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Postgresql - Sql

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PostgreSQL: Documentation: 18: 32.1. Database Connection Control Functions

URL: https://www.postgresql.org/docs/current/libpq-connect.html

Contents:

• 32.1. Database Connection Control Functions #
  • Warning
  • Warning
  • 32.1.1. Connection Strings #
  • 32.1.1.1. Keyword/Value Connection Strings #
  • 32.1.1.2. Connection URIs #
  • 32.1.1.3. Specifying Multiple Hosts #
  • 32.1.2. Parameter Key Words #
  • Warning
  • Warning

The following functions deal with making a connection to a PostgreSQL backend server. An application program can have several backend connections open at one time. (One reason to do that is to access more than one database.) Each connection is represented by a PGconn object, which is obtained from the function PQconnectdb, PQconnectdbParams, or PQsetdbLogin. Note that these functions will always return a non-null object pointer, unless perhaps there is too little memory even to allocate the PGconn object. The PQstatus function should be called to check the return value for a successful connection before queries are sent via the connection object.

If untrusted users have access to a database that has not adopted a secure schema usage pattern, begin each session by removing publicly-writable schemas from search_path. One can set parameter key word options to value -csearch_path=. Alternately, one can issue PQexec(conn, "SELECT pg_catalog.set_config('search_path', '', false)") after connecting. This consideration is not specific to libpq; it applies to every interface for executing arbitrary SQL commands.

On Unix, forking a process with open libpq connections can lead to unpredictable results because the parent and child processes share the same sockets and operating system resources. For this reason, such usage is not recommended, though doing an exec from the child process to load a new executable is safe.

Makes a new connection to the database server.

This function opens a new database connection using the parameters taken from two NULL-terminated arrays. The first, keywords, is defined as an array of strings, each one being a key word. The second, values, gives the value for each key word. Unlike PQsetdbLogin below, the parameter set can be extended without changing the function signature, so use of this function (or its nonblocking analogs PQconnectStartParams and PQconnectPoll) is preferred for new application programming.

The currently recognized parameter key words are listed in Section 32.1.2.

The passed arrays can be empty to use all default parameters, or can contain one or more parameter settings. They must be matched in length. Processing will stop at the first NULL entry in the keywords array. Also, if the values entry associated with a non-NULL keywords entry is NULL or an empty string, that entry is ignored and processing continues with the next pair of array entries.

When expand_dbname is non-zero, the value for the first dbname key word is checked to see if it is a connection string. If so, it is “expanded” into the individual connection parameters extracted from the string. The value is considered to be a connection string, rather than just a database name, if it contains an equal sign (=) or it begins with a URI scheme designator. (More details on connection string formats appear in Section 32.1.1.) Only the first occurrence of dbname is treated in this way; any subsequent dbname parameter is processed as a plain database name.

In general the parameter arrays are processed from start to end. If any key word is repeated, the last value (that is not NULL or empty) is used. This rule applies in particular when a key word found in a connection string conflicts with one appearing in the keywords array. Thus, the programmer may determine whether array entries can override or be overridden by values taken from a connection string. Array entries appearing before an expanded dbname entry can be overridden by fields of the connection string, and in turn those fields are overridden by array entries appearing after dbname (but, again, only if those entries supply non-empty values).

After processing all the array entries and any expanded connection string, any connection parameters that remain unset are filled with default values. If an unset parameter's corresponding environment variable (see Section 32.15) is set, its value is used. If the environment variable is not set either, then the parameter's built-in default value is used.

Makes a new connection to the database server.

This function opens a new database connection using the parameters taken from the string conninfo.

The passed string can be empty to use all default parameters, or it can contain one or more parameter settings separated by whitespace, or it can contain a URI. See Section 32.1.1 for details.

Makes a new connection to the database server.

This is the predecessor of PQconnectdb with a fixed set of parameters. It has the same functionality except that the missing parameters will always take on default values. Write NULL or an empty string for any one of the fixed parameters that is to be defaulted.

If the dbName contains an = sign or has a valid connection URI prefix, it is taken as a conninfo string in exactly the same way as if it had been passed to PQconnectdb, and the remaining parameters are then applied as specified for PQconnectdbParams.

pgtty is no longer used and any value passed will be ignored.

Makes a new connection to the database server.

This is a macro that calls PQsetdbLogin with null pointers for the login and pwd parameters. It is provided for backward compatibility with very old programs.

Make a connection to the database server in a nonblocking manner.

These three functions are used to open a connection to a database server such that your application's thread of execution is not blocked on remote I/O whilst doing so. The point of this approach is that the waits for I/O to complete can occur in the application's main loop, rather than down inside PQconnectdbParams or PQconnectdb, and so the application can manage this operation in parallel with other activities.

With PQconnectStartParams, the database connection is made using the parameters taken from the keywords and values arrays, and controlled by expand_dbname, as described above for PQconnectdbParams.

With PQconnectStart, the database connection is made using the parameters taken from the string conninfo as described above for PQconnectdb.

Neither PQconnectStartParams nor PQconnectStart nor PQconnectPoll will block, so long as a number of restrictions are met:

The hostaddr parameter must be used appropriately to prevent DNS queries from being made. See the documentation of this parameter in Section 32.1.2 for details.

If you call PQtrace, ensure that the stream object into which you trace will not block.

You must ensure that the socket is in the appropriate state before calling PQconnectPoll, as described below.

To begin a nonblocking connection request, call PQconnectStart or PQconnectStartParams. If the result is null, then libpq has been unable to allocate a new PGconn structure. Otherwise, a valid PGconn pointer is returned (though not yet representing a valid connection to the database). Next call PQstatus(conn). If the result is CONNECTION_BAD, the connection attempt has already failed, typically because of invalid connection parameters.

If PQconnectStart or PQconnectStartParams succeeds, the next stage is to poll libpq so that it can proceed with the connection sequence. Use PQsocket(conn) to obtain the descriptor of the socket underlying the database connection. (Caution: do not assume that the socket remains the same across PQconnectPoll calls.) Loop thus: If PQconnectPoll(conn) last returned PGRES_POLLING_READING, wait until the socket is ready to read (as indicated by select(), poll(), or similar system function). Note that PQsocketPoll can help reduce boilerplate by abstracting the setup of select(2) or poll(2) if it is available on your system. Then call PQconnectPoll(conn) again. Conversely, if PQconnectPoll(conn) last returned PGRES_POLLING_WRITING, wait until the socket is ready to write, then call PQconnectPoll(conn) again. On the first iteration, i.e., if you have yet to call PQconnectPoll, behave as if it last returned PGRES_POLLING_WRITING. Continue this loop until PQconnectPoll(conn) returns PGRES_POLLING_FAILED, indicating the connection procedure has failed, or PGRES_POLLING_OK, indicating the connection has been successfully made.

At any time during connection, the status of the connection can be checked by calling PQstatus. If this call returns CONNECTION_BAD, then the connection procedure has failed; if the call returns CONNECTION_OK, then the connection is ready. Both of these states are equally detectable from the return value of PQconnectPoll, described above. Other states might also occur during (and only during) an asynchronous connection procedure. These indicate the current stage of the connection procedure and might be useful to provide feedback to the user for example. These statuses are:

Waiting for connection to be made.

Connection OK; waiting to send.

Waiting for a response from the server.

Received authentication; waiting for backend start-up to finish.

Negotiating SSL encryption.

Negotiating GSS encryption.

Checking if connection is able to handle write transactions.

Checking if connection is to a server in standby mode.

Consuming any remaining response messages on connection.

Note that, although these constants will remain (in order to maintain compatibility), an application should never rely upon these occurring in a particular order, or at all, or on the status always being one of these documented values. An application might do something like this:

The connect_timeout connection parameter is ignored when using PQconnectPoll; it is the application's responsibility to decide whether an excessive amount of time has elapsed. Otherwise, PQconnectStart followed by a PQconnectPoll loop is equivalent to PQconnectdb.

Note that when PQconnectStart or PQconnectStartParams returns a non-null pointer, you must call PQfinish when you are finished with it, in order to dispose of the structure and any associated memory blocks. This must be done even if the connection attempt fails or is abandoned.

Poll a connection's underlying socket descriptor retrieved with PQsocket. The primary use of this function is iterating through the connection sequence described in the documentation of PQconnectStartParams.

This function performs polling of a file descriptor, optionally with a timeout. If forRead is nonzero, the function will terminate when the socket is ready for reading. If forWrite is nonzero, the function will terminate when the socket is ready for writing.

The timeout is specified by end_time, which is the time to stop waiting expressed as a number of microseconds since the Unix epoch (that is, time_t times 1 million). Timeout is infinite if end_time is -1. Timeout is immediate (no blocking) if end_time is 0 (or indeed, any time before now). Timeout values can be calculated conveniently by adding the desired number of microseconds to the result of PQgetCurrentTimeUSec. Note that the underlying system calls may have less than microsecond precision, so that the actual delay may be imprecise.

The function returns a value greater than 0 if the specified condition is met, 0 if a timeout occurred, or -1 if an error occurred. The error can be retrieved by checking the errno(3) value. In the event both forRead and forWrite are zero, the function immediately returns a timeout indication.

PQsocketPoll is implemented using either poll(2) or select(2), depending on platform. See POLLIN and POLLOUT from poll(2), or readfds and writefds from select(2), for more information.

Returns the default connection options.

Returns a connection options array. This can be used to determine all possible PQconnectdb options and their current default values. The return value points to an array of PQconninfoOption structures, which ends with an entry having a null keyword pointer. The null pointer is returned if memory could not be allocated. Note that the current default values (val fields) will depend on environment variables and other context. A missing or invalid service file will be silently ignored. Callers must treat the connection options data as read-only.

After processing the options array, free it by passing it to PQconninfoFree. If this is not done, a small amount of memory is leaked for each call to PQconndefaults.

Returns the connection options used by a live connection.

Returns a connection options array. This can be used to determine all possible PQconnectdb options and the values that were used to connect to the server. The return value points to an array of PQconninfoOption structures, which ends with an entry having a null keyword pointer. All notes above for PQconndefaults also apply to the result of PQconninfo.

Returns parsed connection options from the provided connection string.

Parses a connection string and returns the resulting options as an array; or returns NULL if there is a problem with the connection string. This function can be used to extract the PQconnectdb options in the provided connection string. The return value points to an array of PQconninfoOption structures, which ends with an entry having a null keyword pointer.

All legal options will be present in the result array, but the PQconninfoOption for any option not present in the connection string will have val set to NULL; default values are not inserted.

If errmsg is not NULL, then *errmsg is set to NULL on success, else to a malloc'd error string explaining the problem. (It is also possible for *errmsg to be set to NULL and the function to return NULL; this indicates an out-of-memory condition.)

After processing the options array, free it by passing it to PQconninfoFree. If this is not done, some memory is leaked for each call to PQconninfoParse. Conversely, if an error occurs and errmsg is not NULL, be sure to free the error string using PQfreemem.

Closes the connection to the server. Also frees memory used by the PGconn object.

Note that even if the server connection attempt fails (as indicated by PQstatus), the application should call PQfinish to free the memory used by the PGconn object. The PGconn pointer must not be used again after PQfinish has been called.

Resets the communication channel to the server.

This function will close the connection to the server and attempt to establish a new connection, using all the same parameters previously used. This might be useful for error recovery if a working connection is lost.

Reset the communication channel to the server, in a nonblocking manner.

These functions will close the connection to the server and attempt to establish a new connection, using all the same parameters previously used. This can be useful for error recovery if a working connection is lost. They differ from PQreset (above) in that they act in a nonblocking manner. These functions suffer from the same restrictions as PQconnectStartParams, PQconnectStart and PQconnectPoll.

To initiate a connection reset, call PQresetStart. If it returns 0, the reset has failed. If it returns 1, poll the reset using PQresetPoll in exactly the same way as you would create the connection using PQconnectPoll.

PQpingParams reports the status of the server. It accepts connection parameters identical to those of PQconnectdbParams, described above. It is not necessary to supply correct user name, password, or database name values to obtain the server status; however, if incorrect values are provided, the server will log a failed connection attempt.

The function returns one of the following values:

The server is running and appears to be accepting connections.

The server is running but is in a state that disallows connections (startup, shutdown, or crash recovery).

The server could not be contacted. This might indicate that the server is not running, or that there is something wrong with the given connection parameters (for example, wrong port number), or that there is a network connectivity problem (for example, a firewall blocking the connection request).

No attempt was made to contact the server, because the supplied parameters were obviously incorrect or there was some client-side problem (for example, out of memory).

PQping reports the status of the server. It accepts connection parameters identical to those of PQconnectdb, described above. It is not necessary to supply correct user name, password, or database name values to obtain the server status; however, if incorrect values are provided, the server will log a failed connection attempt.

The return values are the same as for PQpingParams.

PQsetSSLKeyPassHook_OpenSSL lets an application override libpq's default handling of encrypted client certificate key files using sslpassword or interactive prompting.

The application passes a pointer to a callback function with signature:

which libpq will then call instead of its default PQdefaultSSLKeyPassHook_OpenSSL handler. The callback should determine the password for the key and copy it to result-buffer buf of size size. The string in buf must be null-terminated. The callback must return the length of the password stored in buf excluding the null terminator. On failure, the callback should set buf[0] = '\0' and return 0. See PQdefaultSSLKeyPassHook_OpenSSL in libpq's source code for an example.

If the user specified an explicit key location, its path will be in conn->sslkey when the callback is invoked. This will be empty if the default key path is being used. For keys that are engine specifiers, it is up to engine implementations whether they use the OpenSSL password callback or define their own handling.

The app callback may choose to delegate unhandled cases to PQdefaultSSLKeyPassHook_OpenSSL, or call it first and try something else if it returns 0, or completely override it.

The callback must not escape normal flow control with exceptions, longjmp(...), etc. It must return normally.

PQgetSSLKeyPassHook_OpenSSL returns the current client certificate key password hook, or NULL if none has been set.

Several libpq functions parse a user-specified string to obtain connection parameters. There are two accepted formats for these strings: plain keyword/value strings and URIs. URIs generally follow RFC 3986, except that multi-host connection strings are allowed as further described below.

In the keyword/value format, each parameter setting is in the form keyword = value, with space(s) between settings. Spaces around a setting's equal sign are optional. To write an empty value, or a value containing spaces, surround it with single quotes, for example keyword = 'a value'. Single quotes and backslashes within a value must be escaped with a backslash, i.e., \' and \.

The recognized parameter key words are listed in Section 32.1.2.

The general form for a connection URI is:

The URI scheme designator can be either postgresql:// or postgres://. Each of the remaining URI parts is optional. The following examples illustrate valid URI syntax:

Values that would normally appear in the hierarchical part of the URI can alternatively be given as named parameters. For example:

All named parameters must match key words listed in Section 32.1.2, except that for compatibility with JDBC connection URIs, instances of ssl=true are translated into sslmode=require.

The connection URI needs to be encoded with percent-encoding if it includes symbols with special meaning in any of its parts. Here is an example where the equal sign (=) is replaced with %3D and the space character with %20:

The host part may be either a host name or an IP address. To specify an IPv6 address, enclose it in square brackets:

The host part is interpreted as described for the parameter host. In particular, a Unix-domain socket connection is chosen if the host part is either empty or looks like an absolute path name, otherwise a TCP/IP connection is initiated. Note, however, that the slash is a reserved character in the hierarchical part of the URI. So, to specify a non-standard Unix-domain socket directory, either omit the host part of the URI and specify the host as a named parameter, or percent-encode the path in the host part of the URI:

It is possible to specify multiple host components, each with an optional port component, in a single URI. A URI of the form postgresql://host1:port1,host2:port2,host3:port3/ is equivalent to a connection string of the form host=host1,host2,host3 port=port1,port2,port3. As further described below, each host will be tried in turn until a connection is successfully established.

It is possible to specify multiple hosts to connect to, so that they are tried in the given order. In the Keyword/Value format, the host, hostaddr, and port options accept comma-separated lists of values. The same number of elements must be given in each option that is specified, such that e.g., the first hostaddr corresponds to the first host name, the second hostaddr corresponds to the second host name, and so forth. As an exception, if only one port is specified, it applies to all the hosts.

In the connection URI format, you can list multiple host:port pairs separated by commas in the host component of the URI.

In either format, a single host name can translate to multiple network addresses. A common example of this is a host that has both an IPv4 and an IPv6 address.

When multiple hosts are specified, or when a single host name is translated to multiple addresses, all the hosts and addresses will be tried in order, until one succeeds. If none of the hosts can be reached, the connection fails. If a connection is established successfully, but authentication fails, the remaining hosts in the list are not tried.

If a password file is used, you can have different passwords for different hosts. All the other connection options are the same for every host in the list; it is not possible to e.g., specify different usernames for different hosts.

The currently recognized parameter key words are:

Name of host to connect to. If a host name looks like an absolute path name, it specifies Unix-domain communication rather than TCP/IP communication; the value is the name of the directory in which the socket file is stored. (On Unix, an absolute path name begins with a slash. On Windows, paths starting with drive letters are also recognized.) If the host name starts with @, it is taken as a Unix-domain socket in the abstract namespace (currently supported on Linux and Windows). The default behavior when host is not specified, or is empty, is to connect to a Unix-domain socket in /tmp (or whatever socket directory was specified when PostgreSQL was built). On Windows, the default is to connect to localhost.

A comma-separated list of host names is also accepted, in which case each host name in the list is tried in order; an empty item in the list selects the default behavior as explained above. See Section 32.1.1.3 for details.

Numeric IP address of host to connect to. This should be in the standard IPv4 address format, e.g., 172.28.40.9. If your machine supports IPv6, you can also use those addresses. TCP/IP communication is always used when a nonempty string is specified for this parameter. If this parameter is not specified, the value of host will be looked up to find the corresponding IP address — or, if host specifies an IP address, that value will be used directly.

Using hostaddr allows the application to avoid a host name look-up, which might be important in applications with time constraints. However, a host name is required for GSSAPI or SSPI authentication methods, as well as for verify-full SSL certificate verification. The following rules are used:

If host is specified without hostaddr, a host name lookup occurs. (When using PQconnectPoll, the lookup occurs when PQconnectPoll first considers this host name, and it may cause PQconnectPoll to block for a significant amount of time.)

If hostaddr is specified without host, the value for hostaddr gives the server network address. The connection attempt will fail if the authentication method requires a host name.

If both host and hostaddr are specified, the value for hostaddr gives the server network address. The value for host is ignored unless the authentication method requires it, in which case it will be used as the host name.

Note that authentication is likely to fail if host is not the name of the server at network address hostaddr. Also, when both host and hostaddr are specified, host is used to identify the connection in a password file (see Section 32.16).

A comma-separated list of hostaddr values is also accepted, in which case each host in the list is tried in order. An empty item in the list causes the corresponding host name to be used, or the default host name if that is empty as well. See Section 32.1.1.3 for details.

Without either a host name or host address, libpq will connect using a local Unix-domain socket; or on Windows, it will attempt to connect to localhost.

Port number to connect to at the server host, or socket file name extension for Unix-domain connections. If multiple hosts were given in the host or hostaddr parameters, this parameter may specify a comma-separated list of ports of the same length as the host list, or it may specify a single port number to be used for all hosts. An empty string, or an empty item in a comma-separated list, specifies the default port number established when PostgreSQL was built.

The database name. Defaults to be the same as the user name. In certain contexts, the value is checked for extended formats; see Section 32.1.1 for more details on those.

PostgreSQL user name to connect as. Defaults to be the same as the operating system name of the user running the application.

Password to be used if the server demands password authentication.

Specifies the name of the file used to store passwords (see Section 32.16). Defaults to ~/.pgpass, or %APPDATA%\postgresql\pgpass.conf on Microsoft Windows. (No error is reported if this file does not exist.)

Specifies the authentication method that the client requires from the server. If the server does not use the required method to authenticate the client, or if the authentication handshake is not fully completed by the server, the connection will fail. A comma-separated list of methods may also be provided, of which the server must use exactly one in order for the connection to succeed. By default, any authentication method is accepted, and the server is free to skip authentication altogether.

Methods may be negated with the addition of a ! prefix, in which case the server must not attempt the listed method; any other method is accepted, and the server is free not to authenticate the client at all. If a comma-separated list is provided, the server may not attempt any of the listed negated methods. Negated and non-negated forms may not be combined in the same setting.

As a final special case, the none method requires the server not to use an authentication challenge. (It may also be negated, to require some form of authentication.)

The following methods may be specified:

The server must request plaintext password authentication.

The server must request MD5 hashed password authentication.

Support for MD5-encrypted passwords is deprecated and will be removed in a future release of PostgreSQL. Refer to Section 20.5 for details about migrating to another password type.

The server must either request a Kerberos handshake via GSSAPI or establish a GSS-encrypted channel (see also gssencmode).

The server must request Windows SSPI authentication.

The server must successfully complete a SCRAM-SHA-256 authentication exchange with the client.

The server must request an OAuth bearer token from the client.

The server must not prompt the client for an authentication exchange. (This does not prohibit client certificate authentication via TLS, nor GSS authentication via its encrypted transport.)

This option controls the client's use of channel binding. A setting of require means that the connection must employ channel binding, prefer means that the client will choose channel binding if available, and disable prevents the use of channel binding. The default is prefer if PostgreSQL is compiled with SSL support; otherwise the default is disable.

Channel binding is a method for the server to authenticate itself to the client. It is only supported over SSL connections with PostgreSQL 11 or later servers using the SCRAM authentication method.

Maximum time to wait while connecting, in seconds (write as a decimal integer, e.g., 10). Zero, negative, or not specified means wait indefinitely. This timeout applies separately to each host name or IP address. For example, if you specify two hosts and connect_timeout is 5, each host will time out if no connection is made within 5 seconds, so the total time spent waiting for a connection might be up to 10 seconds.

This sets the client_encoding configuration parameter for this connection. In addition to the values accepted by the corresponding server option, you can use auto to determine the right encoding from the current locale in the client (LC_CTYPE environment variable on Unix systems).

Specifies command-line options to send to the server at connection start. For example, setting this to -c geqo=off or --geqo=off sets the session's value of the geqo parameter to off. Spaces within this string are considered to separate command-line arguments, unless escaped with a backslash (); write \ to represent a literal backslash. For a detailed discussion of the available options, consult Chapter 19.

Specifies a value for the application_name configuration parameter.

Specifies a fallback value for the application_name configuration parameter. This value will be used if no value has been given for application_name via a connection parameter or the PGAPPNAME environment variable. Specifying a fallback name is useful in generic utility programs that wish to set a default application name but allow it to be overridden by the user.

Controls whether client-side TCP keepalives are used. The default value is 1, meaning on, but you can change this to 0, meaning off, if keepalives are not wanted. This parameter is ignored for connections made via a Unix-domain socket.

Controls the number of seconds of inactivity after which TCP should send a keepalive message to the server. A value of zero uses the system default. This parameter is ignored for connections made via a Unix-domain socket, or if keepalives are disabled. It is only supported on systems where TCP_KEEPIDLE or an equivalent socket option is available, and on Windows; on other systems, it has no effect.

Controls the number of seconds after which a TCP keepalive message that is not acknowledged by the server should be retransmitted. A value of zero uses the system default. This parameter is ignored for connections made via a Unix-domain socket, or if keepalives are disabled. It is only supported on systems where TCP_KEEPINTVL or an equivalent socket option is available, and on Windows; on other systems, it has no effect.

Controls the number of TCP keepalives that can be lost before the client's connection to the server is considered dead. A value of zero uses the system default. This parameter is ignored for connections made via a Unix-domain socket, or if keepalives are disabled. It is only supported on systems where TCP_KEEPCNT or an equivalent socket option is available; on other systems, it has no effect.

Controls the number of milliseconds that transmitted data may remain unacknowledged before a connection is forcibly closed. A value of zero uses the system default. This parameter is ignored for connections made via a Unix-domain socket. It is only supported on systems where TCP_USER_TIMEOUT is available; on other systems, it has no effect.

This option determines whether the connection should use the replication protocol instead of the normal protocol. This is what PostgreSQL replication connections as well as tools such as pg_basebackup use internally, but it can also be used by third-party applications. For a description of the replication protocol, consult Section 54.4.

The following values, which are case-insensitive, are supported:

The connection goes into physical replication mode.

The connection goes into logical replication mode, connecting to the database specified in the dbname parameter.

The connection is a regular one, which is the default behavior.

In physical or logical replication mode, only the simple query protocol can be used.

This option determines whether or with what priority a secure GSS TCP/IP connection will be negotiated with the server. There are three modes:

only try a non-GSSAPI-encrypted connection

if there are GSSAPI credentials present (i.e., in a credentials cache), first try a GSSAPI-encrypted connection; if that fails or there are no credentials, try a non-GSSAPI-encrypted connection. This is the default when PostgreSQL has been compiled with GSSAPI support.

only try a GSSAPI-encrypted connection

gssencmode is ignored for Unix domain socket communication. If PostgreSQL is compiled without GSSAPI support, using the require option will cause an error, while prefer will be accepted but libpq will not actually attempt a GSSAPI-encrypted connection.

This option determines whether or with what priority a secure SSL TCP/IP connection will be negotiated with the server. There are six modes:

only try a non-SSL connection

first try a non-SSL connection; if that fails, try an SSL connection

first try an SSL connection; if that fails, try a non-SSL connection

only try an SSL connection. If a root CA file is present, verify the certificate in the same way as if verify-ca was specified

only try an SSL connection, and verify that the server certificate is issued by a trusted certificate authority (CA)

only try an SSL connection, verify that the server certificate is issued by a trusted CA and that the requested server host name matches that in the certificate

See Section 32.19 for a detailed description of how these options work.

sslmode is ignored for Unix domain socket communication. If PostgreSQL is compiled without SSL support, using options require, verify-ca, or verify-full will cause an error, while options allow and prefer will be accepted but libpq will not actually attempt an SSL connection.

Note that if GSSAPI encryption is possible, that will be used in preference to SSL encryption, regardless of the value of sslmode. To force use of SSL encryption in an environment that has working GSSAPI infrastructure (such as a Kerberos server), also set gssencmode to disable.

This option is deprecated in favor of the sslmode setting.

If set to 1, an SSL connection to the server is required (this is equivalent to sslmode require). libpq will then refuse to connect if the server does not accept an SSL connection. If set to 0 (default), libpq will negotiate the connection type with the server (equivalent to sslmode prefer). This option is only available if PostgreSQL is compiled with SSL support.

This option controls how SSL encryption is negotiated with the server, if SSL is used. In the default postgres mode, the client first asks the server if SSL is supported. In direct mode, the client starts the standard SSL handshake directly after establishing the TCP/IP connection. Traditional PostgreSQL protocol negotiation is the most flexible with different server configurations. If the server is known to support direct SSL connections then the latter requires one fewer round trip reducing connection latency and also allows the use of protocol agnostic SSL network tools. The direct SSL option was introduced in PostgreSQL version 17.

perform PostgreSQL protocol negotiation. This is the default if the option is not provided.

start SSL handshake directly after establishing the TCP/IP connection. This is only allowed with sslmode=require or higher, because the weaker settings could lead to unintended fallback to plaintext authentication when the server does not support direct SSL handshake.

If set to 1, data sent over SSL connections will be compressed. If set to 0, compression will be disabled. The default is 0. This parameter is ignored if a connection without SSL is made.

SSL compression is nowadays considered insecure and its use is no longer recommended. OpenSSL 1.1.0 disabled compression by default, and many operating system distributions disabled it in prior versions as well, so setting this parameter to on will not have any effect if the server does not accept compression. PostgreSQL 14 disabled compression completely in the backend.

If security is not a primary concern, compression can improve throughput if the network is the bottleneck. Disabling compression can improve response time and throughput if CPU performance is the limiting factor.

This parameter specifies the file name of the client SSL certificate, replacing the default ~/.postgresql/postgresql.crt. This parameter is ignored if an SSL connection is not made.

This parameter specifies the location for the secret key used for the client certificate. It can either specify a file name that will be used instead of the default ~/.postgresql/postgresql.key, or it can specify a key obtained from an external “engine” (engines are OpenSSL loadable modules). An external engine specification should consist of a colon-separated engine name and an engine-specific key identifier. This parameter is ignored if an SSL connection is not made.

This parameter specifies the location where libpq will log keys used in this SSL context. This is useful for debugging PostgreSQL protocol interactions or client connections using network inspection tools like Wireshark. This parameter is ignored if an SSL connection is not made, or if LibreSSL is used (LibreSSL does not support key logging). Keys are logged using the NSS format.

Key logging will expose potentially sensitive information in the keylog file. Keylog files should be handled with the same care as sslkey files.

This parameter specifies the password for the secret key specified in sslkey, allowing client certificate private keys to be stored in encrypted form on disk even when interactive passphrase input is not practical.

Specifying this parameter with any non-empty value suppresses the Enter PEM pass phrase: prompt that OpenSSL will emit by default when an encrypted client certificate key is provided to libpq.

If the key is not encrypted this parameter is ignored. The parameter has no effect on keys specified by OpenSSL engines unless the engine uses the OpenSSL password callback mechanism for prompts.

There is no environment variable equivalent to this option, and no facility for looking it up in .pgpass. It can be used in a service file connection definition. Users with more sophisticated uses should consider using OpenSSL engines and tools like PKCS#11 or USB crypto offload devices.

This option determines whether a client certificate may be sent to the server, and whether the server is required to request one. There are three modes:

A client certificate is never sent, even if one is available (default location or provided via sslcert).

A certificate may be sent, if the server requests one and the client has one to send.

The server must request a certificate. The connection will fail if the client does not send a certificate and the server successfully authenticates the client anyway.

sslcertmode=require doesn't add any additional security, since there is no guarantee that the server is validating the certificate correctly; PostgreSQL servers generally request TLS certificates from clients whether they validate them or not. The option may be useful when troubleshooting more complicated TLS setups.

This parameter specifies the name of a file containing SSL certificate authority (CA) certificate(s). If the file exists, the server's certificate will be verified to be signed by one of these authorities. The default is ~/.postgresql/root.crt.

The special value system may be specified instead, in which case the trusted CA roots from the SSL implementation will be loaded. The exact locations of these root certificates differ by SSL implementation and platform. For OpenSSL in particular, the locations may be further modified by the SSL_CERT_DIR and SSL_CERT_FILE environment variables.

When using sslrootcert=system, the default sslmode is changed to verify-full, and any weaker setting will result in an error. In most cases it is trivial for anyone to obtain a certificate trusted by the system for a hostname they control, rendering verify-ca and all weaker modes useless.

The magic system value will take precedence over a local certificate file with the same name. If for some reason you find yourself in this situation, use an alternative path like sslrootcert=./system instead.

This parameter specifies the file name of the SSL server certificate revocation list (CRL). Certificates listed in this file, if it exists, will be rejected while attempting to authenticate the server's certificate. If neither sslcrl nor sslcrldir is set, this setting is taken as ~/.postgresql/root.crl.

This parameter specifies the directory name of the SSL server certificate revocation list (CRL). Certificates listed in the files in this directory, if it exists, will be rejected while attempting to authenticate the server's certificate.

The directory needs to be prepared with the OpenSSL command openssl rehash or c_rehash. See its documentation for details.

Both sslcrl and sslcrldir can be specified together.

If set to 1 (default), libpq sets the TLS extension “Server Name Indication” (SNI) on SSL-enabled connections. By setting this parameter to 0, this is turned off.

The Server Name Indication can be used by SSL-aware proxies to route connections without having to decrypt the SSL stream. (Note that unless the proxy is aware of the PostgreSQL protocol handshake this would require setting sslnegotiation to direct.) However, SNI makes the destination host name appear in cleartext in the network traffic, so it might be undesirable in some cases.

This parameter specifies the operating-system user name of the server, for example requirepeer=postgres. When making a Unix-domain socket connection, if this parameter is set, the client checks at the beginning of the connection that the server process is running under the specified user name; if it is not, the connection is aborted with an error. This parameter can be used to provide server authentication similar to that available with SSL certificates on TCP/IP connections. (Note that if the Unix-domain socket is in /tmp or another publicly writable location, any user could start a server listening there. Use this parameter to ensure that you are connected to a server run by a trusted user.) This option is only supported on platforms for which the peer authentication method is implemented; see Section 20.9.

This parameter specifies the minimum SSL/TLS protocol version to allow for the connection. Valid values are TLSv1, TLSv1.1, TLSv1.2 and TLSv1.3. The supported protocols depend on the version of OpenSSL used, older versions not supporting the most modern protocol versions. If not specified, the default is TLSv1.2, which satisfies industry best practices as of this writing.

This parameter specifies the maximum SSL/TLS protocol version to allow for the connection. Valid values are TLSv1, TLSv1.1, TLSv1.2 and TLSv1.3. The supported protocols depend on the version of OpenSSL used, older versions not supporting the most modern protocol versions. If not set, this parameter is ignored and the connection will use the maximum bound defined by the backend, if set. Setting the maximum protocol version is mainly useful for testing or if some component has issues working with a newer protocol.

Specifies the minimum protocol version to allow for the connection. The default is to allow any version of the PostgreSQL protocol supported by libpq, which currently means 3.0. If the server does not support at least this protocol version the connection will be closed.

The current supported values are 3.0, 3.2, and latest. The latest value is equivalent to the latest protocol version supported by the libpq version being used, which is currently 3.2.

Specifies the protocol version to request from the server. The default is to use version 3.0 of the PostgreSQL protocol, unless the connection string specifies a feature that relies on a higher protocol version, in which case the latest version supported by libpq is used. If the server does not support the protocol version requested by the client, the connection is automatically downgraded to a lower minor protocol version that the server supports. After the connection attempt has completed you can use PQprotocolVersion to find out which exact protocol version was negotiated.

The current supported values are 3.0, 3.2, and latest. The latest value is equivalent to the latest protocol version supported by the libpq version being used, which is currently 3.2.

Kerberos service name to use when authenticating with GSSAPI. This must match the service name specified in the server configuration for Kerberos authentication to succeed. (See also Section 20.6.) The default value is normally postgres, but that can be changed when building PostgreSQL via the --with-krb-srvnam option of configure. In most environments, this parameter never needs to be changed. Some Kerberos implementations might require a different service name, such as Microsoft Active Directory which requires the service name to be in upper case (POSTGRES).

GSS library to use for GSSAPI authentication. Currently this is disregarded except on Windows builds that include both GSSAPI and SSPI support. In that case, set this to gssapi to cause libpq to use the GSSAPI library for authentication instead of the default SSPI.

Forward (delegate) GSS credentials to the server. The default is 0 which means credentials will not be forwarded to the server. Set this to 1 to have credentials forwarded when possible.

The base64-encoded SCRAM client key. This can be used by foreign-data wrappers or similar middleware to enable pass-through SCRAM authentication. See Section F.38.1.10 for one such implementation. It is not meant to be specified directly by users or client applications.

The base64-encoded SCRAM server key. This can be used by foreign-data wrappers or similar middleware to enable pass-through SCRAM authentication. See Section F.38.1.10 for one such implementation. It is not meant to be specified directly by users or client applications.

Service name to use for additional parameters. It specifies a service name in pg_service.conf that holds additional connection parameters. This allows applications to specify only a service name so connection parameters can be centrally maintained. See Section 32.17.

This option determines whether the session must have certain properties to be acceptable. It's typically used in combination with multiple host names to select the first acceptable alternative among several hosts. There are six modes:

any successful connection is acceptable

session must accept read-write transactions by default (that is, the server must not be in hot standby mode and the default_transaction_read_only parameter must be off)

session must not accept read-write transactions by default (the converse)

server must not be in hot standby mode

server must be in hot standby mode

first try to find a standby server, but if none of the listed hosts is a standby server, try again in any mode

Controls the order in which the client tries to connect to the available hosts and addresses. Once a connection attempt is successful no other hosts and addresses will be tried. This parameter is typically used in combination with multiple host names or a DNS record that returns multiple IPs. This parameter can be used in combination with target_session_attrs to, for example, load balance over standby servers only. Once successfully connected, subsequent queries on the returned connection will all be sent to the same server. There are currently two modes:

No load balancing across hosts is performed. Hosts are tried in the order in which they are provided and addresses are tried in the order they are received from DNS or a hosts file.

Hosts and addresses are tried in random order. This value is mostly useful when opening multiple connections at the same time, possibly from different machines. This way connections can be load balanced across multiple PostgreSQL servers.

While random load balancing, due to its random nature, will almost never result in a completely uniform distribution, it statistically gets quite close. One important aspect here is that this algorithm uses two levels of random choices: First the hosts will be resolved in random order. Then secondly, before resolving the next host, all resolved addresses for the current host will be tried in random order. This behaviour can skew the amount of connections each node gets greatly in certain cases, for instance when some hosts resolve to more addresses than others. But such a skew can also be used on purpose, e.g. to increase the number of connections a larger server gets by providing its hostname multiple times in the host string.

When using this value it's recommended to also configure a reasonable value for connect_timeout. Because then, if one of the nodes that are used for load balancing is not responding, a new node will be tried.

The HTTPS URL of a trusted issuer to contact if the server requests an OAuth token for the connection. This parameter is required for all OAuth connections; it should exactly match the issuer setting in the server's HBA configuration.

As part of the standard authentication handshake, libpq will ask the server for a discovery document: a URL providing a set of OAuth configuration parameters. The server must provide a URL that is directly constructed from the components of the oauth_issuer, and this value must exactly match the issuer identifier that is declared in the discovery document itself, or the connection will fail. This is required to prevent a class of "mix-up attacks" on OAuth clients.

You may also explicitly set oauth_issuer to the /.well-known/ URI used for OAuth discovery. In this case, if the server asks for a different URL, the connection will fail, but a custom OAuth flow may be able to speed up the standard handshake by using previously cached tokens. (In this case, it is recommended that oauth_scope be set as well, since the client will not have a chance to ask the server for a correct scope setting, and the default scopes for a token may not be sufficient to connect.) libpq currently supports the following well-known endpoints:

/.well-known/openid-configuration

/.well-known/oauth-authorization-server

Issuers are highly privileged during the OAuth connection handshake. As a rule of thumb, if you would not trust the operator of a URL to handle access to your servers, or to impersonate you directly, that URL should not be trusted as an oauth_issuer.

An OAuth 2.0 client identifier, as issued by the authorization server. If the PostgreSQL server requests an OAuth token for the connection (and if no custom OAuth hook is installed to provide one), then this parameter must be set; otherwise, the connection will fail.

The client password, if any, to use when contacting the OAuth authorization server. Whether this parameter is required or not is determined by the OAuth provider; "public" clients generally do not use a secret, whereas "confidential" clients generally do.

The scope of the access request sent to the authorization server, specified as a (possibly empty) space-separated list of OAuth scope identifiers. This parameter is optional and intended for advanced usage.

Usually the client will obtain appropriate scope settings from the PostgreSQL server. If this parameter is used, the server's requested scope list will be ignored. This can prevent a less-trusted server from requesting inappropriate access scopes from the end user. However, if the client's scope setting does not contain the server's required scopes, the server is likely to reject the issued token, and the connection will fail.

The meaning of an empty scope list is provider-dependent. An OAuth authorization server may choose to issue a token with "default scope", whatever that happens to be, or it may reject the token request entirely.

Examples:

Example 1 (javascript):

PGconn *PQconnectdbParams(const char * const *keywords,
                          const char * const *values,
                          int expand_dbname);

Example 2 (javascript):

PGconn *PQconnectdb(const char *conninfo);

Example 3 (javascript):

PGconn *PQsetdbLogin(const char *pghost,
                     const char *pgport,
                     const char *pgoptions,
                     const char *pgtty,
                     const char *dbName,
                     const char *login,
                     const char *pwd);

Example 4 (unknown):

PGconn *PQsetdb(char *pghost,
                char *pgport,
                char *pgoptions,
                char *pgtty,
                char *dbName);

---

PostgreSQL: Documentation: 18: 19.6. Replication

URL: https://www.postgresql.org/docs/current/runtime-config-replication.html

Contents:

• 19.6. Replication #
  • 19.6.1. Sending Servers #
  • 19.6.2. Primary Server #
  • Note
  • 19.6.3. Standby Servers #
  • Warning
  • 19.6.4. Subscribers #

These settings control the behavior of the built-in streaming replication feature (see Section 26.2.5), and the built-in logical replication feature (see Chapter 29).

For streaming replication, servers will be either a primary or a standby server. Primaries can send data, while standbys are always receivers of replicated data. When cascading replication (see Section 26.2.7) is used, standby servers can also be senders, as well as receivers. Parameters are mainly for sending and standby servers, though some parameters have meaning only on the primary server. Settings may vary across the cluster without problems if that is required.

For logical replication, publishers (servers that do CREATE PUBLICATION) replicate data to subscribers (servers that do CREATE SUBSCRIPTION). Servers can also be publishers and subscribers at the same time. Note, the following sections refer to publishers as "senders". For more details about logical replication configuration settings refer to Section 29.12.

These parameters can be set on any server that is to send replication data to one or more standby servers. The primary is always a sending server, so these parameters must always be set on the primary. The role and meaning of these parameters does not change after a standby becomes the primary.

Specifies the maximum number of concurrent connections from standby servers or streaming base backup clients (i.e., the maximum number of simultaneously running WAL sender processes). The default is 10. The value 0 means replication is disabled. Abrupt disconnection of a streaming client might leave an orphaned connection slot behind until a timeout is reached, so this parameter should be set slightly higher than the maximum number of expected clients so disconnected clients can immediately reconnect. This parameter can only be set at server start. Also, wal_level must be set to replica or higher to allow connections from standby servers.

When running a standby server, you must set this parameter to the same or higher value than on the primary server. Otherwise, queries will not be allowed in the standby server.

Specifies the maximum number of replication slots (see Section 26.2.6) that the server can support. The default is 10. This parameter can only be set at server start. Setting it to a lower value than the number of currently existing replication slots will prevent the server from starting. Also, wal_level must be set to replica or higher to allow replication slots to be used.

Specifies the minimum size of past WAL files kept in the pg_wal directory, in case a standby server needs to fetch them for streaming replication. If a standby server connected to the sending server falls behind by more than wal_keep_size megabytes, the sending server might remove a WAL segment still needed by the standby, in which case the replication connection will be terminated. Downstream connections will also eventually fail as a result. (However, the standby server can recover by fetching the segment from archive, if WAL archiving is in use.)

This sets only the minimum size of segments retained in pg_wal; the system might need to retain more segments for WAL archival or to recover from a checkpoint. If wal_keep_size is zero (the default), the system doesn't keep any extra segments for standby purposes, so the number of old WAL segments available to standby servers is a function of the location of the previous checkpoint and status of WAL archiving. If this value is specified without units, it is taken as megabytes. This parameter can only be set in the postgresql.conf file or on the server command line.

Specify the maximum size of WAL files that replication slots are allowed to retain in the pg_wal directory at checkpoint time. If max_slot_wal_keep_size is -1 (the default), replication slots may retain an unlimited amount of WAL files. Otherwise, if restart_lsn of a replication slot falls behind the current LSN by more than the given size, the standby using the slot may no longer be able to continue replication due to removal of required WAL files. You can see the WAL availability of replication slots in pg_replication_slots. If this value is specified without units, it is taken as megabytes. This parameter can only be set in the postgresql.conf file or on the server command line.

Invalidate replication slots that have remained inactive (not used by a replication connection) for longer than this duration. If this value is specified without units, it is taken as seconds. A value of zero (the default) disables the idle timeout invalidation mechanism. This parameter can only be set in the postgresql.conf file or on the server command line.

Slot invalidation due to idle timeout occurs during checkpoint. Because checkpoints happen at checkpoint_timeout intervals, there can be some lag between when the idle_replication_slot_timeout was exceeded and when the slot invalidation is triggered at the next checkpoint. To avoid such lags, users can force a checkpoint to promptly invalidate inactive slots. The duration of slot inactivity is calculated using the slot's pg_replication_slots.inactive_since value.

Note that the idle timeout invalidation mechanism is not applicable for slots that do not reserve WAL or for slots on the standby server that are being synced from the primary server (i.e., standby slots having pg_replication_slots.synced value true). Synced slots are always considered to be inactive because they don't perform logical decoding to produce changes.

Terminate replication connections that are inactive for longer than this amount of time. This is useful for the sending server to detect a standby crash or network outage. If this value is specified without units, it is taken as milliseconds. The default value is 60 seconds. A value of zero disables the timeout mechanism.

With a cluster distributed across multiple geographic locations, using different values per location brings more flexibility in the cluster management. A smaller value is useful for faster failure detection with a standby having a low-latency network connection, and a larger value helps in judging better the health of a standby if located on a remote location, with a high-latency network connection.

Record commit time of transactions. This parameter can only be set in postgresql.conf file or on the server command line. The default value is off.

A comma-separated list of streaming replication standby server slot names that logical WAL sender processes will wait for. Logical WAL sender processes will send decoded changes to plugins only after the specified replication slots confirm receiving WAL. This guarantees that logical replication failover slots do not consume changes until those changes are received and flushed to corresponding physical standbys. If a logical replication connection is meant to switch to a physical standby after the standby is promoted, the physical replication slot for the standby should be listed here. Note that logical replication will not proceed if the slots specified in the synchronized_standby_slots do not exist or are invalidated. Additionally, the replication management functions pg_replication_slot_advance, pg_logical_slot_get_changes, and pg_logical_slot_peek_changes, when used with logical failover slots, will block until all physical slots specified in synchronized_standby_slots have confirmed WAL receipt.

The standbys corresponding to the physical replication slots in synchronized_standby_slots must configure sync_replication_slots = true so they can receive logical failover slot changes from the primary.

These parameters can be set on the primary server that is to send replication data to one or more standby servers. Note that in addition to these parameters, wal_level must be set appropriately on the primary server, and optionally WAL archiving can be enabled as well (see Section 19.5.3). The values of these parameters on standby servers are irrelevant, although you may wish to set them there in preparation for the possibility of a standby becoming the primary.

Specifies a list of standby servers that can support synchronous replication, as described in Section 26.2.8. There will be one or more active synchronous standbys; transactions waiting for commit will be allowed to proceed after these standby servers confirm receipt of their data. The synchronous standbys will be those whose names appear in this list, and that are both currently connected and streaming data in real-time (as shown by a state of streaming in the pg_stat_replication view). Specifying more than one synchronous standby can allow for very high availability and protection against data loss.

The name of a standby server for this purpose is the application_name setting of the standby, as set in the standby's connection information. In case of a physical replication standby, this should be set in the primary_conninfo setting; the default is the setting of cluster_name if set, else walreceiver. For logical replication, this can be set in the connection information of the subscription, and it defaults to the subscription name. For other replication stream consumers, consult their documentation.

This parameter specifies a list of standby servers using either of the following syntaxes:

where num_sync is the number of synchronous standbys that transactions need to wait for replies from, and standby_name is the name of a standby server. num_sync must be an integer value greater than zero. FIRST and ANY specify the method to choose synchronous standbys from the listed servers.

The keyword FIRST, coupled with num_sync, specifies a priority-based synchronous replication and makes transaction commits wait until their WAL records are replicated to num_sync synchronous standbys chosen based on their priorities. For example, a setting of FIRST 3 (s1, s2, s3, s4) will cause each commit to wait for replies from three higher-priority standbys chosen from standby servers s1, s2, s3 and s4. The standbys whose names appear earlier in the list are given higher priority and will be considered as synchronous. Other standby servers appearing later in this list represent potential synchronous standbys. If any of the current synchronous standbys disconnects for whatever reason, it will be replaced immediately with the next-highest-priority standby. The keyword FIRST is optional.

The keyword ANY, coupled with num_sync, specifies a quorum-based synchronous replication and makes transaction commits wait until their WAL records are replicated to at least num_sync listed standbys. For example, a setting of ANY 3 (s1, s2, s3, s4) will cause each commit to proceed as soon as at least any three standbys of s1, s2, s3 and s4 reply.

FIRST and ANY are case-insensitive. If these keywords are used as the name of a standby server, its standby_name must be double-quoted.

The third syntax was used before PostgreSQL version 9.6 and is still supported. It's the same as the first syntax with FIRST and num_sync equal to 1. For example, FIRST 1 (s1, s2) and s1, s2 have the same meaning: either s1 or s2 is chosen as a synchronous standby.

The special entry * matches any standby name.

There is no mechanism to enforce uniqueness of standby names. In case of duplicates one of the matching standbys will be considered as higher priority, though exactly which one is indeterminate.

Each standby_name should have the form of a valid SQL identifier, unless it is *. You can use double-quoting if necessary. But note that standby_names are compared to standby application names case-insensitively, whether double-quoted or not.

If no synchronous standby names are specified here, then synchronous replication is not enabled and transaction commits will not wait for replication. This is the default configuration. Even when synchronous replication is enabled, individual transactions can be configured not to wait for replication by setting the synchronous_commit parameter to local or off.

This parameter can only be set in the postgresql.conf file or on the server command line.

These settings control the behavior of a standby server that is to receive replication data. Their values on the primary server are irrelevant.

Specifies a connection string to be used for the standby server to connect with a sending server. This string is in the format described in Section 32.1.1. If any option is unspecified in this string, then the corresponding environment variable (see Section 32.15) is checked. If the environment variable is not set either, then defaults are used.

The connection string should specify the host name (or address) of the sending server, as well as the port number if it is not the same as the standby server's default. Also specify a user name corresponding to a suitably-privileged role on the sending server (see Section 26.2.5.1). A password needs to be provided too, if the sender demands password authentication. It can be provided in the primary_conninfo string, or in a separate ~/.pgpass file on the standby server (use replication as the database name).

For replication slot synchronization (see Section 47.2.3), it is also necessary to specify a valid dbname in the primary_conninfo string. This will only be used for slot synchronization. It is ignored for streaming.

This parameter can only be set in the postgresql.conf file or on the server command line. If this parameter is changed while the WAL receiver process is running, that process is signaled to shut down and expected to restart with the new setting (except if primary_conninfo is an empty string). This setting has no effect if the server is not in standby mode.

Optionally specifies an existing replication slot to be used when connecting to the sending server via streaming replication to control resource removal on the upstream node (see Section 26.2.6). This parameter can only be set in the postgresql.conf file or on the server command line. If this parameter is changed while the WAL receiver process is running, that process is signaled to shut down and expected to restart with the new setting. This setting has no effect if primary_conninfo is not set or the server is not in standby mode.

Specifies whether or not you can connect and run queries during recovery, as described in Section 26.4. The default value is on. This parameter can only be set at server start. It only has effect during archive recovery or in standby mode.

When hot standby is active, this parameter determines how long the standby server should wait before canceling standby queries that conflict with about-to-be-applied WAL entries, as described in Section 26.4.2. max_standby_archive_delay applies when WAL data is being read from WAL archive (and is therefore not current). If this value is specified without units, it is taken as milliseconds. The default is 30 seconds. A value of -1 allows the standby to wait forever for conflicting queries to complete. This parameter can only be set in the postgresql.conf file or on the server command line.

Note that max_standby_archive_delay is not the same as the maximum length of time a query can run before cancellation; rather it is the maximum total time allowed to apply any one WAL segment's data. Thus, if one query has resulted in significant delay earlier in the WAL segment, subsequent conflicting queries will have much less grace time.

When hot standby is active, this parameter determines how long the standby server should wait before canceling standby queries that conflict with about-to-be-applied WAL entries, as described in Section 26.4.2. max_standby_streaming_delay applies when WAL data is being received via streaming replication. If this value is specified without units, it is taken as milliseconds. The default is 30 seconds. A value of -1 allows the standby to wait forever for conflicting queries to complete. This parameter can only be set in the postgresql.conf file or on the server command line.

Note that max_standby_streaming_delay is not the same as the maximum length of time a query can run before cancellation; rather it is the maximum total time allowed to apply WAL data once it has been received from the primary server. Thus, if one query has resulted in significant delay, subsequent conflicting queries will have much less grace time until the standby server has caught up again.

Specifies whether the WAL receiver process should create a temporary replication slot on the remote instance when no permanent replication slot to use has been configured (using primary_slot_name). The default is off. This parameter can only be set in the postgresql.conf file or on the server command line. If this parameter is changed while the WAL receiver process is running, that process is signaled to shut down and expected to restart with the new setting.

Specifies the minimum frequency for the WAL receiver process on the standby to send information about replication progress to the primary or upstream standby, where it can be seen using the pg_stat_replication view. The standby will report the last write-ahead log location it has written, the last position it has flushed to disk, and the last position it has applied. This parameter's value is the maximum amount of time between reports. Updates are sent each time the write or flush positions change, or as often as specified by this parameter if set to a non-zero value. There are additional cases where updates are sent while ignoring this parameter; for example, when processing of the existing WAL completes or when synchronous_commit is set to remote_apply. Thus, the apply position may lag slightly behind the true position. If this value is specified without units, it is taken as seconds. The default value is 10 seconds. This parameter can only be set in the postgresql.conf file or on the server command line.

Specifies whether or not a hot standby will send feedback to the primary or upstream standby about queries currently executing on the standby. This parameter can be used to eliminate query cancels caused by cleanup records, but can cause database bloat on the primary for some workloads. Feedback messages will not be sent more frequently than once per wal_receiver_status_interval. The default value is off. This parameter can only be set in the postgresql.conf file or on the server command line.

If cascaded replication is in use the feedback is passed upstream until it eventually reaches the primary. Standbys make no other use of feedback they receive other than to pass upstream.

Note that if the clock on standby is moved ahead or backward, the feedback message might not be sent at the required interval. In extreme cases, this can lead to a prolonged risk of not removing dead rows on the primary for extended periods, as the feedback mechanism is based on timestamps.

Terminate replication connections that are inactive for longer than this amount of time. This is useful for the receiving standby server to detect a primary node crash or network outage. If this value is specified without units, it is taken as milliseconds. The default value is 60 seconds. A value of zero disables the timeout mechanism. This parameter can only be set in the postgresql.conf file or on the server command line.

Specifies how long the standby server should wait when WAL data is not available from any sources (streaming replication, local pg_wal or WAL archive) before trying again to retrieve WAL data. If this value is specified without units, it is taken as milliseconds. The default value is 5 seconds. This parameter can only be set in the postgresql.conf file or on the server command line.

This parameter is useful in configurations where a node in recovery needs to control the amount of time to wait for new WAL data to be available. For example, in archive recovery, it is possible to make the recovery more responsive in the detection of a new WAL file by reducing the value of this parameter. On a system with low WAL activity, increasing it reduces the amount of requests necessary to access WAL archives, something useful for example in cloud environments where the number of times an infrastructure is accessed is taken into account.

In logical replication, this parameter also limits how often a failing replication apply worker or table synchronization worker will be respawned.

By default, a standby server restores WAL records from the sending server as soon as possible. It may be useful to have a time-delayed copy of the data, offering opportunities to correct data loss errors. This parameter allows you to delay recovery by a specified amount of time. For example, if you set this parameter to 5min, the standby will replay each transaction commit only when the system time on the standby is at least five minutes past the commit time reported by the primary. If this value is specified without units, it is taken as milliseconds. The default is zero, adding no delay.

It is possible that the replication delay between servers exceeds the value of this parameter, in which case no delay is added. Note that the delay is calculated between the WAL time stamp as written on primary and the current time on the standby. Delays in transfer because of network lag or cascading replication configurations may reduce the actual wait time significantly. If the system clocks on primary and standby are not synchronized, this may lead to recovery applying records earlier than expected; but that is not a major issue because useful settings of this parameter are much larger than typical time deviations between servers.

The delay occurs only on WAL records for transaction commits. Other records are replayed as quickly as possible, which is not a problem because MVCC visibility rules ensure their effects are not visible until the corresponding commit record is applied.

The delay occurs once the database in recovery has reached a consistent state, until the standby is promoted or triggered. After that the standby will end recovery without further waiting.

WAL records must be kept on the standby until they are ready to be applied. Therefore, longer delays will result in a greater accumulation of WAL files, increasing disk space requirements for the standby's pg_wal directory.

This parameter is intended for use with streaming replication deployments; however, if the parameter is specified it will be honored in all cases except crash recovery. hot_standby_feedback will be delayed by use of this feature which could lead to bloat on the primary; use both together with care.

Synchronous replication is affected by this setting when synchronous_commit is set to remote_apply; every COMMIT will need to wait to be applied.

This parameter can only be set in the postgresql.conf file or on the server command line.

It enables a physical standby to synchronize logical failover slots from the primary server so that logical subscribers can resume replication from the new primary server after failover.

It is disabled by default. This parameter can only be set in the postgresql.conf file or on the server command line.

These settings control the behavior of a logical replication subscriber. Their values on the publisher are irrelevant. See Section 29.12 for more details.

Specifies how many replication origins (see Chapter 48) can be tracked simultaneously, effectively limiting how many logical replication subscriptions can be created on the server. Setting it to a lower value than the current number of tracked replication origins (reflected in pg_replication_origin_status) will prevent the server from starting. It defaults to 10. This parameter can only be set at server start. max_active_replication_origins must be set to at least the number of subscriptions that will be added to the subscriber, plus some reserve for table synchronization.

Specifies maximum number of logical replication workers. This includes leader apply workers, parallel apply workers, and table synchronization workers.

Logical replication workers are taken from the pool defined by max_worker_processes.

The default value is 4. This parameter can only be set at server start.

Maximum number of synchronization workers per subscription. This parameter controls the amount of parallelism of the initial data copy during the subscription initialization or when new tables are added.

Currently, there can be only one synchronization worker per table.

The synchronization workers are taken from the pool defined by max_logical_replication_workers.

The default value is 2. This parameter can only be set in the postgresql.conf file or on the server command line.

Maximum number of parallel apply workers per subscription. This parameter controls the amount of parallelism for streaming of in-progress transactions with subscription parameter streaming = parallel.

The parallel apply workers are taken from the pool defined by max_logical_replication_workers.

The default value is 2. This parameter can only be set in the postgresql.conf file or on the server command line.

Examples:

Example 1 (unknown):

[FIRST] num_sync ( standby_name [, ...] )
ANY num_sync ( standby_name [, ...] )
standby_name [, ...]

---

PostgreSQL: Documentation: 18: 35.32. key_column_usage

URL: https://www.postgresql.org/docs/current/infoschema-key-column-usage.html

Contents:

• 35.32. key_column_usage #

The view key_column_usage identifies all columns in the current database that are restricted by some unique, primary key, or foreign key constraint. Check constraints are not included in this view. Only those columns are shown that the current user has access to, by way of being the owner or having some privilege.

Table 35.30. key_column_usage Columns

constraint_catalog sql_identifier

Name of the database that contains the constraint (always the current database)

constraint_schema sql_identifier

Name of the schema that contains the constraint

constraint_name sql_identifier

Name of the constraint

table_catalog sql_identifier

Name of the database that contains the table that contains the column that is restricted by this constraint (always the current database)

table_schema sql_identifier

Name of the schema that contains the table that contains the column that is restricted by this constraint

table_name sql_identifier

Name of the table that contains the column that is restricted by this constraint

column_name sql_identifier

Name of the column that is restricted by this constraint

ordinal_position cardinal_number

Ordinal position of the column within the constraint key (count starts at 1)

position_in_unique_constraint cardinal_number

For a foreign-key constraint, ordinal position of the referenced column within its unique constraint (count starts at 1); otherwise null

---

PostgreSQL: Documentation: 18: 35.54. tables

URL: https://www.postgresql.org/docs/current/infoschema-tables.html

Contents:

• 35.54. tables #

The view tables contains all tables and views defined in the current database. Only those tables and views are shown that the current user has access to (by way of being the owner or having some privilege).

Table 35.52. tables Columns

table_catalog sql_identifier

Name of the database that contains the table (always the current database)

table_schema sql_identifier

Name of the schema that contains the table

table_name sql_identifier

table_type character_data

Type of the table: BASE TABLE for a persistent base table (the normal table type), VIEW for a view, FOREIGN for a foreign table, or LOCAL TEMPORARY for a temporary table

self_referencing_column_name sql_identifier

Applies to a feature not available in PostgreSQL

reference_generation character_data

Applies to a feature not available in PostgreSQL

user_defined_type_catalog sql_identifier

If the table is a typed table, the name of the database that contains the underlying data type (always the current database), else null.

user_defined_type_schema sql_identifier

If the table is a typed table, the name of the schema that contains the underlying data type, else null.

user_defined_type_name sql_identifier

If the table is a typed table, the name of the underlying data type, else null.

is_insertable_into yes_or_no

YES if the table is insertable into, NO if not (Base tables are always insertable into, views not necessarily.)

YES if the table is a typed table, NO if not

commit_action character_data

---

PostgreSQL: Documentation: 18: 7.1. Overview

URL: https://www.postgresql.org/docs/current/queries-overview.html

Contents:

• 7.1. Overview #

The process of retrieving or the command to retrieve data from a database is called a query. In SQL the SELECT command is used to specify queries. The general syntax of the SELECT command is

The following sections describe the details of the select list, the table expression, and the sort specification. WITH queries are treated last since they are an advanced feature.

A simple kind of query has the form:

Assuming that there is a table called table1, this command would retrieve all rows and all user-defined columns from table1. (The method of retrieval depends on the client application. For example, the psql program will display an ASCII-art table on the screen, while client libraries will offer functions to extract individual values from the query result.) The select list specification * means all columns that the table expression happens to provide. A select list can also select a subset of the available columns or make calculations using the columns. For example, if table1 has columns named a, b, and c (and perhaps others) you can make the following query:

(assuming that b and c are of a numerical data type). See Section 7.3 for more details.

FROM table1 is a simple kind of table expression: it reads just one table. In general, table expressions can be complex constructs of base tables, joins, and subqueries. But you can also omit the table expression entirely and use the SELECT command as a calculator:

This is more useful if the expressions in the select list return varying results. For example, you could call a function this way:

Examples:

Example 1 (unknown):

[WITH with_queries] SELECT select_list FROM table_expression [sort_specification]

Example 2 (unknown):

SELECT * FROM table1;

Example 3 (unknown):

SELECT a, b + c FROM table1;

Example 4 (unknown):

SELECT 3 * 4;

---

PostgreSQL: Documentation: 18: 21.5. Predefined Roles

URL: https://www.postgresql.org/docs/current/predefined-roles.html

Contents:

• 21.5. Predefined Roles #
  • Warning

PostgreSQL provides a set of predefined roles that provide access to certain, commonly needed, privileged capabilities and information. Administrators (including roles that have the CREATEROLE privilege) can GRANT these roles to users and/or other roles in their environment, providing those users with access to the specified capabilities and information. For example:

Care should be taken when granting these roles to ensure they are only used where needed and with the understanding that these roles grant access to privileged information.

The predefined roles are described below. Note that the specific permissions for each of the roles may change in the future as additional capabilities are added. Administrators should monitor the release notes for changes.

pg_checkpoint allows executing the CHECKPOINT command.

pg_create_subscription allows users with CREATE permission on the database to issue CREATE SUBSCRIPTION.

pg_database_owner always has exactly one implicit member: the current database owner. It cannot be granted membership in any role, and no role can be granted membership in pg_database_owner. However, like any other role, it can own objects and receive grants of access privileges. Consequently, once pg_database_owner has rights within a template database, each owner of a database instantiated from that template will possess those rights. Initially, this role owns the public schema, so each database owner governs local use of that schema.

pg_maintain allows executing VACUUM, ANALYZE, CLUSTER, REFRESH MATERIALIZED VIEW, REINDEX, and LOCK TABLE on all relations, as if having MAINTAIN rights on those objects.

These roles are intended to allow administrators to easily configure a role for the purpose of monitoring the database server. They grant a set of common privileges allowing the role to read various useful configuration settings, statistics, and other system information normally restricted to superusers.

pg_monitor allows reading/executing various monitoring views and functions. This role is a member of pg_read_all_settings, pg_read_all_stats and pg_stat_scan_tables.

pg_read_all_settings allows reading all configuration variables, even those normally visible only to superusers.

pg_read_all_stats allows reading all pgstat* views and use various statistics related extensions, even those normally visible only to superusers.

pg_stat_scan_tables allows executing monitoring functions that may take ACCESS SHARE locks on tables, potentially for a long time (e.g., pgrowlocks(text) in the pgrowlocks extension).

pg_read_all_data allows reading all data (tables, views, sequences), as if having SELECT rights on those objects and USAGE rights on all schemas. This role does not bypass row-level security (RLS) policies. If RLS is being used, an administrator may wish to set BYPASSRLS on roles which this role is granted to.

pg_write_all_data allows writing all data (tables, views, sequences), as if having INSERT, UPDATE, and DELETE rights on those objects and USAGE rights on all schemas. This role does not bypass row-level security (RLS) policies. If RLS is being used, an administrator may wish to set BYPASSRLS on roles which this role is granted to.

These roles are intended to allow administrators to have trusted, but non-superuser, roles which are able to access files and run programs on the database server as the user the database runs as. They bypass all database-level permission checks when accessing files directly and they could be used to gain superuser-level access. Therefore, great care should be taken when granting these roles to users.

pg_read_server_files allows reading files from any location the database can access on the server using COPY and other file-access functions.

pg_write_server_files allows writing to files in any location the database can access on the server using COPY and other file-access functions.

pg_execute_server_program allows executing programs on the database server as the user the database runs as using COPY and other functions which allow executing a server-side program.

pg_signal_autovacuum_worker allows signaling autovacuum workers to cancel the current table's vacuum or terminate its session. See Section 9.28.2.

pg_signal_backend allows signaling another backend to cancel a query or terminate its session. Note that this role does not permit signaling backends owned by a superuser. See Section 9.28.2.

pg_use_reserved_connections allows use of connection slots reserved via reserved_connections.

Examples:

Example 1 (unknown):

GRANT pg_signal_backend TO admin_user;

---

PostgreSQL: Documentation: 18: Chapter 56. Native Language Support

URL: https://www.postgresql.org/docs/current/nls.html

Contents:

• Chapter 56. Native Language Support

---

PostgreSQL: Documentation: 18: 35.41. routine_privileges

URL: https://www.postgresql.org/docs/current/infoschema-routine-privileges.html

Contents:

• 35.41. routine_privileges #

The view routine_privileges identifies all privileges granted on functions to a currently enabled role or by a currently enabled role. There is one row for each combination of function, grantor, and grantee.

Table 35.39. routine_privileges Columns

grantor sql_identifier

Name of the role that granted the privilege

grantee sql_identifier

Name of the role that the privilege was granted to

specific_catalog sql_identifier

Name of the database containing the function (always the current database)

specific_schema sql_identifier

Name of the schema containing the function

specific_name sql_identifier

The “specific name” of the function. See Section 35.45 for more information.

routine_catalog sql_identifier

Name of the database containing the function (always the current database)

routine_schema sql_identifier

Name of the schema containing the function

routine_name sql_identifier

Name of the function (might be duplicated in case of overloading)

privilege_type character_data

Always EXECUTE (the only privilege type for functions)

is_grantable yes_or_no

YES if the privilege is grantable, NO if not

---

PostgreSQL: Documentation: 18: 6.4. Returning Data from Modified Rows

URL: https://www.postgresql.org/docs/current/dml-returning.html

Contents:

• 6.4. Returning Data from Modified Rows #

Sometimes it is useful to obtain data from modified rows while they are being manipulated. The INSERT, UPDATE, DELETE, and MERGE commands all have an optional RETURNING clause that supports this. Use of RETURNING avoids performing an extra database query to collect the data, and is especially valuable when it would otherwise be difficult to identify the modified rows reliably.

The allowed contents of a RETURNING clause are the same as a SELECT command's output list (see Section 7.3). It can contain column names of the command's target table, or value expressions using those columns. A common shorthand is RETURNING *, which selects all columns of the target table in order.

In an INSERT, the default data available to RETURNING is the row as it was inserted. This is not so useful in trivial inserts, since it would just repeat the data provided by the client. But it can be very handy when relying on computed default values. For example, when using a serial column to provide unique identifiers, RETURNING can return the ID assigned to a new row:

The RETURNING clause is also very useful with INSERT ... SELECT.

In an UPDATE, the default data available to RETURNING is the new content of the modified row. For example:

In a DELETE, the default data available to RETURNING is the content of the deleted row. For example:

In a MERGE, the default data available to RETURNING is the content of the source row plus the content of the inserted, updated, or deleted target row. Since it is quite common for the source and target to have many of the same columns, specifying RETURNING * can lead to a lot of duplicated columns, so it is often more useful to qualify it so as to return just the source or target row. For example:

In each of these commands, it is also possible to explicitly return the old and new content of the modified row. For example:

In this example, writing new.price is the same as just writing price, but it makes the meaning clearer.

This syntax for returning old and new values is available in INSERT, UPDATE, DELETE, and MERGE commands, but typically old values will be NULL for an INSERT, and new values will be NULL for a DELETE. However, there are situations where it can still be useful for those commands. For example, in an INSERT with an ON CONFLICT DO UPDATE clause, the old values will be non-NULL for conflicting rows. Similarly, if a DELETE is turned into an UPDATE by a rewrite rule, the new values may be non-NULL.

If there are triggers (Chapter 37) on the target table, the data available to RETURNING is the row as modified by the triggers. Thus, inspecting columns computed by triggers is another common use-case for RETURNING.

Examples:

Example 1 (unknown):

CREATE TABLE users (firstname text, lastname text, id serial primary key);

INSERT INTO users (firstname, lastname) VALUES ('Joe', 'Cool') RETURNING id;

Example 2 (unknown):

UPDATE products SET price = price * 1.10
  WHERE price <= 99.99
  RETURNING name, price AS new_price;

Example 3 (unknown):

DELETE FROM products
  WHERE obsoletion_date = 'today'
  RETURNING *;

Example 4 (unknown):

MERGE INTO products p USING new_products n ON p.product_no = n.product_no
  WHEN NOT MATCHED THEN INSERT VALUES (n.product_no, n.name, n.price)
  WHEN MATCHED THEN UPDATE SET name = n.name, price = n.price
  RETURNING p.*;

---

PostgreSQL: Documentation: 18: 35.40. routine_column_usage

URL: https://www.postgresql.org/docs/current/infoschema-routine-column-usage.html

Contents:

• 35.40. routine_column_usage #

The view routine_column_usage identifies all columns that are used by a function or procedure, either in the SQL body or in parameter default expressions. (This only works for unquoted SQL bodies, not quoted bodies or functions in other languages.) A column is only included if its table is owned by a currently enabled role.

Table 35.38. routine_column_usage Columns

specific_catalog sql_identifier

Name of the database containing the function (always the current database)

specific_schema sql_identifier

Name of the schema containing the function

specific_name sql_identifier

The “specific name” of the function. See Section 35.45 for more information.

routine_catalog sql_identifier

Name of the database containing the function (always the current database)

routine_schema sql_identifier

Name of the schema containing the function

routine_name sql_identifier

Name of the function (might be duplicated in case of overloading)

table_catalog sql_identifier

Name of the database that contains the table that is used by the function (always the current database)

table_schema sql_identifier

Name of the schema that contains the table that is used by the function

table_name sql_identifier

Name of the table that is used by the function

column_name sql_identifier

Name of the column that is used by the function

---

PostgreSQL: Documentation: 18: 19.12. Lock Management

URL: https://www.postgresql.org/docs/current/runtime-config-locks.html

Contents:

• 19.12. Lock Management #

This is the amount of time to wait on a lock before checking to see if there is a deadlock condition. The check for deadlock is relatively expensive, so the server doesn't run it every time it waits for a lock. We optimistically assume that deadlocks are not common in production applications and just wait on the lock for a while before checking for a deadlock. Increasing this value reduces the amount of time wasted in needless deadlock checks, but slows down reporting of real deadlock errors. If this value is specified without units, it is taken as milliseconds. The default is one second (1s), which is probably about the smallest value you would want in practice. On a heavily loaded server you might want to raise it. Ideally the setting should exceed your typical transaction time, so as to improve the odds that a lock will be released before the waiter decides to check for deadlock. Only superusers and users with the appropriate SET privilege can change this setting.

When log_lock_waits is set, this parameter also determines the amount of time to wait before a log message is issued about the lock wait. If you are trying to investigate locking delays you might want to set a shorter than normal deadlock_timeout.

The shared lock table has space for max_locks_per_transaction objects (e.g., tables) per server process or prepared transaction; hence, no more than this many distinct objects can be locked at any one time. This parameter limits the average number of object locks used by each transaction; individual transactions can lock more objects as long as the locks of all transactions fit in the lock table. This is not the number of rows that can be locked; that value is unlimited. The default, 64, has historically proven sufficient, but you might need to raise this value if you have queries that touch many different tables in a single transaction, e.g., query of a parent table with many children. This parameter can only be set at server start.

When running a standby server, you must set this parameter to have the same or higher value as on the primary server. Otherwise, queries will not be allowed in the standby server.

The shared predicate lock table has space for max_pred_locks_per_transaction objects (e.g., tables) per server process or prepared transaction; hence, no more than this many distinct objects can be locked at any one time. This parameter limits the average number of object locks used by each transaction; individual transactions can lock more objects as long as the locks of all transactions fit in the lock table. This is not the number of rows that can be locked; that value is unlimited. The default, 64, has historically proven sufficient, but you might need to raise this value if you have clients that touch many different tables in a single serializable transaction. This parameter can only be set at server start.

This controls how many pages or tuples of a single relation can be predicate-locked before the lock is promoted to covering the whole relation. Values greater than or equal to zero mean an absolute limit, while negative values mean max_pred_locks_per_transaction divided by the absolute value of this setting. The default is -2, which keeps the behavior from previous versions of PostgreSQL. This parameter can only be set in the postgresql.conf file or on the server command line.

This controls how many rows on a single page can be predicate-locked before the lock is promoted to covering the whole page. The default is 2. This parameter can only be set in the postgresql.conf file or on the server command line.

---

PostgreSQL: Documentation: 18: 8.7. Enumerated Types

URL: https://www.postgresql.org/docs/current/datatype-enum.html

Contents:

• 8.7. Enumerated Types #
  • 8.7.1. Declaration of Enumerated Types #
  • 8.7.2. Ordering #
  • 8.7.3. Type Safety #
  • 8.7.4. Implementation Details #

Enumerated (enum) types are data types that comprise a static, ordered set of values. They are equivalent to the enum types supported in a number of programming languages. An example of an enum type might be the days of the week, or a set of status values for a piece of data.

Enum types are created using the CREATE TYPE command, for example:

Once created, the enum type can be used in table and function definitions much like any other type:

The ordering of the values in an enum type is the order in which the values were listed when the type was created. All standard comparison operators and related aggregate functions are supported for enums. For example:

Each enumerated data type is separate and cannot be compared with other enumerated types. See this example:

If you really need to do something like that, you can either write a custom operator or add explicit casts to your query:

Enum labels are case sensitive, so 'happy' is not the same as 'HAPPY'. White space in the labels is significant too.

Although enum types are primarily intended for static sets of values, there is support for adding new values to an existing enum type, and for renaming values (see ALTER TYPE). Existing values cannot be removed from an enum type, nor can the sort ordering of such values be changed, short of dropping and re-creating the enum type.

An enum value occupies four bytes on disk. The length of an enum value's textual label is limited by the NAMEDATALEN setting compiled into PostgreSQL; in standard builds this means at most 63 bytes.

The translations from internal enum values to textual labels are kept in the system catalog pg_enum. Querying this catalog directly can be useful.

Examples:

Example 1 (unknown):

CREATE TYPE mood AS ENUM ('sad', 'ok', 'happy');

Example 2 (unknown):

CREATE TYPE mood AS ENUM ('sad', 'ok', 'happy');
CREATE TABLE person (
    name text,
    current_mood mood
);
INSERT INTO person VALUES ('Moe', 'happy');
SELECT * FROM person WHERE current_mood = 'happy';
 name | current_mood
------+--------------
 Moe  | happy
(1 row)

Example 3 (unknown):

INSERT INTO person VALUES ('Larry', 'sad');
INSERT INTO person VALUES ('Curly', 'ok');
SELECT * FROM person WHERE current_mood > 'sad';
 name  | current_mood
-------+--------------
 Moe   | happy
 Curly | ok
(2 rows)

SELECT * FROM person WHERE current_mood > 'sad' ORDER BY current_mood;
 name  | current_mood
-------+--------------
 Curly | ok
 Moe   | happy
(2 rows)

SELECT name
FROM person
WHERE current_mood = (SELECT MIN(current_mood) FROM person);
 name
-------
 Larry
(1 row)

Example 4 (unknown):

CREATE TYPE happiness AS ENUM ('happy', 'very happy', 'ecstatic');
CREATE TABLE holidays (
    num_weeks integer,
    happiness happiness
);
INSERT INTO holidays(num_weeks,happiness) VALUES (4, 'happy');
INSERT INTO holidays(num_weeks,happiness) VALUES (6, 'very happy');
INSERT INTO holidays(num_weeks,happiness) VALUES (8, 'ecstatic');
INSERT INTO holidays(num_weeks,happiness) VALUES (2, 'sad');
ERROR:  invalid input value for enum happiness: "sad"
SELECT person.name, holidays.num_weeks FROM person, holidays
  WHERE person.current_mood = holidays.happiness;
ERROR:  operator does not exist: mood = happiness

---

PostgreSQL: Documentation: 18: TYPE

URL: https://www.postgresql.org/docs/current/ecpg-sql-type.html

Contents:

• TYPE
• Synopsis
• Description
• Parameters
• Examples
• Compatibility

TYPE — define a new data type

The TYPE command defines a new C type. It is equivalent to putting a typedef into a declare section.

This command is only recognized when ecpg is run with the -c option.

The name for the new type. It must be a valid C type name.

A C type specification.

Here is an example program that uses EXEC SQL TYPE:

The output from this program looks like this:

The TYPE command is a PostgreSQL extension.

Examples:

Example 1 (unknown):

TYPE type_name IS ctype

Example 2 (unknown):

EXEC SQL TYPE customer IS
    struct
    {
        varchar name[50];
        int     phone;
    };

EXEC SQL TYPE cust_ind IS
    struct ind
    {
        short   name_ind;
        short   phone_ind;
    };

EXEC SQL TYPE c IS char reference;
EXEC SQL TYPE ind IS union { int integer; short smallint; };
EXEC SQL TYPE intarray IS int[AMOUNT];
EXEC SQL TYPE str IS varchar[BUFFERSIZ];
EXEC SQL TYPE string IS char[11];

Example 3 (unknown):

EXEC SQL WHENEVER SQLERROR SQLPRINT;

EXEC SQL TYPE tt IS
    struct
    {
        varchar v[256];
        int     i;
    };

EXEC SQL TYPE tt_ind IS
    struct ind {
        short   v_ind;
        short   i_ind;
    };

int
main(void)
{
EXEC SQL BEGIN DECLARE SECTION;
    tt t;
    tt_ind t_ind;
EXEC SQL END DECLARE SECTION;

    EXEC SQL CONNECT TO testdb AS con1;
    EXEC SQL SELECT pg_catalog.set_config('search_path', '', false); EXEC SQL COMMIT;

    EXEC SQL SELECT current_database(), 256 INTO :t:t_ind LIMIT 1;

    printf("t.v = %s\n", t.v.arr);
    printf("t.i = %d\n", t.i);

    printf("t_ind.v_ind = %d\n", t_ind.v_ind);
    printf("t_ind.i_ind = %d\n", t_ind.i_ind);

    EXEC SQL DISCONNECT con1;

    return 0;
}

Example 4 (unknown):

t.v = testdb
t.i = 256
t_ind.v_ind = 0
t_ind.i_ind = 0

---

PostgreSQL: Documentation: 18: 14.2. Statistics Used by the Planner

URL: https://www.postgresql.org/docs/current/planner-stats.html

Contents:

• 14.2. Statistics Used by the Planner #
  • 14.2.1. Single-Column Statistics #
  • 14.2.2. Extended Statistics #
  • 14.2.2.1. Functional Dependencies #
    • 14.2.2.1.1. Limitations of Functional Dependencies #
  • 14.2.2.2. Multivariate N-Distinct Counts #
  • 14.2.2.3. Multivariate MCV Lists #

As we saw in the previous section, the query planner needs to estimate the number of rows retrieved by a query in order to make good choices of query plans. This section provides a quick look at the statistics that the system uses for these estimates.

One component of the statistics is the total number of entries in each table and index, as well as the number of disk blocks occupied by each table and index. This information is kept in the table pg_class, in the columns reltuples and relpages. We can look at it with queries similar to this one:

Here we can see that tenk1 contains 10000 rows, as do its indexes, but the indexes are (unsurprisingly) much smaller than the table.

For efficiency reasons, reltuples and relpages are not updated on-the-fly, and so they usually contain somewhat out-of-date values. They are updated by VACUUM, ANALYZE, and a few DDL commands such as CREATE INDEX. A VACUUM or ANALYZE operation that does not scan the entire table (which is commonly the case) will incrementally update the reltuples count on the basis of the part of the table it did scan, resulting in an approximate value. In any case, the planner will scale the values it finds in pg_class to match the current physical table size, thus obtaining a closer approximation.

Most queries retrieve only a fraction of the rows in a table, due to WHERE clauses that restrict the rows to be examined. The planner thus needs to make an estimate of the selectivity of WHERE clauses, that is, the fraction of rows that match each condition in the WHERE clause. The information used for this task is stored in the pg_statistic system catalog. Entries in pg_statistic are updated by the ANALYZE and VACUUM ANALYZE commands, and are always approximate even when freshly updated.

Rather than look at pg_statistic directly, it's better to look at its view pg_stats when examining the statistics manually. pg_stats is designed to be more easily readable. Furthermore, pg_stats is readable by all, whereas pg_statistic is only readable by a superuser. (This prevents unprivileged users from learning something about the contents of other people's tables from the statistics. The pg_stats view is restricted to show only rows about tables that the current user can read.) For example, we might do:

Note that two rows are displayed for the same column, one corresponding to the complete inheritance hierarchy starting at the road table (inherited=t), and another one including only the road table itself (inherited=f). (For brevity, we have only shown the first ten most-common values for the name column.)

The amount of information stored in pg_statistic by ANALYZE, in particular the maximum number of entries in the most_common_vals and histogram_bounds arrays for each column, can be set on a column-by-column basis using the ALTER TABLE SET STATISTICS command, or globally by setting the default_statistics_target configuration variable. The default limit is presently 100 entries. Raising the limit might allow more accurate planner estimates to be made, particularly for columns with irregular data distributions, at the price of consuming more space in pg_statistic and slightly more time to compute the estimates. Conversely, a lower limit might be sufficient for columns with simple data distributions.

Further details about the planner's use of statistics can be found in Chapter 69.

It is common to see slow queries running bad execution plans because multiple columns used in the query clauses are correlated. The planner normally assumes that multiple conditions are independent of each other, an assumption that does not hold when column values are correlated. Regular statistics, because of their per-individual-column nature, cannot capture any knowledge about cross-column correlation. However, PostgreSQL has the ability to compute multivariate statistics, which can capture such information.

Because the number of possible column combinations is very large, it's impractical to compute multivariate statistics automatically. Instead, extended statistics objects, more often called just statistics objects, can be created to instruct the server to obtain statistics across interesting sets of columns.

Statistics objects are created using the CREATE STATISTICS command. Creation of such an object merely creates a catalog entry expressing interest in the statistics. Actual data collection is performed by ANALYZE (either a manual command, or background auto-analyze). The collected values can be examined in the pg_statistic_ext_data catalog.

ANALYZE computes extended statistics based on the same sample of table rows that it takes for computing regular single-column statistics. Since the sample size is increased by increasing the statistics target for the table or any of its columns (as described in the previous section), a larger statistics target will normally result in more accurate extended statistics, as well as more time spent calculating them.

The following subsections describe the kinds of extended statistics that are currently supported.

The simplest kind of extended statistics tracks functional dependencies, a concept used in definitions of database normal forms. We say that column b is functionally dependent on column a if knowledge of the value of a is sufficient to determine the value of b, that is there are no two rows having the same value of a but different values of b. In a fully normalized database, functional dependencies should exist only on primary keys and superkeys. However, in practice many data sets are not fully normalized for various reasons; intentional denormalization for performance reasons is a common example. Even in a fully normalized database, there may be partial correlation between some columns, which can be expressed as partial functional dependency.

The existence of functional dependencies directly affects the accuracy of estimates in certain queries. If a query contains conditions on both the independent and the dependent column(s), the conditions on the dependent columns do not further reduce the result size; but without knowledge of the functional dependency, the query planner will assume that the conditions are independent, resulting in underestimating the result size.

To inform the planner about functional dependencies, ANALYZE can collect measurements of cross-column dependency. Assessing the degree of dependency between all sets of columns would be prohibitively expensive, so data collection is limited to those groups of columns appearing together in a statistics object defined with the dependencies option. It is advisable to create dependencies statistics only for column groups that are strongly correlated, to avoid unnecessary overhead in both ANALYZE and later query planning.

Here is an example of collecting functional-dependency statistics:

Here it can be seen that column 1 (zip code) fully determines column 5 (city) so the coefficient is 1.0, while city only determines zip code about 42% of the time, meaning that there are many cities (58%) that are represented by more than a single ZIP code.

When computing the selectivity for a query involving functionally dependent columns, the planner adjusts the per-condition selectivity estimates using the dependency coefficients so as not to produce an underestimate.

Functional dependencies are currently only applied when considering simple equality conditions that compare columns to constant values, and IN clauses with constant values. They are not used to improve estimates for equality conditions comparing two columns or comparing a column to an expression, nor for range clauses, LIKE or any other type of condition.

When estimating with functional dependencies, the planner assumes that conditions on the involved columns are compatible and hence redundant. If they are incompatible, the correct estimate would be zero rows, but that possibility is not considered. For example, given a query like

the planner will disregard the city clause as not changing the selectivity, which is correct. However, it will make the same assumption about

even though there will really be zero rows satisfying this query. Functional dependency statistics do not provide enough information to conclude that, however.

In many practical situations, this assumption is usually satisfied; for example, there might be a GUI in the application that only allows selecting compatible city and ZIP code values to use in a query. But if that's not the case, functional dependencies may not be a viable option.

Single-column statistics store the number of distinct values in each column. Estimates of the number of distinct values when combining more than one column (for example, for GROUP BY a, b) are frequently wrong when the planner only has single-column statistical data, causing it to select bad plans.

To improve such estimates, ANALYZE can collect n-distinct statistics for groups of columns. As before, it's impractical to do this for every possible column grouping, so data is collected only for those groups of columns appearing together in a statistics object defined with the ndistinct option. Data will be collected for each possible combination of two or more columns from the set of listed columns.

Continuing the previous example, the n-distinct counts in a table of ZIP codes might look like the following:

This indicates that there are three combinations of columns that have 33178 distinct values: ZIP code and state; ZIP code and city; and ZIP code, city and state (the fact that they are all equal is expected given that ZIP code alone is unique in this table). On the other hand, the combination of city and state has only 27435 distinct values.

It's advisable to create ndistinct statistics objects only on combinations of columns that are actually used for grouping, and for which misestimation of the number of groups is resulting in bad plans. Otherwise, the ANALYZE cycles are just wasted.

Another type of statistic stored for each column are most-common value lists. This allows very accurate estimates for individual columns, but may result in significant misestimates for queries with conditions on multiple columns.

To improve such estimates, ANALYZE can collect MCV lists on combinations of columns. Similarly to functional dependencies and n-distinct coefficients, it's impractical to do this for every possible column grouping. Even more so in this case, as the MCV list (unlike functional dependencies and n-distinct coefficients) does store the common column values. So data is collected only for those groups of columns appearing together in a statistics object defined with the mcv option.

Continuing the previous example, the MCV list for a table of ZIP codes might look like the following (unlike for simpler types of statistics, a function is required for inspection of MCV contents):

This indicates that the most common combination of city and state is Washington in DC, with actual frequency (in the sample) about 0.35%. The base frequency of the combination (as computed from the simple per-column frequencies) is only 0.0027%, resulting in two orders of magnitude under-estimates.

It's advisable to create MCV statistics objects only on combinations of columns that are actually used in conditions together, and for which misestimation of the number of groups is resulting in bad plans. Otherwise, the ANALYZE and planning cycles are just wasted.

Examples:

Example 1 (unknown):

SELECT relname, relkind, reltuples, relpages
FROM pg_class
WHERE relname LIKE 'tenk1%';

       relname        | relkind | reltuples | relpages
----------------------+---------+-----------+----------
 tenk1                | r       |     10000 |      345
 tenk1_hundred        | i       |     10000 |       11
 tenk1_thous_tenthous | i       |     10000 |       30
 tenk1_unique1        | i       |     10000 |       30
 tenk1_unique2        | i       |     10000 |       30
(5 rows)

Example 2 (unknown):

SELECT attname, inherited, n_distinct,
       array_to_string(most_common_vals, E'\n') as most_common_vals
FROM pg_stats
WHERE tablename = 'road';

 attname | inherited | n_distinct |          most_common_vals
---------+-----------+------------+------------------------------------
 name    | f         | -0.5681108 | I- 580                        Ramp+
         |           |            | I- 880                        Ramp+
         |           |            | Sp Railroad                       +
         |           |            | I- 580                            +
         |           |            | I- 680                        Ramp+
         |           |            | I- 80                         Ramp+
         |           |            | 14th                          St  +
         |           |            | I- 880                            +
         |           |            | Mac Arthur                    Blvd+
         |           |            | Mission                       Blvd+
...
 name    | t         |    -0.5125 | I- 580                        Ramp+
         |           |            | I- 880                        Ramp+
         |           |            | I- 580                            +
         |           |            | I- 680                        Ramp+
         |           |            | I- 80                         Ramp+
         |           |            | Sp Railroad                       +
         |           |            | I- 880                            +
         |           |            | State Hwy 13                  Ramp+
         |           |            | I- 80                             +
         |           |            | State Hwy 24                  Ramp+
...
 thepath | f         |          0 |
 thepath | t         |          0 |
(4 rows)

Example 3 (javascript):

CREATE STATISTICS stts (dependencies) ON city, zip FROM zipcodes;

ANALYZE zipcodes;

SELECT stxname, stxkeys, stxddependencies
  FROM pg_statistic_ext join pg_statistic_ext_data on (oid = stxoid)
  WHERE stxname = 'stts';
 stxname | stxkeys |             stxddependencies
---------+---------+------------------------------------------
 stts    | 1 5     | {"1 => 5": 1.000000, "5 => 1": 0.423130}
(1 row)

Example 4 (unknown):

SELECT * FROM zipcodes WHERE city = 'San Francisco' AND zip = '94105';

---

PostgreSQL: Documentation: 18: CREATE VIEW

URL: https://www.postgresql.org/docs/current/sql-createview.html

Contents:

• CREATE VIEW
• Synopsis
• Description
• Parameters
• Notes
  • Updatable Views
• Examples
• Compatibility
• See Also

CREATE VIEW — define a new view

CREATE VIEW defines a view of a query. The view is not physically materialized. Instead, the query is run every time the view is referenced in a query.

CREATE OR REPLACE VIEW is similar, but if a view of the same name already exists, it is replaced. The new query must generate the same columns that were generated by the existing view query (that is, the same column names in the same order and with the same data types), but it may add additional columns to the end of the list. The calculations giving rise to the output columns may be completely different.

If a schema name is given (for example, CREATE VIEW myschema.myview ...) then the view is created in the specified schema. Otherwise it is created in the current schema. Temporary views exist in a special schema, so a schema name cannot be given when creating a temporary view. The name of the view must be distinct from the name of any other relation (table, sequence, index, view, materialized view, or foreign table) in the same schema.

If specified, the view is created as a temporary view. Temporary views are automatically dropped at the end of the current session. Existing permanent relations with the same name are not visible to the current session while the temporary view exists, unless they are referenced with schema-qualified names.

If any of the tables referenced by the view are temporary, the view is created as a temporary view (whether TEMPORARY is specified or not).

Creates a recursive view. The syntax

A view column name list must be specified for a recursive view.

The name (optionally schema-qualified) of a view to be created.

An optional list of names to be used for columns of the view. If not given, the column names are deduced from the query.

This clause specifies optional parameters for a view; the following parameters are supported:

This parameter may be either local or cascaded, and is equivalent to specifying WITH [ CASCADED | LOCAL ] CHECK OPTION (see below).

This should be used if the view is intended to provide row-level security. See Section 39.5 for full details.

This option causes the underlying base relations to be checked against the privileges of the user of the view rather than the view owner. See the notes below for full details.

All of the above options can be changed on existing views using ALTER VIEW.

A SELECT or VALUES command which will provide the columns and rows of the view.

This option controls the behavior of automatically updatable views. When this option is specified, INSERT, UPDATE, and MERGE commands on the view will be checked to ensure that new rows satisfy the view-defining condition (that is, the new rows are checked to ensure that they are visible through the view). If they are not, the update will be rejected. If the CHECK OPTION is not specified, INSERT, UPDATE, and MERGE commands on the view are allowed to create rows that are not visible through the view. The following check options are supported:

New rows are only checked against the conditions defined directly in the view itself. Any conditions defined on underlying base views are not checked (unless they also specify the CHECK OPTION).

New rows are checked against the conditions of the view and all underlying base views. If the CHECK OPTION is specified, and neither LOCAL nor CASCADED is specified, then CASCADED is assumed.

The CHECK OPTION may not be used with RECURSIVE views.

Note that the CHECK OPTION is only supported on views that are automatically updatable, and do not have INSTEAD OF triggers or INSTEAD rules. If an automatically updatable view is defined on top of a base view that has INSTEAD OF triggers, then the LOCAL CHECK OPTION may be used to check the conditions on the automatically updatable view, but the conditions on the base view with INSTEAD OF triggers will not be checked (a cascaded check option will not cascade down to a trigger-updatable view, and any check options defined directly on a trigger-updatable view will be ignored). If the view or any of its base relations has an INSTEAD rule that causes the INSERT or UPDATE command to be rewritten, then all check options will be ignored in the rewritten query, including any checks from automatically updatable views defined on top of the relation with the INSTEAD rule. MERGE is not supported if the view or any of its base relations have rules.

Use the DROP VIEW statement to drop views.

Be careful that the names and types of the view's columns will be assigned the way you want. For example:

is bad form because the column name defaults to ?column?; also, the column data type defaults to text, which might not be what you wanted. Better style for a string literal in a view's result is something like:

By default, access to the underlying base relations referenced in the view is determined by the permissions of the view owner. In some cases, this can be used to provide secure but restricted access to the underlying tables. However, not all views are secure against tampering; see Section 39.5 for details.

If the view has the security_invoker property set to true, access to the underlying base relations is determined by the permissions of the user executing the query, rather than the view owner. Thus, the user of a security invoker view must have the relevant permissions on the view and its underlying base relations.

If any of the underlying base relations is a security invoker view, it will be treated as if it had been accessed directly from the original query. Thus, a security invoker view will always check its underlying base relations using the permissions of the current user, even if it is accessed from a view without the security_invoker property.

If any of the underlying base relations has row-level security enabled, then by default, the row-level security policies of the view owner are applied, and access to any additional relations referred to by those policies is determined by the permissions of the view owner. However, if the view has security_invoker set to true, then the policies and permissions of the invoking user are used instead, as if the base relations had been referenced directly from the query using the view.

Functions called in the view are treated the same as if they had been called directly from the query using the view. Therefore, the user of a view must have permissions to call all functions used by the view. Functions in the view are executed with the privileges of the user executing the query or the function owner, depending on whether the functions are defined as SECURITY INVOKER or SECURITY DEFINER. Thus, for example, calling CURRENT_USER directly in a view will always return the invoking user, not the view owner. This is not affected by the view's security_invoker setting, and so a view with security_invoker set to false is not equivalent to a SECURITY DEFINER function and those concepts should not be confused.

The user creating or replacing a view must have USAGE privileges on any schemas referred to in the view query, in order to look up the referenced objects in those schemas. Note, however, that this lookup only happens when the view is created or replaced. Therefore, the user of the view only requires the USAGE privilege on the schema containing the view, not on the schemas referred to in the view query, even for a security invoker view.

When CREATE OR REPLACE VIEW is used on an existing view, only the view's defining SELECT rule, plus any WITH ( ... ) parameters and its CHECK OPTION are changed. Other view properties, including ownership, permissions, and non-SELECT rules, remain unchanged. You must own the view to replace it (this includes being a member of the owning role).

Simple views are automatically updatable: the system will allow INSERT, UPDATE, DELETE, and MERGE statements to be used on the view in the same way as on a regular table. A view is automatically updatable if it satisfies all of the following conditions:

The view must have exactly one entry in its FROM list, which must be a table or another updatable view.

The view definition must not contain WITH, DISTINCT, GROUP BY, HAVING, LIMIT, or OFFSET clauses at the top level.

The view definition must not contain set operations (UNION, INTERSECT or EXCEPT) at the top level.

The view's select list must not contain any aggregates, window functions or set-returning functions.

An automatically updatable view may contain a mix of updatable and non-updatable columns. A column is updatable if it is a simple reference to an updatable column of the underlying base relation; otherwise the column is read-only, and an error will be raised if an INSERT, UPDATE, or MERGE statement attempts to assign a value to it.

If the view is automatically updatable the system will convert any INSERT, UPDATE, DELETE, or MERGE statement on the view into the corresponding statement on the underlying base relation. INSERT statements that have an ON CONFLICT UPDATE clause are fully supported.

If an automatically updatable view contains a WHERE condition, the condition restricts which rows of the base relation are available to be modified by UPDATE, DELETE, and MERGE statements on the view. However, an UPDATE or MERGE is allowed to change a row so that it no longer satisfies the WHERE condition, and thus is no longer visible through the view. Similarly, an INSERT or MERGE command can potentially insert base-relation rows that do not satisfy the WHERE condition and thus are not visible through the view (ON CONFLICT UPDATE may similarly affect an existing row not visible through the view). The CHECK OPTION may be used to prevent INSERT, UPDATE, and MERGE commands from creating such rows that are not visible through the view.

If an automatically updatable view is marked with the security_barrier property then all the view's WHERE conditions (and any conditions using operators which are marked as LEAKPROOF) will always be evaluated before any conditions that a user of the view has added. See Section 39.5 for full details. Note that, due to this, rows which are not ultimately returned (because they do not pass the user's WHERE conditions) may still end up being locked. EXPLAIN can be used to see which conditions are applied at the relation level (and therefore do not lock rows) and which are not.

A more complex view that does not satisfy all these conditions is read-only by default: the system will not allow an INSERT, UPDATE, DELETE, or MERGE on the view. You can get the effect of an updatable view by creating INSTEAD OF triggers on the view, which must convert attempted inserts, etc. on the view into appropriate actions on other tables. For more information see CREATE TRIGGER. Another possibility is to create rules (see CREATE RULE), but in practice triggers are easier to understand and use correctly. Also note that MERGE is not supported on relations with rules.

Note that the user performing the insert, update or delete on the view must have the corresponding insert, update or delete privilege on the view. In addition, by default, the view's owner must have the relevant privileges on the underlying base relations, whereas the user performing the update does not need any permissions on the underlying base relations (see Section 39.5). However, if the view has security_invoker set to true, the user performing the update, rather than the view owner, must have the relevant privileges on the underlying base relations.

Create a view consisting of all comedy films:

This will create a view containing the columns that are in the film table at the time of view creation. Though * was used to create the view, columns added later to the table will not be part of the view.

Create a view with LOCAL CHECK OPTION:

This will create a view based on the comedies view, showing only films with kind = 'Comedy' and classification = 'U'. Any attempt to INSERT or UPDATE a row in the view will be rejected if the new row doesn't have classification = 'U', but the film kind will not be checked.

Create a view with CASCADED CHECK OPTION:

This will create a view that checks both the kind and classification of new rows.

Create a view with a mix of updatable and non-updatable columns:

This view will support INSERT, UPDATE and DELETE. All the columns from the films table will be updatable, whereas the computed columns country and avg_rating will be read-only.

Create a recursive view consisting of the numbers from 1 to 100:

Notice that although the recursive view's name is schema-qualified in this CREATE, its internal self-reference is not schema-qualified. This is because the implicitly-created CTE's name cannot be schema-qualified.

CREATE OR REPLACE VIEW is a PostgreSQL language extension. So is the concept of a temporary view. The WITH ( ... ) clause is an extension as well, as are security barrier views and security invoker views.

Examples:

Example 1 (unknown):

CREATE [ OR REPLACE ] [ TEMP | TEMPORARY ] [ RECURSIVE ] VIEW name [ ( column_name [, ...] ) ]
    [ WITH ( view_option_name [= view_option_value] [, ... ] ) ]
    AS query
    [ WITH [ CASCADED | LOCAL ] CHECK OPTION ]

Example 2 (unknown):

CREATE RECURSIVE VIEW [ schema . ] view_name (column_names) AS SELECT ...;

Example 3 (unknown):

CREATE VIEW [ schema . ] view_name AS WITH RECURSIVE view_name (column_names) AS (SELECT ...) SELECT column_names FROM view_name;

Example 4 (unknown):

CREATE VIEW vista AS SELECT 'Hello World';

---

PostgreSQL: Documentation: 18: Chapter 5. Data Definition

URL: https://www.postgresql.org/docs/current/ddl.html

Contents:

• Chapter 5. Data Definition

This chapter covers how one creates the database structures that will hold one's data. In a relational database, the raw data is stored in tables, so the majority of this chapter is devoted to explaining how tables are created and modified and what features are available to control what data is stored in the tables. Subsequently, we discuss how tables can be organized into schemas, and how privileges can be assigned to tables. Finally, we will briefly look at other features that affect the data storage, such as inheritance, table partitioning, views, functions, and triggers.

---

PostgreSQL: Documentation: 18: 7.8. WITH Queries (Common Table Expressions)

URL: https://www.postgresql.org/docs/current/queries-with.html

Contents:

• 7.8. WITH Queries (Common Table Expressions) #
  • 7.8.1. SELECT in WITH #
  • 7.8.2. Recursive Queries #
  • Note
  • 7.8.2.1. Search Order #
  • Tip
  • Tip
  • 7.8.2.2. Cycle Detection #
  • Tip
  • Tip

WITH provides a way to write auxiliary statements for use in a larger query. These statements, which are often referred to as Common Table Expressions or CTEs, can be thought of as defining temporary tables that exist just for one query. Each auxiliary statement in a WITH clause can be a SELECT, INSERT, UPDATE, DELETE, or MERGE; and the WITH clause itself is attached to a primary statement that can also be a SELECT, INSERT, UPDATE, DELETE, or MERGE.

The basic value of SELECT in WITH is to break down complicated queries into simpler parts. An example is:

which displays per-product sales totals in only the top sales regions. The WITH clause defines two auxiliary statements named regional_sales and top_regions, where the output of regional_sales is used in top_regions and the output of top_regions is used in the primary SELECT query. This example could have been written without WITH, but we'd have needed two levels of nested sub-SELECTs. It's a bit easier to follow this way.

The optional RECURSIVE modifier changes WITH from a mere syntactic convenience into a feature that accomplishes things not otherwise possible in standard SQL. Using RECURSIVE, a WITH query can refer to its own output. A very simple example is this query to sum the integers from 1 through 100:

The general form of a recursive WITH query is always a non-recursive term, then UNION (or UNION ALL), then a recursive term, where only the recursive term can contain a reference to the query's own output. Such a query is executed as follows:

Recursive Query Evaluation

Evaluate the non-recursive term. For UNION (but not UNION ALL), discard duplicate rows. Include all remaining rows in the result of the recursive query, and also place them in a temporary working table.

So long as the working table is not empty, repeat these steps:

Evaluate the recursive term, substituting the current contents of the working table for the recursive self-reference. For UNION (but not UNION ALL), discard duplicate rows and rows that duplicate any previous result row. Include all remaining rows in the result of the recursive query, and also place them in a temporary intermediate table.

Replace the contents of the working table with the contents of the intermediate table, then empty the intermediate table.

While RECURSIVE allows queries to be specified recursively, internally such queries are evaluated iteratively.

In the example above, the working table has just a single row in each step, and it takes on the values from 1 through 100 in successive steps. In the 100th step, there is no output because of the WHERE clause, and so the query terminates.

Recursive queries are typically used to deal with hierarchical or tree-structured data. A useful example is this query to find all the direct and indirect sub-parts of a product, given only a table that shows immediate inclusions:

When computing a tree traversal using a recursive query, you might want to order the results in either depth-first or breadth-first order. This can be done by computing an ordering column alongside the other data columns and using that to sort the results at the end. Note that this does not actually control in which order the query evaluation visits the rows; that is as always in SQL implementation-dependent. This approach merely provides a convenient way to order the results afterwards.

To create a depth-first order, we compute for each result row an array of rows that we have visited so far. For example, consider the following query that searches a table tree using a link field:

To add depth-first ordering information, you can write this:

In the general case where more than one field needs to be used to identify a row, use an array of rows. For example, if we needed to track fields f1 and f2:

Omit the ROW() syntax in the common case where only one field needs to be tracked. This allows a simple array rather than a composite-type array to be used, gaining efficiency.

To create a breadth-first order, you can add a column that tracks the depth of the search, for example:

To get a stable sort, add data columns as secondary sorting columns.

The recursive query evaluation algorithm produces its output in breadth-first search order. However, this is an implementation detail and it is perhaps unsound to rely on it. The order of the rows within each level is certainly undefined, so some explicit ordering might be desired in any case.

There is built-in syntax to compute a depth- or breadth-first sort column. For example:

This syntax is internally expanded to something similar to the above hand-written forms. The SEARCH clause specifies whether depth- or breadth first search is wanted, the list of columns to track for sorting, and a column name that will contain the result data that can be used for sorting. That column will implicitly be added to the output rows of the CTE.

When working with recursive queries it is important to be sure that the recursive part of the query will eventually return no tuples, or else the query will loop indefinitely. Sometimes, using UNION instead of UNION ALL can accomplish this by discarding rows that duplicate previous output rows. However, often a cycle does not involve output rows that are completely duplicate: it may be necessary to check just one or a few fields to see if the same point has been reached before. The standard method for handling such situations is to compute an array of the already-visited values. For example, consider again the following query that searches a table graph using a link field:

This query will loop if the link relationships contain cycles. Because we require a “depth” output, just changing UNION ALL to UNION would not eliminate the looping. Instead we need to recognize whether we have reached the same row again while following a particular path of links. We add two columns is_cycle and path to the loop-prone query:

Aside from preventing cycles, the array value is often useful in its own right as representing the “path” taken to reach any particular row.

In the general case where more than one field needs to be checked to recognize a cycle, use an array of rows. For example, if we needed to compare fields f1 and f2:

Omit the ROW() syntax in the common case where only one field needs to be checked to recognize a cycle. This allows a simple array rather than a composite-type array to be used, gaining efficiency.

There is built-in syntax to simplify cycle detection. The above query can also be written like this:

and it will be internally rewritten to the above form. The CYCLE clause specifies first the list of columns to track for cycle detection, then a column name that will show whether a cycle has been detected, and finally the name of another column that will track the path. The cycle and path columns will implicitly be added to the output rows of the CTE.

The cycle path column is computed in the same way as the depth-first ordering column show in the previous section. A query can have both a SEARCH and a CYCLE clause, but a depth-first search specification and a cycle detection specification would create redundant computations, so it's more efficient to just use the CYCLE clause and order by the path column. If breadth-first ordering is wanted, then specifying both SEARCH and CYCLE can be useful.

A helpful trick for testing queries when you are not certain if they might loop is to place a LIMIT in the parent query. For example, this query would loop forever without the LIMIT:

This works because PostgreSQL's implementation evaluates only as many rows of a WITH query as are actually fetched by the parent query. Using this trick in production is not recommended, because other systems might work differently. Also, it usually won't work if you make the outer query sort the recursive query's results or join them to some other table, because in such cases the outer query will usually try to fetch all of the WITH query's output anyway.

A useful property of WITH queries is that they are normally evaluated only once per execution of the parent query, even if they are referred to more than once by the parent query or sibling WITH queries. Thus, expensive calculations that are needed in multiple places can be placed within a WITH query to avoid redundant work. Another possible application is to prevent unwanted multiple evaluations of functions with side-effects. However, the other side of this coin is that the optimizer is not able to push restrictions from the parent query down into a multiply-referenced WITH query, since that might affect all uses of the WITH query's output when it should affect only one. The multiply-referenced WITH query will be evaluated as written, without suppression of rows that the parent query might discard afterwards. (But, as mentioned above, evaluation might stop early if the reference(s) to the query demand only a limited number of rows.)

However, if a WITH query is non-recursive and side-effect-free (that is, it is a SELECT containing no volatile functions) then it can be folded into the parent query, allowing joint optimization of the two query levels. By default, this happens if the parent query references the WITH query just once, but not if it references the WITH query more than once. You can override that decision by specifying MATERIALIZED to force separate calculation of the WITH query, or by specifying NOT MATERIALIZED to force it to be merged into the parent query. The latter choice risks duplicate computation of the WITH query, but it can still give a net savings if each usage of the WITH query needs only a small part of the WITH query's full output.

A simple example of these rules is

This WITH query will be folded, producing the same execution plan as

In particular, if there's an index on key, it will probably be used to fetch just the rows having key = 123. On the other hand, in

the WITH query will be materialized, producing a temporary copy of big_table that is then joined with itself — without benefit of any index. This query will be executed much more efficiently if written as

so that the parent query's restrictions can be applied directly to scans of big_table.

An example where NOT MATERIALIZED could be undesirable is

Here, materialization of the WITH query ensures that very_expensive_function is evaluated only once per table row, not twice.

The examples above only show WITH being used with SELECT, but it can be attached in the same way to INSERT, UPDATE, DELETE, or MERGE. In each case it effectively provides temporary table(s) that can be referred to in the main command.

You can use data-modifying statements (INSERT, UPDATE, DELETE, or MERGE) in WITH. This allows you to perform several different operations in the same query. An example is:

This query effectively moves rows from products to products_log. The DELETE in WITH deletes the specified rows from products, returning their contents by means of its RETURNING clause; and then the primary query reads that output and inserts it into products_log.

A fine point of the above example is that the WITH clause is attached to the INSERT, not the sub-SELECT within the INSERT. This is necessary because data-modifying statements are only allowed in WITH clauses that are attached to the top-level statement. However, normal WITH visibility rules apply, so it is possible to refer to the WITH statement's output from the sub-SELECT.

Data-modifying statements in WITH usually have RETURNING clauses (see Section 6.4), as shown in the example above. It is the output of the RETURNING clause, not the target table of the data-modifying statement, that forms the temporary table that can be referred to by the rest of the query. If a data-modifying statement in WITH lacks a RETURNING clause, then it forms no temporary table and cannot be referred to in the rest of the query. Such a statement will be executed nonetheless. A not-particularly-useful example is:

This example would remove all rows from tables foo and bar. The number of affected rows reported to the client would only include rows removed from bar.

Recursive self-references in data-modifying statements are not allowed. In some cases it is possible to work around this limitation by referring to the output of a recursive WITH, for example:

This query would remove all direct and indirect subparts of a product.

Data-modifying statements in WITH are executed exactly once, and always to completion, independently of whether the primary query reads all (or indeed any) of their output. Notice that this is different from the rule for SELECT in WITH: as stated in the previous section, execution of a SELECT is carried only as far as the primary query demands its output.

The sub-statements in WITH are executed concurrently with each other and with the main query. Therefore, when using data-modifying statements in WITH, the order in which the specified updates actually happen is unpredictable. All the statements are executed with the same snapshot (see Chapter 13), so they cannot “see” one another's effects on the target tables. This alleviates the effects of the unpredictability of the actual order of row updates, and means that RETURNING data is the only way to communicate changes between different WITH sub-statements and the main query. An example of this is that in

the outer SELECT would return the original prices before the action of the UPDATE, while in

the outer SELECT would return the updated data.

Trying to update the same row twice in a single statement is not supported. Only one of the modifications takes place, but it is not easy (and sometimes not possible) to reliably predict which one. This also applies to deleting a row that was already updated in the same statement: only the update is performed. Therefore you should generally avoid trying to modify a single row twice in a single statement. In particular avoid writing WITH sub-statements that could affect the same rows changed by the main statement or a sibling sub-statement. The effects of such a statement will not be predictable.

At present, any table used as the target of a data-modifying statement in WITH must not have a conditional rule, nor an ALSO rule, nor an INSTEAD rule that expands to multiple statements.

Examples:

Example 1 (unknown):

WITH regional_sales AS (
    SELECT region, SUM(amount) AS total_sales
    FROM orders
    GROUP BY region
), top_regions AS (
    SELECT region
    FROM regional_sales
    WHERE total_sales > (SELECT SUM(total_sales)/10 FROM regional_sales)
)
SELECT region,
       product,
       SUM(quantity) AS product_units,
       SUM(amount) AS product_sales
FROM orders
WHERE region IN (SELECT region FROM top_regions)
GROUP BY region, product;

Example 2 (unknown):

WITH RECURSIVE t(n) AS (
    VALUES (1)
  UNION ALL
    SELECT n+1 FROM t WHERE n < 100
)
SELECT sum(n) FROM t;

Example 3 (unknown):

WITH RECURSIVE included_parts(sub_part, part, quantity) AS (
    SELECT sub_part, part, quantity FROM parts WHERE part = 'our_product'
  UNION ALL
    SELECT p.sub_part, p.part, p.quantity * pr.quantity
    FROM included_parts pr, parts p
    WHERE p.part = pr.sub_part
)
SELECT sub_part, SUM(quantity) as total_quantity
FROM included_parts
GROUP BY sub_part

Example 4 (unknown):

WITH RECURSIVE search_tree(id, link, data) AS (
    SELECT t.id, t.link, t.data
    FROM tree t
  UNION ALL
    SELECT t.id, t.link, t.data
    FROM tree t, search_tree st
    WHERE t.id = st.link
)
SELECT * FROM search_tree;

---

PostgreSQL: Documentation: 18: 7.6. LIMIT and OFFSET

URL: https://www.postgresql.org/docs/current/queries-limit.html

Contents:

• 7.6. LIMIT and OFFSET #

LIMIT and OFFSET allow you to retrieve just a portion of the rows that are generated by the rest of the query:

If a limit count is given, no more than that many rows will be returned (but possibly fewer, if the query itself yields fewer rows). LIMIT ALL is the same as omitting the LIMIT clause, as is LIMIT with a NULL argument.

OFFSET says to skip that many rows before beginning to return rows. OFFSET 0 is the same as omitting the OFFSET clause, as is OFFSET with a NULL argument.

If both OFFSET and LIMIT appear, then OFFSET rows are skipped before starting to count the LIMIT rows that are returned.

When using LIMIT, it is important to use an ORDER BY clause that constrains the result rows into a unique order. Otherwise you will get an unpredictable subset of the query's rows. You might be asking for the tenth through twentieth rows, but tenth through twentieth in what ordering? The ordering is unknown, unless you specified ORDER BY.

The query optimizer takes LIMIT into account when generating query plans, so you are very likely to get different plans (yielding different row orders) depending on what you give for LIMIT and OFFSET. Thus, using different LIMIT/OFFSET values to select different subsets of a query result will give inconsistent results unless you enforce a predictable result ordering with ORDER BY. This is not a bug; it is an inherent consequence of the fact that SQL does not promise to deliver the results of a query in any particular order unless ORDER BY is used to constrain the order.

The rows skipped by an OFFSET clause still have to be computed inside the server; therefore a large OFFSET might be inefficient.

Examples:

Example 1 (unknown):

SELECT select_list
    FROM table_expression
    [ ORDER BY ... ]
    [ LIMIT { count | ALL } ]
    [ OFFSET start ]

---

PostgreSQL: Documentation: 18: 27.5. Dynamic Tracing

URL: https://www.postgresql.org/docs/current/dynamic-trace.html

Contents:

• 27.5. Dynamic Tracing #
  • 27.5.1. Compiling for Dynamic Tracing #
  • 27.5.2. Built-in Probes #
  • 27.5.3. Using Probes #
  • Note
  • 27.5.4. Defining New Probes #

PostgreSQL provides facilities to support dynamic tracing of the database server. This allows an external utility to be called at specific points in the code and thereby trace execution.

A number of probes or trace points are already inserted into the source code. These probes are intended to be used by database developers and administrators. By default the probes are not compiled into PostgreSQL; the user needs to explicitly tell the configure script to make the probes available.

Currently, the DTrace utility is supported, which, at the time of this writing, is available on Solaris, macOS, FreeBSD, NetBSD, and Oracle Linux. The SystemTap project for Linux provides a DTrace equivalent and can also be used. Supporting other dynamic tracing utilities is theoretically possible by changing the definitions for the macros in src/include/utils/probes.h.

By default, probes are not available, so you will need to explicitly tell the configure script to make the probes available in PostgreSQL. To include DTrace support specify --enable-dtrace to configure. See Section 17.3.3.6 for further information.

A number of standard probes are provided in the source code, as shown in Table 27.49; Table 27.50 shows the types used in the probes. More probes can certainly be added to enhance PostgreSQL's observability.

Table 27.49. Built-in DTrace Probes

Table 27.50. Defined Types Used in Probe Parameters

The example below shows a DTrace script for analyzing transaction counts in the system, as an alternative to snapshotting pg_stat_database before and after a performance test:

When executed, the example D script gives output such as:

SystemTap uses a different notation for trace scripts than DTrace does, even though the underlying trace points are compatible. One point worth noting is that at this writing, SystemTap scripts must reference probe names using double underscores in place of hyphens. This is expected to be fixed in future SystemTap releases.

You should remember that DTrace scripts need to be carefully written and debugged, otherwise the trace information collected might be meaningless. In most cases where problems are found it is the instrumentation that is at fault, not the underlying system. When discussing information found using dynamic tracing, be sure to enclose the script used to allow that too to be checked and discussed.

New probes can be defined within the code wherever the developer desires, though this will require a recompilation. Below are the steps for inserting new probes:

Decide on probe names and data to be made available through the probes

Add the probe definitions to src/backend/utils/probes.d

Include pg_trace.h if it is not already present in the module(s) containing the probe points, and insert TRACE_POSTGRESQL probe macros at the desired locations in the source code

Recompile and verify that the new probes are available

Example: Here is an example of how you would add a probe to trace all new transactions by transaction ID.

Decide that the probe will be named transaction-start and requires a parameter of type LocalTransactionId

Add the probe definition to src/backend/utils/probes.d:

Note the use of the double underline in the probe name. In a DTrace script using the probe, the double underline needs to be replaced with a hyphen, so transaction-start is the name to document for users.

At compile time, transaction__start is converted to a macro called TRACE_POSTGRESQL_TRANSACTION_START (notice the underscores are single here), which is available by including pg_trace.h. Add the macro call to the appropriate location in the source code. In this case, it looks like the following:

After recompiling and running the new binary, check that your newly added probe is available by executing the following DTrace command. You should see similar output:

There are a few things to be careful about when adding trace macros to the C code:

You should take care that the data types specified for a probe's parameters match the data types of the variables used in the macro. Otherwise, you will get compilation errors.

On most platforms, if PostgreSQL is built with --enable-dtrace, the arguments to a trace macro will be evaluated whenever control passes through the macro, even if no tracing is being done. This is usually not worth worrying about if you are just reporting the values of a few local variables. But beware of putting expensive function calls into the arguments. If you need to do that, consider protecting the macro with a check to see if the trace is actually enabled:

Each trace macro has a corresponding ENABLED macro.

Examples:

Example 1 (unknown):

#!/usr/sbin/dtrace -qs

postgresql$1:::transaction-start
{
      @start["Start"] = count();
      self->ts  = timestamp;
}

postgresql$1:::transaction-abort
{
      @abort["Abort"] = count();
}

postgresql$1:::transaction-commit
/self->ts/
{
      @commit["Commit"] = count();
      @time["Total time (ns)"] = sum(timestamp - self->ts);
      self->ts=0;
}

Example 2 (unknown):

# ./txn_count.d `pgrep -n postgres` or ./txn_count.d <PID>
^C

Start                                          71
Commit                                         70
Total time (ns)                        2312105013

Example 3 (unknown):

probe transaction__start(LocalTransactionId);

Example 4 (unknown):

TRACE_POSTGRESQL_TRANSACTION_START(vxid.localTransactionId);

---

PostgreSQL: Documentation: 18: Chapter 8. Data Types

URL: https://www.postgresql.org/docs/current/datatype.html

Contents:

• Chapter 8. Data Types
  • Compatibility

PostgreSQL has a rich set of native data types available to users. Users can add new types to PostgreSQL using the CREATE TYPE command.

Table 8.1 shows all the built-in general-purpose data types. Most of the alternative names listed in the “Aliases” column are the names used internally by PostgreSQL for historical reasons. In addition, some internally used or deprecated types are available, but are not listed here.

Table 8.1. Data Types

The following types (or spellings thereof) are specified by SQL: bigint, bit, bit varying, boolean, char, character varying, character, varchar, date, double precision, integer, interval, numeric, decimal, real, smallint, time (with or without time zone), timestamp (with or without time zone), xml.

Each data type has an external representation determined by its input and output functions. Many of the built-in types have obvious external formats. However, several types are either unique to PostgreSQL, such as geometric paths, or have several possible formats, such as the date and time types. Some of the input and output functions are not invertible, i.e., the result of an output function might lose accuracy when compared to the original input.

---

PostgreSQL: Documentation: 18: Appendix G. Additional Supplied Programs

URL: https://www.postgresql.org/docs/current/contrib-prog.html

Contents:

• Appendix G. Additional Supplied Programs

This appendix and the previous one contain information regarding the modules that can be found in the contrib directory of the PostgreSQL distribution. See Appendix F for more information about the contrib section in general and server extensions and plug-ins found in contrib specifically.

This appendix covers utility programs found in contrib. Once installed, either from source or a packaging system, they are found in the bin directory of the PostgreSQL installation and can be used like any other program.

---

PostgreSQL: Documentation: 18: 8.6. Boolean Type

URL: https://www.postgresql.org/docs/current/datatype-boolean.html

Contents:

• 8.6. Boolean Type #

PostgreSQL provides the standard SQL type boolean; see Table 8.19. The boolean type can have several states: “true”, “false”, and a third state, “unknown”, which is represented by the SQL null value.

Table 8.19. Boolean Data Type

Boolean constants can be represented in SQL queries by the SQL key words TRUE, FALSE, and NULL.

The datatype input function for type boolean accepts these string representations for the “true” state:

and these representations for the “false” state:

Unique prefixes of these strings are also accepted, for example t or n. Leading or trailing whitespace is ignored, and case does not matter.

The datatype output function for type boolean always emits either t or f, as shown in Example 8.2.

Example 8.2. Using the boolean Type

The key words TRUE and FALSE are the preferred (SQL-compliant) method for writing Boolean constants in SQL queries. But you can also use the string representations by following the generic string-literal constant syntax described in Section 4.1.2.7, for example 'yes'::boolean.

Note that the parser automatically understands that TRUE and FALSE are of type boolean, but this is not so for NULL because that can have any type. So in some contexts you might have to cast NULL to boolean explicitly, for example NULL::boolean. Conversely, the cast can be omitted from a string-literal Boolean value in contexts where the parser can deduce that the literal must be of type boolean.

Examples:

Example 1 (unknown):

CREATE TABLE test1 (a boolean, b text);
INSERT INTO test1 VALUES (TRUE, 'sic est');
INSERT INTO test1 VALUES (FALSE, 'non est');
SELECT * FROM test1;
 a |    b
---+---------
 t | sic est
 f | non est

SELECT * FROM test1 WHERE a;
 a |    b
---+---------
 t | sic est

---

PostgreSQL: Documentation: 18: Chapter 27. Monitoring Database Activity

URL: https://www.postgresql.org/docs/current/monitoring.html

Contents:

• Chapter 27. Monitoring Database Activity

A database administrator frequently wonders, “What is the system doing right now?” This chapter discusses how to find that out.

Several tools are available for monitoring database activity and analyzing performance. Most of this chapter is devoted to describing PostgreSQL's cumulative statistics system, but one should not neglect regular Unix monitoring programs such as ps, top, iostat, and vmstat. Also, once one has identified a poorly-performing query, further investigation might be needed using PostgreSQL's EXPLAIN command. Section 14.1 discusses EXPLAIN and other methods for understanding the behavior of an individual query.

---

PostgreSQL: Documentation: 18: Chapter 46. Background Worker Processes

URL: https://www.postgresql.org/docs/current/bgworker.html

Contents:

• Chapter 46. Background Worker Processes
  • Warning

PostgreSQL can be extended to run user-supplied code in separate processes. Such processes are started, stopped and monitored by postgres, which permits them to have a lifetime closely linked to the server's status. These processes are attached to PostgreSQL's shared memory area and have the option to connect to databases internally; they can also run multiple transactions serially, just like a regular client-connected server process. Also, by linking to libpq they can connect to the server and behave like a regular client application.

There are considerable robustness and security risks in using background worker processes because, being written in the C language, they have unrestricted access to data. Administrators wishing to enable modules that include background worker processes should exercise extreme caution. Only carefully audited modules should be permitted to run background worker processes.

Background workers can be initialized at the time that PostgreSQL is started by including the module name in shared_preload_libraries. A module wishing to run a background worker can register it by calling RegisterBackgroundWorker(BackgroundWorker *worker) from its _PG_init() function. Background workers can also be started after the system is up and running by calling RegisterDynamicBackgroundWorker(BackgroundWorker *worker, BackgroundWorkerHandle **handle). Unlike RegisterBackgroundWorker, which can only be called from within the postmaster process, RegisterDynamicBackgroundWorker must be called from a regular backend or another background worker.

The structure BackgroundWorker is defined thus:

bgw_name and bgw_type are strings to be used in log messages, process listings and similar contexts. bgw_type should be the same for all background workers of the same type, so that it is possible to group such workers in a process listing, for example. bgw_name on the other hand can contain additional information about the specific process. (Typically, the string for bgw_name will contain the type somehow, but that is not strictly required.)

bgw_flags is a bitwise-or'd bit mask indicating the capabilities that the module wants. Possible values are:

Requests shared memory access. This flag is required.

Requests the ability to establish a database connection through which it can later run transactions and queries. A background worker using BGWORKER_BACKEND_DATABASE_CONNECTION to connect to a database must also attach shared memory using BGWORKER_SHMEM_ACCESS, or worker start-up will fail.

bgw_start_time is the server state during which postgres should start the process; it can be one of BgWorkerStart_PostmasterStart (start as soon as postgres itself has finished its own initialization; processes requesting this are not eligible for database connections), BgWorkerStart_ConsistentState (start as soon as a consistent state has been reached in a hot standby, allowing processes to connect to databases and run read-only queries), and BgWorkerStart_RecoveryFinished (start as soon as the system has entered normal read-write state). Note the last two values are equivalent in a server that's not a hot standby. Note that this setting only indicates when the processes are to be started; they do not stop when a different state is reached.

bgw_restart_time is the interval, in seconds, that postgres should wait before restarting the process in the event that it crashes. It can be any positive value, or BGW_NEVER_RESTART, indicating not to restart the process in case of a crash.

bgw_library_name is the name of a library in which the initial entry point for the background worker should be sought. The named library will be dynamically loaded by the worker process and bgw_function_name will be used to identify the function to be called. If calling a function in the core code, this must be set to "postgres".

bgw_function_name is the name of the function to use as the initial entry point for the new background worker. If this function is in a dynamically loaded library, it must be marked PGDLLEXPORT (and not static).

bgw_main_arg is the Datum argument to the background worker main function. This main function should take a single argument of type Datum and return void. bgw_main_arg will be passed as the argument. In addition, the global variable MyBgworkerEntry points to a copy of the BackgroundWorker structure passed at registration time; the worker may find it helpful to examine this structure.

On Windows (and anywhere else where EXEC_BACKEND is defined) or in dynamic background workers it is not safe to pass a Datum by reference, only by value. If an argument is required, it is safest to pass an int32 or other small value and use that as an index into an array allocated in shared memory. If a value like a cstring or text is passed then the pointer won't be valid from the new background worker process.

bgw_extra can contain extra data to be passed to the background worker. Unlike bgw_main_arg, this data is not passed as an argument to the worker's main function, but it can be accessed via MyBgworkerEntry, as discussed above.

bgw_notify_pid is the PID of a PostgreSQL backend process to which the postmaster should send SIGUSR1 when the process is started or exits. It should be 0 for workers registered at postmaster startup time, or when the backend registering the worker does not wish to wait for the worker to start up. Otherwise, it should be initialized to MyProcPid.

Once running, the process can connect to a database by calling BackgroundWorkerInitializeConnection(char *dbname, char *username, uint32 flags) or BackgroundWorkerInitializeConnectionByOid(Oid dboid, Oid useroid, uint32 flags). This allows the process to run transactions and queries using the SPI interface. If dbname is NULL or dboid is InvalidOid, the session is not connected to any particular database, but shared catalogs can be accessed. If username is NULL or useroid is InvalidOid, the process will run as the superuser created during initdb. If BGWORKER_BYPASS_ALLOWCONN is specified as flags it is possible to bypass the restriction to connect to databases not allowing user connections. If BGWORKER_BYPASS_ROLELOGINCHECK is specified as flags it is possible to bypass the login check for the role used to connect to databases. A background worker can only call one of these two functions, and only once. It is not possible to switch databases.

Signals are initially blocked when control reaches the background worker's main function, and must be unblocked by it; this is to allow the process to customize its signal handlers, if necessary. Signals can be unblocked in the new process by calling BackgroundWorkerUnblockSignals and blocked by calling BackgroundWorkerBlockSignals.

If bgw_restart_time for a background worker is configured as BGW_NEVER_RESTART, or if it exits with an exit code of 0 or is terminated by TerminateBackgroundWorker, it will be automatically unregistered by the postmaster on exit. Otherwise, it will be restarted after the time period configured via bgw_restart_time, or immediately if the postmaster reinitializes the cluster due to a backend failure. Backends which need to suspend execution only temporarily should use an interruptible sleep rather than exiting; this can be achieved by calling WaitLatch(). Make sure the WL_POSTMASTER_DEATH flag is set when calling that function, and verify the return code for a prompt exit in the emergency case that postgres itself has terminated.

When a background worker is registered using the RegisterDynamicBackgroundWorker function, it is possible for the backend performing the registration to obtain information regarding the status of the worker. Backends wishing to do this should pass the address of a BackgroundWorkerHandle * as the second argument to RegisterDynamicBackgroundWorker. If the worker is successfully registered, this pointer will be initialized with an opaque handle that can subsequently be passed to GetBackgroundWorkerPid(BackgroundWorkerHandle *, pid_t *) or TerminateBackgroundWorker(BackgroundWorkerHandle *). GetBackgroundWorkerPid can be used to poll the status of the worker: a return value of BGWH_NOT_YET_STARTED indicates that the worker has not yet been started by the postmaster; BGWH_STOPPED indicates that it has been started but is no longer running; and BGWH_STARTED indicates that it is currently running. In this last case, the PID will also be returned via the second argument. TerminateBackgroundWorker causes the postmaster to send SIGTERM to the worker if it is running, and to unregister it as soon as it is not.

In some cases, a process which registers a background worker may wish to wait for the worker to start up. This can be accomplished by initializing bgw_notify_pid to MyProcPid and then passing the BackgroundWorkerHandle * obtained at registration time to WaitForBackgroundWorkerStartup(BackgroundWorkerHandle *handle, pid_t *) function. This function will block until the postmaster has attempted to start the background worker, or until the postmaster dies. If the background worker is running, the return value will be BGWH_STARTED, and the PID will be written to the provided address. Otherwise, the return value will be BGWH_STOPPED or BGWH_POSTMASTER_DIED.

A process can also wait for a background worker to shut down, by using the WaitForBackgroundWorkerShutdown(BackgroundWorkerHandle *handle) function and passing the BackgroundWorkerHandle * obtained at registration. This function will block until the background worker exits, or postmaster dies. When the background worker exits, the return value is BGWH_STOPPED, if postmaster dies it will return BGWH_POSTMASTER_DIED.

Background workers can send asynchronous notification messages, either by using the NOTIFY command via SPI, or directly via Async_Notify(). Such notifications will be sent at transaction commit. Background workers should not register to receive asynchronous notifications with the LISTEN command, as there is no infrastructure for a worker to consume such notifications.

The src/test/modules/worker_spi module contains a working example, which demonstrates some useful techniques.

The maximum number of registered background workers is limited by max_worker_processes.

Examples:

Example 1 (unknown):

typedef void (*bgworker_main_type)(Datum main_arg);
typedef struct BackgroundWorker
{
    char        bgw_name[BGW_MAXLEN];
    char        bgw_type[BGW_MAXLEN];
    int         bgw_flags;
    BgWorkerStartTime bgw_start_time;
    int         bgw_restart_time;       /* in seconds, or BGW_NEVER_RESTART */
    char        bgw_library_name[MAXPGPATH];
    char        bgw_function_name[BGW_MAXLEN];
    Datum       bgw_main_arg;
    char        bgw_extra[BGW_EXTRALEN];
    pid_t       bgw_notify_pid;
} BackgroundWorker;

---

PostgreSQL: Documentation: 18: 19.14. Error Handling

URL: https://www.postgresql.org/docs/current/runtime-config-error-handling.html

Contents:

• 19.14. Error Handling #

If on, any error will terminate the current session. By default, this is set to off, so that only FATAL errors will terminate the session.

When set to on, which is the default, PostgreSQL will automatically reinitialize after a backend crash. Leaving this value set to on is normally the best way to maximize the availability of the database. However, in some circumstances, such as when PostgreSQL is being invoked by clusterware, it may be useful to disable the restart so that the clusterware can gain control and take any actions it deems appropriate.

This parameter can only be set in the postgresql.conf file or on the server command line.

When set to off, which is the default, PostgreSQL will raise a PANIC-level error on failure to flush modified data files to the file system. This causes the database server to crash. This parameter can only be set at server start.

On some operating systems, the status of data in the kernel's page cache is unknown after a write-back failure. In some cases it might have been entirely forgotten, making it unsafe to retry; the second attempt may be reported as successful, when in fact the data has been lost. In these circumstances, the only way to avoid data loss is to recover from the WAL after any failure is reported, preferably after investigating the root cause of the failure and replacing any faulty hardware.

If set to on, PostgreSQL will instead report an error but continue to run so that the data flushing operation can be retried in a later checkpoint. Only set it to on after investigating the operating system's treatment of buffered data in case of write-back failure.

When set to fsync, which is the default, PostgreSQL will recursively open and synchronize all files in the data directory before crash recovery begins. The search for files will follow symbolic links for the WAL directory and each configured tablespace (but not any other symbolic links). This is intended to make sure that all WAL and data files are durably stored on disk before replaying changes. This applies whenever starting a database cluster that did not shut down cleanly, including copies created with pg_basebackup.

On Linux, syncfs may be used instead, to ask the operating system to synchronize the file systems that contain the data directory, the WAL files and each tablespace (but not any other file systems that may be reachable through symbolic links). This may be a lot faster than the fsync setting, because it doesn't need to open each file one by one. On the other hand, it may be slower if a file system is shared by other applications that modify a lot of files, since those files will also be written to disk. Furthermore, on versions of Linux before 5.8, I/O errors encountered while writing data to disk may not be reported to PostgreSQL, and relevant error messages may appear only in kernel logs.

This parameter can only be set in the postgresql.conf file or on the server command line.

---

PostgreSQL: Documentation: 18: Chapter 55. PostgreSQL Coding Conventions

URL: https://www.postgresql.org/docs/current/source.html

Contents:

• Chapter 55. PostgreSQL Coding Conventions

---

PostgreSQL: Documentation: 18: 14.5. Non-Durable Settings

URL: https://www.postgresql.org/docs/current/non-durability.html

Contents:

• 14.5. Non-Durable Settings #

Durability is a database feature that guarantees the recording of committed transactions even if the server crashes or loses power. However, durability adds significant database overhead, so if your site does not require such a guarantee, PostgreSQL can be configured to run much faster. The following are configuration changes you can make to improve performance in such cases. Except as noted below, durability is still guaranteed in case of a crash of the database software; only an abrupt operating system crash creates a risk of data loss or corruption when these settings are used.

Place the database cluster's data directory in a memory-backed file system (i.e., RAM disk). This eliminates all database disk I/O, but limits data storage to the amount of available memory (and perhaps swap).

Turn off fsync; there is no need to flush data to disk.

Turn off synchronous_commit; there might be no need to force WAL writes to disk on every commit. This setting does risk transaction loss (though not data corruption) in case of a crash of the database.

Turn off full_page_writes; there is no need to guard against partial page writes.

Increase max_wal_size and checkpoint_timeout; this reduces the frequency of checkpoints, but increases the storage requirements of /pg_wal.

Create unlogged tables to avoid WAL writes, though it makes the tables non-crash-safe.

---

PostgreSQL: Documentation: 18: 8.18. Domain Types

URL: https://www.postgresql.org/docs/current/domains.html

Contents:

• 8.18. Domain Types #

A domain is a user-defined data type that is based on another underlying type. Optionally, it can have constraints that restrict its valid values to a subset of what the underlying type would allow. Otherwise it behaves like the underlying type — for example, any operator or function that can be applied to the underlying type will work on the domain type. The underlying type can be any built-in or user-defined base type, enum type, array type, composite type, range type, or another domain.

For example, we could create a domain over integers that accepts only positive integers:

When an operator or function of the underlying type is applied to a domain value, the domain is automatically down-cast to the underlying type. Thus, for example, the result of mytable.id - 1 is considered to be of type integer not posint. We could write (mytable.id - 1)::posint to cast the result back to posint, causing the domain's constraints to be rechecked. In this case, that would result in an error if the expression had been applied to an id value of 1. Assigning a value of the underlying type to a field or variable of the domain type is allowed without writing an explicit cast, but the domain's constraints will be checked.

For additional information see CREATE DOMAIN.

Examples:

Example 1 (unknown):

CREATE DOMAIN posint AS integer CHECK (VALUE > 0);
CREATE TABLE mytable (id posint);
INSERT INTO mytable VALUES(1);   -- works
INSERT INTO mytable VALUES(-1);  -- fails

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PostgreSQL: Documentation: 18: EXECUTE IMMEDIATE

URL: https://www.postgresql.org/docs/current/ecpg-sql-execute-immediate.html

Contents:

• EXECUTE IMMEDIATE
• Synopsis
• Description
• Parameters
• Notes
• Examples
• Compatibility

EXECUTE IMMEDIATE — dynamically prepare and execute a statement

EXECUTE IMMEDIATE immediately prepares and executes a dynamically specified SQL statement, without retrieving result rows.

A literal string or a host variable containing the SQL statement to be executed.

In typical usage, the string is a host variable reference to a string containing a dynamically-constructed SQL statement. The case of a literal string is not very useful; you might as well just write the SQL statement directly, without the extra typing of EXECUTE IMMEDIATE.

If you do use a literal string, keep in mind that any double quotes you might wish to include in the SQL statement must be written as octal escapes (\042) not the usual C idiom \". This is because the string is inside an EXEC SQL section, so the ECPG lexer parses it according to SQL rules not C rules. Any embedded backslashes will later be handled according to C rules; but \" causes an immediate syntax error because it is seen as ending the literal.

Here is an example that executes an INSERT statement using EXECUTE IMMEDIATE and a host variable named command:

EXECUTE IMMEDIATE is specified in the SQL standard.

Examples:

Example 1 (unknown):

EXECUTE IMMEDIATE string

Example 2 (unknown):

sprintf(command, "INSERT INTO test (name, amount, letter) VALUES ('db: ''r1''', 1, 'f')");
EXEC SQL EXECUTE IMMEDIATE :command;

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PostgreSQL: Documentation: 18: 36.4. User-Defined Procedures

URL: https://www.postgresql.org/docs/current/xproc.html

Contents:

• 36.4. User-Defined Procedures #

A procedure is a database object similar to a function. The key differences are:

Procedures are defined with the CREATE PROCEDURE command, not CREATE FUNCTION.

Procedures do not return a function value; hence CREATE PROCEDURE lacks a RETURNS clause. However, procedures can instead return data to their callers via output parameters.

While a function is called as part of a query or DML command, a procedure is called in isolation using the CALL command.

A procedure can commit or roll back transactions during its execution (then automatically beginning a new transaction), so long as the invoking CALL command is not part of an explicit transaction block. A function cannot do that.

Certain function attributes, such as strictness, don't apply to procedures. Those attributes control how the function is used in a query, which isn't relevant to procedures.

The explanations in the following sections about how to define user-defined functions apply to procedures as well, except for the points made above.

Collectively, functions and procedures are also known as routines. There are commands such as ALTER ROUTINE and DROP ROUTINE that can operate on functions and procedures without having to know which kind it is. Note, however, that there is no CREATE ROUTINE command.

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PostgreSQL: Documentation: 18: 35.8. check_constraint_routine_usage

URL: https://www.postgresql.org/docs/current/infoschema-check-constraint-routine-usage.html

Contents:

• 35.8. check_constraint_routine_usage #

The view check_constraint_routine_usage identifies routines (functions and procedures) that are used by a check constraint. Only those routines are shown that are owned by a currently enabled role.

Table 35.6. check_constraint_routine_usage Columns

constraint_catalog sql_identifier

Name of the database containing the constraint (always the current database)

constraint_schema sql_identifier

Name of the schema containing the constraint

constraint_name sql_identifier

Name of the constraint

specific_catalog sql_identifier

Name of the database containing the function (always the current database)

specific_schema sql_identifier

Name of the schema containing the function

specific_name sql_identifier

The “specific name” of the function. See Section 35.45 for more information.

---

PostgreSQL: Documentation: 18: 26.2. Log-Shipping Standby Servers

URL: https://www.postgresql.org/docs/current/warm-standby.html

Contents:

• 26.2. Log-Shipping Standby Servers #
  • 26.2.1. Planning #
  • 26.2.2. Standby Server Operation #
  • 26.2.3. Preparing the Primary for Standby Servers #
  • 26.2.4. Setting Up a Standby Server #
  • Note
  • 26.2.5. Streaming Replication #
  • 26.2.5.1. Authentication #
  • 26.2.5.2. Monitoring #
  • 26.2.6. Replication Slots #

Continuous archiving can be used to create a high availability (HA) cluster configuration with one or more standby servers ready to take over operations if the primary server fails. This capability is widely referred to as warm standby or log shipping.

The primary and standby server work together to provide this capability, though the servers are only loosely coupled. The primary server operates in continuous archiving mode, while each standby server operates in continuous recovery mode, reading the WAL files from the primary. No changes to the database tables are required to enable this capability, so it offers low administration overhead compared to some other replication solutions. This configuration also has relatively low performance impact on the primary server.

Directly moving WAL records from one database server to another is typically described as log shipping. PostgreSQL implements file-based log shipping by transferring WAL records one file (WAL segment) at a time. WAL files (16MB) can be shipped easily and cheaply over any distance, whether it be to an adjacent system, another system at the same site, or another system on the far side of the globe. The bandwidth required for this technique varies according to the transaction rate of the primary server. Record-based log shipping is more granular and streams WAL changes incrementally over a network connection (see Section 26.2.5).

It should be noted that log shipping is asynchronous, i.e., the WAL records are shipped after transaction commit. As a result, there is a window for data loss should the primary server suffer a catastrophic failure; transactions not yet shipped will be lost. The size of the data loss window in file-based log shipping can be limited by use of the archive_timeout parameter, which can be set as low as a few seconds. However such a low setting will substantially increase the bandwidth required for file shipping. Streaming replication (see Section 26.2.5) allows a much smaller window of data loss.

Recovery performance is sufficiently good that the standby will typically be only moments away from full availability once it has been activated. As a result, this is called a warm standby configuration which offers high availability. Restoring a server from an archived base backup and rollforward will take considerably longer, so that technique only offers a solution for disaster recovery, not high availability. A standby server can also be used for read-only queries, in which case it is called a hot standby server. See Section 26.4 for more information.

It is usually wise to create the primary and standby servers so that they are as similar as possible, at least from the perspective of the database server. In particular, the path names associated with tablespaces will be passed across unmodified, so both primary and standby servers must have the same mount paths for tablespaces if that feature is used. Keep in mind that if CREATE TABLESPACE is executed on the primary, any new mount point needed for it must be created on the primary and all standby servers before the command is executed. Hardware need not be exactly the same, but experience shows that maintaining two identical systems is easier than maintaining two dissimilar ones over the lifetime of the application and system. In any case the hardware architecture must be the same — shipping from, say, a 32-bit to a 64-bit system will not work.

In general, log shipping between servers running different major PostgreSQL release levels is not possible. It is the policy of the PostgreSQL Global Development Group not to make changes to disk formats during minor release upgrades, so it is likely that running different minor release levels on primary and standby servers will work successfully. However, no formal support for that is offered and you are advised to keep primary and standby servers at the same release level as much as possible. When updating to a new minor release, the safest policy is to update the standby servers first — a new minor release is more likely to be able to read WAL files from a previous minor release than vice versa.

A server enters standby mode if a standby.signal file exists in the data directory when the server is started.

In standby mode, the server continuously applies WAL received from the primary server. The standby server can read WAL from a WAL archive (see restore_command) or directly from the primary over a TCP connection (streaming replication). The standby server will also attempt to restore any WAL found in the standby cluster's pg_wal directory. That typically happens after a server restart, when the standby replays again WAL that was streamed from the primary before the restart, but you can also manually copy files to pg_wal at any time to have them replayed.

At startup, the standby begins by restoring all WAL available in the archive location, calling restore_command. Once it reaches the end of WAL available there and restore_command fails, it tries to restore any WAL available in the pg_wal directory. If that fails, and streaming replication has been configured, the standby tries to connect to the primary server and start streaming WAL from the last valid record found in archive or pg_wal. If that fails or streaming replication is not configured, or if the connection is later disconnected, the standby goes back to step 1 and tries to restore the file from the archive again. This loop of retries from the archive, pg_wal, and via streaming replication goes on until the server is stopped or is promoted.

Standby mode is exited and the server switches to normal operation when pg_ctl promote is run, or pg_promote() is called. Before failover, any WAL immediately available in the archive or in pg_wal will be restored, but no attempt is made to connect to the primary.

Set up continuous archiving on the primary to an archive directory accessible from the standby, as described in Section 25.3. The archive location should be accessible from the standby even when the primary is down, i.e., it should reside on the standby server itself or another trusted server, not on the primary server.

If you want to use streaming replication, set up authentication on the primary server to allow replication connections from the standby server(s); that is, create a role and provide a suitable entry or entries in pg_hba.conf with the database field set to replication. Also ensure max_wal_senders is set to a sufficiently large value in the configuration file of the primary server. If replication slots will be used, ensure that max_replication_slots is set sufficiently high as well.

Take a base backup as described in Section 25.3.2 to bootstrap the standby server.

To set up the standby server, restore the base backup taken from primary server (see Section 25.3.5). Create a file standby.signal in the standby's cluster data directory. Set restore_command to a simple command to copy files from the WAL archive. If you plan to have multiple standby servers for high availability purposes, make sure that recovery_target_timeline is set to latest (the default), to make the standby server follow the timeline change that occurs at failover to another standby.

restore_command should return immediately if the file does not exist; the server will retry the command again if necessary.

If you want to use streaming replication, fill in primary_conninfo with a libpq connection string, including the host name (or IP address) and any additional details needed to connect to the primary server. If the primary needs a password for authentication, the password needs to be specified in primary_conninfo as well.

If you're setting up the standby server for high availability purposes, set up WAL archiving, connections and authentication like the primary server, because the standby server will work as a primary server after failover.

If you're using a WAL archive, its size can be minimized using the archive_cleanup_command parameter to remove files that are no longer required by the standby server. The pg_archivecleanup utility is designed specifically to be used with archive_cleanup_command in typical single-standby configurations, see pg_archivecleanup. Note however, that if you're using the archive for backup purposes, you need to retain files needed to recover from at least the latest base backup, even if they're no longer needed by the standby.

A simple example of configuration is:

You can have any number of standby servers, but if you use streaming replication, make sure you set max_wal_senders high enough in the primary to allow them to be connected simultaneously.

Streaming replication allows a standby server to stay more up-to-date than is possible with file-based log shipping. The standby connects to the primary, which streams WAL records to the standby as they're generated, without waiting for the WAL file to be filled.

Streaming replication is asynchronous by default (see Section 26.2.8), in which case there is a small delay between committing a transaction in the primary and the changes becoming visible in the standby. This delay is however much smaller than with file-based log shipping, typically under one second assuming the standby is powerful enough to keep up with the load. With streaming replication, archive_timeout is not required to reduce the data loss window.

If you use streaming replication without file-based continuous archiving, the server might recycle old WAL segments before the standby has received them. If this occurs, the standby will need to be reinitialized from a new base backup. You can avoid this by setting wal_keep_size to a value large enough to ensure that WAL segments are not recycled too early, or by configuring a replication slot for the standby. If you set up a WAL archive that's accessible from the standby, these solutions are not required, since the standby can always use the archive to catch up provided it retains enough segments.

To use streaming replication, set up a file-based log-shipping standby server as described in Section 26.2. The step that turns a file-based log-shipping standby into streaming replication standby is setting the primary_conninfo setting to point to the primary server. Set listen_addresses and authentication options (see pg_hba.conf) on the primary so that the standby server can connect to the replication pseudo-database on the primary server (see Section 26.2.5.1).

On systems that support the keepalive socket option, setting tcp_keepalives_idle, tcp_keepalives_interval and tcp_keepalives_count helps the primary promptly notice a broken connection.

Set the maximum number of concurrent connections from the standby servers (see max_wal_senders for details).

When the standby is started and primary_conninfo is set correctly, the standby will connect to the primary after replaying all WAL files available in the archive. If the connection is established successfully, you will see a walreceiver in the standby, and a corresponding walsender process in the primary.

It is very important that the access privileges for replication be set up so that only trusted users can read the WAL stream, because it is easy to extract privileged information from it. Standby servers must authenticate to the primary as an account that has the REPLICATION privilege or a superuser. It is recommended to create a dedicated user account with REPLICATION and LOGIN privileges for replication. While REPLICATION privilege gives very high permissions, it does not allow the user to modify any data on the primary system, which the SUPERUSER privilege does.

Client authentication for replication is controlled by a pg_hba.conf record specifying replication in the database field. For example, if the standby is running on host IP 192.168.1.100 and the account name for replication is foo, the administrator can add the following line to the pg_hba.conf file on the primary:

The host name and port number of the primary, connection user name, and password are specified in the primary_conninfo. The password can also be set in the ~/.pgpass file on the standby (specify replication in the database field). For example, if the primary is running on host IP 192.168.1.50, port 5432, the account name for replication is foo, and the password is foopass, the administrator can add the following line to the postgresql.conf file on the standby:

An important health indicator of streaming replication is the amount of WAL records generated in the primary, but not yet applied in the standby. You can calculate this lag by comparing the current WAL write location on the primary with the last WAL location received by the standby. These locations can be retrieved using pg_current_wal_lsn on the primary and pg_last_wal_receive_lsn on the standby, respectively (see Table 9.97 and Table 9.98 for details). The last WAL receive location in the standby is also displayed in the process status of the WAL receiver process, displayed using the ps command (see Section 27.1 for details).

You can retrieve a list of WAL sender processes via the pg_stat_replication view. Large differences between pg_current_wal_lsn and the view's sent_lsn field might indicate that the primary server is under heavy load, while differences between sent_lsn and pg_last_wal_receive_lsn on the standby might indicate network delay, or that the standby is under heavy load.

On a hot standby, the status of the WAL receiver process can be retrieved via the pg_stat_wal_receiver view. A large difference between pg_last_wal_replay_lsn and the view's flushed_lsn indicates that WAL is being received faster than it can be replayed.

Replication slots provide an automated way to ensure that the primary server does not remove WAL segments until they have been received by all standbys, and that the primary does not remove rows which could cause a recovery conflict even when the standby is disconnected.

In lieu of using replication slots, it is possible to prevent the removal of old WAL segments using wal_keep_size, or by storing the segments in an archive using archive_command or archive_library. A disadvantage of these methods is that they often result in retaining more WAL segments than required, whereas replication slots retain only the number of segments known to be needed.

Similarly, hot_standby_feedback on its own, without also using a replication slot, provides protection against relevant rows being removed by vacuum, but provides no protection during any time period when the standby is not connected.

Beware that replication slots can cause the server to retain so many WAL segments that they fill up the space allocated for pg_wal. max_slot_wal_keep_size can be used to limit the size of WAL files retained by replication slots.

Each replication slot has a name, which can contain lower-case letters, numbers, and the underscore character.

Existing replication slots and their state can be seen in the pg_replication_slots view.

Slots can be created and dropped either via the streaming replication protocol (see Section 54.4) or via SQL functions (see Section 9.28.6).

You can create a replication slot like this:

To configure the standby to use this slot, primary_slot_name should be configured on the standby. Here is a simple example:

The cascading replication feature allows a standby server to accept replication connections and stream WAL records to other standbys, acting as a relay. This can be used to reduce the number of direct connections to the primary and also to minimize inter-site bandwidth overheads.

A standby acting as both a receiver and a sender is known as a cascading standby. Standbys that are more directly connected to the primary are known as upstream servers, while those standby servers further away are downstream servers. Cascading replication does not place limits on the number or arrangement of downstream servers, though each standby connects to only one upstream server which eventually links to a single primary server.

A cascading standby sends not only WAL records received from the primary but also those restored from the archive. So even if the replication connection in some upstream connection is terminated, streaming replication continues downstream for as long as new WAL records are available.

Cascading replication is currently asynchronous. Synchronous replication (see Section 26.2.8) settings have no effect on cascading replication at present.

Hot standby feedback propagates upstream, whatever the cascaded arrangement.

If an upstream standby server is promoted to become the new primary, downstream servers will continue to stream from the new primary if recovery_target_timeline is set to 'latest' (the default).

To use cascading replication, set up the cascading standby so that it can accept replication connections (that is, set max_wal_senders and hot_standby, and configure host-based authentication). You will also need to set primary_conninfo in the downstream standby to point to the cascading standby.

PostgreSQL streaming replication is asynchronous by default. If the primary server crashes then some transactions that were committed may not have been replicated to the standby server, causing data loss. The amount of data loss is proportional to the replication delay at the time of failover.

Synchronous replication offers the ability to confirm that all changes made by a transaction have been transferred to one or more synchronous standby servers. This extends that standard level of durability offered by a transaction commit. This level of protection is referred to as 2-safe replication in computer science theory, and group-1-safe (group-safe and 1-safe) when synchronous_commit is set to remote_write.

When requesting synchronous replication, each commit of a write transaction will wait until confirmation is received that the commit has been written to the write-ahead log on disk of both the primary and standby server. The only possibility that data can be lost is if both the primary and the standby suffer crashes at the same time. This can provide a much higher level of durability, though only if the sysadmin is cautious about the placement and management of the two servers. Waiting for confirmation increases the user's confidence that the changes will not be lost in the event of server crashes but it also necessarily increases the response time for the requesting transaction. The minimum wait time is the round-trip time between primary and standby.

Read-only transactions and transaction rollbacks need not wait for replies from standby servers. Subtransaction commits do not wait for responses from standby servers, only top-level commits. Long running actions such as data loading or index building do not wait until the very final commit message. All two-phase commit actions require commit waits, including both prepare and commit.

A synchronous standby can be a physical replication standby or a logical replication subscriber. It can also be any other physical or logical WAL replication stream consumer that knows how to send the appropriate feedback messages. Besides the built-in physical and logical replication systems, this includes special programs such as pg_receivewal and pg_recvlogical as well as some third-party replication systems and custom programs. Check the respective documentation for details on synchronous replication support.

Once streaming replication has been configured, configuring synchronous replication requires only one additional configuration step: synchronous_standby_names must be set to a non-empty value. synchronous_commit must also be set to on, but since this is the default value, typically no change is required. (See Section 19.5.1 and Section 19.6.2.) This configuration will cause each commit to wait for confirmation that the standby has written the commit record to durable storage. synchronous_commit can be set by individual users, so it can be configured in the configuration file, for particular users or databases, or dynamically by applications, in order to control the durability guarantee on a per-transaction basis.

After a commit record has been written to disk on the primary, the WAL record is then sent to the standby. The standby sends reply messages each time a new batch of WAL data is written to disk, unless wal_receiver_status_interval is set to zero on the standby. In the case that synchronous_commit is set to remote_apply, the standby sends reply messages when the commit record is replayed, making the transaction visible. If the standby is chosen as a synchronous standby, according to the setting of synchronous_standby_names on the primary, the reply messages from that standby will be considered along with those from other synchronous standbys to decide when to release transactions waiting for confirmation that the commit record has been received. These parameters allow the administrator to specify which standby servers should be synchronous standbys. Note that the configuration of synchronous replication is mainly on the primary. Named standbys must be directly connected to the primary; the primary knows nothing about downstream standby servers using cascaded replication.

Setting synchronous_commit to remote_write will cause each commit to wait for confirmation that the standby has received the commit record and written it out to its own operating system, but not for the data to be flushed to disk on the standby. This setting provides a weaker guarantee of durability than on does: the standby could lose the data in the event of an operating system crash, though not a PostgreSQL crash. However, it's a useful setting in practice because it can decrease the response time for the transaction. Data loss could only occur if both the primary and the standby crash and the database of the primary gets corrupted at the same time.

Setting synchronous_commit to remote_apply will cause each commit to wait until the current synchronous standbys report that they have replayed the transaction, making it visible to user queries. In simple cases, this allows for load balancing with causal consistency.

Users will stop waiting if a fast shutdown is requested. However, as when using asynchronous replication, the server will not fully shutdown until all outstanding WAL records are transferred to the currently connected standby servers.

Synchronous replication supports one or more synchronous standby servers; transactions will wait until all the standby servers which are considered as synchronous confirm receipt of their data. The number of synchronous standbys that transactions must wait for replies from is specified in synchronous_standby_names. This parameter also specifies a list of standby names and the method (FIRST and ANY) to choose synchronous standbys from the listed ones.

The method FIRST specifies a priority-based synchronous replication and makes transaction commits wait until their WAL records are replicated to the requested number of synchronous standbys chosen based on their priorities. The standbys whose names appear earlier in the list are given higher priority and will be considered as synchronous. Other standby servers appearing later in this list represent potential synchronous standbys. If any of the current synchronous standbys disconnects for whatever reason, it will be replaced immediately with the next-highest-priority standby.

An example of synchronous_standby_names for a priority-based multiple synchronous standbys is:

In this example, if four standby servers s1, s2, s3 and s4 are running, the two standbys s1 and s2 will be chosen as synchronous standbys because their names appear early in the list of standby names. s3 is a potential synchronous standby and will take over the role of synchronous standby when either of s1 or s2 fails. s4 is an asynchronous standby since its name is not in the list.

The method ANY specifies a quorum-based synchronous replication and makes transaction commits wait until their WAL records are replicated to at least the requested number of synchronous standbys in the list.

An example of synchronous_standby_names for a quorum-based multiple synchronous standbys is:

In this example, if four standby servers s1, s2, s3 and s4 are running, transaction commits will wait for replies from at least any two standbys of s1, s2 and s3. s4 is an asynchronous standby since its name is not in the list.

The synchronous states of standby servers can be viewed using the pg_stat_replication view.

Synchronous replication usually requires carefully planned and placed standby servers to ensure applications perform acceptably. Waiting doesn't utilize system resources, but transaction locks continue to be held until the transfer is confirmed. As a result, incautious use of synchronous replication will reduce performance for database applications because of increased response times and higher contention.

PostgreSQL allows the application developer to specify the durability level required via replication. This can be specified for the system overall, though it can also be specified for specific users or connections, or even individual transactions.

For example, an application workload might consist of: 10% of changes are important customer details, while 90% of changes are less important data that the business can more easily survive if it is lost, such as chat messages between users.

With synchronous replication options specified at the application level (on the primary) we can offer synchronous replication for the most important changes, without slowing down the bulk of the total workload. Application level options are an important and practical tool for allowing the benefits of synchronous replication for high performance applications.

You should consider that the network bandwidth must be higher than the rate of generation of WAL data.

synchronous_standby_names specifies the number and names of synchronous standbys that transaction commits made when synchronous_commit is set to on, remote_apply or remote_write will wait for responses from. Such transaction commits may never be completed if any one of the synchronous standbys should crash.

The best solution for high availability is to ensure you keep as many synchronous standbys as requested. This can be achieved by naming multiple potential synchronous standbys using synchronous_standby_names.

In a priority-based synchronous replication, the standbys whose names appear earlier in the list will be used as synchronous standbys. Standbys listed after these will take over the role of synchronous standby if one of current ones should fail.

In a quorum-based synchronous replication, all the standbys appearing in the list will be used as candidates for synchronous standbys. Even if one of them should fail, the other standbys will keep performing the role of candidates of synchronous standby.

When a standby first attaches to the primary, it will not yet be properly synchronized. This is described as catchup mode. Once the lag between standby and primary reaches zero for the first time we move to real-time streaming state. The catch-up duration may be long immediately after the standby has been created. If the standby is shut down, then the catch-up period will increase according to the length of time the standby has been down. The standby is only able to become a synchronous standby once it has reached streaming state. This state can be viewed using the pg_stat_replication view.

If primary restarts while commits are waiting for acknowledgment, those waiting transactions will be marked fully committed once the primary database recovers. There is no way to be certain that all standbys have received all outstanding WAL data at time of the crash of the primary. Some transactions may not show as committed on the standby, even though they show as committed on the primary. The guarantee we offer is that the application will not receive explicit acknowledgment of the successful commit of a transaction until the WAL data is known to be safely received by all the synchronous standbys.

If you really cannot keep as many synchronous standbys as requested then you should decrease the number of synchronous standbys that transaction commits must wait for responses from in synchronous_standby_names (or disable it) and reload the configuration file on the primary server.

If the primary is isolated from remaining standby servers you should fail over to the best candidate of those other remaining standby servers.

If you need to re-create a standby server while transactions are waiting, make sure that the functions pg_backup_start() and pg_backup_stop() are run in a session with synchronous_commit = off, otherwise those requests will wait forever for the standby to appear.

When continuous WAL archiving is used in a standby, there are two different scenarios: the WAL archive can be shared between the primary and the standby, or the standby can have its own WAL archive. When the standby has its own WAL archive, set archive_mode to always, and the standby will call the archive command for every WAL segment it receives, whether it's by restoring from the archive or by streaming replication. The shared archive can be handled similarly, but the archive_command or archive_library must test if the file being archived exists already, and if the existing file has identical contents. This requires more care in the archive_command or archive_library, as it must be careful to not overwrite an existing file with different contents, but return success if the exactly same file is archived twice. And all that must be done free of race conditions, if two servers attempt to archive the same file at the same time.

If archive_mode is set to on, the archiver is not enabled during recovery or standby mode. If the standby server is promoted, it will start archiving after the promotion, but will not archive any WAL or timeline history files that it did not generate itself. To get a complete series of WAL files in the archive, you must ensure that all WAL is archived, before it reaches the standby. This is inherently true with file-based log shipping, as the standby can only restore files that are found in the archive, but not if streaming replication is enabled. When a server is not in recovery mode, there is no difference between on and always modes.

Examples:

Example 1 (unknown):

primary_conninfo = 'host=192.168.1.50 port=5432 user=foo password=foopass options=''-c wal_sender_timeout=5000'''
restore_command = 'cp /path/to/archive/%f %p'
archive_cleanup_command = 'pg_archivecleanup /path/to/archive %r'

Example 2 (unknown):

# Allow the user "foo" from host 192.168.1.100 to connect to the primary
# as a replication standby if the user's password is correctly supplied.
#
# TYPE  DATABASE        USER            ADDRESS                 METHOD
host    replication     foo             192.168.1.100/32        md5

Example 3 (unknown):

# The standby connects to the primary that is running on host 192.168.1.50
# and port 5432 as the user "foo" whose password is "foopass".
primary_conninfo = 'host=192.168.1.50 port=5432 user=foo password=foopass'

Example 4 (unknown):

postgres=# SELECT * FROM pg_create_physical_replication_slot('node_a_slot');
  slot_name  | lsn
-------------+-----
 node_a_slot |

postgres=# SELECT slot_name, slot_type, active FROM pg_replication_slots;
  slot_name  | slot_type | active
-------------+-----------+--------
 node_a_slot | physical  | f
(1 row)

---

PostgreSQL: Documentation: 18: GET DESCRIPTOR

URL: https://www.postgresql.org/docs/current/ecpg-sql-get-descriptor.html

Contents:

• GET DESCRIPTOR
• Synopsis
• Description
• Parameters
• Examples
• Compatibility
• See Also

GET DESCRIPTOR — get information from an SQL descriptor area

GET DESCRIPTOR retrieves information about a query result set from an SQL descriptor area and stores it into host variables. A descriptor area is typically populated using FETCH or SELECT before using this command to transfer the information into host language variables.

This command has two forms: The first form retrieves descriptor “header” items, which apply to the result set in its entirety. One example is the row count. The second form, which requires the column number as additional parameter, retrieves information about a particular column. Examples are the column name and the actual column value.

A token identifying which header information item to retrieve. Only COUNT, to get the number of columns in the result set, is currently supported.

The number of the column about which information is to be retrieved. The count starts at 1.

A token identifying which item of information about a column to retrieve. See Section 34.7.1 for a list of supported items.

A host variable that will receive the data retrieved from the descriptor area.

An example to retrieve the number of columns in a result set:

An example to retrieve a data length in the first column:

An example to retrieve the data body of the second column as a string:

Here is an example for a whole procedure of executing SELECT current_database(); and showing the number of columns, the column data length, and the column data:

When the example is executed, the result will look like this:

GET DESCRIPTOR is specified in the SQL standard.

Examples:

Example 1 (unknown):

GET DESCRIPTOR descriptor_name :cvariable = descriptor_header_item [, ... ]
GET DESCRIPTOR descriptor_name VALUE column_number :cvariable = descriptor_item [, ... ]

Example 2 (unknown):

EXEC SQL GET DESCRIPTOR d :d_count = COUNT;

Example 3 (unknown):

EXEC SQL GET DESCRIPTOR d VALUE 1 :d_returned_octet_length = RETURNED_OCTET_LENGTH;

Example 4 (unknown):

EXEC SQL GET DESCRIPTOR d VALUE 2 :d_data = DATA;

---

PostgreSQL: Documentation: 18: Part II. The SQL Language

URL: https://www.postgresql.org/docs/current/sql.html

Contents:

• Part II. The SQL Language

This part describes the use of the SQL language in PostgreSQL. We start with describing the general syntax of SQL, then how to create tables, how to populate the database, and how to query it. The middle part lists the available data types and functions for use in SQL commands. Lastly, we address several aspects of importance for tuning a database.

The information is arranged so that a novice user can follow it from start to end and gain a full understanding of the topics without having to refer forward too many times. The chapters are intended to be self-contained, so that advanced users can read the chapters individually as they choose. The information is presented in narrative form with topical units. Readers looking for a complete description of a particular command are encouraged to review the Part VI.

Readers should know how to connect to a PostgreSQL database and issue SQL commands. Readers that are unfamiliar with these issues are encouraged to read Part I first. SQL commands are typically entered using the PostgreSQL interactive terminal psql, but other programs that have similar functionality can be used as well.

---

PostgreSQL: Documentation: 18: 18.4. Managing Kernel Resources

URL: https://www.postgresql.org/docs/current/kernel-resources.html

Contents:

• 18.4. Managing Kernel Resources #
  • 18.4.1. Shared Memory and Semaphores #
  • 18.4.2. systemd RemoveIPC #
  • Caution
  • 18.4.3. Resource Limits #
  • 18.4.4. Linux Memory Overcommit #
  • 18.4.5. Linux Huge Pages #

PostgreSQL can sometimes exhaust various operating system resource limits, especially when multiple copies of the server are running on the same system, or in very large installations. This section explains the kernel resources used by PostgreSQL and the steps you can take to resolve problems related to kernel resource consumption.

PostgreSQL requires the operating system to provide inter-process communication (IPC) features, specifically shared memory and semaphores. Unix-derived systems typically provide “System V” IPC, “POSIX” IPC, or both. Windows has its own implementation of these features and is not discussed here.

By default, PostgreSQL allocates a very small amount of System V shared memory, as well as a much larger amount of anonymous mmap shared memory. Alternatively, a single large System V shared memory region can be used (see shared_memory_type). In addition a significant number of semaphores, which can be either System V or POSIX style, are created at server startup. Currently, POSIX semaphores are used on Linux and FreeBSD systems while other platforms use System V semaphores.

System V IPC features are typically constrained by system-wide allocation limits. When PostgreSQL exceeds one of these limits, the server will refuse to start and should leave an instructive error message describing the problem and what to do about it. (See also Section 18.3.1.) The relevant kernel parameters are named consistently across different systems; Table 18.1 gives an overview. The methods to set them, however, vary. Suggestions for some platforms are given below.

Table 18.1. System V IPC Parameters

PostgreSQL requires a few bytes of System V shared memory (typically 48 bytes, on 64-bit platforms) for each copy of the server. On most modern operating systems, this amount can easily be allocated. However, if you are running many copies of the server or you explicitly configure the server to use large amounts of System V shared memory (see shared_memory_type and dynamic_shared_memory_type), it may be necessary to increase SHMALL, which is the total amount of System V shared memory system-wide. Note that SHMALL is measured in pages rather than bytes on many systems.

Less likely to cause problems is the minimum size for shared memory segments (SHMMIN), which should be at most approximately 32 bytes for PostgreSQL (it is usually just 1). The maximum number of segments system-wide (SHMMNI) or per-process (SHMSEG) are unlikely to cause a problem unless your system has them set to zero.

When using System V semaphores, PostgreSQL uses one semaphore per allowed connection (max_connections), allowed autovacuum worker process (autovacuum_worker_slots), allowed WAL sender process (max_wal_senders), allowed background process (max_worker_processes), etc., in sets of 16. The runtime-computed parameter num_os_semaphores reports the number of semaphores required. This parameter can be viewed before starting the server with a postgres command like:

Each set of 16 semaphores will also contain a 17th semaphore which contains a “magic number”, to detect collision with semaphore sets used by other applications. The maximum number of semaphores in the system is set by SEMMNS, which consequently must be at least as high as num_os_semaphores plus one extra for each set of 16 required semaphores (see the formula in Table 18.1). The parameter SEMMNI determines the limit on the number of semaphore sets that can exist on the system at one time. Hence this parameter must be at least ceil(num_os_semaphores / 16). Lowering the number of allowed connections is a temporary workaround for failures, which are usually confusingly worded “No space left on device”, from the function semget.

In some cases it might also be necessary to increase SEMMAP to be at least on the order of SEMMNS. If the system has this parameter (many do not), it defines the size of the semaphore resource map, in which each contiguous block of available semaphores needs an entry. When a semaphore set is freed it is either added to an existing entry that is adjacent to the freed block or it is registered under a new map entry. If the map is full, the freed semaphores get lost (until reboot). Fragmentation of the semaphore space could over time lead to fewer available semaphores than there should be.

Various other settings related to “semaphore undo”, such as SEMMNU and SEMUME, do not affect PostgreSQL.

When using POSIX semaphores, the number of semaphores needed is the same as for System V, that is one semaphore per allowed connection (max_connections), allowed autovacuum worker process (autovacuum_worker_slots), allowed WAL sender process (max_wal_senders), allowed background process (max_worker_processes), etc. On the platforms where this option is preferred, there is no specific kernel limit on the number of POSIX semaphores.

The default shared memory settings are usually good enough, unless you have set shared_memory_type to sysv. System V semaphores are not used on this platform.

The default IPC settings can be changed using the sysctl or loader interfaces. The following parameters can be set using sysctl:

To make these settings persist over reboots, modify /etc/sysctl.conf.

If you have set shared_memory_type to sysv, you might also want to configure your kernel to lock System V shared memory into RAM and prevent it from being paged out to swap. This can be accomplished using the sysctl setting kern.ipc.shm_use_phys.

If running in a FreeBSD jail, you should set its sysvshm parameter to new, so that it has its own separate System V shared memory namespace. (Before FreeBSD 11.0, it was necessary to enable shared access to the host's IPC namespace from jails, and take measures to avoid collisions.)

The default shared memory settings are usually good enough, unless you have set shared_memory_type to sysv. However, you will need to increase kern.ipc.semmni and kern.ipc.semmns, as NetBSD's default settings for these are unworkably small.

IPC parameters can be adjusted using sysctl, for example:

To make these settings persist over reboots, modify /etc/sysctl.conf.

If you have set shared_memory_type to sysv, you might also want to configure your kernel to lock System V shared memory into RAM and prevent it from being paged out to swap. This can be accomplished using the sysctl setting kern.ipc.shm_use_phys.

The default shared memory settings are usually good enough, unless you have set shared_memory_type to sysv. However, you will need to increase kern.seminfo.semmni and kern.seminfo.semmns, as OpenBSD's default settings for these are unworkably small.

IPC parameters can be adjusted using sysctl, for example:

To make these settings persist over reboots, modify /etc/sysctl.conf.

The default shared memory settings are usually good enough, unless you have set shared_memory_type to sysv, and even then only on older kernel versions that shipped with low defaults. System V semaphores are not used on this platform.

The shared memory size settings can be changed via the sysctl interface. For example, to allow 16 GB:

To make these settings persist over reboots, see /etc/sysctl.conf.

The default shared memory and semaphore settings are usually good enough, unless you have set shared_memory_type to sysv.

The recommended method for configuring shared memory in macOS is to create a file named /etc/sysctl.conf, containing variable assignments such as:

Note that in some macOS versions, all five shared-memory parameters must be set in /etc/sysctl.conf, else the values will be ignored.

SHMMAX can only be set to a multiple of 4096.

SHMALL is measured in 4 kB pages on this platform.

It is possible to change all but SHMMNI on the fly, using sysctl. But it's still best to set up your preferred values via /etc/sysctl.conf, so that the values will be kept across reboots.

The default shared memory and semaphore settings are usually good enough for most PostgreSQL applications. Solaris defaults to a SHMMAX of one-quarter of system RAM. To further adjust this setting, use a project setting associated with the postgres user. For example, run the following as root:

This command adds the user.postgres project and sets the shared memory maximum for the postgres user to 8GB, and takes effect the next time that user logs in, or when you restart PostgreSQL (not reload). The above assumes that PostgreSQL is run by the postgres user in the postgres group. No server reboot is required.

Other recommended kernel setting changes for database servers which will have a large number of connections are:

Additionally, if you are running PostgreSQL inside a zone, you may need to raise the zone resource usage limits as well. See "Chapter2: Projects and Tasks" in the System Administrator's Guide for more information on projects and prctl.

If systemd is in use, some care must be taken that IPC resources (including shared memory) are not prematurely removed by the operating system. This is especially of concern when installing PostgreSQL from source. Users of distribution packages of PostgreSQL are less likely to be affected, as the postgres user is then normally created as a system user.

The setting RemoveIPC in logind.conf controls whether IPC objects are removed when a user fully logs out. System users are exempt. This setting defaults to on in stock systemd, but some operating system distributions default it to off.

A typical observed effect when this setting is on is that shared memory objects used for parallel query execution are removed at apparently random times, leading to errors and warnings while attempting to open and remove them, like

Different types of IPC objects (shared memory vs. semaphores, System V vs. POSIX) are treated slightly differently by systemd, so one might observe that some IPC resources are not removed in the same way as others. But it is not advisable to rely on these subtle differences.

A “user logging out” might happen as part of a maintenance job or manually when an administrator logs in as the postgres user or something similar, so it is hard to prevent in general.

What is a “system user” is determined at systemd compile time from the SYS_UID_MAX setting in /etc/login.defs.

Packaging and deployment scripts should be careful to create the postgres user as a system user by using useradd -r, adduser --system, or equivalent.

Alternatively, if the user account was created incorrectly or cannot be changed, it is recommended to set

in /etc/systemd/logind.conf or another appropriate configuration file.

At least one of these two things has to be ensured, or the PostgreSQL server will be very unreliable.

Unix-like operating systems enforce various kinds of resource limits that might interfere with the operation of your PostgreSQL server. Of particular importance are limits on the number of processes per user, the number of open files per process, and the amount of memory available to each process. Each of these have a “hard” and a “soft” limit. The soft limit is what actually counts but it can be changed by the user up to the hard limit. The hard limit can only be changed by the root user. The system call setrlimit is responsible for setting these parameters. The shell's built-in command ulimit (Bourne shells) or limit (csh) is used to control the resource limits from the command line. On BSD-derived systems the file /etc/login.conf controls the various resource limits set during login. See the operating system documentation for details. The relevant parameters are maxproc, openfiles, and datasize. For example:

(-cur is the soft limit. Append -max to set the hard limit.)

Kernels can also have system-wide limits on some resources.

On Linux the kernel parameter fs.file-max determines the maximum number of open files that the kernel will support. It can be changed with sysctl -w fs.file-max=N. To make the setting persist across reboots, add an assignment in /etc/sysctl.conf. The maximum limit of files per process is fixed at the time the kernel is compiled; see /usr/src/linux/Documentation/proc.txt for more information.

The PostgreSQL server uses one process per connection so you should provide for at least as many processes as allowed connections, in addition to what you need for the rest of your system. This is usually not a problem but if you run several servers on one machine things might get tight.

The factory default limit on open files is often set to “socially friendly” values that allow many users to coexist on a machine without using an inappropriate fraction of the system resources. If you run many servers on a machine this is perhaps what you want, but on dedicated servers you might want to raise this limit.

On the other side of the coin, some systems allow individual processes to open large numbers of files; if more than a few processes do so then the system-wide limit can easily be exceeded. If you find this happening, and you do not want to alter the system-wide limit, you can set PostgreSQL's max_files_per_process configuration parameter to limit the consumption of open files.

Another kernel limit that may be of concern when supporting large numbers of client connections is the maximum socket connection queue length. If more than that many connection requests arrive within a very short period, some may get rejected before the PostgreSQL server can service the requests, with those clients receiving unhelpful connection failure errors such as “Resource temporarily unavailable” or “Connection refused”. The default queue length limit is 128 on many platforms. To raise it, adjust the appropriate kernel parameter via sysctl, then restart the PostgreSQL server. The parameter is variously named net.core.somaxconn on Linux, kern.ipc.soacceptqueue on newer FreeBSD, and kern.ipc.somaxconn on macOS and other BSD variants.

The default virtual memory behavior on Linux is not optimal for PostgreSQL. Because of the way that the kernel implements memory overcommit, the kernel might terminate the PostgreSQL postmaster (the supervisor server process) if the memory demands of either PostgreSQL or another process cause the system to run out of virtual memory.

If this happens, you will see a kernel message that looks like this (consult your system documentation and configuration on where to look for such a message):

This indicates that the postgres process has been terminated due to memory pressure. Although existing database connections will continue to function normally, no new connections will be accepted. To recover, PostgreSQL will need to be restarted.

One way to avoid this problem is to run PostgreSQL on a machine where you can be sure that other processes will not run the machine out of memory. If memory is tight, increasing the swap space of the operating system can help avoid the problem, because the out-of-memory (OOM) killer is invoked only when physical memory and swap space are exhausted.

If PostgreSQL itself is the cause of the system running out of memory, you can avoid the problem by changing your configuration. In some cases, it may help to lower memory-related configuration parameters, particularly shared_buffers, work_mem, and hash_mem_multiplier. In other cases, the problem may be caused by allowing too many connections to the database server itself. In many cases, it may be better to reduce max_connections and instead make use of external connection-pooling software.

It is possible to modify the kernel's behavior so that it will not “overcommit” memory. Although this setting will not prevent the OOM killer from being invoked altogether, it will lower the chances significantly and will therefore lead to more robust system behavior. This is done by selecting strict overcommit mode via sysctl:

or placing an equivalent entry in /etc/sysctl.conf. You might also wish to modify the related setting vm.overcommit_ratio. For details see the kernel documentation file https://www.kernel.org/doc/Documentation/vm/overcommit-accounting.

Another approach, which can be used with or without altering vm.overcommit_memory, is to set the process-specific OOM score adjustment value for the postmaster process to -1000, thereby guaranteeing it will not be targeted by the OOM killer. The simplest way to do this is to execute

in the PostgreSQL startup script just before invoking postgres. Note that this action must be done as root, or it will have no effect; so a root-owned startup script is the easiest place to do it. If you do this, you should also set these environment variables in the startup script before invoking postgres:

These settings will cause postmaster child processes to run with the normal OOM score adjustment of zero, so that the OOM killer can still target them at need. You could use some other value for PG_OOM_ADJUST_VALUE if you want the child processes to run with some other OOM score adjustment. (PG_OOM_ADJUST_VALUE can also be omitted, in which case it defaults to zero.) If you do not set PG_OOM_ADJUST_FILE, the child processes will run with the same OOM score adjustment as the postmaster, which is unwise since the whole point is to ensure that the postmaster has a preferential setting.

Using huge pages reduces overhead when using large contiguous chunks of memory, as PostgreSQL does, particularly when using large values of shared_buffers. To use this feature in PostgreSQL you need a kernel with CONFIG_HUGETLBFS=y and CONFIG_HUGETLB_PAGE=y. You will also have to configure the operating system to provide enough huge pages of the desired size. The runtime-computed parameter shared_memory_size_in_huge_pages reports the number of huge pages required. This parameter can be viewed before starting the server with a postgres command like:

In this example the default is 2MB, but you can also explicitly request either 2MB or 1GB with huge_page_size to adapt the number of pages calculated by shared_memory_size_in_huge_pages. While we need at least 3170 huge pages in this example, a larger setting would be appropriate if other programs on the machine also need huge pages. We can set this with:

Don't forget to add this setting to /etc/sysctl.conf so that it is reapplied after reboots. For non-default huge page sizes, we can instead use:

It is also possible to provide these settings at boot time using kernel parameters such as hugepagesz=2M hugepages=3170.

Sometimes the kernel is not able to allocate the desired number of huge pages immediately due to fragmentation, so it might be necessary to repeat the command or to reboot. (Immediately after a reboot, most of the machine's memory should be available to convert into huge pages.) To verify the huge page allocation situation for a given size, use:

It may also be necessary to give the database server's operating system user permission to use huge pages by setting vm.hugetlb_shm_group via sysctl, and/or give permission to lock memory with ulimit -l.

The default behavior for huge pages in PostgreSQL is to use them when possible, with the system's default huge page size, and to fall back to normal pages on failure. To enforce the use of huge pages, you can set huge_pages to on in postgresql.conf. Note that with this setting PostgreSQL will fail to start if not enough huge pages are available.

For a detailed description of the Linux huge pages feature have a look at https://www.kernel.org/doc/Documentation/vm/hugetlbpage.txt.

Examples:

Example 1 (unknown):

$ postgres -D $PGDATA -C num_os_semaphores

Example 2 (unknown):

# sysctl kern.ipc.shmall=32768
# sysctl kern.ipc.shmmax=134217728

Example 3 (unknown):

# sysctl -w kern.ipc.semmni=100

Example 4 (unknown):

# sysctl kern.seminfo.semmni=100

---

PostgreSQL: Documentation: 18: 32.23. Example Programs

URL: https://www.postgresql.org/docs/current/libpq-example.html

Contents:

• 32.23. Example Programs #

These examples and others can be found in the directory src/test/examples in the source code distribution.

Example 32.1. libpq Example Program 1

Example 32.2. libpq Example Program 2

Example 32.3. libpq Example Program 3

Examples:

Example 1 (javascript):

/*
 * src/test/examples/testlibpq.c
 *
 *
 * testlibpq.c
 *
 *      Test the C version of libpq, the PostgreSQL frontend library.
 */
#include <stdio.h>
#include <stdlib.h>
#include "libpq-fe.h"

static void
exit_nicely(PGconn *conn)
{
    PQfinish(conn);
    exit(1);
}

int
main(int argc, char **argv)
{
    const char *conninfo;
    PGconn     *conn;
    PGresult   *res;
    int         nFields;
    int         i,
                j;

    /*
     * If the user supplies a parameter on the command line, use it as the
     * conninfo string; otherwise default to setting dbname=postgres and using
     * environment variables or defaults for all other connection parameters.
     */
    if (argc > 1)
        conninfo = argv[1];
    else
        conninfo = "dbname = postgres";

    /* Make a connection to the database */
    conn = PQconnectdb(conninfo);

    /* Check to see that the backend connection was successfully made */
    if (PQstatus(conn) != CONNECTION_OK)
    {
        fprintf(stderr, "%s", PQerrorMessage(conn));
        exit_nicely(conn);
    }

    /* Set always-secure search path, so malicious users can't take control. */
    res = PQexec(conn,
                 "SELECT pg_catalog.set_config('search_path', '', false)");
    if (PQresultStatus(res) != PGRES_TUPLES_OK)
    {
        fprintf(stderr, "SET failed: %s", PQerrorMessage(conn));
        PQclear(res);
        exit_nicely(conn);
    }

    /*
     * Should PQclear PGresult whenever it is no longer needed to avoid memory
     * leaks
     */
    PQclear(res);

    /*
     * Our test case here involves using a cursor, for which we must be inside
     * a transaction block.  We could do the whole thing with a single
     * PQexec() of "select * from pg_database", but that's too trivial to make
     * a good example.
     */

    /* Start a transaction block */
    res = PQexec(conn, "BEGIN");
    if (PQresultStatus(res) != PGRES_COMMAND_OK)
    {
        fprintf(stderr, "BEGIN command failed: %s", PQerrorMessage(conn));
        PQclear(res);
        exit_nicely(conn);
    }
    PQclear(res);

    /*
     * Fetch rows from pg_database, the system catalog of databases
     */
    res = PQexec(conn, "DECLARE myportal CURSOR FOR select * from pg_database");
    if (PQresultStatus(res) != PGRES_COMMAND_OK)
    {
        fprintf(stderr, "DECLARE CURSOR failed: %s", PQerrorMessage(conn));
        PQclear(res);
        exit_nicely(conn);
    }
    PQclear(res);

    res = PQexec(conn, "FETCH ALL in myportal");
    if (PQresultStatus(res) != PGRES_TUPLES_OK)
    {
        fprintf(stderr, "FETCH ALL failed: %s", PQerrorMessage(conn));
        PQclear(res);
        exit_nicely(conn);
    }

    /* first, print out the attribute names */
    nFields = PQnfields(res);
    for (i = 0; i < nFields; i++)
        printf("%-15s", PQfname(res, i));
    printf("\n\n");

    /* next, print out the rows */
    for (i = 0; i < PQntuples(res); i++)
    {
        for (j = 0; j < nFields; j++)
            printf("%-15s", PQgetvalue(res, i, j));
        printf("\n");
    }

    PQclear(res);

    /* close the portal ... we don't bother to check for errors ... */
    res = PQexec(conn, "CLOSE myportal");
    PQclear(res);

    /* end the transaction */
    res = PQexec(conn, "END");
    PQclear(res);

    /* close the connection to the database and cleanup */
    PQfinish(conn);

    return 0;
}

Example 2 (javascript):

/*
 * src/test/examples/testlibpq2.c
 *
 *
 * testlibpq2.c
 *      Test of the asynchronous notification interface
 *
 * Start this program, then from psql in another window do
 *   NOTIFY TBL2;
 * Repeat four times to get this program to exit.
 *
 * Or, if you want to get fancy, try this:
 * populate a database with the following commands
 * (provided in src/test/examples/testlibpq2.sql):
 *
 *   CREATE SCHEMA TESTLIBPQ2;
 *   SET search_path = TESTLIBPQ2;
 *   CREATE TABLE TBL1 (i int4);
 *   CREATE TABLE TBL2 (i int4);
 *   CREATE RULE r1 AS ON INSERT TO TBL1 DO
 *     (INSERT INTO TBL2 VALUES (new.i); NOTIFY TBL2);
 *
 * Start this program, then from psql do this four times:
 *
 *   INSERT INTO TESTLIBPQ2.TBL1 VALUES (10);
 */

#ifdef WIN32
#include <windows.h>
#endif
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <errno.h>
#include <sys/select.h>
#include <sys/time.h>
#include <sys/types.h>

#include "libpq-fe.h"

static void
exit_nicely(PGconn *conn)
{
    PQfinish(conn);
    exit(1);
}

int
main(int argc, char **argv)
{
    const char *conninfo;
    PGconn     *conn;
    PGresult   *res;
    PGnotify   *notify;
    int         nnotifies;

    /*
     * If the user supplies a parameter on the command line, use it as the
     * conninfo string; otherwise default to setting dbname=postgres and using
     * environment variables or defaults for all other connection parameters.
     */
    if (argc > 1)
        conninfo = argv[1];
    else
        conninfo = "dbname = postgres";

    /* Make a connection to the database */
    conn = PQconnectdb(conninfo);

    /* Check to see that the backend connection was successfully made */
    if (PQstatus(conn) != CONNECTION_OK)
    {
        fprintf(stderr, "%s", PQerrorMessage(conn));
        exit_nicely(conn);
    }

    /* Set always-secure search path, so malicious users can't take control. */
    res = PQexec(conn,
                 "SELECT pg_catalog.set_config('search_path', '', false)");
    if (PQresultStatus(res) != PGRES_TUPLES_OK)
    {
        fprintf(stderr, "SET failed: %s", PQerrorMessage(conn));
        PQclear(res);
        exit_nicely(conn);
    }

    /*
     * Should PQclear PGresult whenever it is no longer needed to avoid memory
     * leaks
     */
    PQclear(res);

    /*
     * Issue LISTEN command to enable notifications from the rule's NOTIFY.
     */
    res = PQexec(conn, "LISTEN TBL2");
    if (PQresultStatus(res) != PGRES_COMMAND_OK)
    {
        fprintf(stderr, "LISTEN command failed: %s", PQerrorMessage(conn));
        PQclear(res);
        exit_nicely(conn);
    }
    PQclear(res);

    /* Quit after four notifies are received. */
    nnotifies = 0;
    while (nnotifies < 4)
    {
        /*
         * Sleep until something happens on the connection.  We use select(2)
         * to wait for input, but you could also use poll() or similar
         * facilities.
         */
        int         sock;
        fd_set      input_mask;

        sock = PQsocket(conn);

        if (sock < 0)
            break;              /* shouldn't happen */

        FD_ZERO(&input_mask);
        FD_SET(sock, &input_mask);

        if (select(sock + 1, &input_mask, NULL, NULL, NULL) < 0)
        {
            fprintf(stderr, "select() failed: %s\n", strerror(errno));
            exit_nicely(conn);
        }

        /* Now check for input */
        PQconsumeInput(conn);
        while ((notify = PQnotifies(conn)) != NULL)
        {
            fprintf(stderr,
                    "ASYNC NOTIFY of '%s' received from backend PID %d\n",
                    notify->relname, notify->be_pid);
            PQfreemem(notify);
            nnotifies++;
            PQconsumeInput(conn);
        }
    }

    fprintf(stderr, "Done.\n");

    /* close the connection to the database and cleanup */
    PQfinish(conn);

    return 0;
}

Example 3 (javascript):

/*
 * src/test/examples/testlibpq3.c
 *
 *
 * testlibpq3.c
 *      Test out-of-line parameters and binary I/O.
 *
 * Before running this, populate a database with the following commands
 * (provided in src/test/examples/testlibpq3.sql):
 *
 * CREATE SCHEMA testlibpq3;
 * SET search_path = testlibpq3;
 * SET standard_conforming_strings = ON;
 * CREATE TABLE test1 (i int4, t text, b bytea);
 * INSERT INTO test1 values (1, 'joe''s place', '\000\001\002\003\004');
 * INSERT INTO test1 values (2, 'ho there', '\004\003\002\001\000');
 *
 * The expected output is:
 *
 * tuple 0: got
 *  i = (4 bytes) 1
 *  t = (11 bytes) 'joe's place'
 *  b = (5 bytes) \000\001\002\003\004
 *
 * tuple 0: got
 *  i = (4 bytes) 2
 *  t = (8 bytes) 'ho there'
 *  b = (5 bytes) \004\003\002\001\000
 */

#ifdef WIN32
#include <windows.h>
#endif

#include <stdio.h>
#include <stdlib.h>
#include <stdint.h>
#include <string.h>
#include <sys/types.h>
#include "libpq-fe.h"

/* for ntohl/htonl */
#include <netinet/in.h>
#include <arpa/inet.h>


static void
exit_nicely(PGconn *conn)
{
    PQfinish(conn);
    exit(1);
}

/*
 * This function prints a query result that is a binary-format fetch from
 * a table defined as in the comment above.  We split it out because the
 * main() function uses it twice.
 */
static void
show_binary_results(PGresult *res)
{
    int         i,
                j;
    int         i_fnum,
                t_fnum,
                b_fnum;

    /* Use PQfnumber to avoid assumptions about field order in result */
    i_fnum = PQfnumber(res, "i");
    t_fnum = PQfnumber(res, "t");
    b_fnum = PQfnumber(res, "b");

    for (i = 0; i < PQntuples(res); i++)
    {
        char       *iptr;
        char       *tptr;
        char       *bptr;
        int         blen;
        int         ival;

        /* Get the field values (we ignore possibility they are null!) */
        iptr = PQgetvalue(res, i, i_fnum);
        tptr = PQgetvalue(res, i, t_fnum);
        bptr = PQgetvalue(res, i, b_fnum);

        /*
         * The binary representation of INT4 is in network byte order, which
         * we'd better coerce to the local byte order.
         */
        ival = ntohl(*((uint32_t *) iptr));

        /*
         * The binary representation of TEXT is, well, text, and since libpq
         * was nice enough to append a zero byte to it, it'll work just fine
         * as a C string.
         *
         * The binary representation of BYTEA is a bunch of bytes, which could
         * include embedded nulls so we have to pay attention to field length.
         */
        blen = PQgetlength(res, i, b_fnum);

        printf("tuple %d: got\n", i);
        printf(" i = (%d bytes) %d\n",
               PQgetlength(res, i, i_fnum), ival);
        printf(" t = (%d bytes) '%s'\n",
               PQgetlength(res, i, t_fnum), tptr);
        printf(" b = (%d bytes) ", blen);
        for (j = 0; j < blen; j++)
            printf("\\%03o", bptr[j]);
        printf("\n\n");
    }
}

int
main(int argc, char **argv)
{
    const char *conninfo;
    PGconn     *conn;
    PGresult   *res;
    const char *paramValues[1];
    int         paramLengths[1];
    int         paramFormats[1];
    uint32_t    binaryIntVal;

    /*
     * If the user supplies a parameter on the command line, use it as the
     * conninfo string; otherwise default to setting dbname=postgres and using
     * environment variables or defaults for all other connection parameters.
     */
    if (argc > 1)
        conninfo = argv[1];
    else
        conninfo = "dbname = postgres";

    /* Make a connection to the database */
    conn = PQconnectdb(conninfo);

    /* Check to see that the backend connection was successfully made */
    if (PQstatus(conn) != CONNECTION_OK)
    {
        fprintf(stderr, "%s", PQerrorMessage(conn));
        exit_nicely(conn);
    }

    /* Set always-secure search path, so malicious users can't take control. */
    res = PQexec(conn, "SET search_path = testlibpq3");
    if (PQresultStatus(res) != PGRES_COMMAND_OK)
    {
        fprintf(stderr, "SET failed: %s", PQerrorMessage(conn));
        PQclear(res);
        exit_nicely(conn);
    }
    PQclear(res);

    /*
     * The point of this program is to illustrate use of PQexecParams() with
     * out-of-line parameters, as well as binary transmission of data.
     *
     * This first example transmits the parameters as text, but receives the
     * results in binary format.  By using out-of-line parameters we can avoid
     * a lot of tedious mucking about with quoting and escaping, even though
     * the data is text.  Notice how we don't have to do anything special with
     * the quote mark in the parameter value.
     */

    /* Here is our out-of-line parameter value */
    paramValues[0] = "joe's place";

    res = PQexecParams(conn,
                       "SELECT * FROM test1 WHERE t = $1",
                       1,       /* one param */
                       NULL,    /* let the backend deduce param type */
                       paramValues,
                       NULL,    /* don't need param lengths since text */
                       NULL,    /* default to all text params */
                       1);      /* ask for binary results */

    if (PQresultStatus(res) != PGRES_TUPLES_OK)
    {
        fprintf(stderr, "SELECT failed: %s", PQerrorMessage(conn));
        PQclear(res);
        exit_nicely(conn);
    }

    show_binary_results(res);

    PQclear(res);

    /*
     * In this second example we transmit an integer parameter in binary form,
     * and again retrieve the results in binary form.
     *
     * Although we tell PQexecParams we are letting the backend deduce
     * parameter type, we really force the decision by casting the parameter
     * symbol in the query text.  This is a good safety measure when sending
     * binary parameters.
     */

    /* Convert integer value "2" to network byte order */
    binaryIntVal = htonl((uint32_t) 2);

    /* Set up parameter arrays for PQexecParams */
    paramValues[0] = (char *) &binaryIntVal;
    paramLengths[0] = sizeof(binaryIntVal);
    paramFormats[0] = 1;        /* binary */

    res = PQexecParams(conn,
                       "SELECT * FROM test1 WHERE i = $1::int4",
                       1,       /* one param */
                       NULL,    /* let the backend deduce param type */
                       paramValues,
                       paramLengths,
                       paramFormats,
                       1);      /* ask for binary results */

    if (PQresultStatus(res) != PGRES_TUPLES_OK)
    {
        fprintf(stderr, "SELECT failed: %s", PQerrorMessage(conn));
        PQclear(res);
        exit_nicely(conn);
    }

    show_binary_results(res);

    PQclear(res);

    /* close the connection to the database and cleanup */
    PQfinish(conn);

    return 0;
}

---

PostgreSQL: Documentation: 18: PostgreSQL Client Applications

URL: https://www.postgresql.org/docs/current/reference-client.html

Contents:

• PostgreSQL Client Applications

This part contains reference information for PostgreSQL client applications and utilities. Not all of these commands are of general utility; some might require special privileges. The common feature of these applications is that they can be run on any host, independent of where the database server resides.

When specified on the command line, user and database names have their case preserved — the presence of spaces or special characters might require quoting. Table names and other identifiers do not have their case preserved, except where documented, and might require quoting.

---

PostgreSQL: Documentation: 18: 35.25. enabled_roles

URL: https://www.postgresql.org/docs/current/infoschema-enabled-roles.html

Contents:

• 35.25. enabled_roles #

The view enabled_roles identifies the currently “enabled roles”. The enabled roles are recursively defined as the current user together with all roles that have been granted to the enabled roles with automatic inheritance. In other words, these are all roles that the current user has direct or indirect, automatically inheriting membership in.

For permission checking, the set of “applicable roles” is applied, which can be broader than the set of enabled roles. So generally, it is better to use the view applicable_roles instead of this one; See Section 35.5 for details on applicable_roles view.

Table 35.23. enabled_roles Columns

role_name sql_identifier

---

PostgreSQL: Documentation: 18: 34.1. The Concept

URL: https://www.postgresql.org/docs/current/ecpg-concept.html

Contents:

• 34.1. The Concept #

An embedded SQL program consists of code written in an ordinary programming language, in this case C, mixed with SQL commands in specially marked sections. To build the program, the source code (.pgc) is first passed through the embedded SQL preprocessor, which converts it to an ordinary C program (.c), and afterwards it can be processed by a C compiler. (For details about the compiling and linking see Section 34.10.) Converted ECPG applications call functions in the libpq library through the embedded SQL library (ecpglib), and communicate with the PostgreSQL server using the normal frontend-backend protocol.

Embedded SQL has advantages over other methods for handling SQL commands from C code. First, it takes care of the tedious passing of information to and from variables in your C program. Second, the SQL code in the program is checked at build time for syntactical correctness. Third, embedded SQL in C is specified in the SQL standard and supported by many other SQL database systems. The PostgreSQL implementation is designed to match this standard as much as possible, and it is usually possible to port embedded SQL programs written for other SQL databases to PostgreSQL with relative ease.

As already stated, programs written for the embedded SQL interface are normal C programs with special code inserted to perform database-related actions. This special code always has the form:

These statements syntactically take the place of a C statement. Depending on the particular statement, they can appear at the global level or within a function.

Embedded SQL statements follow the case-sensitivity rules of normal SQL code, and not those of C. Also they allow nested C-style comments as per the SQL standard. The C part of the program, however, follows the C standard of not accepting nested comments. Embedded SQL statements likewise use SQL rules, not C rules, for parsing quoted strings and identifiers. (See Section 4.1.2.1 and Section 4.1.1 respectively. Note that ECPG assumes that standard_conforming_strings is on.) Of course, the C part of the program follows C quoting rules.

The following sections explain all the embedded SQL statements.

Examples:

Example 1 (unknown):

EXEC SQL ...;

---

PostgreSQL: Documentation: 18: 34.5. Dynamic SQL

URL: https://www.postgresql.org/docs/current/ecpg-dynamic.html

Contents:

• 34.5. Dynamic SQL #
  • 34.5.1. Executing Statements without a Result Set #
  • 34.5.2. Executing a Statement with Input Parameters #
  • 34.5.3. Executing a Statement with a Result Set #

In many cases, the particular SQL statements that an application has to execute are known at the time the application is written. In some cases, however, the SQL statements are composed at run time or provided by an external source. In these cases you cannot embed the SQL statements directly into the C source code, but there is a facility that allows you to call arbitrary SQL statements that you provide in a string variable.

The simplest way to execute an arbitrary SQL statement is to use the command EXECUTE IMMEDIATE. For example:

EXECUTE IMMEDIATE can be used for SQL statements that do not return a result set (e.g., DDL, INSERT, UPDATE, DELETE). You cannot execute statements that retrieve data (e.g., SELECT) this way. The next section describes how to do that.

A more powerful way to execute arbitrary SQL statements is to prepare them once and execute the prepared statement as often as you like. It is also possible to prepare a generalized version of a statement and then execute specific versions of it by substituting parameters. When preparing the statement, write question marks where you want to substitute parameters later. For example:

When you don't need the prepared statement anymore, you should deallocate it:

To execute an SQL statement with a single result row, EXECUTE can be used. To save the result, add an INTO clause.

An EXECUTE command can have an INTO clause, a USING clause, both, or neither.

If a query is expected to return more than one result row, a cursor should be used, as in the following example. (See Section 34.3.2 for more details about the cursor.)

Examples:

Example 1 (javascript):

EXEC SQL BEGIN DECLARE SECTION;
const char *stmt = "CREATE TABLE test1 (...);";
EXEC SQL END DECLARE SECTION;

EXEC SQL EXECUTE IMMEDIATE :stmt;

Example 2 (javascript):

EXEC SQL BEGIN DECLARE SECTION;
const char *stmt = "INSERT INTO test1 VALUES(?, ?);";
EXEC SQL END DECLARE SECTION;

EXEC SQL PREPARE mystmt FROM :stmt;
 ...
EXEC SQL EXECUTE mystmt USING 42, 'foobar';

Example 3 (unknown):

EXEC SQL DEALLOCATE PREPARE name;

Example 4 (javascript):

EXEC SQL BEGIN DECLARE SECTION;
const char *stmt = "SELECT a, b, c FROM test1 WHERE a > ?";
int v1, v2;
VARCHAR v3[50];
EXEC SQL END DECLARE SECTION;

EXEC SQL PREPARE mystmt FROM :stmt;
 ...
EXEC SQL EXECUTE mystmt INTO :v1, :v2, :v3 USING 37;

---

PostgreSQL: Documentation: 18: 5.15. Dependency Tracking

URL: https://www.postgresql.org/docs/current/ddl-depend.html

Contents:

• 5.15. Dependency Tracking #
  • Note

When you create complex database structures involving many tables with foreign key constraints, views, triggers, functions, etc. you implicitly create a net of dependencies between the objects. For instance, a table with a foreign key constraint depends on the table it references.

To ensure the integrity of the entire database structure, PostgreSQL makes sure that you cannot drop objects that other objects still depend on. For example, attempting to drop the products table we considered in Section 5.5.5, with the orders table depending on it, would result in an error message like this:

The error message contains a useful hint: if you do not want to bother deleting all the dependent objects individually, you can run:

and all the dependent objects will be removed, as will any objects that depend on them, recursively. In this case, it doesn't remove the orders table, it only removes the foreign key constraint. It stops there because nothing depends on the foreign key constraint. (If you want to check what DROP ... CASCADE will do, run DROP without CASCADE and read the DETAIL output.)

Almost all DROP commands in PostgreSQL support specifying CASCADE. Of course, the nature of the possible dependencies varies with the type of the object. You can also write RESTRICT instead of CASCADE to get the default behavior, which is to prevent dropping objects that any other objects depend on.

According to the SQL standard, specifying either RESTRICT or CASCADE is required in a DROP command. No database system actually enforces that rule, but whether the default behavior is RESTRICT or CASCADE varies across systems.

If a DROP command lists multiple objects, CASCADE is only required when there are dependencies outside the specified group. For example, when saying DROP TABLE tab1, tab2 the existence of a foreign key referencing tab1 from tab2 would not mean that CASCADE is needed to succeed.

For a user-defined function or procedure whose body is defined as a string literal, PostgreSQL tracks dependencies associated with the function's externally-visible properties, such as its argument and result types, but not dependencies that could only be known by examining the function body. As an example, consider this situation:

(See Section 36.5 for an explanation of SQL-language functions.) PostgreSQL will be aware that the get_color_note function depends on the rainbow type: dropping the type would force dropping the function, because its argument type would no longer be defined. But PostgreSQL will not consider get_color_note to depend on the my_colors table, and so will not drop the function if the table is dropped. While there are disadvantages to this approach, there are also benefits. The function is still valid in some sense if the table is missing, though executing it would cause an error; creating a new table of the same name would allow the function to work again.

On the other hand, for an SQL-language function or procedure whose body is written in SQL-standard style, the body is parsed at function definition time and all dependencies recognized by the parser are stored. Thus, if we write the function above as

then the function's dependency on the my_colors table will be known and enforced by DROP.

Examples:

Example 1 (unknown):

DROP TABLE products;

ERROR:  cannot drop table products because other objects depend on it
DETAIL:  constraint orders_product_no_fkey on table orders depends on table products
HINT:  Use DROP ... CASCADE to drop the dependent objects too.

Example 2 (unknown):

DROP TABLE products CASCADE;

Example 3 (unknown):

CREATE TYPE rainbow AS ENUM ('red', 'orange', 'yellow',
                             'green', 'blue', 'purple');

CREATE TABLE my_colors (color rainbow, note text);

CREATE FUNCTION get_color_note (rainbow) RETURNS text AS
  'SELECT note FROM my_colors WHERE color = $1'
  LANGUAGE SQL;

Example 4 (unknown):

CREATE FUNCTION get_color_note (rainbow) RETURNS text
BEGIN ATOMIC
  SELECT note FROM my_colors WHERE color = $1;
END;

---

PostgreSQL: Documentation: 18: 35.22. domain_udt_usage

URL: https://www.postgresql.org/docs/current/infoschema-domain-udt-usage.html

Contents:

• 35.22. domain_udt_usage #

The view domain_udt_usage identifies all domains that are based on data types owned by a currently enabled role. Note that in PostgreSQL, built-in data types behave like user-defined types, so they are included here as well.

Table 35.20. domain_udt_usage Columns

udt_catalog sql_identifier

Name of the database that the domain data type is defined in (always the current database)

udt_schema sql_identifier

Name of the schema that the domain data type is defined in

udt_name sql_identifier

Name of the domain data type

domain_catalog sql_identifier

Name of the database that contains the domain (always the current database)

domain_schema sql_identifier

Name of the schema that contains the domain

domain_name sql_identifier

---

PostgreSQL: Documentation: 18: 9.15. XML Functions

URL: https://www.postgresql.org/docs/current/functions-xml.html

Contents:

• 9.15. XML Functions #
  • 9.15.1. Producing XML Content #
  • 9.15.1.1. xmltext #
  • 9.15.1.2. xmlcomment #
  • 9.15.1.3. xmlconcat #
  • 9.15.1.4. xmlelement #
  • 9.15.1.5. xmlforest #
  • 9.15.1.6. xmlpi #
  • 9.15.1.7. xmlroot #
  • 9.15.1.8. xmlagg #

The functions and function-like expressions described in this section operate on values of type xml. See Section 8.13 for information about the xml type. The function-like expressions xmlparse and xmlserialize for converting to and from type xml are documented there, not in this section.

Use of most of these functions requires PostgreSQL to have been built with configure --with-libxml.

A set of functions and function-like expressions is available for producing XML content from SQL data. As such, they are particularly suitable for formatting query results into XML documents for processing in client applications.

The function xmltext returns an XML value with a single text node containing the input argument as its content. Predefined entities like ampersand (&), left and right angle brackets (< >), and quotation marks ("") are escaped.

The function xmlcomment creates an XML value containing an XML comment with the specified text as content. The text cannot contain “--” or end with a “-”, otherwise the resulting construct would not be a valid XML comment. If the argument is null, the result is null.

The function xmlconcat concatenates a list of individual XML values to create a single value containing an XML content fragment. Null values are omitted; the result is only null if there are no nonnull arguments.

XML declarations, if present, are combined as follows. If all argument values have the same XML version declaration, that version is used in the result, else no version is used. If all argument values have the standalone declaration value “yes”, then that value is used in the result. If all argument values have a standalone declaration value and at least one is “no”, then that is used in the result. Else the result will have no standalone declaration. If the result is determined to require a standalone declaration but no version declaration, a version declaration with version 1.0 will be used because XML requires an XML declaration to contain a version declaration. Encoding declarations are ignored and removed in all cases.

The xmlelement expression produces an XML element with the given name, attributes, and content. The name and attname items shown in the syntax are simple identifiers, not values. The attvalue and content items are expressions, which can yield any PostgreSQL data type. The argument(s) within XMLATTRIBUTES generate attributes of the XML element; the content value(s) are concatenated to form its content.

Element and attribute names that are not valid XML names are escaped by replacing the offending characters by the sequence xHHHH, where HHHH is the character's Unicode codepoint in hexadecimal notation. For example:

An explicit attribute name need not be specified if the attribute value is a column reference, in which case the column's name will be used as the attribute name by default. In other cases, the attribute must be given an explicit name. So this example is valid:

Element content, if specified, will be formatted according to its data type. If the content is itself of type xml, complex XML documents can be constructed. For example:

Content of other types will be formatted into valid XML character data. This means in particular that the characters <, >, and & will be converted to entities. Binary data (data type bytea) will be represented in base64 or hex encoding, depending on the setting of the configuration parameter xmlbinary. The particular behavior for individual data types is expected to evolve in order to align the PostgreSQL mappings with those specified in SQL:2006 and later, as discussed in Section D.3.1.3.

The xmlforest expression produces an XML forest (sequence) of elements using the given names and content. As for xmlelement, each name must be a simple identifier, while the content expressions can have any data type.

As seen in the second example, the element name can be omitted if the content value is a column reference, in which case the column name is used by default. Otherwise, a name must be specified.

Element names that are not valid XML names are escaped as shown for xmlelement above. Similarly, content data is escaped to make valid XML content, unless it is already of type xml.

Note that XML forests are not valid XML documents if they consist of more than one element, so it might be useful to wrap xmlforest expressions in xmlelement.

The xmlpi expression creates an XML processing instruction. As for xmlelement, the name must be a simple identifier, while the content expression can have any data type. The content, if present, must not contain the character sequence ?>.

The xmlroot expression alters the properties of the root node of an XML value. If a version is specified, it replaces the value in the root node's version declaration; if a standalone setting is specified, it replaces the value in the root node's standalone declaration.

The function xmlagg is, unlike the other functions described here, an aggregate function. It concatenates the input values to the aggregate function call, much like xmlconcat does, except that concatenation occurs across rows rather than across expressions in a single row. See Section 9.21 for additional information about aggregate functions.

To determine the order of the concatenation, an ORDER BY clause may be added to the aggregate call as described in Section 4.2.7. For example:

The following non-standard approach used to be recommended in previous versions, and may still be useful in specific cases:

The expressions described in this section check properties of xml values.

The expression IS DOCUMENT returns true if the argument XML value is a proper XML document, false if it is not (that is, it is a content fragment), or null if the argument is null. See Section 8.13 about the difference between documents and content fragments.

The expression IS NOT DOCUMENT returns false if the argument XML value is a proper XML document, true if it is not (that is, it is a content fragment), or null if the argument is null.

The function xmlexists evaluates an XPath 1.0 expression (the first argument), with the passed XML value as its context item. The function returns false if the result of that evaluation yields an empty node-set, true if it yields any other value. The function returns null if any argument is null. A nonnull value passed as the context item must be an XML document, not a content fragment or any non-XML value.

The BY REF and BY VALUE clauses are accepted in PostgreSQL, but are ignored, as discussed in Section D.3.2.

In the SQL standard, the xmlexists function evaluates an expression in the XML Query language, but PostgreSQL allows only an XPath 1.0 expression, as discussed in Section D.3.1.

These functions check whether a text string represents well-formed XML, returning a Boolean result. xml_is_well_formed_document checks for a well-formed document, while xml_is_well_formed_content checks for well-formed content. xml_is_well_formed does the former if the xmloption configuration parameter is set to DOCUMENT, or the latter if it is set to CONTENT. This means that xml_is_well_formed is useful for seeing whether a simple cast to type xml will succeed, whereas the other two functions are useful for seeing whether the corresponding variants of XMLPARSE will succeed.

The last example shows that the checks include whether namespaces are correctly matched.

To process values of data type xml, PostgreSQL offers the functions xpath and xpath_exists, which evaluate XPath 1.0 expressions, and the XMLTABLE table function.

The function xpath evaluates the XPath 1.0 expression xpath (given as text) against the XML value xml. It returns an array of XML values corresponding to the node-set produced by the XPath expression. If the XPath expression returns a scalar value rather than a node-set, a single-element array is returned.

The second argument must be a well formed XML document. In particular, it must have a single root node element.

The optional third argument of the function is an array of namespace mappings. This array should be a two-dimensional text array with the length of the second axis being equal to 2 (i.e., it should be an array of arrays, each of which consists of exactly 2 elements). The first element of each array entry is the namespace name (alias), the second the namespace URI. It is not required that aliases provided in this array be the same as those being used in the XML document itself (in other words, both in the XML document and in the xpath function context, aliases are local).

To deal with default (anonymous) namespaces, do something like this:

The function xpath_exists is a specialized form of the xpath function. Instead of returning the individual XML values that satisfy the XPath 1.0 expression, this function returns a Boolean indicating whether the query was satisfied or not (specifically, whether it produced any value other than an empty node-set). This function is equivalent to the XMLEXISTS predicate, except that it also offers support for a namespace mapping argument.

The xmltable expression produces a table based on an XML value, an XPath filter to extract rows, and a set of column definitions. Although it syntactically resembles a function, it can only appear as a table in a query's FROM clause.

The optional XMLNAMESPACES clause gives a comma-separated list of namespace definitions, where each namespace_uri is a text expression and each namespace_name is a simple identifier. It specifies the XML namespaces used in the document and their aliases. A default namespace specification is not currently supported.

The required row_expression argument is an XPath 1.0 expression (given as text) that is evaluated, passing the XML value document_expression as its context item, to obtain a set of XML nodes. These nodes are what xmltable transforms into output rows. No rows will be produced if the document_expression is null, nor if the row_expression produces an empty node-set or any value other than a node-set.

document_expression provides the context item for the row_expression. It must be a well-formed XML document; fragments/forests are not accepted. The BY REF and BY VALUE clauses are accepted but ignored, as discussed in Section D.3.2.

In the SQL standard, the xmltable function evaluates expressions in the XML Query language, but PostgreSQL allows only XPath 1.0 expressions, as discussed in Section D.3.1.

The required COLUMNS clause specifies the column(s) that will be produced in the output table. See the syntax summary above for the format. A name is required for each column, as is a data type (unless FOR ORDINALITY is specified, in which case type integer is implicit). The path, default and nullability clauses are optional.

A column marked FOR ORDINALITY will be populated with row numbers, starting with 1, in the order of nodes retrieved from the row_expression's result node-set. At most one column may be marked FOR ORDINALITY.

XPath 1.0 does not specify an order for nodes in a node-set, so code that relies on a particular order of the results will be implementation-dependent. Details can be found in Section D.3.1.2.

The column_expression for a column is an XPath 1.0 expression that is evaluated for each row, with the current node from the row_expression result as its context item, to find the value of the column. If no column_expression is given, then the column name is used as an implicit path.

If a column's XPath expression returns a non-XML value (which is limited to string, boolean, or double in XPath 1.0) and the column has a PostgreSQL type other than xml, the column will be set as if by assigning the value's string representation to the PostgreSQL type. (If the value is a boolean, its string representation is taken to be 1 or 0 if the output column's type category is numeric, otherwise true or false.)

If a column's XPath expression returns a non-empty set of XML nodes and the column's PostgreSQL type is xml, the column will be assigned the expression result exactly, if it is of document or content form. [8]

A non-XML result assigned to an xml output column produces content, a single text node with the string value of the result. An XML result assigned to a column of any other type may not have more than one node, or an error is raised. If there is exactly one node, the column will be set as if by assigning the node's string value (as defined for the XPath 1.0 string function) to the PostgreSQL type.

The string value of an XML element is the concatenation, in document order, of all text nodes contained in that element and its descendants. The string value of an element with no descendant text nodes is an empty string (not NULL). Any xsi:nil attributes are ignored. Note that the whitespace-only text() node between two non-text elements is preserved, and that leading whitespace on a text() node is not flattened. The XPath 1.0 string function may be consulted for the rules defining the string value of other XML node types and non-XML values.

The conversion rules presented here are not exactly those of the SQL standard, as discussed in Section D.3.1.3.

If the path expression returns an empty node-set (typically, when it does not match) for a given row, the column will be set to NULL, unless a default_expression is specified; then the value resulting from evaluating that expression is used.

A default_expression, rather than being evaluated immediately when xmltable is called, is evaluated each time a default is needed for the column. If the expression qualifies as stable or immutable, the repeat evaluation may be skipped. This means that you can usefully use volatile functions like nextval in default_expression.

Columns may be marked NOT NULL. If the column_expression for a NOT NULL column does not match anything and there is no DEFAULT or the default_expression also evaluates to null, an error is reported.

The following example shows concatenation of multiple text() nodes, usage of the column name as XPath filter, and the treatment of whitespace, XML comments and processing instructions:

The following example illustrates how the XMLNAMESPACES clause can be used to specify a list of namespaces used in the XML document as well as in the XPath expressions:

The following functions map the contents of relational tables to XML values. They can be thought of as XML export functionality:

table_to_xml maps the content of the named table, passed as parameter table. The regclass type accepts strings identifying tables using the usual notation, including optional schema qualification and double quotes (see Section 8.19 for details). query_to_xml executes the query whose text is passed as parameter query and maps the result set. cursor_to_xml fetches the indicated number of rows from the cursor specified by the parameter cursor. This variant is recommended if large tables have to be mapped, because the result value is built up in memory by each function.

If tableforest is false, then the resulting XML document looks like this:

If tableforest is true, the result is an XML content fragment that looks like this:

If no table name is available, that is, when mapping a query or a cursor, the string table is used in the first format, row in the second format.

The choice between these formats is up to the user. The first format is a proper XML document, which will be important in many applications. The second format tends to be more useful in the cursor_to_xml function if the result values are to be reassembled into one document later on. The functions for producing XML content discussed above, in particular xmlelement, can be used to alter the results to taste.

The data values are mapped in the same way as described for the function xmlelement above.

The parameter nulls determines whether null values should be included in the output. If true, null values in columns are represented as:

where xsi is the XML namespace prefix for XML Schema Instance. An appropriate namespace declaration will be added to the result value. If false, columns containing null values are simply omitted from the output.

The parameter targetns specifies the desired XML namespace of the result. If no particular namespace is wanted, an empty string should be passed.

The following functions return XML Schema documents describing the mappings performed by the corresponding functions above:

It is essential that the same parameters are passed in order to obtain matching XML data mappings and XML Schema documents.

The following functions produce XML data mappings and the corresponding XML Schema in one document (or forest), linked together. They can be useful where self-contained and self-describing results are wanted:

In addition, the following functions are available to produce analogous mappings of entire schemas or the entire current database:

These functions ignore tables that are not readable by the current user. The database-wide functions additionally ignore schemas that the current user does not have USAGE (lookup) privilege for.

Note that these potentially produce a lot of data, which needs to be built up in memory. When requesting content mappings of large schemas or databases, it might be worthwhile to consider mapping the tables separately instead, possibly even through a cursor.

The result of a schema content mapping looks like this:

where the format of a table mapping depends on the tableforest parameter as explained above.

The result of a database content mapping looks like this:

where the schema mapping is as above.

As an example of using the output produced by these functions, Example 9.1 shows an XSLT stylesheet that converts the output of table_to_xml_and_xmlschema to an HTML document containing a tabular rendition of the table data. In a similar manner, the results from these functions can be converted into other XML-based formats.

Example 9.1. XSLT Stylesheet for Converting SQL/XML Output to HTML

[8] A result containing more than one element node at the top level, or non-whitespace text outside of an element, is an example of content form. An XPath result can be of neither form, for example if it returns an attribute node selected from the element that contains it. Such a result will be put into content form with each such disallowed node replaced by its string value, as defined for the XPath 1.0 string function.

Examples:

Example 1 (unknown):

xmltext ( text ) → xml

Example 2 (unknown):

SELECT xmltext('< foo & bar >');
         xmltext
-------------------------
 &lt; foo &amp; bar &gt;

Example 3 (unknown):

xmlcomment ( text ) → xml

Example 4 (unknown):

SELECT xmlcomment('hello');

  xmlcomment
--------------
 <!--hello-->

---

PostgreSQL: Documentation: 18: 34.17. Internals

URL: https://www.postgresql.org/docs/current/ecpg-develop.html

Contents:

• 34.17. Internals #

This section explains how ECPG works internally. This information can occasionally be useful to help users understand how to use ECPG.

The first four lines written by ecpg to the output are fixed lines. Two are comments and two are include lines necessary to interface to the library. Then the preprocessor reads through the file and writes output. Normally it just echoes everything to the output.

When it sees an EXEC SQL statement, it intervenes and changes it. The command starts with EXEC SQL and ends with ;. Everything in between is treated as an SQL statement and parsed for variable substitution.

Variable substitution occurs when a symbol starts with a colon (:). The variable with that name is looked up among the variables that were previously declared within a EXEC SQL DECLARE section.

The most important function in the library is ECPGdo, which takes care of executing most commands. It takes a variable number of arguments. This can easily add up to 50 or so arguments, and we hope this will not be a problem on any platform.

This is the line number of the original line; used in error messages only.

This is the SQL command that is to be issued. It is modified by the input variables, i.e., the variables that where not known at compile time but are to be entered in the command. Where the variables should go the string contains ?.

Every input variable causes ten arguments to be created. (See below.)

An enum telling that there are no more input variables.

Every output variable causes ten arguments to be created. (See below.) These variables are filled by the function.

An enum telling that there are no more variables.

For every variable that is part of the SQL command, the function gets ten arguments:

The type as a special symbol.

A pointer to the value or a pointer to the pointer.

The size of the variable if it is a char or varchar.

The number of elements in the array (for array fetches).

The offset to the next element in the array (for array fetches).

The type of the indicator variable as a special symbol.

A pointer to the indicator variable.

The number of elements in the indicator array (for array fetches).

The offset to the next element in the indicator array (for array fetches).

Note that not all SQL commands are treated in this way. For instance, an open cursor statement like:

is not copied to the output. Instead, the cursor's DECLARE command is used at the position of the OPEN command because it indeed opens the cursor.

Here is a complete example describing the output of the preprocessor of a file foo.pgc (details might change with each particular version of the preprocessor):

(The indentation here is added for readability and not something the preprocessor does.)

Examples:

Example 1 (unknown):

EXEC SQL OPEN cursor;

Example 2 (unknown):

EXEC SQL BEGIN DECLARE SECTION;
int index;
int result;
EXEC SQL END DECLARE SECTION;
...
EXEC SQL SELECT res INTO :result FROM mytable WHERE index = :index;

Example 3 (cpp):

/* Processed by ecpg (2.6.0) */
/* These two include files are added by the preprocessor */
#include <ecpgtype.h>;
#include <ecpglib.h>;

/* exec sql begin declare section */

#line 1 "foo.pgc"

 int index;
 int result;
/* exec sql end declare section */
...
ECPGdo(__LINE__, NULL, "SELECT res FROM mytable WHERE index = ?     ",
        ECPGt_int,&(index),1L,1L,sizeof(int),
        ECPGt_NO_INDICATOR, NULL , 0L, 0L, 0L, ECPGt_EOIT,
        ECPGt_int,&(result),1L,1L,sizeof(int),
        ECPGt_NO_INDICATOR, NULL , 0L, 0L, 0L, ECPGt_EORT);
#line 147 "foo.pgc"

---

PostgreSQL: Documentation: 18: 6.2. Updating Data

URL: https://www.postgresql.org/docs/current/dml-update.html

Contents:

• 6.2. Updating Data #

The modification of data that is already in the database is referred to as updating. You can update individual rows, all the rows in a table, or a subset of all rows. Each column can be updated separately; the other columns are not affected.

To update existing rows, use the UPDATE command. This requires three pieces of information:

The name of the table and column to update

The new value of the column

Which row(s) to update

Recall from Chapter 5 that SQL does not, in general, provide a unique identifier for rows. Therefore it is not always possible to directly specify which row to update. Instead, you specify which conditions a row must meet in order to be updated. Only if you have a primary key in the table (independent of whether you declared it or not) can you reliably address individual rows by choosing a condition that matches the primary key. Graphical database access tools rely on this fact to allow you to update rows individually.

For example, this command updates all products that have a price of 5 to have a price of 10:

This might cause zero, one, or many rows to be updated. It is not an error to attempt an update that does not match any rows.

Let's look at that command in detail. First is the key word UPDATE followed by the table name. As usual, the table name can be schema-qualified, otherwise it is looked up in the path. Next is the key word SET followed by the column name, an equal sign, and the new column value. The new column value can be any scalar expression, not just a constant. For example, if you want to raise the price of all products by 10% you could use:

As you see, the expression for the new value can refer to the existing value(s) in the row. We also left out the WHERE clause. If it is omitted, it means that all rows in the table are updated. If it is present, only those rows that match the WHERE condition are updated. Note that the equals sign in the SET clause is an assignment while the one in the WHERE clause is a comparison, but this does not create any ambiguity. Of course, the WHERE condition does not have to be an equality test. Many other operators are available (see Chapter 9). But the expression needs to evaluate to a Boolean result.

You can update more than one column in an UPDATE command by listing more than one assignment in the SET clause. For example:

Examples:

Example 1 (unknown):

UPDATE products SET price = 10 WHERE price = 5;

Example 2 (unknown):

UPDATE products SET price = price * 1.10;

Example 3 (unknown):

UPDATE mytable SET a = 5, b = 3, c = 1 WHERE a > 0;

---

PostgreSQL: Documentation: 18: 35.55. transforms

URL: https://www.postgresql.org/docs/current/infoschema-transforms.html

Contents:

• 35.55. transforms #

The view transforms contains information about the transforms defined in the current database. More precisely, it contains a row for each function contained in a transform (the “from SQL” or “to SQL” function).

Table 35.53. transforms Columns

udt_catalog sql_identifier

Name of the database that contains the type the transform is for (always the current database)

udt_schema sql_identifier

Name of the schema that contains the type the transform is for

udt_name sql_identifier

Name of the type the transform is for

specific_catalog sql_identifier

Name of the database containing the function (always the current database)

specific_schema sql_identifier

Name of the schema containing the function

specific_name sql_identifier

The “specific name” of the function. See Section 35.45 for more information.

group_name sql_identifier

The SQL standard allows defining transforms in “groups”, and selecting a group at run time. PostgreSQL does not support this. Instead, transforms are specific to a language. As a compromise, this field contains the language the transform is for.

transform_type character_data

---

PostgreSQL: Documentation: 18: 28.2. Data Checksums

URL: https://www.postgresql.org/docs/current/checksums.html

Contents:

• 28.2. Data Checksums #
  • 28.2.1. Off-line Enabling of Checksums #

By default, data pages are protected by checksums, but this can optionally be disabled for a cluster. When enabled, each data page includes a checksum that is updated when the page is written and verified each time the page is read. Only data pages are protected by checksums; internal data structures and temporary files are not.

Checksums can be disabled when the cluster is initialized using initdb. They can also be enabled or disabled at a later time as an offline operation. Data checksums are enabled or disabled at the full cluster level, and cannot be specified individually for databases or tables.

The current state of checksums in the cluster can be verified by viewing the value of the read-only configuration variable data_checksums by issuing the command SHOW data_checksums.

When attempting to recover from page corruptions, it may be necessary to bypass the checksum protection. To do this, temporarily set the configuration parameter ignore_checksum_failure.

The pg_checksums application can be used to enable or disable data checksums, as well as verify checksums, on an offline cluster.

---

PostgreSQL: Documentation: 18: 9.19. Array Functions and Operators

URL: https://www.postgresql.org/docs/current/functions-array.html

Contents:

• 9.19. Array Functions and Operators #

Table 9.56 shows the specialized operators available for array types. In addition to those, the usual comparison operators shown in Table 9.1 are available for arrays. The comparison operators compare the array contents element-by-element, using the default B-tree comparison function for the element data type, and sort based on the first difference. In multidimensional arrays the elements are visited in row-major order (last subscript varies most rapidly). If the contents of two arrays are equal but the dimensionality is different, the first difference in the dimensionality information determines the sort order.

Table 9.56. Array Operators

anyarray @> anyarray → boolean

Does the first array contain the second, that is, does each element appearing in the second array equal some element of the first array? (Duplicates are not treated specially, thus ARRAY[1] and ARRAY[1,1] are each considered to contain the other.)

ARRAY[1,4,3] @> ARRAY[3,1,3] → t

anyarray <@ anyarray → boolean

Is the first array contained by the second?

ARRAY[2,2,7] <@ ARRAY[1,7,4,2,6] → t

anyarray && anyarray → boolean

Do the arrays overlap, that is, have any elements in common?

ARRAY[1,4,3] && ARRAY[2,1] → t

anycompatiblearray || anycompatiblearray → anycompatiblearray

Concatenates the two arrays. Concatenating a null or empty array is a no-op; otherwise the arrays must have the same number of dimensions (as illustrated by the first example) or differ in number of dimensions by one (as illustrated by the second). If the arrays are not of identical element types, they will be coerced to a common type (see Section 10.5).

ARRAY[1,2,3] || ARRAY[4,5,6,7] → {1,2,3,4,5,6,7}

ARRAY[1,2,3] || ARRAY[[4,5,6],[7,8,9.9]] → {{1,2,3},{4,5,6},{7,8,9.9}}

anycompatible || anycompatiblearray → anycompatiblearray

Concatenates an element onto the front of an array (which must be empty or one-dimensional).

3 || ARRAY[4,5,6] → {3,4,5,6}

anycompatiblearray || anycompatible → anycompatiblearray

Concatenates an element onto the end of an array (which must be empty or one-dimensional).

ARRAY[4,5,6] || 7 → {4,5,6,7}

See Section 8.15 for more details about array operator behavior. See Section 11.2 for more details about which operators support indexed operations.

Table 9.57 shows the functions available for use with array types. See Section 8.15 for more information and examples of the use of these functions.

Table 9.57. Array Functions

array_append ( anycompatiblearray, anycompatible ) → anycompatiblearray

Appends an element to the end of an array (same as the anycompatiblearray || anycompatible operator).

array_append(ARRAY[1,2], 3) → {1,2,3}

array_cat ( anycompatiblearray, anycompatiblearray ) → anycompatiblearray

Concatenates two arrays (same as the anycompatiblearray || anycompatiblearray operator).

array_cat(ARRAY[1,2,3], ARRAY[4,5]) → {1,2,3,4,5}

array_dims ( anyarray ) → text

Returns a text representation of the array's dimensions.

array_dims(ARRAY[[1,2,3], [4,5,6]]) → [1:2][1:3]

array_fill ( anyelement, integer[] [, integer[] ] ) → anyarray

Returns an array filled with copies of the given value, having dimensions of the lengths specified by the second argument. The optional third argument supplies lower-bound values for each dimension (which default to all 1).

array_fill(11, ARRAY[2,3]) → {{11,11,11},{11,11,11}}

array_fill(7, ARRAY[3], ARRAY[2]) → [2:4]={7,7,7}

array_length ( anyarray, integer ) → integer

Returns the length of the requested array dimension. (Produces NULL instead of 0 for empty or missing array dimensions.)

array_length(array[1,2,3], 1) → 3

array_length(array[]::int[], 1) → NULL

array_length(array['text'], 2) → NULL

array_lower ( anyarray, integer ) → integer

Returns the lower bound of the requested array dimension.

array_lower('[0:2]={1,2,3}'::integer[], 1) → 0

array_ndims ( anyarray ) → integer

Returns the number of dimensions of the array.

array_ndims(ARRAY[[1,2,3], [4,5,6]]) → 2

array_position ( anycompatiblearray, anycompatible [, integer ] ) → integer

Returns the subscript of the first occurrence of the second argument in the array, or NULL if it's not present. If the third argument is given, the search begins at that subscript. The array must be one-dimensional. Comparisons are done using IS NOT DISTINCT FROM semantics, so it is possible to search for NULL.

array_position(ARRAY['sun', 'mon', 'tue', 'wed', 'thu', 'fri', 'sat'], 'mon') → 2

array_positions ( anycompatiblearray, anycompatible ) → integer[]

Returns an array of the subscripts of all occurrences of the second argument in the array given as first argument. The array must be one-dimensional. Comparisons are done using IS NOT DISTINCT FROM semantics, so it is possible to search for NULL. NULL is returned only if the array is NULL; if the value is not found in the array, an empty array is returned.

array_positions(ARRAY['A','A','B','A'], 'A') → {1,2,4}

array_prepend ( anycompatible, anycompatiblearray ) → anycompatiblearray

Prepends an element to the beginning of an array (same as the anycompatible || anycompatiblearray operator).

array_prepend(1, ARRAY[2,3]) → {1,2,3}

array_remove ( anycompatiblearray, anycompatible ) → anycompatiblearray

Removes all elements equal to the given value from the array. The array must be one-dimensional. Comparisons are done using IS NOT DISTINCT FROM semantics, so it is possible to remove NULLs.

array_remove(ARRAY[1,2,3,2], 2) → {1,3}

array_replace ( anycompatiblearray, anycompatible, anycompatible ) → anycompatiblearray

Replaces each array element equal to the second argument with the third argument.

array_replace(ARRAY[1,2,5,4], 5, 3) → {1,2,3,4}

array_reverse ( anyarray ) → anyarray

Reverses the first dimension of the array.

array_reverse(ARRAY[[1,2],[3,4],[5,6]]) → {{5,6},{3,4},{1,2}}

array_sample ( array anyarray, n integer ) → anyarray

Returns an array of n items randomly selected from array. n may not exceed the length of array's first dimension. If array is multi-dimensional, an “item” is a slice having a given first subscript.

array_sample(ARRAY[1,2,3,4,5,6], 3) → {2,6,1}

array_sample(ARRAY[[1,2],[3,4],[5,6]], 2) → {{5,6},{1,2}}

array_shuffle ( anyarray ) → anyarray

Randomly shuffles the first dimension of the array.

array_shuffle(ARRAY[[1,2],[3,4],[5,6]]) → {{5,6},{1,2},{3,4}}

array_sort ( array anyarray [, descending boolean [, nulls_first boolean ]] ) → anyarray

Sorts the first dimension of the array. The sort order is determined by the default sort ordering of the array's element type; however, if the element type is collatable, the collation to use can be specified by adding a COLLATE clause to the array argument.

If descending is true then sort in descending order, otherwise ascending order. If omitted, the default is ascending order. If nulls_first is true then nulls appear before non-null values, otherwise nulls appear after non-null values. If omitted, nulls_first is taken to have the same value as descending.

array_sort(ARRAY[[2,4],[2,1],[6,5]]) → {{2,1},{2,4},{6,5}}

array_to_string ( array anyarray, delimiter text [, null_string text ] ) → text

Converts each array element to its text representation, and concatenates those separated by the delimiter string. If null_string is given and is not NULL, then NULL array entries are represented by that string; otherwise, they are omitted. See also string_to_array.

array_to_string(ARRAY[1, 2, 3, NULL, 5], ',', '') → 1,2,3,,5

array_upper ( anyarray, integer ) → integer

Returns the upper bound of the requested array dimension.

array_upper(ARRAY[1,8,3,7], 1) → 4

cardinality ( anyarray ) → integer

Returns the total number of elements in the array, or 0 if the array is empty.

cardinality(ARRAY[[1,2],[3,4]]) → 4

trim_array ( array anyarray, n integer ) → anyarray

Trims an array by removing the last n elements. If the array is multidimensional, only the first dimension is trimmed.

trim_array(ARRAY[1,2,3,4,5,6], 2) → {1,2,3,4}

unnest ( anyarray ) → setof anyelement

Expands an array into a set of rows. The array's elements are read out in storage order.

unnest(ARRAY[['foo','bar'],['baz','quux']]) →

unnest ( anyarray, anyarray [, ... ] ) → setof anyelement, anyelement [, ... ]

Expands multiple arrays (possibly of different data types) into a set of rows. If the arrays are not all the same length then the shorter ones are padded with NULLs. This form is only allowed in a query's FROM clause; see Section 7.2.1.4.

select * from unnest(ARRAY[1,2], ARRAY['foo','bar','baz']) as x(a,b) →

See also Section 9.21 about the aggregate function array_agg for use with arrays.

Examples:

Example 1 (unknown):

foo
 bar
 baz
 quux

Example 2 (unknown):

a |  b
---+-----
 1 | foo
 2 | bar
   | baz

---

PostgreSQL: Documentation: 18: Chapter 67. Transaction Processing

URL: https://www.postgresql.org/docs/current/transactions.html

Contents:

• Chapter 67. Transaction Processing

This chapter provides an overview of the internals of PostgreSQL's transaction management system. The word transaction is often abbreviated as xact.

---

PostgreSQL: Documentation: 18: 20.2. User Name Maps

URL: https://www.postgresql.org/docs/current/auth-username-maps.html

Contents:

• 20.2. User Name Maps #
  • Tip

When using an external authentication system such as Ident or GSSAPI, the name of the operating system user that initiated the connection might not be the same as the database user (role) that is to be used. In this case, a user name map can be applied to map the operating system user name to a database user. To use user name mapping, specify map=map-name in the options field in pg_hba.conf. This option is supported for all authentication methods that receive external user names. Since different mappings might be needed for different connections, the name of the map to be used is specified in the map-name parameter in pg_hba.conf to indicate which map to use for each individual connection.

User name maps are defined in the ident map file, which by default is named pg_ident.conf and is stored in the cluster's data directory. (It is possible to place the map file elsewhere, however; see the ident_file configuration parameter.) The ident map file contains lines of the general forms:

Comments, whitespace and line continuations are handled in the same way as in pg_hba.conf. The map-name is an arbitrary name that will be used to refer to this mapping in pg_hba.conf. The other two fields specify an operating system user name and a matching database user name. The same map-name can be used repeatedly to specify multiple user-mappings within a single map.

As for pg_hba.conf, the lines in this file can be include directives, following the same rules.

The pg_ident.conf file is read on start-up and when the main server process receives a SIGHUP signal. If you edit the file on an active system, you will need to signal the postmaster (using pg_ctl reload, calling the SQL function pg_reload_conf(), or using kill -HUP) to make it re-read the file.

The system view pg_ident_file_mappings can be helpful for pre-testing changes to the pg_ident.conf file, or for diagnosing problems if loading of the file did not have the desired effects. Rows in the view with non-null error fields indicate problems in the corresponding lines of the file.

There is no restriction regarding how many database users a given operating system user can correspond to, nor vice versa. Thus, entries in a map should be thought of as meaning “this operating system user is allowed to connect as this database user”, rather than implying that they are equivalent. The connection will be allowed if there is any map entry that pairs the user name obtained from the external authentication system with the database user name that the user has requested to connect as. The value all can be used as the database-username to specify that if the system-username matches, then this user is allowed to log in as any of the existing database users. Quoting all makes the keyword lose its special meaning.

If the database-username begins with a + character, then the operating system user can login as any user belonging to that role, similarly to how user names beginning with + are treated in pg_hba.conf. Thus, a + mark means “match any of the roles that are directly or indirectly members of this role”, while a name without a + mark matches only that specific role. Quoting a username starting with a + makes the + lose its special meaning.

If the system-username field starts with a slash (/), the remainder of the field is treated as a regular expression. (See Section 9.7.3.1 for details of PostgreSQL's regular expression syntax.) The regular expression can include a single capture, or parenthesized subexpression. The portion of the system user name that matched the capture can then be referenced in the database-username field as \1 (backslash-one). This allows the mapping of multiple user names in a single line, which is particularly useful for simple syntax substitutions. For example, these entries

will remove the domain part for users with system user names that end with @mydomain.com, and allow any user whose system name ends with @otherdomain.com to log in as guest. Quoting a database-username containing \1 does not make \1 lose its special meaning.

If the database-username field starts with a slash (/), the remainder of the field is treated as a regular expression. When the database-username field is a regular expression, it is not possible to use \1 within it to refer to a capture from the system-username field.

Keep in mind that by default, a regular expression can match just part of a string. It's usually wise to use ^ and $, as shown in the above example, to force the match to be to the entire system user name.

A pg_ident.conf file that could be used in conjunction with the pg_hba.conf file in Example 20.1 is shown in Example 20.2. In this example, anyone logged in to a machine on the 192.168 network that does not have the operating system user name bryanh, ann, or robert would not be granted access. Unix user robert would only be allowed access when he tries to connect as PostgreSQL user bob, not as robert or anyone else. ann would only be allowed to connect as ann. User bryanh would be allowed to connect as either bryanh or as guest1.

Example 20.2. An Example pg_ident.conf File

Examples:

Example 1 (unknown):

map-name system-username database-username
include file
include_if_exists file
include_dir directory

Example 2 (unknown):

mymap   /^(.*)@mydomain\.com$      \1
mymap   /^(.*)@otherdomain\.com$   guest

Example 3 (unknown):

# MAPNAME       SYSTEM-USERNAME         PG-USERNAME

omicron         bryanh                  bryanh
omicron         ann                     ann
# bob has user name robert on these machines
omicron         robert                  bob
# bryanh can also connect as guest1
omicron         bryanh                  guest1

---

PostgreSQL: Documentation: 18: 8.2. Monetary Types

URL: https://www.postgresql.org/docs/current/datatype-money.html

Contents:

• 8.2. Monetary Types #

The money type stores a currency amount with a fixed fractional precision; see Table 8.3. The fractional precision is determined by the database's lc_monetary setting. The range shown in the table assumes there are two fractional digits. Input is accepted in a variety of formats, including integer and floating-point literals, as well as typical currency formatting, such as '$1,000.00'. Output is generally in the latter form but depends on the locale.

Table 8.3. Monetary Types

Since the output of this data type is locale-sensitive, it might not work to load money data into a database that has a different setting of lc_monetary. To avoid problems, before restoring a dump into a new database make sure lc_monetary has the same or equivalent value as in the database that was dumped.

Values of the numeric, int, and bigint data types can be cast to money. Conversion from the real and double precision data types can be done by casting to numeric first, for example:

However, this is not recommended. Floating point numbers should not be used to handle money due to the potential for rounding errors.

A money value can be cast to numeric without loss of precision. Conversion to other types could potentially lose precision, and must also be done in two stages:

Division of a money value by an integer value is performed with truncation of the fractional part towards zero. To get a rounded result, divide by a floating-point value, or cast the money value to numeric before dividing and back to money afterwards. (The latter is preferable to avoid risking precision loss.) When a money value is divided by another money value, the result is double precision (i.e., a pure number, not money); the currency units cancel each other out in the division.

Examples:

Example 1 (unknown):

SELECT '12.34'::float8::numeric::money;

Example 2 (unknown):

SELECT '52093.89'::money::numeric::float8;

---

PostgreSQL: Documentation: 18: Chapter 4. SQL Syntax

URL: https://www.postgresql.org/docs/current/sql-syntax.html

Contents:

• Chapter 4. SQL Syntax

This chapter describes the syntax of SQL. It forms the foundation for understanding the following chapters which will go into detail about how SQL commands are applied to define and modify data.

We also advise users who are already familiar with SQL to read this chapter carefully because it contains several rules and concepts that are implemented inconsistently among SQL databases or that are specific to PostgreSQL.

---

PostgreSQL: Documentation: 18: 17.6. Supported Platforms

URL: https://www.postgresql.org/docs/current/supported-platforms.html

Contents:

• 17.6. Supported Platforms #

A platform (that is, a CPU architecture and operating system combination) is considered supported by the PostgreSQL development community if the code contains provisions to work on that platform and it has recently been verified to build and pass its regression tests on that platform. Currently, most testing of platform compatibility is done automatically by test machines in the PostgreSQL Build Farm. If you are interested in using PostgreSQL on a platform that is not represented in the build farm, but on which the code works or can be made to work, you are strongly encouraged to set up a build farm member machine so that continued compatibility can be assured.

In general, PostgreSQL can be expected to work on these CPU architectures: x86, PowerPC, S/390, SPARC, ARM, MIPS, and RISC-V, including big-endian, little-endian, 32-bit, and 64-bit variants where applicable.

PostgreSQL can be expected to work on current versions of these operating systems: Linux, Windows, FreeBSD, OpenBSD, NetBSD, DragonFlyBSD, macOS, Solaris, and illumos. Other Unix-like systems may also work but are not currently being tested. In most cases, all CPU architectures supported by a given operating system will work. Look in Section 17.7 below to see if there is information specific to your operating system, particularly if using an older system.

If you have installation problems on a platform that is known to be supported according to recent build farm results, please report it to pgsql-bugs@lists.postgresql.org. If you are interested in porting PostgreSQL to a new platform, pgsql-hackers@lists.postgresql.org is the appropriate place to discuss that.

Historical versions of PostgreSQL or POSTGRES also ran on CPU architectures including Alpha, Itanium, M32R, M68K, M88K, NS32K, PA-RISC, SuperH, and VAX, and operating systems including 4.3BSD, AIX, BEOS, BSD/OS, DG/UX, Dynix, HP-UX, IRIX, NeXTSTEP, QNX, SCO, SINIX, Sprite, SunOS, Tru64 UNIX, and ULTRIX.

---

PostgreSQL: Documentation: 18: 35.63. view_column_usage

URL: https://www.postgresql.org/docs/current/infoschema-view-column-usage.html

Contents:

• 35.63. view_column_usage #
  • Note

The view view_column_usage identifies all columns that are used in the query expression of a view (the SELECT statement that defines the view). A column is only included if the table that contains the column is owned by a currently enabled role.

Columns of system tables are not included. This should be fixed sometime.

Table 35.61. view_column_usage Columns

view_catalog sql_identifier

Name of the database that contains the view (always the current database)

view_schema sql_identifier

Name of the schema that contains the view

view_name sql_identifier

table_catalog sql_identifier

Name of the database that contains the table that contains the column that is used by the view (always the current database)

table_schema sql_identifier

Name of the schema that contains the table that contains the column that is used by the view

table_name sql_identifier

Name of the table that contains the column that is used by the view

column_name sql_identifier

Name of the column that is used by the view

---

PostgreSQL: Documentation: 18: 35.16. column_udt_usage

URL: https://www.postgresql.org/docs/current/infoschema-column-udt-usage.html

Contents:

• 35.16. column_udt_usage #

The view column_udt_usage identifies all columns that use data types owned by a currently enabled role. Note that in PostgreSQL, built-in data types behave like user-defined types, so they are included here as well. See also Section 35.17 for details.

Table 35.14. column_udt_usage Columns

udt_catalog sql_identifier

Name of the database that the column data type (the underlying type of the domain, if applicable) is defined in (always the current database)

udt_schema sql_identifier

Name of the schema that the column data type (the underlying type of the domain, if applicable) is defined in

udt_name sql_identifier

Name of the column data type (the underlying type of the domain, if applicable)

table_catalog sql_identifier

Name of the database containing the table (always the current database)

table_schema sql_identifier

Name of the schema containing the table

table_name sql_identifier

column_name sql_identifier

---

PostgreSQL: Documentation: 18: Chapter 47. Logical Decoding

URL: https://www.postgresql.org/docs/current/logicaldecoding.html

Contents:

• Chapter 47. Logical Decoding

PostgreSQL provides infrastructure to stream the modifications performed via SQL to external consumers. This functionality can be used for a variety of purposes, including replication solutions and auditing.

Changes are sent out in streams identified by logical replication slots.

The format in which those changes are streamed is determined by the output plugin used. An example plugin is provided in the PostgreSQL distribution. Additional plugins can be written to extend the choice of available formats without modifying any core code. Every output plugin has access to each individual new row produced by INSERT and the new row version created by UPDATE. Availability of old row versions for UPDATE and DELETE depends on the configured replica identity (see REPLICA IDENTITY).

Changes can be consumed either using the streaming replication protocol (see Section 54.4 and Section 47.3), or by calling functions via SQL (see Section 47.4). It is also possible to write additional methods of consuming the output of a replication slot without modifying core code (see Section 47.7).

---

PostgreSQL: Documentation: 18: 35.14. column_options

URL: https://www.postgresql.org/docs/current/infoschema-column-options.html

Contents:

• 35.14. column_options #

The view column_options contains all the options defined for foreign table columns in the current database. Only those foreign table columns are shown that the current user has access to (by way of being the owner or having some privilege).

Table 35.12. column_options Columns

table_catalog sql_identifier

Name of the database that contains the foreign table (always the current database)

table_schema sql_identifier

Name of the schema that contains the foreign table

table_name sql_identifier

Name of the foreign table

column_name sql_identifier

option_name sql_identifier

option_value character_data

---

PostgreSQL: Documentation: 18: 24.3. Log File Maintenance

URL: https://www.postgresql.org/docs/current/logfile-maintenance.html

Contents:

• 24.3. Log File Maintenance #
  • Note
  • Note

It is a good idea to save the database server's log output somewhere, rather than just discarding it via /dev/null. The log output is invaluable when diagnosing problems.

The server log can contain sensitive information and needs to be protected, no matter how or where it is stored, or the destination to which it is routed. For example, some DDL statements might contain plaintext passwords or other authentication details. Logged statements at the ERROR level might show the SQL source code for applications and might also contain some parts of data rows. Recording data, events and related information is the intended function of this facility, so this is not a leakage or a bug. Please ensure the server logs are visible only to appropriately authorized people.

Log output tends to be voluminous (especially at higher debug levels) so you won't want to save it indefinitely. You need to rotate the log files so that new log files are started and old ones removed after a reasonable period of time.

If you simply direct the stderr of postgres into a file, you will have log output, but the only way to truncate the log file is to stop and restart the server. This might be acceptable if you are using PostgreSQL in a development environment, but few production servers would find this behavior acceptable.

A better approach is to send the server's stderr output to some type of log rotation program. There is a built-in log rotation facility, which you can use by setting the configuration parameter logging_collector to true in postgresql.conf. The control parameters for this program are described in Section 19.8.1. You can also use this approach to capture the log data in machine readable CSV (comma-separated values) format.

Alternatively, you might prefer to use an external log rotation program if you have one that you are already using with other server software. For example, the rotatelogs tool included in the Apache distribution can be used with PostgreSQL. One way to do this is to pipe the server's stderr output to the desired program. If you start the server with pg_ctl, then stderr is already redirected to stdout, so you just need a pipe command, for example:

You can combine these approaches by setting up logrotate to collect log files produced by PostgreSQL built-in logging collector. In this case, the logging collector defines the names and location of the log files, while logrotate periodically archives these files. When initiating log rotation, logrotate must ensure that the application sends further output to the new file. This is commonly done with a postrotate script that sends a SIGHUP signal to the application, which then reopens the log file. In PostgreSQL, you can run pg_ctl with the logrotate option instead. When the server receives this command, the server either switches to a new log file or reopens the existing file, depending on the logging configuration (see Section 19.8.1).

When using static log file names, the server might fail to reopen the log file if the max open file limit is reached or a file table overflow occurs. In this case, log messages are sent to the old log file until a successful log rotation. If logrotate is configured to compress the log file and delete it, the server may lose the messages logged in this time frame. To avoid this issue, you can configure the logging collector to dynamically assign log file names and use a prerotate script to ignore open log files.

Another production-grade approach to managing log output is to send it to syslog and let syslog deal with file rotation. To do this, set the configuration parameter log_destination to syslog (to log to syslog only) in postgresql.conf. Then you can send a SIGHUP signal to the syslog daemon whenever you want to force it to start writing a new log file. If you want to automate log rotation, the logrotate program can be configured to work with log files from syslog.

On many systems, however, syslog is not very reliable, particularly with large log messages; it might truncate or drop messages just when you need them the most. Also, on Linux, syslog will flush each message to disk, yielding poor performance. (You can use a “-” at the start of the file name in the syslog configuration file to disable syncing.)

Note that all the solutions described above take care of starting new log files at configurable intervals, but they do not handle deletion of old, no-longer-useful log files. You will probably want to set up a batch job to periodically delete old log files. Another possibility is to configure the rotation program so that old log files are overwritten cyclically.

pgBadger is an external project that does sophisticated log file analysis. check_postgres provides Nagios alerts when important messages appear in the log files, as well as detection of many other extraordinary conditions.

Examples:

Example 1 (unknown):

pg_ctl start | rotatelogs /var/log/pgsql_log 86400

---

PostgreSQL: Documentation: 18: 31.1. Running the Tests

URL: https://www.postgresql.org/docs/current/regress-run.html

Contents:

• 31.1. Running the Tests #
  • 31.1.1. Running the Tests Against a Temporary Installation #
  • 31.1.2. Running the Tests Against an Existing Installation #
  • 31.1.3. Additional Test Suites #
  • 31.1.4. Locale and Encoding #
  • 31.1.5. Custom Server Settings #
  • 31.1.6. Extra Tests #

The regression tests can be run against an already installed and running server, or using a temporary installation within the build tree. Furthermore, there is a “parallel” and a “sequential” mode for running the tests. The sequential method runs each test script alone, while the parallel method starts up multiple server processes to run groups of tests in parallel. Parallel testing adds confidence that interprocess communication and locking are working correctly. Some tests may run sequentially even in the “parallel” mode in case this is required by the test.

To run the parallel regression tests after building but before installation, type:

in the top-level directory. (Or you can change to src/test/regress and run the command there.) Tests which are run in parallel are prefixed with “+”, and tests which run sequentially are prefixed with “-”. At the end you should see something like:

or otherwise a note about which tests failed. See Section 31.2 below before assuming that a “failure” represents a serious problem.

Because this test method runs a temporary server, it will not work if you did the build as the root user, since the server will not start as root. Recommended procedure is not to do the build as root, or else to perform testing after completing the installation.

If you have configured PostgreSQL to install into a location where an older PostgreSQL installation already exists, and you perform make check before installing the new version, you might find that the tests fail because the new programs try to use the already-installed shared libraries. (Typical symptoms are complaints about undefined symbols.) If you wish to run the tests before overwriting the old installation, you'll need to build with configure --disable-rpath. It is not recommended that you use this option for the final installation, however.

The parallel regression test starts quite a few processes under your user ID. Presently, the maximum concurrency is twenty parallel test scripts, which means forty processes: there's a server process and a psql process for each test script. So if your system enforces a per-user limit on the number of processes, make sure this limit is at least fifty or so, else you might get random-seeming failures in the parallel test. If you are not in a position to raise the limit, you can cut down the degree of parallelism by setting the MAX_CONNECTIONS parameter. For example:

runs no more than ten tests concurrently.

To run the tests after installation (see Chapter 17), initialize a data directory and start the server as explained in Chapter 18, then type:

or for a parallel test:

The tests will expect to contact the server at the local host and the default port number, unless directed otherwise by PGHOST and PGPORT environment variables. The tests will be run in a database named regression; any existing database by this name will be dropped.

The tests will also transiently create some cluster-wide objects, such as roles, tablespaces, and subscriptions. These objects will have names beginning with regress_. Beware of using installcheck mode with an installation that has any actual global objects named that way.

The make check and make installcheck commands run only the “core” regression tests, which test built-in functionality of the PostgreSQL server. The source distribution contains many additional test suites, most of them having to do with add-on functionality such as optional procedural languages.

To run all test suites applicable to the modules that have been selected to be built, including the core tests, type one of these commands at the top of the build tree:

These commands run the tests using temporary servers or an already-installed server, respectively, just as previously explained for make check and make installcheck. Other considerations are the same as previously explained for each method. Note that make check-world builds a separate instance (temporary data directory) for each tested module, so it requires more time and disk space than make installcheck-world.

On a modern machine with multiple CPU cores and no tight operating-system limits, you can make things go substantially faster with parallelism. The recipe that most PostgreSQL developers actually use for running all tests is something like

with a -j limit near to or a bit more than the number of available cores. Discarding stdout eliminates chatter that's not interesting when you just want to verify success. (In case of failure, the stderr messages are usually enough to determine where to look closer.)

Alternatively, you can run individual test suites by typing make check or make installcheck in the appropriate subdirectory of the build tree. Keep in mind that make installcheck assumes you've installed the relevant module(s), not only the core server.

The additional tests that can be invoked this way include:

Regression tests for optional procedural languages. These are located under src/pl.

Regression tests for contrib modules, located under contrib. Not all contrib modules have tests.

Regression tests for the interface libraries, located in src/interfaces/libpq/test and src/interfaces/ecpg/test.

Tests for core-supported authentication methods, located in src/test/authentication. (See below for additional authentication-related tests.)

Tests stressing behavior of concurrent sessions, located in src/test/isolation.

Tests for crash recovery and physical replication, located in src/test/recovery.

Tests for logical replication, located in src/test/subscription.

Tests of client programs, located under src/bin.

When using installcheck mode, these tests will create and destroy test databases whose names include regression, for example pl_regression or contrib_regression. Beware of using installcheck mode with an installation that has any non-test databases named that way.

Some of these auxiliary test suites use the TAP infrastructure explained in Section 31.4. The TAP-based tests are run only when PostgreSQL was configured with the option --enable-tap-tests. This is recommended for development, but can be omitted if there is no suitable Perl installation.

Some test suites are not run by default, either because they are not secure to run on a multiuser system, because they require special software or because they are resource intensive. You can decide which test suites to run additionally by setting the make or environment variable PG_TEST_EXTRA to a whitespace-separated list, for example:

The following values are currently supported:

Runs the test suite under src/test/kerberos. This requires an MIT Kerberos installation and opens TCP/IP listen sockets.

Runs the test suite under src/test/ldap. This requires an OpenLDAP installation and opens TCP/IP listen sockets.

Runs the test src/interfaces/libpq/t/005_negotiate_encryption.pl. This opens TCP/IP listen sockets. If PG_TEST_EXTRA also includes kerberos, additional tests that require an MIT Kerberos installation are enabled.

Runs the test src/interfaces/libpq/t/004_load_balance_dns.pl. This requires editing the system hosts file and opens TCP/IP listen sockets.

Runs the test suite under src/test/modules/oauth_validator. This opens TCP/IP listen sockets for a test server running HTTPS.

Runs an additional test suite in src/bin/pg_upgrade/t/002_pg_upgrade.pl which cycles the regression database through pg_dump/ pg_restore. Not enabled by default because it is resource intensive.

Runs the test suite under contrib/sepgsql. This requires an SELinux environment that is set up in a specific way; see Section F.40.3.

Runs the test suite under src/test/ssl. This opens TCP/IP listen sockets.

Uses wal_consistency_checking=all while running certain tests under src/test/recovery. Not enabled by default because it is resource intensive.

Runs the test suite under src/test/modules/xid_wraparound. Not enabled by default because it is resource intensive.

Tests for features that are not supported by the current build configuration are not run even if they are mentioned in PG_TEST_EXTRA.

In addition, there are tests in src/test/modules which will be run by make check-world but not by make installcheck-world. This is because they install non-production extensions or have other side-effects that are considered undesirable for a production installation. You can use make install and make installcheck in one of those subdirectories if you wish, but it's not recommended to do so with a non-test server.

By default, tests using a temporary installation use the locale defined in the current environment and the corresponding database encoding as determined by initdb. It can be useful to test different locales by setting the appropriate environment variables, for example:

For implementation reasons, setting LC_ALL does not work for this purpose; all the other locale-related environment variables do work.

When testing against an existing installation, the locale is determined by the existing database cluster and cannot be set separately for the test run.

You can also choose the database encoding explicitly by setting the variable ENCODING, for example:

Setting the database encoding this way typically only makes sense if the locale is C; otherwise the encoding is chosen automatically from the locale, and specifying an encoding that does not match the locale will result in an error.

The database encoding can be set for tests against either a temporary or an existing installation, though in the latter case it must be compatible with the installation's locale.

There are several ways to use custom server settings when running a test suite. This can be useful to enable additional logging, adjust resource limits, or enable extra run-time checks such as debug_discard_caches. But note that not all tests can be expected to pass cleanly with arbitrary settings.

Extra options can be passed to the various initdb commands that are run internally during test setup using the environment variable PG_TEST_INITDB_EXTRA_OPTS. For example, to run a test with checksums enabled and a custom WAL segment size and work_mem setting, use:

For the core regression test suite and other tests driven by pg_regress, custom run-time server settings can also be set in the PGOPTIONS environment variable (for settings that allow this), for example:

(This makes use of functionality provided by libpq; see options for details.)

When running against a temporary installation, custom settings can also be set by supplying a pre-written postgresql.conf:

The core regression test suite contains a few test files that are not run by default, because they might be platform-dependent or take a very long time to run. You can run these or other extra test files by setting the variable EXTRA_TESTS. For example, to run the numeric_big test:

Examples:

Example 1 (unknown):

# All 213 tests passed.

Example 2 (unknown):

make MAX_CONNECTIONS=10 check

Example 3 (unknown):

make installcheck

Example 4 (unknown):

make installcheck-parallel

---

PostgreSQL: Documentation: 18: 8.10. Bit String Types

URL: https://www.postgresql.org/docs/current/datatype-bit.html

Contents:

• 8.10. Bit String Types #
  • Note

Bit strings are strings of 1's and 0's. They can be used to store or visualize bit masks. There are two SQL bit types: bit(n) and bit varying(n), where n is a positive integer.

bit type data must match the length n exactly; it is an error to attempt to store shorter or longer bit strings. bit varying data is of variable length up to the maximum length n; longer strings will be rejected. Writing bit without a length is equivalent to bit(1), while bit varying without a length specification means unlimited length.

If one explicitly casts a bit-string value to bit(n), it will be truncated or zero-padded on the right to be exactly n bits, without raising an error. Similarly, if one explicitly casts a bit-string value to bit varying(n), it will be truncated on the right if it is more than n bits.

Refer to Section 4.1.2.5 for information about the syntax of bit string constants. Bit-logical operators and string manipulation functions are available; see Section 9.6.

Example 8.3. Using the Bit String Types

A bit string value requires 1 byte for each group of 8 bits, plus 5 or 8 bytes overhead depending on the length of the string (but long values may be compressed or moved out-of-line, as explained in Section 8.3 for character strings).

Examples:

Example 1 (unknown):

CREATE TABLE test (a BIT(3), b BIT VARYING(5));
INSERT INTO test VALUES (B'101', B'00');
INSERT INTO test VALUES (B'10', B'101');

ERROR:  bit string length 2 does not match type bit(3)

INSERT INTO test VALUES (B'10'::bit(3), B'101');
SELECT * FROM test;

  a  |  b
-----+-----
 101 | 00
 100 | 101

---

PostgreSQL: Documentation: 18: 35.34. referential_constraints

URL: https://www.postgresql.org/docs/current/infoschema-referential-constraints.html

Contents:

• 35.34. referential_constraints #

The view referential_constraints contains all referential (foreign key) constraints in the current database. Only those constraints are shown for which the current user has write access to the referencing table (by way of being the owner or having some privilege other than SELECT).

Table 35.32. referential_constraints Columns

constraint_catalog sql_identifier

Name of the database containing the constraint (always the current database)

constraint_schema sql_identifier

Name of the schema containing the constraint

constraint_name sql_identifier

Name of the constraint

unique_constraint_catalog sql_identifier

Name of the database that contains the unique or primary key constraint that the foreign key constraint references (always the current database)

unique_constraint_schema sql_identifier

Name of the schema that contains the unique or primary key constraint that the foreign key constraint references

unique_constraint_name sql_identifier

Name of the unique or primary key constraint that the foreign key constraint references

match_option character_data

Match option of the foreign key constraint: FULL, PARTIAL, or NONE.

update_rule character_data

Update rule of the foreign key constraint: CASCADE, SET NULL, SET DEFAULT, RESTRICT, or NO ACTION.

delete_rule character_data

Delete rule of the foreign key constraint: CASCADE, SET NULL, SET DEFAULT, RESTRICT, or NO ACTION.

---

PostgreSQL: Documentation: 18: 19.3. Connections and Authentication

URL: https://www.postgresql.org/docs/current/runtime-config-connection.html

Contents:

• 19.3. Connections and Authentication #
  • 19.3.1. Connection Settings #
  • 19.3.2. TCP Settings #
  • 19.3.3. Authentication #
  • Warning
  • 19.3.4. SSL #

Specifies the TCP/IP address(es) on which the server is to listen for connections from client applications. The value takes the form of a comma-separated list of host names and/or numeric IP addresses. The special entry * corresponds to all available IP interfaces. The entry 0.0.0.0 allows listening for all IPv4 addresses and :: allows listening for all IPv6 addresses. If the list is empty, the server does not listen on any IP interface at all, in which case only Unix-domain sockets can be used to connect to it. If the list is not empty, the server will start if it can listen on at least one TCP/IP address. A warning will be emitted for any TCP/IP address which cannot be opened. The default value is localhost, which allows only local TCP/IP “loopback” connections to be made.

While client authentication (Chapter 20) allows fine-grained control over who can access the server, listen_addresses controls which interfaces accept connection attempts, which can help prevent repeated malicious connection requests on insecure network interfaces. This parameter can only be set at server start.

The TCP port the server listens on; 5432 by default. Note that the same port number is used for all IP addresses the server listens on. This parameter can only be set at server start.

Determines the maximum number of concurrent connections to the database server. The default is typically 100 connections, but might be less if your kernel settings will not support it (as determined during initdb). This parameter can only be set at server start.

PostgreSQL sizes certain resources based directly on the value of max_connections. Increasing its value leads to higher allocation of those resources, including shared memory.

When running a standby server, you must set this parameter to the same or higher value than on the primary server. Otherwise, queries will not be allowed in the standby server.

Determines the number of connection “slots” that are reserved for connections by roles with privileges of the pg_use_reserved_connections role. Whenever the number of free connection slots is greater than superuser_reserved_connections but less than or equal to the sum of superuser_reserved_connections and reserved_connections, new connections will be accepted only for superusers and roles with privileges of pg_use_reserved_connections. If superuser_reserved_connections or fewer connection slots are available, new connections will be accepted only for superusers.

The default value is zero connections. The value must be less than max_connections minus superuser_reserved_connections. This parameter can only be set at server start.

Determines the number of connection “slots” that are reserved for connections by PostgreSQL superusers. At most max_connections connections can ever be active simultaneously. Whenever the number of active concurrent connections is at least max_connections minus superuser_reserved_connections, new connections will be accepted only for superusers. The connection slots reserved by this parameter are intended as final reserve for emergency use after the slots reserved by reserved_connections have been exhausted.

The default value is three connections. The value must be less than max_connections minus reserved_connections. This parameter can only be set at server start.

Specifies the directory of the Unix-domain socket(s) on which the server is to listen for connections from client applications. Multiple sockets can be created by listing multiple directories separated by commas. Whitespace between entries is ignored; surround a directory name with double quotes if you need to include whitespace or commas in the name. An empty value specifies not listening on any Unix-domain sockets, in which case only TCP/IP sockets can be used to connect to the server.

A value that starts with @ specifies that a Unix-domain socket in the abstract namespace should be created (currently supported on Linux only). In that case, this value does not specify a “directory” but a prefix from which the actual socket name is computed in the same manner as for the file-system namespace. While the abstract socket name prefix can be chosen freely, since it is not a file-system location, the convention is to nonetheless use file-system-like values such as @/tmp.

The default value is normally /tmp, but that can be changed at build time. On Windows, the default is empty, which means no Unix-domain socket is created by default. This parameter can only be set at server start.

In addition to the socket file itself, which is named .s.PGSQL.nnnn where nnnn is the server's port number, an ordinary file named .s.PGSQL.nnnn.lock will be created in each of the unix_socket_directories directories. Neither file should ever be removed manually. For sockets in the abstract namespace, no lock file is created.

Sets the owning group of the Unix-domain socket(s). (The owning user of the sockets is always the user that starts the server.) In combination with the parameter unix_socket_permissions this can be used as an additional access control mechanism for Unix-domain connections. By default this is the empty string, which uses the default group of the server user. This parameter can only be set at server start.

This parameter is not supported on Windows. Any setting will be ignored. Also, sockets in the abstract namespace have no file owner, so this setting is also ignored in that case.

Sets the access permissions of the Unix-domain socket(s). Unix-domain sockets use the usual Unix file system permission set. The parameter value is expected to be a numeric mode specified in the format accepted by the chmod and umask system calls. (To use the customary octal format the number must start with a 0 (zero).)

The default permissions are 0777, meaning anyone can connect. Reasonable alternatives are 0770 (only user and group, see also unix_socket_group) and 0700 (only user). (Note that for a Unix-domain socket, only write permission matters, so there is no point in setting or revoking read or execute permissions.)

This access control mechanism is independent of the one described in Chapter 20.

This parameter can only be set at server start.

This parameter is irrelevant on systems, notably Solaris as of Solaris 10, that ignore socket permissions entirely. There, one can achieve a similar effect by pointing unix_socket_directories to a directory having search permission limited to the desired audience.

Sockets in the abstract namespace have no file permissions, so this setting is also ignored in that case.

Enables advertising the server's existence via Bonjour. The default is off. This parameter can only be set at server start.

Specifies the Bonjour service name. The computer name is used if this parameter is set to the empty string '' (which is the default). This parameter is ignored if the server was not compiled with Bonjour support. This parameter can only be set at server start.

Specifies the amount of time with no network activity after which the operating system should send a TCP keepalive message to the client. If this value is specified without units, it is taken as seconds. A value of 0 (the default) selects the operating system's default. On Windows, setting a value of 0 will set this parameter to 2 hours, since Windows does not provide a way to read the system default value. This parameter is supported only on systems that support TCP_KEEPIDLE or an equivalent socket option, and on Windows; on other systems, it must be zero. In sessions connected via a Unix-domain socket, this parameter is ignored and always reads as zero.

Specifies the amount of time after which a TCP keepalive message that has not been acknowledged by the client should be retransmitted. If this value is specified without units, it is taken as seconds. A value of 0 (the default) selects the operating system's default. On Windows, setting a value of 0 will set this parameter to 1 second, since Windows does not provide a way to read the system default value. This parameter is supported only on systems that support TCP_KEEPINTVL or an equivalent socket option, and on Windows; on other systems, it must be zero. In sessions connected via a Unix-domain socket, this parameter is ignored and always reads as zero.

Specifies the number of TCP keepalive messages that can be lost before the server's connection to the client is considered dead. A value of 0 (the default) selects the operating system's default. This parameter is supported only on systems that support TCP_KEEPCNT or an equivalent socket option (which does not include Windows); on other systems, it must be zero. In sessions connected via a Unix-domain socket, this parameter is ignored and always reads as zero.

Specifies the amount of time that transmitted data may remain unacknowledged before the TCP connection is forcibly closed. If this value is specified without units, it is taken as milliseconds. A value of 0 (the default) selects the operating system's default. This parameter is supported only on systems that support TCP_USER_TIMEOUT (which does not include Windows); on other systems, it must be zero. In sessions connected via a Unix-domain socket, this parameter is ignored and always reads as zero.

Sets the time interval between optional checks that the client is still connected, while running queries. The check is performed by polling the socket, and allows long running queries to be aborted sooner if the kernel reports that the connection is closed.

This option relies on kernel events exposed by Linux, macOS, illumos and the BSD family of operating systems, and is not currently available on other systems.

If the value is specified without units, it is taken as milliseconds. The default value is 0, which disables connection checks. Without connection checks, the server will detect the loss of the connection only at the next interaction with the socket, when it waits for, receives or sends data.

For the kernel itself to detect lost TCP connections reliably and within a known timeframe in all scenarios including network failure, it may also be necessary to adjust the TCP keepalive settings of the operating system, or the tcp_keepalives_idle, tcp_keepalives_interval and tcp_keepalives_count settings of PostgreSQL.

Maximum amount of time allowed to complete client authentication. If a would-be client has not completed the authentication protocol in this much time, the server closes the connection. This prevents hung clients from occupying a connection indefinitely. If this value is specified without units, it is taken as seconds. The default is one minute (1m). This parameter can only be set in the postgresql.conf file or on the server command line.

When a password is specified in CREATE ROLE or ALTER ROLE, this parameter determines the algorithm to use to encrypt the password. Possible values are scram-sha-256, which will encrypt the password with SCRAM-SHA-256, and md5, which stores the password as an MD5 hash. The default is scram-sha-256.

Note that older clients might lack support for the SCRAM authentication mechanism, and hence not work with passwords encrypted with SCRAM-SHA-256. See Section 20.5 for more details.

Support for MD5-encrypted passwords is deprecated and will be removed in a future release of PostgreSQL. Refer to Section 20.5 for details about migrating to another password type.

The number of computational iterations to be performed when encrypting a password using SCRAM-SHA-256. The default is 4096. A higher number of iterations provides additional protection against brute-force attacks on stored passwords, but makes authentication slower. Changing the value has no effect on existing passwords encrypted with SCRAM-SHA-256 as the iteration count is fixed at the time of encryption. In order to make use of a changed value, a new password must be set.

Controls whether a WARNING about MD5 password deprecation is produced when a CREATE ROLE or ALTER ROLE statement sets an MD5-encrypted password. The default value is on.

Sets the location of the server's Kerberos key file. The default is FILE:/usr/local/pgsql/etc/krb5.keytab (where the directory part is whatever was specified as sysconfdir at build time; use pg_config --sysconfdir to determine that). If this parameter is set to an empty string, it is ignored and a system-dependent default is used. This parameter can only be set in the postgresql.conf file or on the server command line. See Section 20.6 for more information.

Sets whether GSSAPI user names should be treated case-insensitively. The default is off (case sensitive). This parameter can only be set in the postgresql.conf file or on the server command line.

Sets whether GSSAPI delegation should be accepted from the client. The default is off meaning credentials from the client will not be accepted. Changing this to on will make the server accept credentials delegated to it from the client. This parameter can only be set in the postgresql.conf file or on the server command line.

The library/libraries to use for validating OAuth connection tokens. If only one validator library is provided, it will be used by default for any OAuth connections; otherwise, all oauth HBA entries must explicitly set a validator chosen from this list. If set to an empty string (the default), OAuth connections will be refused. This parameter can only be set in the postgresql.conf file.

Validator modules must be implemented/obtained separately; PostgreSQL does not ship with any default implementations. For more information on implementing OAuth validators, see Chapter 50.

See Section 18.9 for more information about setting up SSL. The configuration parameters for controlling transfer encryption using TLS protocols are named ssl for historic reasons, even though support for the SSL protocol has been deprecated. SSL is in this context used interchangeably with TLS.

Enables SSL connections. This parameter can only be set in the postgresql.conf file or on the server command line. The default is off.

Specifies the name of the file containing the SSL server certificate authority (CA). Relative paths are relative to the data directory. This parameter can only be set in the postgresql.conf file or on the server command line. The default is empty, meaning no CA file is loaded, and client certificate verification is not performed.

Specifies the name of the file containing the SSL server certificate. Relative paths are relative to the data directory. This parameter can only be set in the postgresql.conf file or on the server command line. The default is server.crt.

Specifies the name of the file containing the SSL client certificate revocation list (CRL). Relative paths are relative to the data directory. This parameter can only be set in the postgresql.conf file or on the server command line. The default is empty, meaning no CRL file is loaded (unless ssl_crl_dir is set).

Specifies the name of the directory containing the SSL client certificate revocation list (CRL). Relative paths are relative to the data directory. This parameter can only be set in the postgresql.conf file or on the server command line. The default is empty, meaning no CRLs are used (unless ssl_crl_file is set).

The directory needs to be prepared with the OpenSSL command openssl rehash or c_rehash. See its documentation for details.

When using this setting, CRLs in the specified directory are loaded on-demand at connection time. New CRLs can be added to the directory and will be used immediately. This is unlike ssl_crl_file, which causes the CRL in the file to be loaded at server start time or when the configuration is reloaded. Both settings can be used together.

Specifies the name of the file containing the SSL server private key. Relative paths are relative to the data directory. This parameter can only be set in the postgresql.conf file or on the server command line. The default is server.key.

Specifies a list of cipher suites that are allowed by connections using TLS version 1.3. Multiple cipher suites can be specified by using a colon separated list. If left blank, the default set of cipher suites in OpenSSL will be used.

This parameter can only be set in the postgresql.conf file or on the server command line.

Specifies a list of SSL ciphers that are allowed by connections using TLS version 1.2 and lower, see ssl_tls13_ciphers for TLS version 1.3 connections. See the ciphers manual page in the OpenSSL package for the syntax of this setting and a list of supported values. The default value is HIGH:MEDIUM:+3DES:!aNULL. The default is usually a reasonable choice unless you have specific security requirements.

This parameter can only be set in the postgresql.conf file or on the server command line.

Explanation of the default value:

Cipher suites that use ciphers from HIGH group (e.g., AES, Camellia, 3DES)

Cipher suites that use ciphers from MEDIUM group (e.g., RC4, SEED)

The OpenSSL default order for HIGH is problematic because it orders 3DES higher than AES128. This is wrong because 3DES offers less security than AES128, and it is also much slower. +3DES reorders it after all other HIGH and MEDIUM ciphers.

Disables anonymous cipher suites that do no authentication. Such cipher suites are vulnerable to MITM attacks and therefore should not be used.

Available cipher suite details will vary across OpenSSL versions. Use the command openssl ciphers -v 'HIGH:MEDIUM:+3DES:!aNULL' to see actual details for the currently installed OpenSSL version. Note that this list is filtered at run time based on the server key type.

Specifies whether to use the server's SSL cipher preferences, rather than the client's. This parameter can only be set in the postgresql.conf file or on the server command line. The default is on.

PostgreSQL versions before 9.4 do not have this setting and always use the client's preferences. This setting is mainly for backward compatibility with those versions. Using the server's preferences is usually better because it is more likely that the server is appropriately configured.

Specifies the name of the curve to use in ECDH key exchange. It needs to be supported by all clients that connect. Multiple curves can be specified by using a colon-separated list. It does not need to be the same curve used by the server's Elliptic Curve key. This parameter can only be set in the postgresql.conf file or on the server command line. The default is X25519:prime256v1.

OpenSSL names for the most common curves are: prime256v1 (NIST P-256), secp384r1 (NIST P-384), secp521r1 (NIST P-521). An incomplete list of available groups can be shown with the command openssl ecparam -list_curves. Not all of them are usable with TLS though, and many supported group names and aliases are omitted.

In PostgreSQL versions before 18.0 this setting was named ssl_ecdh_curve and only accepted a single value.

Sets the minimum SSL/TLS protocol version to use. Valid values are currently: TLSv1, TLSv1.1, TLSv1.2, TLSv1.3. Older versions of the OpenSSL library do not support all values; an error will be raised if an unsupported setting is chosen. Protocol versions before TLS 1.0, namely SSL version 2 and 3, are always disabled.

The default is TLSv1.2, which satisfies industry best practices as of this writing.

This parameter can only be set in the postgresql.conf file or on the server command line.

Sets the maximum SSL/TLS protocol version to use. Valid values are as for ssl_min_protocol_version, with addition of an empty string, which allows any protocol version. The default is to allow any version. Setting the maximum protocol version is mainly useful for testing or if some component has issues working with a newer protocol.

This parameter can only be set in the postgresql.conf file or on the server command line.

Specifies the name of the file containing Diffie-Hellman parameters used for so-called ephemeral DH family of SSL ciphers. The default is empty, in which case compiled-in default DH parameters used. Using custom DH parameters reduces the exposure if an attacker manages to crack the well-known compiled-in DH parameters. You can create your own DH parameters file with the command openssl dhparam -out dhparams.pem 2048.

This parameter can only be set in the postgresql.conf file or on the server command line.

Sets an external command to be invoked when a passphrase for decrypting an SSL file such as a private key needs to be obtained. By default, this parameter is empty, which means the built-in prompting mechanism is used.

The command must print the passphrase to the standard output and exit with code 0. In the parameter value, %p is replaced by a prompt string. (Write %% for a literal %.) Note that the prompt string will probably contain whitespace, so be sure to quote adequately. A single newline is stripped from the end of the output if present.

The command does not actually have to prompt the user for a passphrase. It can read it from a file, obtain it from a keychain facility, or similar. It is up to the user to make sure the chosen mechanism is adequately secure.

This parameter can only be set in the postgresql.conf file or on the server command line.

This parameter determines whether the passphrase command set by ssl_passphrase_command will also be called during a configuration reload if a key file needs a passphrase. If this parameter is off (the default), then ssl_passphrase_command will be ignored during a reload and the SSL configuration will not be reloaded if a passphrase is needed. That setting is appropriate for a command that requires a TTY for prompting, which might not be available when the server is running. Setting this parameter to on might be appropriate if the passphrase is obtained from a file, for example.

This parameter can only be set in the postgresql.conf file or on the server command line.

---

PostgreSQL: Documentation: 18: 34.13. C++ Applications

URL: https://www.postgresql.org/docs/current/ecpg-cpp.html

Contents:

• 34.13. C++ Applications #
  • 34.13.1. Scope for Host Variables #
  • 34.13.2. C++ Application Development with External C Module #

ECPG has some limited support for C++ applications. This section describes some caveats.

The ecpg preprocessor takes an input file written in C (or something like C) and embedded SQL commands, converts the embedded SQL commands into C language chunks, and finally generates a .c file. The header file declarations of the library functions used by the C language chunks that ecpg generates are wrapped in extern "C" { ... } blocks when used under C++, so they should work seamlessly in C++.

In general, however, the ecpg preprocessor only understands C; it does not handle the special syntax and reserved words of the C++ language. So, some embedded SQL code written in C++ application code that uses complicated features specific to C++ might fail to be preprocessed correctly or might not work as expected.

A safe way to use the embedded SQL code in a C++ application is hiding the ECPG calls in a C module, which the C++ application code calls into to access the database, and linking that together with the rest of the C++ code. See Section 34.13.2 about that.

The ecpg preprocessor understands the scope of variables in C. In the C language, this is rather simple because the scopes of variables is based on their code blocks. In C++, however, the class member variables are referenced in a different code block from the declared position, so the ecpg preprocessor will not understand the scope of the class member variables.

For example, in the following case, the ecpg preprocessor cannot find any declaration for the variable dbname in the test method, so an error will occur.

This code will result in an error like this:

To avoid this scope issue, the test method could be modified to use a local variable as intermediate storage. But this approach is only a poor workaround, because it uglifies the code and reduces performance.

If you understand these technical limitations of the ecpg preprocessor in C++, you might come to the conclusion that linking C objects and C++ objects at the link stage to enable C++ applications to use ECPG features could be better than writing some embedded SQL commands in C++ code directly. This section describes a way to separate some embedded SQL commands from C++ application code with a simple example. In this example, the application is implemented in C++, while C and ECPG is used to connect to the PostgreSQL server.

Three kinds of files have to be created: a C file (*.pgc), a header file, and a C++ file:

A sub-routine module to execute SQL commands embedded in C. It is going to be converted into test_mod.c by the preprocessor.

A header file with declarations of the functions in the C module (test_mod.pgc). It is included by test_cpp.cpp. This file has to have an extern "C" block around the declarations, because it will be linked from the C++ module.

The main code for the application, including the main routine, and in this example a C++ class.

To build the application, proceed as follows. Convert test_mod.pgc into test_mod.c by running ecpg, and generate test_mod.o by compiling test_mod.c with the C compiler:

Next, generate test_cpp.o by compiling test_cpp.cpp with the C++ compiler:

Finally, link these object files, test_cpp.o and test_mod.o, into one executable, using the C++ compiler driver:

Examples:

Example 1 (unknown):

class TestCpp
{
    EXEC SQL BEGIN DECLARE SECTION;
    char dbname[1024];
    EXEC SQL END DECLARE SECTION;

  public:
    TestCpp();
    void test();
    ~TestCpp();
};

TestCpp::TestCpp()
{
    EXEC SQL CONNECT TO testdb1;
    EXEC SQL SELECT pg_catalog.set_config('search_path', '', false); EXEC SQL COMMIT;
}

void Test::test()
{
    EXEC SQL SELECT current_database() INTO :dbname;
    printf("current_database = %s\n", dbname);
}

TestCpp::~TestCpp()
{
    EXEC SQL DISCONNECT ALL;
}

Example 2 (unknown):

ecpg test_cpp.pgc
test_cpp.pgc:28: ERROR: variable "dbname" is not declared

Example 3 (unknown):

void TestCpp::test()
{
    EXEC SQL BEGIN DECLARE SECTION;
    char tmp[1024];
    EXEC SQL END DECLARE SECTION;

    EXEC SQL SELECT current_database() INTO :tmp;
    strlcpy(dbname, tmp, sizeof(tmp));

    printf("current_database = %s\n", dbname);
}

Example 4 (cpp):

#include "test_mod.h"
#include <stdio.h>

void
db_connect()
{
    EXEC SQL CONNECT TO testdb1;
    EXEC SQL SELECT pg_catalog.set_config('search_path', '', false); EXEC SQL COMMIT;
}

void
db_test()
{
    EXEC SQL BEGIN DECLARE SECTION;
    char dbname[1024];
    EXEC SQL END DECLARE SECTION;

    EXEC SQL SELECT current_database() INTO :dbname;
    printf("current_database = %s\n", dbname);
}

void
db_disconnect()
{
    EXEC SQL DISCONNECT ALL;
}

---

PostgreSQL: Documentation: 18: 13.5. Serialization Failure Handling

URL: https://www.postgresql.org/docs/current/mvcc-serialization-failure-handling.html

Contents:

• 13.5. Serialization Failure Handling #

Both Repeatable Read and Serializable isolation levels can produce errors that are designed to prevent serialization anomalies. As previously stated, applications using these levels must be prepared to retry transactions that fail due to serialization errors. Such an error's message text will vary according to the precise circumstances, but it will always have the SQLSTATE code 40001 (serialization_failure).

It may also be advisable to retry deadlock failures. These have the SQLSTATE code 40P01 (deadlock_detected).

In some cases it is also appropriate to retry unique-key failures, which have SQLSTATE code 23505 (unique_violation), and exclusion constraint failures, which have SQLSTATE code 23P01 (exclusion_violation). For example, if the application selects a new value for a primary key column after inspecting the currently stored keys, it could get a unique-key failure because another application instance selected the same new key concurrently. This is effectively a serialization failure, but the server will not detect it as such because it cannot “see” the connection between the inserted value and the previous reads. There are also some corner cases in which the server will issue a unique-key or exclusion constraint error even though in principle it has enough information to determine that a serialization problem is the underlying cause. While it's recommendable to just retry serialization_failure errors unconditionally, more care is needed when retrying these other error codes, since they might represent persistent error conditions rather than transient failures.

It is important to retry the complete transaction, including all logic that decides which SQL to issue and/or which values to use. Therefore, PostgreSQL does not offer an automatic retry facility, since it cannot do so with any guarantee of correctness.

Transaction retry does not guarantee that the retried transaction will complete; multiple retries may be needed. In cases with very high contention, it is possible that completion of a transaction may take many attempts. In cases involving a conflicting prepared transaction, it may not be possible to make progress until the prepared transaction commits or rolls back.

---

PostgreSQL: Documentation: 18: 22.1. Overview

URL: https://www.postgresql.org/docs/current/manage-ag-overview.html

Contents:

• 22.1. Overview #
  • Note

A small number of objects, like role, database, and tablespace names, are defined at the cluster level and stored in the pg_global tablespace. Inside the cluster are multiple databases, which are isolated from each other but can access cluster-level objects. Inside each database are multiple schemas, which contain objects like tables and functions. So the full hierarchy is: cluster, database, schema, table (or some other kind of object, such as a function).

When connecting to the database server, a client must specify the database name in its connection request. It is not possible to access more than one database per connection. However, clients can open multiple connections to the same database, or different databases. Database-level security has two components: access control (see Section 20.1), managed at the connection level, and authorization control (see Section 5.8), managed via the grant system. Foreign data wrappers (see postgres_fdw) allow for objects within one database to act as proxies for objects in other database or clusters. The older dblink module (see dblink) provides a similar capability. By default, all users can connect to all databases using all connection methods.

If one PostgreSQL server cluster is planned to contain unrelated projects or users that should be, for the most part, unaware of each other, it is recommended to put them into separate databases and adjust authorizations and access controls accordingly. If the projects or users are interrelated, and thus should be able to use each other's resources, they should be put in the same database but probably into separate schemas; this provides a modular structure with namespace isolation and authorization control. More information about managing schemas is in Section 5.10.

While multiple databases can be created within a single cluster, it is advised to consider carefully whether the benefits outweigh the risks and limitations. In particular, the impact that having a shared WAL (see Chapter 28) has on backup and recovery options. While individual databases in the cluster are isolated when considered from the user's perspective, they are closely bound from the database administrator's point-of-view.

Databases are created with the CREATE DATABASE command (see Section 22.2) and destroyed with the DROP DATABASE command (see Section 22.5). To determine the set of existing databases, examine the pg_database system catalog, for example

The psql program's \l meta-command and -l command-line option are also useful for listing the existing databases.

The SQL standard calls databases “catalogs”, but there is no difference in practice.

Examples:

Example 1 (unknown):

SELECT datname FROM pg_database;

---

PostgreSQL: Documentation: 18: 35.18. constraint_column_usage

URL: https://www.postgresql.org/docs/current/infoschema-constraint-column-usage.html

Contents:

• 35.18. constraint_column_usage #

The view constraint_column_usage identifies all columns in the current database that are used by some constraint. Only those columns are shown that are contained in a table owned by a currently enabled role. For a check constraint, this view identifies the columns that are used in the check expression. For a not-null constraint, this view identifies the column that the constraint is defined on. For a foreign key constraint, this view identifies the columns that the foreign key references. For a unique or primary key constraint, this view identifies the constrained columns.

Table 35.16. constraint_column_usage Columns

table_catalog sql_identifier

Name of the database that contains the table that contains the column that is used by some constraint (always the current database)

table_schema sql_identifier

Name of the schema that contains the table that contains the column that is used by some constraint

table_name sql_identifier

Name of the table that contains the column that is used by some constraint

column_name sql_identifier

Name of the column that is used by some constraint

constraint_catalog sql_identifier

Name of the database that contains the constraint (always the current database)

constraint_schema sql_identifier

Name of the schema that contains the constraint

constraint_name sql_identifier

Name of the constraint

---

PostgreSQL: Documentation: 18: 5.10. Schemas

URL: https://www.postgresql.org/docs/current/ddl-schemas.html

Contents:

• 5.10. Schemas #
  • Note
  • 5.10.1. Creating a Schema #
  • 5.10.2. The Public Schema #
  • 5.10.3. The Schema Search Path #
  • 5.10.4. Schemas and Privileges #
  • 5.10.5. The System Catalog Schema #
  • 5.10.6. Usage Patterns #
  • 5.10.7. Portability #

A PostgreSQL database cluster contains one or more named databases. Roles and a few other object types are shared across the entire cluster. A client connection to the server can only access data in a single database, the one specified in the connection request.

Users of a cluster do not necessarily have the privilege to access every database in the cluster. Sharing of role names means that there cannot be different roles named, say, joe in two databases in the same cluster; but the system can be configured to allow joe access to only some of the databases.

A database contains one or more named schemas, which in turn contain tables. Schemas also contain other kinds of named objects, including data types, functions, and operators. Within one schema, two objects of the same type cannot have the same name. Furthermore, tables, sequences, indexes, views, materialized views, and foreign tables share the same namespace, so that, for example, an index and a table must have different names if they are in the same schema. The same object name can be used in different schemas without conflict; for example, both schema1 and myschema can contain tables named mytable. Unlike databases, schemas are not rigidly separated: a user can access objects in any of the schemas in the database they are connected to, if they have privileges to do so.

There are several reasons why one might want to use schemas:

To allow many users to use one database without interfering with each other.

To organize database objects into logical groups to make them more manageable.

Third-party applications can be put into separate schemas so they do not collide with the names of other objects.

Schemas are analogous to directories at the operating system level, except that schemas cannot be nested.

To create a schema, use the CREATE SCHEMA command. Give the schema a name of your choice. For example:

To create or access objects in a schema, write a qualified name consisting of the schema name and table name separated by a dot:

This works anywhere a table name is expected, including the table modification commands and the data access commands discussed in the following chapters. (For brevity we will speak of tables only, but the same ideas apply to other kinds of named objects, such as types and functions.)

Actually, the even more general syntax

can be used too, but at present this is just for pro forma compliance with the SQL standard. If you write a database name, it must be the same as the database you are connected to.

So to create a table in the new schema, use:

To drop a schema if it's empty (all objects in it have been dropped), use:

To drop a schema including all contained objects, use:

See Section 5.15 for a description of the general mechanism behind this.

Often you will want to create a schema owned by someone else (since this is one of the ways to restrict the activities of your users to well-defined namespaces). The syntax for that is:

You can even omit the schema name, in which case the schema name will be the same as the user name. See Section 5.10.6 for how this can be useful.

Schema names beginning with pg_ are reserved for system purposes and cannot be created by users.

In the previous sections we created tables without specifying any schema names. By default such tables (and other objects) are automatically put into a schema named “public”. Every new database contains such a schema. Thus, the following are equivalent:

Qualified names are tedious to write, and it's often best not to wire a particular schema name into applications anyway. Therefore tables are often referred to by unqualified names, which consist of just the table name. The system determines which table is meant by following a search path, which is a list of schemas to look in. The first matching table in the search path is taken to be the one wanted. If there is no match in the search path, an error is reported, even if matching table names exist in other schemas in the database.

The ability to create like-named objects in different schemas complicates writing a query that references precisely the same objects every time. It also opens up the potential for users to change the behavior of other users' queries, maliciously or accidentally. Due to the prevalence of unqualified names in queries and their use in PostgreSQL internals, adding a schema to search_path effectively trusts all users having CREATE privilege on that schema. When you run an ordinary query, a malicious user able to create objects in a schema of your search path can take control and execute arbitrary SQL functions as though you executed them.

The first schema named in the search path is called the current schema. Aside from being the first schema searched, it is also the schema in which new tables will be created if the CREATE TABLE command does not specify a schema name.

To show the current search path, use the following command:

In the default setup this returns:

The first element specifies that a schema with the same name as the current user is to be searched. If no such schema exists, the entry is ignored. The second element refers to the public schema that we have seen already.

The first schema in the search path that exists is the default location for creating new objects. That is the reason that by default objects are created in the public schema. When objects are referenced in any other context without schema qualification (table modification, data modification, or query commands) the search path is traversed until a matching object is found. Therefore, in the default configuration, any unqualified access again can only refer to the public schema.

To put our new schema in the path, we use:

(We omit the $user here because we have no immediate need for it.) And then we can access the table without schema qualification:

Also, since myschema is the first element in the path, new objects would by default be created in it.

We could also have written:

Then we no longer have access to the public schema without explicit qualification. There is nothing special about the public schema except that it exists by default. It can be dropped, too.

See also Section 9.27 for other ways to manipulate the schema search path.

The search path works in the same way for data type names, function names, and operator names as it does for table names. Data type and function names can be qualified in exactly the same way as table names. If you need to write a qualified operator name in an expression, there is a special provision: you must write

This is needed to avoid syntactic ambiguity. An example is:

In practice one usually relies on the search path for operators, so as not to have to write anything so ugly as that.

By default, users cannot access any objects in schemas they do not own. To allow that, the owner of the schema must grant the USAGE privilege on the schema. By default, everyone has that privilege on the schema public. To allow users to make use of the objects in a schema, additional privileges might need to be granted, as appropriate for the object.

A user can also be allowed to create objects in someone else's schema. To allow that, the CREATE privilege on the schema needs to be granted. In databases upgraded from PostgreSQL 14 or earlier, everyone has that privilege on the schema public. Some usage patterns call for revoking that privilege:

(The first “public” is the schema, the second “public” means “every user”. In the first sense it is an identifier, in the second sense it is a key word, hence the different capitalization; recall the guidelines from Section 4.1.1.)

In addition to public and user-created schemas, each database contains a pg_catalog schema, which contains the system tables and all the built-in data types, functions, and operators. pg_catalog is always effectively part of the search path. If it is not named explicitly in the path then it is implicitly searched before searching the path's schemas. This ensures that built-in names will always be findable. However, you can explicitly place pg_catalog at the end of your search path if you prefer to have user-defined names override built-in names.

Since system table names begin with pg, it is best to avoid such names to ensure that you won't suffer a conflict if some future version defines a system table named the same as your table. (With the default search path, an unqualified reference to your table name would then be resolved as the system table instead.) System tables will continue to follow the convention of having names beginning with pg, so that they will not conflict with unqualified user-table names so long as users avoid the pg_ prefix.

Schemas can be used to organize your data in many ways. A secure schema usage pattern prevents untrusted users from changing the behavior of other users' queries. When a database does not use a secure schema usage pattern, users wishing to securely query that database would take protective action at the beginning of each session. Specifically, they would begin each session by setting search_path to the empty string or otherwise removing schemas that are writable by non-superusers from search_path. There are a few usage patterns easily supported by the default configuration:

Constrain ordinary users to user-private schemas. To implement this pattern, first ensure that no schemas have public CREATE privileges. Then, for every user needing to create non-temporary objects, create a schema with the same name as that user, for example CREATE SCHEMA alice AUTHORIZATION alice. (Recall that the default search path starts with $user, which resolves to the user name. Therefore, if each user has a separate schema, they access their own schemas by default.) This pattern is a secure schema usage pattern unless an untrusted user is the database owner or has been granted ADMIN OPTION on a relevant role, in which case no secure schema usage pattern exists.

In PostgreSQL 15 and later, the default configuration supports this usage pattern. In prior versions, or when using a database that has been upgraded from a prior version, you will need to remove the public CREATE privilege from the public schema (issue REVOKE CREATE ON SCHEMA public FROM PUBLIC). Then consider auditing the public schema for objects named like objects in schema pg_catalog.

Remove the public schema from the default search path, by modifying postgresql.conf or by issuing ALTER ROLE ALL SET search_path = "$user". Then, grant privileges to create in the public schema. Only qualified names will choose public schema objects. While qualified table references are fine, calls to functions in the public schema will be unsafe or unreliable. If you create functions or extensions in the public schema, use the first pattern instead. Otherwise, like the first pattern, this is secure unless an untrusted user is the database owner or has been granted ADMIN OPTION on a relevant role.

Keep the default search path, and grant privileges to create in the public schema. All users access the public schema implicitly. This simulates the situation where schemas are not available at all, giving a smooth transition from the non-schema-aware world. However, this is never a secure pattern. It is acceptable only when the database has a single user or a few mutually-trusting users. In databases upgraded from PostgreSQL 14 or earlier, this is the default.

For any pattern, to install shared applications (tables to be used by everyone, additional functions provided by third parties, etc.), put them into separate schemas. Remember to grant appropriate privileges to allow the other users to access them. Users can then refer to these additional objects by qualifying the names with a schema name, or they can put the additional schemas into their search path, as they choose.

In the SQL standard, the notion of objects in the same schema being owned by different users does not exist. Moreover, some implementations do not allow you to create schemas that have a different name than their owner. In fact, the concepts of schema and user are nearly equivalent in a database system that implements only the basic schema support specified in the standard. Therefore, many users consider qualified names to really consist of user_name.table_name. This is how PostgreSQL will effectively behave if you create a per-user schema for every user.

Also, there is no concept of a public schema in the SQL standard. For maximum conformance to the standard, you should not use the public schema.

Of course, some SQL database systems might not implement schemas at all, or provide namespace support by allowing (possibly limited) cross-database access. If you need to work with those systems, then maximum portability would be achieved by not using schemas at all.

Examples:

Example 1 (unknown):

CREATE SCHEMA myschema;

Example 2 (unknown):

schema.table

Example 3 (unknown):

database.schema.table

Example 4 (unknown):

CREATE TABLE myschema.mytable (
 ...
);

---

PostgreSQL: Documentation: 18: 32.11. Control Functions

URL: https://www.postgresql.org/docs/current/libpq-control.html

Contents:

• 32.11. Control Functions #
  • Note

These functions control miscellaneous details of libpq's behavior.

Returns the client encoding.

Note that it returns the encoding ID, not a symbolic string such as EUC_JP. If unsuccessful, it returns -1. To convert an encoding ID to an encoding name, you can use:

Sets the client encoding.

conn is a connection to the server, and encoding is the encoding you want to use. If the function successfully sets the encoding, it returns 0, otherwise -1. The current encoding for this connection can be determined by using PQclientEncoding.

Determines the verbosity of messages returned by PQerrorMessage and PQresultErrorMessage.

PQsetErrorVerbosity sets the verbosity mode, returning the connection's previous setting. In TERSE mode, returned messages include severity, primary text, and position only; this will normally fit on a single line. The DEFAULT mode produces messages that include the above plus any detail, hint, or context fields (these might span multiple lines). The VERBOSE mode includes all available fields. The SQLSTATE mode includes only the error severity and the SQLSTATE error code, if one is available (if not, the output is like TERSE mode).

Changing the verbosity setting does not affect the messages available from already-existing PGresult objects, only subsequently-created ones. (But see PQresultVerboseErrorMessage if you want to print a previous error with a different verbosity.)

Determines the handling of CONTEXT fields in messages returned by PQerrorMessage and PQresultErrorMessage.

PQsetErrorContextVisibility sets the context display mode, returning the connection's previous setting. This mode controls whether the CONTEXT field is included in messages. The NEVER mode never includes CONTEXT, while ALWAYS always includes it if available. In ERRORS mode (the default), CONTEXT fields are included only in error messages, not in notices and warnings. (However, if the verbosity setting is TERSE or SQLSTATE, CONTEXT fields are omitted regardless of the context display mode.)

Changing this mode does not affect the messages available from already-existing PGresult objects, only subsequently-created ones. (But see PQresultVerboseErrorMessage if you want to print a previous error with a different display mode.)

Enables tracing of the client/server communication to a debugging file stream.

Each line consists of: an optional timestamp, a direction indicator (F for messages from client to server or B for messages from server to client), message length, message type, and message contents. Non-message contents fields (timestamp, direction, length and message type) are separated by a tab. Message contents are separated by a space. Protocol strings are enclosed in double quotes, while strings used as data values are enclosed in single quotes. Non-printable chars are printed as hexadecimal escapes. Further message-type-specific detail can be found in Section 54.7.

On Windows, if the libpq library and an application are compiled with different flags, this function call will crash the application because the internal representation of the FILE pointers differ. Specifically, multithreaded/single-threaded, release/debug, and static/dynamic flags should be the same for the library and all applications using that library.

Controls the tracing behavior of client/server communication.

flags contains flag bits describing the operating mode of tracing. If flags contains PQTRACE_SUPPRESS_TIMESTAMPS, then the timestamp is not included when printing each message. If flags contains PQTRACE_REGRESS_MODE, then some fields are redacted when printing each message, such as object OIDs, to make the output more convenient to use in testing frameworks. This function must be called after calling PQtrace.

Disables tracing started by PQtrace.

Examples:

Example 1 (javascript):

int PQclientEncoding(const PGconn *conn);

Example 2 (unknown):

char *pg_encoding_to_char(int encoding_id);

Example 3 (javascript):

int PQsetClientEncoding(PGconn *conn, const char *encoding);

Example 4 (unknown):

typedef enum
{
    PQERRORS_TERSE,
    PQERRORS_DEFAULT,
    PQERRORS_VERBOSE,
    PQERRORS_SQLSTATE
} PGVerbosity;

PGVerbosity PQsetErrorVerbosity(PGconn *conn, PGVerbosity verbosity);

---

PostgreSQL: Documentation: 18: 36.13. User-Defined Types

URL: https://www.postgresql.org/docs/current/xtypes.html

Contents:

• 36.13. User-Defined Types #
  • 36.13.1. TOAST Considerations #
  • Note

As described in Section 36.2, PostgreSQL can be extended to support new data types. This section describes how to define new base types, which are data types defined below the level of the SQL language. Creating a new base type requires implementing functions to operate on the type in a low-level language, usually C.

The examples in this section can be found in complex.sql and complex.c in the src/tutorial directory of the source distribution. See the README file in that directory for instructions about running the examples.

A user-defined type must always have input and output functions. These functions determine how the type appears in strings (for input by the user and output to the user) and how the type is organized in memory. The input function takes a null-terminated character string as its argument and returns the internal (in memory) representation of the type. The output function takes the internal representation of the type as argument and returns a null-terminated character string. If we want to do anything more with the type than merely store it, we must provide additional functions to implement whatever operations we'd like to have for the type.

Suppose we want to define a type complex that represents complex numbers. A natural way to represent a complex number in memory would be the following C structure:

We will need to make this a pass-by-reference type, since it's too large to fit into a single Datum value.

As the external string representation of the type, we choose a string of the form (x,y).

The input and output functions are usually not hard to write, especially the output function. But when defining the external string representation of the type, remember that you must eventually write a complete and robust parser for that representation as your input function. For instance:

The output function can simply be:

You should be careful to make the input and output functions inverses of each other. If you do not, you will have severe problems when you need to dump your data into a file and then read it back in. This is a particularly common problem when floating-point numbers are involved.

Optionally, a user-defined type can provide binary input and output routines. Binary I/O is normally faster but less portable than textual I/O. As with textual I/O, it is up to you to define exactly what the external binary representation is. Most of the built-in data types try to provide a machine-independent binary representation. For complex, we will piggy-back on the binary I/O converters for type float8:

Once we have written the I/O functions and compiled them into a shared library, we can define the complex type in SQL. First we declare it as a shell type:

This serves as a placeholder that allows us to reference the type while defining its I/O functions. Now we can define the I/O functions:

Finally, we can provide the full definition of the data type:

When you define a new base type, PostgreSQL automatically provides support for arrays of that type. The array type typically has the same name as the base type with the underscore character (_) prepended.

Once the data type exists, we can declare additional functions to provide useful operations on the data type. Operators can then be defined atop the functions, and if needed, operator classes can be created to support indexing of the data type. These additional layers are discussed in following sections.

If the internal representation of the data type is variable-length, the internal representation must follow the standard layout for variable-length data: the first four bytes must be a char[4] field which is never accessed directly (customarily named vllen). You must use the SET_VARSIZE() macro to store the total size of the datum (including the length field itself) in this field and VARSIZE() to retrieve it. (These macros exist because the length field may be encoded depending on platform.)

For further details see the description of the CREATE TYPE command.

If the values of your data type vary in size (in internal form), it's usually desirable to make the data type TOAST-able (see Section 66.2). You should do this even if the values are always too small to be compressed or stored externally, because TOAST can save space on small data too, by reducing header overhead.

To support TOAST storage, the C functions operating on the data type must always be careful to unpack any toasted values they are handed by using PG_DETOAST_DATUM. (This detail is customarily hidden by defining type-specific GETARG_DATATYPE_P macros.) Then, when running the CREATE TYPE command, specify the internal length as variable and select some appropriate storage option other than plain.

If data alignment is unimportant (either just for a specific function or because the data type specifies byte alignment anyway) then it's possible to avoid some of the overhead of PG_DETOAST_DATUM. You can use PG_DETOAST_DATUM_PACKED instead (customarily hidden by defining a GETARG_DATATYPE_PP macro) and using the macros VARSIZE_ANY_EXHDR and VARDATA_ANY to access a potentially-packed datum. Again, the data returned by these macros is not aligned even if the data type definition specifies an alignment. If the alignment is important you must go through the regular PG_DETOAST_DATUM interface.

Older code frequently declares vllen as an int32 field instead of char[4]. This is OK as long as the struct definition has other fields that have at least int32 alignment. But it is dangerous to use such a struct definition when working with a potentially unaligned datum; the compiler may take it as license to assume the datum actually is aligned, leading to core dumps on architectures that are strict about alignment.

Another feature that's enabled by TOAST support is the possibility of having an expanded in-memory data representation that is more convenient to work with than the format that is stored on disk. The regular or “flat” varlena storage format is ultimately just a blob of bytes; it cannot for example contain pointers, since it may get copied to other locations in memory. For complex data types, the flat format may be quite expensive to work with, so PostgreSQL provides a way to “expand” the flat format into a representation that is more suited to computation, and then pass that format in-memory between functions of the data type.

To use expanded storage, a data type must define an expanded format that follows the rules given in src/include/utils/expandeddatum.h, and provide functions to “expand” a flat varlena value into expanded format and “flatten” the expanded format back to the regular varlena representation. Then ensure that all C functions for the data type can accept either representation, possibly by converting one into the other immediately upon receipt. This does not require fixing all existing functions for the data type at once, because the standard PG_DETOAST_DATUM macro is defined to convert expanded inputs into regular flat format. Therefore, existing functions that work with the flat varlena format will continue to work, though slightly inefficiently, with expanded inputs; they need not be converted until and unless better performance is important.

C functions that know how to work with an expanded representation typically fall into two categories: those that can only handle expanded format, and those that can handle either expanded or flat varlena inputs. The former are easier to write but may be less efficient overall, because converting a flat input to expanded form for use by a single function may cost more than is saved by operating on the expanded format. When only expanded format need be handled, conversion of flat inputs to expanded form can be hidden inside an argument-fetching macro, so that the function appears no more complex than one working with traditional varlena input. To handle both types of input, write an argument-fetching function that will detoast external, short-header, and compressed varlena inputs, but not expanded inputs. Such a function can be defined as returning a pointer to a union of the flat varlena format and the expanded format. Callers can use the VARATT_IS_EXPANDED_HEADER() macro to determine which format they received.

The TOAST infrastructure not only allows regular varlena values to be distinguished from expanded values, but also distinguishes “read-write” and “read-only” pointers to expanded values. C functions that only need to examine an expanded value, or will only change it in safe and non-semantically-visible ways, need not care which type of pointer they receive. C functions that produce a modified version of an input value are allowed to modify an expanded input value in-place if they receive a read-write pointer, but must not modify the input if they receive a read-only pointer; in that case they have to copy the value first, producing a new value to modify. A C function that has constructed a new expanded value should always return a read-write pointer to it. Also, a C function that is modifying a read-write expanded value in-place should take care to leave the value in a sane state if it fails partway through.

For examples of working with expanded values, see the standard array infrastructure, particularly src/backend/utils/adt/array_expanded.c.

Examples:

Example 1 (unknown):

typedef struct Complex {
    double      x;
    double      y;
} Complex;

Example 2 (unknown):

PG_FUNCTION_INFO_V1(complex_in);

Datum
complex_in(PG_FUNCTION_ARGS)
{
    char       *str = PG_GETARG_CSTRING(0);
    double      x,
                y;
    Complex    *result;

    if (sscanf(str, " ( %lf , %lf )", &x, &y) != 2)
        ereport(ERROR,
                (errcode(ERRCODE_INVALID_TEXT_REPRESENTATION),
                 errmsg("invalid input syntax for type %s: \"%s\"",
                        "complex", str)));

    result = (Complex *) palloc(sizeof(Complex));
    result->x = x;
    result->y = y;
    PG_RETURN_POINTER(result);
}

Example 3 (unknown):

PG_FUNCTION_INFO_V1(complex_out);

Datum
complex_out(PG_FUNCTION_ARGS)
{
    Complex    *complex = (Complex *) PG_GETARG_POINTER(0);
    char       *result;

    result = psprintf("(%g,%g)", complex->x, complex->y);
    PG_RETURN_CSTRING(result);
}

Example 4 (unknown):

PG_FUNCTION_INFO_V1(complex_recv);

Datum
complex_recv(PG_FUNCTION_ARGS)
{
    StringInfo  buf = (StringInfo) PG_GETARG_POINTER(0);
    Complex    *result;

    result = (Complex *) palloc(sizeof(Complex));
    result->x = pq_getmsgfloat8(buf);
    result->y = pq_getmsgfloat8(buf);
    PG_RETURN_POINTER(result);
}

PG_FUNCTION_INFO_V1(complex_send);

Datum
complex_send(PG_FUNCTION_ARGS)
{
    Complex    *complex = (Complex *) PG_GETARG_POINTER(0);
    StringInfoData buf;

    pq_begintypsend(&buf);
    pq_sendfloat8(&buf, complex->x);
    pq_sendfloat8(&buf, complex->y);
    PG_RETURN_BYTEA_P(pq_endtypsend(&buf));
}

---

PostgreSQL: Documentation: 18: Chapter 14. Performance Tips

URL: https://www.postgresql.org/docs/current/performance-tips.html

Contents:

• Chapter 14. Performance Tips

Query performance can be affected by many things. Some of these can be controlled by the user, while others are fundamental to the underlying design of the system. This chapter provides some hints about understanding and tuning PostgreSQL performance.

---

PostgreSQL: Documentation: 18: PostgreSQL Server Applications

URL: https://www.postgresql.org/docs/current/reference-server.html

Contents:

• PostgreSQL Server Applications

This part contains reference information for PostgreSQL server applications and support utilities. These commands can only be run usefully on the host where the database server resides. Other utility programs are listed in PostgreSQL Client Applications.

---

PostgreSQL: Documentation: 18: Chapter 28. Reliability and the Write-Ahead Log

URL: https://www.postgresql.org/docs/current/wal.html

Contents:

• Chapter 28. Reliability and the Write-Ahead Log

This chapter explains how to control the reliability of PostgreSQL, including details about the Write-Ahead Log.

---

PostgreSQL: Documentation: 18: 19.4. Resource Consumption

URL: https://www.postgresql.org/docs/current/runtime-config-resource.html

Contents:

• 19.4. Resource Consumption #
  • 19.4.1. Memory #
  • 19.4.2. Disk #
  • 19.4.3. Kernel Resource Usage #
  • 19.4.4. Background Writer #
  • 19.4.5. I/O #
  • 19.4.6. Worker Processes #

Sets the amount of memory the database server uses for shared memory buffers. The default is typically 128 megabytes (128MB), but might be less if your kernel settings will not support it (as determined during initdb). This setting must be at least 128 kilobytes. However, settings significantly higher than the minimum are usually needed for good performance. If this value is specified without units, it is taken as blocks, that is BLCKSZ bytes, typically 8kB. (Non-default values of BLCKSZ change the minimum value.) This parameter can only be set at server start.

If you have a dedicated database server with 1GB or more of RAM, a reasonable starting value for shared_buffers is 25% of the memory in your system. There are some workloads where even larger settings for shared_buffers are effective, but because PostgreSQL also relies on the operating system cache, it is unlikely that an allocation of more than 40% of RAM to shared_buffers will work better than a smaller amount. Larger settings for shared_buffers usually require a corresponding increase in max_wal_size, in order to spread out the process of writing large quantities of new or changed data over a longer period of time.

On systems with less than 1GB of RAM, a smaller percentage of RAM is appropriate, so as to leave adequate space for the operating system.

Controls whether huge pages are requested for the main shared memory area. Valid values are try (the default), on, and off. With huge_pages set to try, the server will try to request huge pages, but fall back to the default if that fails. With on, failure to request huge pages will prevent the server from starting up. With off, huge pages will not be requested. The actual state of huge pages is indicated by the server variable huge_pages_status.

At present, this setting is supported only on Linux and Windows. The setting is ignored on other systems when set to try. On Linux, it is only supported when shared_memory_type is set to mmap (the default).

The use of huge pages results in smaller page tables and less CPU time spent on memory management, increasing performance. For more details about using huge pages on Linux, see Section 18.4.5.

Huge pages are known as large pages on Windows. To use them, you need to assign the user right “Lock pages in memory” to the Windows user account that runs PostgreSQL. You can use Windows Group Policy tool (gpedit.msc) to assign the user right “Lock pages in memory”. To start the database server on the command prompt as a standalone process, not as a Windows service, the command prompt must be run as an administrator or User Access Control (UAC) must be disabled. When the UAC is enabled, the normal command prompt revokes the user right “Lock pages in memory” when started.

Note that this setting only affects the main shared memory area. Operating systems such as Linux, FreeBSD, and Illumos can also use huge pages (also known as “super” pages or “large” pages) automatically for normal memory allocation, without an explicit request from PostgreSQL. On Linux, this is called “transparent huge pages” (THP). That feature has been known to cause performance degradation with PostgreSQL for some users on some Linux versions, so its use is currently discouraged (unlike explicit use of huge_pages).

Controls the size of huge pages, when they are enabled with huge_pages. The default is zero (0). When set to 0, the default huge page size on the system will be used. This parameter can only be set at server start.

Some commonly available page sizes on modern 64 bit server architectures include: 2MB and 1GB (Intel and AMD), 16MB and 16GB (IBM POWER), and 64kB, 2MB, 32MB and 1GB (ARM). For more information about usage and support, see Section 18.4.5.

Non-default settings are currently supported only on Linux.

Sets the maximum amount of memory used for temporary buffers within each database session. These are session-local buffers used only for access to temporary tables. If this value is specified without units, it is taken as blocks, that is BLCKSZ bytes, typically 8kB. The default is eight megabytes (8MB). (If BLCKSZ is not 8kB, the default value scales proportionally to it.) This setting can be changed within individual sessions, but only before the first use of temporary tables within the session; subsequent attempts to change the value will have no effect on that session.

A session will allocate temporary buffers as needed up to the limit given by temp_buffers. The cost of setting a large value in sessions that do not actually need many temporary buffers is only a buffer descriptor, or about 64 bytes, per increment in temp_buffers. However if a buffer is actually used an additional 8192 bytes will be consumed for it (or in general, BLCKSZ bytes).

Sets the maximum number of transactions that can be in the “prepared” state simultaneously (see PREPARE TRANSACTION). Setting this parameter to zero (which is the default) disables the prepared-transaction feature. This parameter can only be set at server start.

If you are not planning to use prepared transactions, this parameter should be set to zero to prevent accidental creation of prepared transactions. If you are using prepared transactions, you will probably want max_prepared_transactions to be at least as large as max_connections, so that every session can have a prepared transaction pending.

When running a standby server, you must set this parameter to the same or higher value than on the primary server. Otherwise, queries will not be allowed in the standby server.

Sets the base maximum amount of memory to be used by a query operation (such as a sort or hash table) before writing to temporary disk files. If this value is specified without units, it is taken as kilobytes. The default value is four megabytes (4MB). Note that a complex query might perform several sort and hash operations at the same time, with each operation generally being allowed to use as much memory as this value specifies before it starts to write data into temporary files. Also, several running sessions could be doing such operations concurrently. Therefore, the total memory used could be many times the value of work_mem; it is necessary to keep this fact in mind when choosing the value. Sort operations are used for ORDER BY, DISTINCT, and merge joins. Hash tables are used in hash joins, hash-based aggregation, memoize nodes and hash-based processing of IN subqueries.

Hash-based operations are generally more sensitive to memory availability than equivalent sort-based operations. The memory limit for a hash table is computed by multiplying work_mem by hash_mem_multiplier. This makes it possible for hash-based operations to use an amount of memory that exceeds the usual work_mem base amount.

Used to compute the maximum amount of memory that hash-based operations can use. The final limit is determined by multiplying work_mem by hash_mem_multiplier. The default value is 2.0, which makes hash-based operations use twice the usual work_mem base amount.

Consider increasing hash_mem_multiplier in environments where spilling by query operations is a regular occurrence, especially when simply increasing work_mem results in memory pressure (memory pressure typically takes the form of intermittent out of memory errors). The default setting of 2.0 is often effective with mixed workloads. Higher settings in the range of 2.0 - 8.0 or more may be effective in environments where work_mem has already been increased to 40MB or more.

Specifies the maximum amount of memory to be used by maintenance operations, such as VACUUM, CREATE INDEX, and ALTER TABLE ADD FOREIGN KEY. If this value is specified without units, it is taken as kilobytes. It defaults to 64 megabytes (64MB). Since only one of these operations can be executed at a time by a database session, and an installation normally doesn't have many of them running concurrently, it's safe to set this value significantly larger than work_mem. Larger settings might improve performance for vacuuming and for restoring database dumps.

Note that when autovacuum runs, up to autovacuum_max_workers times this memory may be allocated, so be careful not to set the default value too high. It may be useful to control for this by separately setting autovacuum_work_mem.

Specifies the maximum amount of memory to be used by each autovacuum worker process. If this value is specified without units, it is taken as kilobytes. It defaults to -1, indicating that the value of maintenance_work_mem should be used instead. The setting has no effect on the behavior of VACUUM when run in other contexts. This parameter can only be set in the postgresql.conf file or on the server command line.

Specifies the size of the Buffer Access Strategy used by the VACUUM and ANALYZE commands. A setting of 0 will allow the operation to use any number of shared_buffers. Otherwise valid sizes range from 128 kB to 16 GB. If the specified size would exceed 1/8 the size of shared_buffers, the size is silently capped to that value. The default value is 2MB. If this value is specified without units, it is taken as kilobytes. This parameter can be set at any time. It can be overridden for VACUUM and ANALYZE when passing the BUFFER_USAGE_LIMIT option. Higher settings can allow VACUUM and ANALYZE to run more quickly, but having too large a setting may cause too many other useful pages to be evicted from shared buffers.

Specifies the maximum amount of memory to be used by logical decoding, before some of the decoded changes are written to local disk. This limits the amount of memory used by logical streaming replication connections. It defaults to 64 megabytes (64MB). Since each replication connection only uses a single buffer of this size, and an installation normally doesn't have many such connections concurrently (as limited by max_wal_senders), it's safe to set this value significantly higher than work_mem, reducing the amount of decoded changes written to disk.

Specifies the amount of memory to use to cache the contents of pg_commit_ts (see Table 66.1). If this value is specified without units, it is taken as blocks, that is BLCKSZ bytes, typically 8kB. The default value is 0, which requests shared_buffers/512 up to 1024 blocks, but not fewer than 16 blocks. This parameter can only be set at server start.

Specifies the amount of shared memory to use to cache the contents of pg_multixact/members (see Table 66.1). If this value is specified without units, it is taken as blocks, that is BLCKSZ bytes, typically 8kB. The default value is 32. This parameter can only be set at server start.

Specifies the amount of shared memory to use to cache the contents of pg_multixact/offsets (see Table 66.1). If this value is specified without units, it is taken as blocks, that is BLCKSZ bytes, typically 8kB. The default value is 16. This parameter can only be set at server start.

Specifies the amount of shared memory to use to cache the contents of pg_notify (see Table 66.1). If this value is specified without units, it is taken as blocks, that is BLCKSZ bytes, typically 8kB. The default value is 16. This parameter can only be set at server start.

Specifies the amount of shared memory to use to cache the contents of pg_serial (see Table 66.1). If this value is specified without units, it is taken as blocks, that is BLCKSZ bytes, typically 8kB. The default value is 32. This parameter can only be set at server start.

Specifies the amount of shared memory to use to cache the contents of pg_subtrans (see Table 66.1). If this value is specified without units, it is taken as blocks, that is BLCKSZ bytes, typically 8kB. The default value is 0, which requests shared_buffers/512 up to 1024 blocks, but not fewer than 16 blocks. This parameter can only be set at server start.

Specifies the amount of shared memory to use to cache the contents of pg_xact (see Table 66.1). If this value is specified without units, it is taken as blocks, that is BLCKSZ bytes, typically 8kB. The default value is 0, which requests shared_buffers/512 up to 1024 blocks, but not fewer than 16 blocks. This parameter can only be set at server start.

Specifies the maximum safe depth of the server's execution stack. The ideal setting for this parameter is the actual stack size limit enforced by the kernel (as set by ulimit -s or local equivalent), less a safety margin of a megabyte or so. The safety margin is needed because the stack depth is not checked in every routine in the server, but only in key potentially-recursive routines. If this value is specified without units, it is taken as kilobytes. The default setting is two megabytes (2MB), which is conservatively small and unlikely to risk crashes. However, it might be too small to allow execution of complex functions. Only superusers and users with the appropriate SET privilege can change this setting.

Setting max_stack_depth higher than the actual kernel limit will mean that a runaway recursive function can crash an individual backend process. On platforms where PostgreSQL can determine the kernel limit, the server will not allow this variable to be set to an unsafe value. However, not all platforms provide the information, so caution is recommended in selecting a value.

Specifies the shared memory implementation that the server should use for the main shared memory region that holds PostgreSQL's shared buffers and other shared data. Possible values are mmap (for anonymous shared memory allocated using mmap), sysv (for System V shared memory allocated via shmget) and windows (for Windows shared memory). Not all values are supported on all platforms; the first supported option is the default for that platform. The use of the sysv option, which is not the default on any platform, is generally discouraged because it typically requires non-default kernel settings to allow for large allocations (see Section 18.4.1).

Specifies the dynamic shared memory implementation that the server should use. Possible values are posix (for POSIX shared memory allocated using shm_open), sysv (for System V shared memory allocated via shmget), windows (for Windows shared memory), and mmap (to simulate shared memory using memory-mapped files stored in the data directory). Not all values are supported on all platforms; the first supported option is usually the default for that platform. The use of the mmap option, which is not the default on any platform, is generally discouraged because the operating system may write modified pages back to disk repeatedly, increasing system I/O load; however, it may be useful for debugging, when the pg_dynshmem directory is stored on a RAM disk, or when other shared memory facilities are not available.

Specifies the amount of memory that should be allocated at server startup for use by parallel queries. When this memory region is insufficient or exhausted by concurrent queries, new parallel queries try to allocate extra shared memory temporarily from the operating system using the method configured with dynamic_shared_memory_type, which may be slower due to memory management overheads. Memory that is allocated at startup with min_dynamic_shared_memory is affected by the huge_pages setting on operating systems where that is supported, and may be more likely to benefit from larger pages on operating systems where that is managed automatically. The default value is 0 (none). This parameter can only be set at server start.

Specifies the maximum amount of disk space that a process can use for temporary files, such as sort and hash temporary files, or the storage file for a held cursor. A transaction attempting to exceed this limit will be canceled. If this value is specified without units, it is taken as kilobytes. -1 (the default) means no limit. Only superusers and users with the appropriate SET privilege can change this setting.

This setting constrains the total space used at any instant by all temporary files used by a given PostgreSQL process. It should be noted that disk space used for explicit temporary tables, as opposed to temporary files used behind-the-scenes in query execution, does not count against this limit.

Specifies the method used to copy files. Possible values are COPY (default) and CLONE (if operating support is available).

This parameter affects:

CREATE DATABASE ... STRATEGY=FILE_COPY

ALTER DATABASE ... SET TABLESPACE ...

CLONE uses the copy_file_range() (Linux, FreeBSD) or copyfile (macOS) system calls, giving the kernel the opportunity to share disk blocks or push work down to lower layers on some file systems.

Specifies the maximum amount of allocated pages for NOTIFY / LISTEN queue. The default value is 1048576. For 8 KB pages it allows to consume up to 8 GB of disk space.

Sets the maximum number of open files each server subprocess is allowed to open simultaneously; files already opened in the postmaster are not counted toward this limit. The default is one thousand files.

If the kernel is enforcing a safe per-process limit, you don't need to worry about this setting. But on some platforms (notably, most BSD systems), the kernel will allow individual processes to open many more files than the system can actually support if many processes all try to open that many files. If you find yourself seeing “Too many open files” failures, try reducing this setting. This parameter can only be set at server start.

There is a separate server process called the background writer, whose function is to issue writes of “dirty” (new or modified) shared buffers. When the number of clean shared buffers appears to be insufficient, the background writer writes some dirty buffers to the file system and marks them as clean. This reduces the likelihood that server processes handling user queries will be unable to find clean buffers and have to write dirty buffers themselves. However, the background writer does cause a net overall increase in I/O load, because while a repeatedly-dirtied page might otherwise be written only once per checkpoint interval, the background writer might write it several times as it is dirtied in the same interval. The parameters discussed in this subsection can be used to tune the behavior for local needs.

Specifies the delay between activity rounds for the background writer. In each round the writer issues writes for some number of dirty buffers (controllable by the following parameters). It then sleeps for the length of bgwriter_delay, and repeats. When there are no dirty buffers in the buffer pool, though, it goes into a longer sleep regardless of bgwriter_delay. If this value is specified without units, it is taken as milliseconds. The default value is 200 milliseconds (200ms). Note that on some systems, the effective resolution of sleep delays is 10 milliseconds; setting bgwriter_delay to a value that is not a multiple of 10 might have the same results as setting it to the next higher multiple of 10. This parameter can only be set in the postgresql.conf file or on the server command line.

In each round, no more than this many buffers will be written by the background writer. Setting this to zero disables background writing. (Note that checkpoints, which are managed by a separate, dedicated auxiliary process, are unaffected.) The default value is 100 buffers. This parameter can only be set in the postgresql.conf file or on the server command line.

The number of dirty buffers written in each round is based on the number of new buffers that have been needed by server processes during recent rounds. The average recent need is multiplied by bgwriter_lru_multiplier to arrive at an estimate of the number of buffers that will be needed during the next round. Dirty buffers are written until there are that many clean, reusable buffers available. (However, no more than bgwriter_lru_maxpages buffers will be written per round.) Thus, a setting of 1.0 represents a “just in time” policy of writing exactly the number of buffers predicted to be needed. Larger values provide some cushion against spikes in demand, while smaller values intentionally leave writes to be done by server processes. The default is 2.0. This parameter can only be set in the postgresql.conf file or on the server command line.

Whenever more than this amount of data has been written by the background writer, attempt to force the OS to issue these writes to the underlying storage. Doing so will limit the amount of dirty data in the kernel's page cache, reducing the likelihood of stalls when an fsync is issued at the end of a checkpoint, or when the OS writes data back in larger batches in the background. Often that will result in greatly reduced transaction latency, but there also are some cases, especially with workloads that are bigger than shared_buffers, but smaller than the OS's page cache, where performance might degrade. This setting may have no effect on some platforms. If this value is specified without units, it is taken as blocks, that is BLCKSZ bytes, typically 8kB. The valid range is between 0, which disables forced writeback, and 2MB. The default is 512kB on Linux, 0 elsewhere. (If BLCKSZ is not 8kB, the default and maximum values scale proportionally to it.) This parameter can only be set in the postgresql.conf file or on the server command line.

Smaller values of bgwriter_lru_maxpages and bgwriter_lru_multiplier reduce the extra I/O load caused by the background writer, but make it more likely that server processes will have to issue writes for themselves, delaying interactive queries.

Whenever more than this amount of data has been written by a single backend, attempt to force the OS to issue these writes to the underlying storage. Doing so will limit the amount of dirty data in the kernel's page cache, reducing the likelihood of stalls when an fsync is issued at the end of a checkpoint, or when the OS writes data back in larger batches in the background. Often that will result in greatly reduced transaction latency, but there also are some cases, especially with workloads that are bigger than shared_buffers, but smaller than the OS's page cache, where performance might degrade. This setting may have no effect on some platforms. If this value is specified without units, it is taken as blocks, that is BLCKSZ bytes, typically 8kB. The valid range is between 0, which disables forced writeback, and 2MB. The default is 0, i.e., no forced writeback. (If BLCKSZ is not 8kB, the maximum value scales proportionally to it.)

Sets the number of concurrent storage I/O operations that PostgreSQL expects can be executed simultaneously. Raising this value will increase the number of I/O operations that any individual PostgreSQL session attempts to initiate in parallel. The allowed range is 1 to 1000, or 0 to disable issuance of asynchronous I/O requests. The default is 16.

Higher values will have the most impact on higher latency storage where queries otherwise experience noticeable I/O stalls and on devices with high IOPs. Unnecessarily high values may increase I/O latency for all queries on the system

On systems with prefetch advice support, effective_io_concurrency also controls the prefetch distance.

This value can be overridden for tables in a particular tablespace by setting the tablespace parameter of the same name (see ALTER TABLESPACE).

Similar to effective_io_concurrency, but used for maintenance work that is done on behalf of many client sessions.

The default is 16. This value can be overridden for tables in a particular tablespace by setting the tablespace parameter of the same name (see ALTER TABLESPACE).

Controls the largest I/O size in operations that combine I/O, and silently limits the user-settable parameter io_combine_limit. This parameter can only be set in the postgresql.conf file or on the server command line. The maximum possible size depends on the operating system and block size, but is typically 1MB on Unix and 128kB on Windows. The default is 128kB.

Controls the largest I/O size in operations that combine I/O. If set higher than the io_max_combine_limit parameter, the lower value will silently be used instead, so both may need to be raised to increase the I/O size. The maximum possible size depends on the operating system and block size, but is typically 1MB on Unix and 128kB on Windows. The default is 128kB.

Controls the maximum number of I/O operations that one process can execute simultaneously.

The default setting of -1 selects a number based on shared_buffers and the maximum number of processes (max_connections, autovacuum_worker_slots, max_worker_processes and max_wal_senders), but not more than 64.

This parameter can only be set at server start.

Selects the method for executing asynchronous I/O. Possible values are:

worker (execute asynchronous I/O using worker processes)

io_uring (execute asynchronous I/O using io_uring, requires a build with --with-liburing / -Dliburing)

sync (execute asynchronous-eligible I/O synchronously)

The default is worker.

This parameter can only be set at server start.

Selects the number of I/O worker processes to use. The default is 3. This parameter can only be set in the postgresql.conf file or on the server command line.

Only has an effect if io_method is set to worker.

Sets the maximum number of background processes that the cluster can support. This parameter can only be set at server start. The default is 8.

When running a standby server, you must set this parameter to the same or higher value than on the primary server. Otherwise, queries will not be allowed in the standby server.

When changing this value, consider also adjusting max_parallel_workers, max_parallel_maintenance_workers, and max_parallel_workers_per_gather.

Sets the maximum number of workers that can be started by a single Gather or Gather Merge node. Parallel workers are taken from the pool of processes established by max_worker_processes, limited by max_parallel_workers. Note that the requested number of workers may not actually be available at run time. If this occurs, the plan will run with fewer workers than expected, which may be inefficient. The default value is 2. Setting this value to 0 disables parallel query execution.

Note that parallel queries may consume very substantially more resources than non-parallel queries, because each worker process is a completely separate process which has roughly the same impact on the system as an additional user session. This should be taken into account when choosing a value for this setting, as well as when configuring other settings that control resource utilization, such as work_mem. Resource limits such as work_mem are applied individually to each worker, which means the total utilization may be much higher across all processes than it would normally be for any single process. For example, a parallel query using 4 workers may use up to 5 times as much CPU time, memory, I/O bandwidth, and so forth as a query which uses no workers at all.

For more information on parallel query, see Chapter 15.

Sets the maximum number of parallel workers that can be started by a single utility command. Currently, the parallel utility commands that support the use of parallel workers are CREATE INDEX when building a B-tree, GIN, or BRIN index, and VACUUM without FULL option. Parallel workers are taken from the pool of processes established by max_worker_processes, limited by max_parallel_workers. Note that the requested number of workers may not actually be available at run time. If this occurs, the utility operation will run with fewer workers than expected. The default value is 2. Setting this value to 0 disables the use of parallel workers by utility commands.

Note that parallel utility commands should not consume substantially more memory than equivalent non-parallel operations. This strategy differs from that of parallel query, where resource limits generally apply per worker process. Parallel utility commands treat the resource limit maintenance_work_mem as a limit to be applied to the entire utility command, regardless of the number of parallel worker processes. However, parallel utility commands may still consume substantially more CPU resources and I/O bandwidth.

Sets the maximum number of workers that the cluster can support for parallel operations. The default value is 8. When increasing or decreasing this value, consider also adjusting max_parallel_maintenance_workers and max_parallel_workers_per_gather. Also, note that a setting for this value which is higher than max_worker_processes will have no effect, since parallel workers are taken from the pool of worker processes established by that setting.

Allows the leader process to execute the query plan under Gather and Gather Merge nodes instead of waiting for worker processes. The default is on. Setting this value to off reduces the likelihood that workers will become blocked because the leader is not reading tuples fast enough, but requires the leader process to wait for worker processes to start up before the first tuples can be produced. The degree to which the leader can help or hinder performance depends on the plan type, number of workers and query duration.

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PostgreSQL: Documentation: 18: 20.10. LDAP Authentication

URL: https://www.postgresql.org/docs/current/auth-ldap.html

Contents:

• 20.10. LDAP Authentication #
  • Tip

This authentication method operates similarly to password except that it uses LDAP as the password verification method. LDAP is used only to validate the user name/password pairs. Therefore the user must already exist in the database before LDAP can be used for authentication.

LDAP authentication can operate in two modes. In the first mode, which we will call the simple bind mode, the server will bind to the distinguished name constructed as prefix username suffix. Typically, the prefix parameter is used to specify cn=, or DOMAIN\ in an Active Directory environment. suffix is used to specify the remaining part of the DN in a non-Active Directory environment.

In the second mode, which we will call the search+bind mode, the server first binds to the LDAP directory with a fixed user name and password, specified with ldapbinddn and ldapbindpasswd, and performs a search for the user trying to log in to the database. If no user and password is configured, an anonymous bind will be attempted to the directory. The search will be performed over the subtree at ldapbasedn, and will try to do an exact match of the attribute specified in ldapsearchattribute. Once the user has been found in this search, the server re-binds to the directory as this user, using the password specified by the client, to verify that the login is correct. This mode is the same as that used by LDAP authentication schemes in other software, such as Apache mod_authnz_ldap and pam_ldap. This method allows for significantly more flexibility in where the user objects are located in the directory, but will cause two additional requests to the LDAP server to be made.

The following configuration options are used in both modes:

Names or IP addresses of LDAP servers to connect to. Multiple servers may be specified, separated by spaces.

Port number on LDAP server to connect to. If no port is specified, the LDAP library's default port setting will be used.

Set to ldaps to use LDAPS. This is a non-standard way of using LDAP over SSL, supported by some LDAP server implementations. See also the ldaptls option for an alternative.

Set to 1 to make the connection between PostgreSQL and the LDAP server use TLS encryption. This uses the StartTLS operation per RFC 4513. See also the ldapscheme option for an alternative.

Note that using ldapscheme or ldaptls only encrypts the traffic between the PostgreSQL server and the LDAP server. The connection between the PostgreSQL server and the PostgreSQL client will still be unencrypted unless SSL is used there as well.

The following options are used in simple bind mode only:

String to prepend to the user name when forming the DN to bind as, when doing simple bind authentication.

String to append to the user name when forming the DN to bind as, when doing simple bind authentication.

The following options are used in search+bind mode only:

Root DN to begin the search for the user in, when doing search+bind authentication.

DN of user to bind to the directory with to perform the search when doing search+bind authentication.

Password for user to bind to the directory with to perform the search when doing search+bind authentication.

Attribute to match against the user name in the search when doing search+bind authentication. If no attribute is specified, the uid attribute will be used.

The search filter to use when doing search+bind authentication. Occurrences of $username will be replaced with the user name. This allows for more flexible search filters than ldapsearchattribute.

The following option may be used as an alternative way to write some of the above LDAP options in a more compact and standard form:

An RFC 4516 LDAP URL. The format is

scope must be one of base, one, sub, typically the last. (The default is base, which is normally not useful in this application.) attribute can nominate a single attribute, in which case it is used as a value for ldapsearchattribute. If attribute is empty then filter can be used as a value for ldapsearchfilter.

The URL scheme ldaps chooses the LDAPS method for making LDAP connections over SSL, equivalent to using ldapscheme=ldaps. To use encrypted LDAP connections using the StartTLS operation, use the normal URL scheme ldap and specify the ldaptls option in addition to ldapurl.

For non-anonymous binds, ldapbinddn and ldapbindpasswd must be specified as separate options.

LDAP URLs are currently only supported with OpenLDAP, not on Windows.

It is an error to mix configuration options for simple bind with options for search+bind. To use ldapurl in simple bind mode, the URL must not contain a basedn or query elements.

When using search+bind mode, the search can be performed using a single attribute specified with ldapsearchattribute, or using a custom search filter specified with ldapsearchfilter. Specifying ldapsearchattribute=foo is equivalent to specifying ldapsearchfilter="(foo=$username)". If neither option is specified the default is ldapsearchattribute=uid.

If PostgreSQL was compiled with OpenLDAP as the LDAP client library, the ldapserver setting may be omitted. In that case, a list of host names and ports is looked up via RFC 2782 DNS SRV records. The name _ldap._tcp.DOMAIN is looked up, where DOMAIN is extracted from ldapbasedn.

Here is an example for a simple-bind LDAP configuration:

When a connection to the database server as database user someuser is requested, PostgreSQL will attempt to bind to the LDAP server using the DN cn=someuser, dc=example, dc=net and the password provided by the client. If that connection succeeds, the database access is granted.

Here is a different simple-bind configuration, which uses the LDAPS scheme and a custom port number, written as a URL:

This is slightly more compact than specifying ldapserver, ldapscheme, and ldapport separately.

Here is an example for a search+bind configuration:

When a connection to the database server as database user someuser is requested, PostgreSQL will attempt to bind anonymously (since ldapbinddn was not specified) to the LDAP server, perform a search for (uid=someuser) under the specified base DN. If an entry is found, it will then attempt to bind using that found information and the password supplied by the client. If that second bind succeeds, the database access is granted.

Here is the same search+bind configuration written as a URL:

Some other software that supports authentication against LDAP uses the same URL format, so it will be easier to share the configuration.

Here is an example for a search+bind configuration that uses ldapsearchfilter instead of ldapsearchattribute to allow authentication by user ID or email address:

Here is an example for a search+bind configuration that uses DNS SRV discovery to find the host name(s) and port(s) for the LDAP service for the domain name example.net:

Since LDAP often uses commas and spaces to separate the different parts of a DN, it is often necessary to use double-quoted parameter values when configuring LDAP options, as shown in the examples.

Examples:

Example 1 (unknown):

ldap[s]://host[:port]/basedn[?[attribute][?[scope][?[filter]]]]

Example 2 (unknown):

host ... ldap ldapserver=ldap.example.net ldapprefix="cn=" ldapsuffix=", dc=example, dc=net"

Example 3 (unknown):

host ... ldap ldapurl="ldaps://ldap.example.net:49151" ldapprefix="cn=" ldapsuffix=", dc=example, dc=net"

Example 4 (unknown):

host ... ldap ldapserver=ldap.example.net ldapbasedn="dc=example, dc=net" ldapsearchattribute=uid

---

PostgreSQL: Documentation: 18: 34.9. Preprocessor Directives

URL: https://www.postgresql.org/docs/current/ecpg-preproc.html

Contents:

• 34.9. Preprocessor Directives #
  • 34.9.1. Including Files #
  • Note
  • 34.9.2. The define and undef Directives #
  • 34.9.3. ifdef, ifndef, elif, else, and endif Directives #

Several preprocessor directives are available that modify how the ecpg preprocessor parses and processes a file.

To include an external file into your embedded SQL program, use:

The embedded SQL preprocessor will look for a file named filename.h, preprocess it, and include it in the resulting C output. Thus, embedded SQL statements in the included file are handled correctly.

The ecpg preprocessor will search a file at several directories in following order:

But when EXEC SQL INCLUDE "filename" is used, only the current directory is searched.

In each directory, the preprocessor will first look for the file name as given, and if not found will append .h to the file name and try again (unless the specified file name already has that suffix).

Note that EXEC SQL INCLUDE is not the same as:

because this file would not be subject to SQL command preprocessing. Naturally, you can continue to use the C #include directive to include other header files.

The include file name is case-sensitive, even though the rest of the EXEC SQL INCLUDE command follows the normal SQL case-sensitivity rules.

Similar to the directive #define that is known from C, embedded SQL has a similar concept:

So you can define a name:

And you can also define constants:

Use undef to remove a previous definition:

Of course you can continue to use the C versions #define and #undef in your embedded SQL program. The difference is where your defined values get evaluated. If you use EXEC SQL DEFINE then the ecpg preprocessor evaluates the defines and substitutes the values. For example if you write:

then ecpg will already do the substitution and your C compiler will never see any name or identifier MYNUMBER. Note that you cannot use #define for a constant that you are going to use in an embedded SQL query because in this case the embedded SQL precompiler is not able to see this declaration.

If multiple input files are named on the ecpg preprocessor's command line, the effects of EXEC SQL DEFINE and EXEC SQL UNDEF do not carry across files: each file starts with only the symbols defined by -D switches on the command line.

You can use the following directives to compile code sections conditionally:

Checks a name and processes subsequent lines if name has been defined via EXEC SQL define name.

Checks a name and processes subsequent lines if name has not been defined via EXEC SQL define name.

Begins an optional alternative section after an EXEC SQL ifdef name or EXEC SQL ifndef name directive. Any number of elif sections can appear. Lines following an elif will be processed if name has been defined and no previous section of the same ifdef/ifndef...endif construct has been processed.

Begins an optional, final alternative section after an EXEC SQL ifdef name or EXEC SQL ifndef name directive. Subsequent lines will be processed if no previous section of the same ifdef/ifndef...endif construct has been processed.

Ends an ifdef/ifndef...endif construct. Subsequent lines are processed normally.

ifdef/ifndef...endif constructs can be nested, up to 127 levels deep.

This example will compile exactly one of the three SET TIMEZONE commands:

Examples:

Example 1 (unknown):

EXEC SQL INCLUDE filename;
EXEC SQL INCLUDE <filename>;
EXEC SQL INCLUDE "filename";

Example 2 (cpp):

#include <filename.h>

Example 3 (unknown):

EXEC SQL DEFINE name;
EXEC SQL DEFINE name value;

Example 4 (unknown):

EXEC SQL DEFINE HAVE_FEATURE;

---

PostgreSQL: Documentation: 18: 20.8. Ident Authentication

URL: https://www.postgresql.org/docs/current/auth-ident.html

Contents:

• 20.8. Ident Authentication #
  • Note

The ident authentication method works by obtaining the client's operating system user name from an ident server and using it as the allowed database user name (with an optional user name mapping). This is only supported on TCP/IP connections.

When ident is specified for a local (non-TCP/IP) connection, peer authentication (see Section 20.9) will be used instead.

The following configuration options are supported for ident:

Allows for mapping between system and database user names. See Section 20.2 for details.

The “Identification Protocol” is described in RFC 1413. Virtually every Unix-like operating system ships with an ident server that listens on TCP port 113 by default. The basic functionality of an ident server is to answer questions like “What user initiated the connection that goes out of your port X and connects to my port Y?”. Since PostgreSQL knows both X and Y when a physical connection is established, it can interrogate the ident server on the host of the connecting client and can theoretically determine the operating system user for any given connection.

The drawback of this procedure is that it depends on the integrity of the client: if the client machine is untrusted or compromised, an attacker could run just about any program on port 113 and return any user name they choose. This authentication method is therefore only appropriate for closed networks where each client machine is under tight control and where the database and system administrators operate in close contact. In other words, you must trust the machine running the ident server. Heed the warning:

The Identification Protocol is not intended as an authorization or access control protocol.

Some ident servers have a nonstandard option that causes the returned user name to be encrypted, using a key that only the originating machine's administrator knows. This option must not be used when using the ident server with PostgreSQL, since PostgreSQL does not have any way to decrypt the returned string to determine the actual user name.

---

PostgreSQL: Documentation: 18: 29.2. Subscription

URL: https://www.postgresql.org/docs/current/logical-replication-subscription.html

Contents:

• 29.2. Subscription #
  • 29.2.1. Replication Slot Management #
  • 29.2.2. Examples: Set Up Logical Replication #
  • 29.2.3. Examples: Deferred Replication Slot Creation #

A subscription is the downstream side of logical replication. The node where a subscription is defined is referred to as the subscriber. A subscription defines the connection to another database and set of publications (one or more) to which it wants to subscribe.

The subscriber database behaves in the same way as any other PostgreSQL instance and can be used as a publisher for other databases by defining its own publications.

A subscriber node may have multiple subscriptions if desired. It is possible to define multiple subscriptions between a single publisher-subscriber pair, in which case care must be taken to ensure that the subscribed publication objects don't overlap.

Each subscription will receive changes via one replication slot (see Section 26.2.6). Additional replication slots may be required for the initial data synchronization of pre-existing table data and those will be dropped at the end of data synchronization.

A logical replication subscription can be a standby for synchronous replication (see Section 26.2.8). The standby name is by default the subscription name. An alternative name can be specified as application_name in the connection information of the subscription.

Subscriptions are dumped by pg_dump if the current user is a superuser. Otherwise a warning is written and subscriptions are skipped, because non-superusers cannot read all subscription information from the pg_subscription catalog.

The subscription is added using CREATE SUBSCRIPTION and can be stopped/resumed at any time using the ALTER SUBSCRIPTION command and removed using DROP SUBSCRIPTION.

When a subscription is dropped and recreated, the synchronization information is lost. This means that the data has to be resynchronized afterwards.

The schema definitions are not replicated, and the published tables must exist on the subscriber. Only regular tables may be the target of replication. For example, you can't replicate to a view.

The tables are matched between the publisher and the subscriber using the fully qualified table name. Replication to differently-named tables on the subscriber is not supported.

Columns of a table are also matched by name. The order of columns in the subscriber table does not need to match that of the publisher. The data types of the columns do not need to match, as long as the text representation of the data can be converted to the target type. For example, you can replicate from a column of type integer to a column of type bigint. The target table can also have additional columns not provided by the published table. Any such columns will be filled with the default value as specified in the definition of the target table. However, logical replication in binary format is more restrictive. See the binary option of CREATE SUBSCRIPTION for details.

As mentioned earlier, each (active) subscription receives changes from a replication slot on the remote (publishing) side.

Additional table synchronization slots are normally transient, created internally to perform initial table synchronization and dropped automatically when they are no longer needed. These table synchronization slots have generated names: “pg_%usync%u_%llu” (parameters: Subscription oid, Table relid, system identifier sysid)

Normally, the remote replication slot is created automatically when the subscription is created using CREATE SUBSCRIPTION and it is dropped automatically when the subscription is dropped using DROP SUBSCRIPTION. In some situations, however, it can be useful or necessary to manipulate the subscription and the underlying replication slot separately. Here are some scenarios:

When creating a subscription, the replication slot already exists. In that case, the subscription can be created using the create_slot = false option to associate with the existing slot.

When creating a subscription, the remote host is not reachable or in an unclear state. In that case, the subscription can be created using the connect = false option. The remote host will then not be contacted at all. This is what pg_dump uses. The remote replication slot will then have to be created manually before the subscription can be activated.

When dropping a subscription, the replication slot should be kept. This could be useful when the subscriber database is being moved to a different host and will be activated from there. In that case, disassociate the slot from the subscription using ALTER SUBSCRIPTION before attempting to drop the subscription.

When dropping a subscription, the remote host is not reachable. In that case, disassociate the slot from the subscription using ALTER SUBSCRIPTION before attempting to drop the subscription. If the remote database instance no longer exists, no further action is then necessary. If, however, the remote database instance is just unreachable, the replication slot (and any still remaining table synchronization slots) should then be dropped manually; otherwise it/they would continue to reserve WAL and might eventually cause the disk to fill up. Such cases should be carefully investigated.

Create some test tables on the publisher.

Create the same tables on the subscriber.

Insert data to the tables at the publisher side.

Create publications for the tables. The publications pub2 and pub3a disallow some publish operations. The publication pub3b has a row filter (see Section 29.4).

Create subscriptions for the publications. The subscription sub3 subscribes to both pub3a and pub3b. All subscriptions will copy initial data by default.

Observe that initial table data is copied, regardless of the publish operation of the publication.

Furthermore, because the initial data copy ignores the publish operation, and because publication pub3a has no row filter, it means the copied table t3 contains all rows even when they do not match the row filter of publication pub3b.

Insert more data to the tables at the publisher side.

Now the publisher side data looks like:

Observe that during normal replication the appropriate publish operations are used. This means publications pub2 and pub3a will not replicate the INSERT. Also, publication pub3b will only replicate data that matches the row filter of pub3b. Now the subscriber side data looks like:

There are some cases (e.g. Section 29.2.1) where, if the remote replication slot was not created automatically, the user must create it manually before the subscription can be activated. The steps to create the slot and activate the subscription are shown in the following examples. These examples specify the standard logical decoding output plugin (pgoutput), which is what the built-in logical replication uses.

First, create a publication for the examples to use.

Example 1: Where the subscription says connect = false

Create the subscription.

On the publisher, manually create a slot. Because the name was not specified during CREATE SUBSCRIPTION, the name of the slot to create is same as the subscription name, e.g. "sub1".

On the subscriber, complete the activation of the subscription. After this the tables of pub1 will start replicating.

Example 2: Where the subscription says connect = false, but also specifies the slot_name option.

Create the subscription.

On the publisher, manually create a slot using the same name that was specified during CREATE SUBSCRIPTION, e.g. "myslot".

On the subscriber, the remaining subscription activation steps are the same as before.

Example 3: Where the subscription specifies slot_name = NONE

Create the subscription. When slot_name = NONE then enabled = false, and create_slot = false are also needed.

On the publisher, manually create a slot using any name, e.g. "myslot".

On the subscriber, associate the subscription with the slot name just created.

The remaining subscription activation steps are same as before.

Examples:

Example 1 (unknown):

/* pub # */ CREATE TABLE t1(a int, b text, PRIMARY KEY(a));
/* pub # */ CREATE TABLE t2(c int, d text, PRIMARY KEY(c));
/* pub # */ CREATE TABLE t3(e int, f text, PRIMARY KEY(e));

Example 2 (unknown):

/* sub # */ CREATE TABLE t1(a int, b text, PRIMARY KEY(a));
/* sub # */ CREATE TABLE t2(c int, d text, PRIMARY KEY(c));
/* sub # */ CREATE TABLE t3(e int, f text, PRIMARY KEY(e));

Example 3 (unknown):

/* pub # */ INSERT INTO t1 VALUES (1, 'one'), (2, 'two'), (3, 'three');
/* pub # */ INSERT INTO t2 VALUES (1, 'A'), (2, 'B'), (3, 'C');
/* pub # */ INSERT INTO t3 VALUES (1, 'i'), (2, 'ii'), (3, 'iii');

Example 4 (unknown):

/* pub # */ CREATE PUBLICATION pub1 FOR TABLE t1;
/* pub # */ CREATE PUBLICATION pub2 FOR TABLE t2 WITH (publish = 'truncate');
/* pub # */ CREATE PUBLICATION pub3a FOR TABLE t3 WITH (publish = 'truncate');
/* pub # */ CREATE PUBLICATION pub3b FOR TABLE t3 WHERE (e > 5);

---

PostgreSQL: Documentation: 18: 11.8. Partial Indexes

URL: https://www.postgresql.org/docs/current/indexes-partial.html

Contents:

• 11.8. Partial Indexes #

A partial index is an index built over a subset of a table; the subset is defined by a conditional expression (called the predicate of the partial index). The index contains entries only for those table rows that satisfy the predicate. Partial indexes are a specialized feature, but there are several situations in which they are useful.

One major reason for using a partial index is to avoid indexing common values. Since a query searching for a common value (one that accounts for more than a few percent of all the table rows) will not use the index anyway, there is no point in keeping those rows in the index at all. This reduces the size of the index, which will speed up those queries that do use the index. It will also speed up many table update operations because the index does not need to be updated in all cases. Example 11.1 shows a possible application of this idea.

Example 11.1. Setting up a Partial Index to Exclude Common Values

Suppose you are storing web server access logs in a database. Most accesses originate from the IP address range of your organization but some are from elsewhere (say, employees on dial-up connections). If your searches by IP are primarily for outside accesses, you probably do not need to index the IP range that corresponds to your organization's subnet.

Assume a table like this:

To create a partial index that suits our example, use a command such as this:

A typical query that can use this index would be:

Here the query's IP address is covered by the partial index. The following query cannot use the partial index, as it uses an IP address that is excluded from the index:

Observe that this kind of partial index requires that the common values be predetermined, so such partial indexes are best used for data distributions that do not change. Such indexes can be recreated occasionally to adjust for new data distributions, but this adds maintenance effort.

Another possible use for a partial index is to exclude values from the index that the typical query workload is not interested in; this is shown in Example 11.2. This results in the same advantages as listed above, but it prevents the “uninteresting” values from being accessed via that index, even if an index scan might be profitable in that case. Obviously, setting up partial indexes for this kind of scenario will require a lot of care and experimentation.

Example 11.2. Setting up a Partial Index to Exclude Uninteresting Values

If you have a table that contains both billed and unbilled orders, where the unbilled orders take up a small fraction of the total table and yet those are the most-accessed rows, you can improve performance by creating an index on just the unbilled rows. The command to create the index would look like this:

A possible query to use this index would be:

However, the index can also be used in queries that do not involve order_nr at all, e.g.:

This is not as efficient as a partial index on the amount column would be, since the system has to scan the entire index. Yet, if there are relatively few unbilled orders, using this partial index just to find the unbilled orders could be a win.

Note that this query cannot use this index:

The order 3501 might be among the billed or unbilled orders.

Example 11.2 also illustrates that the indexed column and the column used in the predicate do not need to match. PostgreSQL supports partial indexes with arbitrary predicates, so long as only columns of the table being indexed are involved. However, keep in mind that the predicate must match the conditions used in the queries that are supposed to benefit from the index. To be precise, a partial index can be used in a query only if the system can recognize that the WHERE condition of the query mathematically implies the predicate of the index. PostgreSQL does not have a sophisticated theorem prover that can recognize mathematically equivalent expressions that are written in different forms. (Not only is such a general theorem prover extremely difficult to create, it would probably be too slow to be of any real use.) The system can recognize simple inequality implications, for example “x < 1” implies “x < 2”; otherwise the predicate condition must exactly match part of the query's WHERE condition or the index will not be recognized as usable. Matching takes place at query planning time, not at run time. As a result, parameterized query clauses do not work with a partial index. For example a prepared query with a parameter might specify “x < ?” which will never imply “x < 2” for all possible values of the parameter.

A third possible use for partial indexes does not require the index to be used in queries at all. The idea here is to create a unique index over a subset of a table, as in Example 11.3. This enforces uniqueness among the rows that satisfy the index predicate, without constraining those that do not.

Example 11.3. Setting up a Partial Unique Index

Suppose that we have a table describing test outcomes. We wish to ensure that there is only one “successful” entry for a given subject and target combination, but there might be any number of “unsuccessful” entries. Here is one way to do it:

This is a particularly efficient approach when there are few successful tests and many unsuccessful ones. It is also possible to allow only one null in a column by creating a unique partial index with an IS NULL restriction.

Finally, a partial index can also be used to override the system's query plan choices. Also, data sets with peculiar distributions might cause the system to use an index when it really should not. In that case the index can be set up so that it is not available for the offending query. Normally, PostgreSQL makes reasonable choices about index usage (e.g., it avoids them when retrieving common values, so the earlier example really only saves index size, it is not required to avoid index usage), and grossly incorrect plan choices are cause for a bug report.

Keep in mind that setting up a partial index indicates that you know at least as much as the query planner knows, in particular you know when an index might be profitable. Forming this knowledge requires experience and understanding of how indexes in PostgreSQL work. In most cases, the advantage of a partial index over a regular index will be minimal. There are cases where they are quite counterproductive, as in Example 11.4.

Example 11.4. Do Not Use Partial Indexes as a Substitute for Partitioning

You might be tempted to create a large set of non-overlapping partial indexes, for example

This is a bad idea! Almost certainly, you'll be better off with a single non-partial index, declared like

(Put the category column first, for the reasons described in Section 11.3.) While a search in this larger index might have to descend through a couple more tree levels than a search in a smaller index, that's almost certainly going to be cheaper than the planner effort needed to select the appropriate one of the partial indexes. The core of the problem is that the system does not understand the relationship among the partial indexes, and will laboriously test each one to see if it's applicable to the current query.

If your table is large enough that a single index really is a bad idea, you should look into using partitioning instead (see Section 5.12). With that mechanism, the system does understand that the tables and indexes are non-overlapping, so far better performance is possible.

More information about partial indexes can be found in [ston89b], [olson93], and [seshadri95].

Examples:

Example 1 (unknown):

CREATE TABLE access_log (
    url varchar,
    client_ip inet,
    ...
);

Example 2 (unknown):

CREATE INDEX access_log_client_ip_ix ON access_log (client_ip)
WHERE NOT (client_ip > inet '192.168.100.0' AND
           client_ip < inet '192.168.100.255');

Example 3 (unknown):

SELECT *
FROM access_log
WHERE url = '/index.html' AND client_ip = inet '212.78.10.32';

Example 4 (unknown):

SELECT *
FROM access_log
WHERE url = '/index.html' AND client_ip = inet '192.168.100.23';

---

PostgreSQL: Documentation: 18: Chapter 22. Managing Databases

URL: https://www.postgresql.org/docs/current/managing-databases.html

Contents:

• Chapter 22. Managing Databases

Every instance of a running PostgreSQL server manages one or more databases. Databases are therefore the topmost hierarchical level for organizing SQL objects (“database objects”). This chapter describes the properties of databases, and how to create, manage, and destroy them.

---

PostgreSQL: Documentation: 18: 35.27. foreign_data_wrappers

URL: https://www.postgresql.org/docs/current/infoschema-foreign-data-wrappers.html

Contents:

• 35.27. foreign_data_wrappers #

The view foreign_data_wrappers contains all foreign-data wrappers defined in the current database. Only those foreign-data wrappers are shown that the current user has access to (by way of being the owner or having some privilege).

Table 35.25. foreign_data_wrappers Columns

foreign_data_wrapper_catalog sql_identifier

Name of the database that contains the foreign-data wrapper (always the current database)

foreign_data_wrapper_name sql_identifier

Name of the foreign-data wrapper

authorization_identifier sql_identifier

Name of the owner of the foreign server

library_name character_data

File name of the library that implementing this foreign-data wrapper

foreign_data_wrapper_language character_data

Language used to implement this foreign-data wrapper

---

PostgreSQL: Documentation: 18: Chapter 31. Regression Tests

URL: https://www.postgresql.org/docs/current/regress.html

Contents:

• Chapter 31. Regression Tests

The regression tests are a comprehensive set of tests for the SQL implementation in PostgreSQL. They test standard SQL operations as well as the extended capabilities of PostgreSQL.

---

PostgreSQL: Documentation: 18: 35.11. collation_character_set_​applicability

URL: https://www.postgresql.org/docs/current/infoschema-collation-character-set-applicab.html

Contents:

• 35.11. collation_character_set_​applicability #

The view collation_character_set_applicability identifies which character set the available collations are applicable to. In PostgreSQL, there is only one character set per database (see explanation in Section 35.7), so this view does not provide much useful information.

Table 35.9. collation_character_set_applicability Columns

collation_catalog sql_identifier

Name of the database containing the collation (always the current database)

collation_schema sql_identifier

Name of the schema containing the collation

collation_name sql_identifier

Name of the default collation

character_set_catalog sql_identifier

Character sets are currently not implemented as schema objects, so this column is null

character_set_schema sql_identifier

Character sets are currently not implemented as schema objects, so this column is null

character_set_name sql_identifier

Name of the character set

---

PostgreSQL: Documentation: 18: Chapter 53. System Views

URL: https://www.postgresql.org/docs/current/views.html

Contents:

• Chapter 53. System Views

In addition to the system catalogs, PostgreSQL provides a number of built-in views. Some system views provide convenient access to some commonly used queries on the system catalogs. Other views provide access to internal server state.

The information schema (Chapter 35) provides an alternative set of views which overlap the functionality of the system views. Since the information schema is SQL-standard whereas the views described here are PostgreSQL-specific, it's usually better to use the information schema if it provides all the information you need.

Table 53.1 lists the system views described here. More detailed documentation of each view follows below. There are some additional views that provide access to accumulated statistics; they are described in Table 27.2.

---

PostgreSQL: Documentation: 18: 10.3. Functions

URL: https://www.postgresql.org/docs/current/typeconv-func.html

Contents:

• 10.3. Functions #
  • Note

The specific function that is referenced by a function call is determined using the following procedure.

Function Type Resolution

Select the functions to be considered from the pg_proc system catalog. If a non-schema-qualified function name was used, the functions considered are those with the matching name and argument count that are visible in the current search path (see Section 5.10.3). If a qualified function name was given, only functions in the specified schema are considered.

If the search path finds multiple functions of identical argument types, only the one appearing earliest in the path is considered. Functions of different argument types are considered on an equal footing regardless of search path position.

If a function is declared with a VARIADIC array parameter, and the call does not use the VARIADIC keyword, then the function is treated as if the array parameter were replaced by one or more occurrences of its element type, as needed to match the call. After such expansion the function might have effective argument types identical to some non-variadic function. In that case the function appearing earlier in the search path is used, or if the two functions are in the same schema, the non-variadic one is preferred.

This creates a security hazard when calling, via qualified name [10], a variadic function found in a schema that permits untrusted users to create objects. A malicious user can take control and execute arbitrary SQL functions as though you executed them. Substitute a call bearing the VARIADIC keyword, which bypasses this hazard. Calls populating VARIADIC "any" parameters often have no equivalent formulation containing the VARIADIC keyword. To issue those calls safely, the function's schema must permit only trusted users to create objects.

Functions that have default values for parameters are considered to match any call that omits zero or more of the defaultable parameter positions. If more than one such function matches a call, the one appearing earliest in the search path is used. If there are two or more such functions in the same schema with identical parameter types in the non-defaulted positions (which is possible if they have different sets of defaultable parameters), the system will not be able to determine which to prefer, and so an “ambiguous function call” error will result if no better match to the call can be found.

This creates an availability hazard when calling, via qualified name[10], any function found in a schema that permits untrusted users to create objects. A malicious user can create a function with the name of an existing function, replicating that function's parameters and appending novel parameters having default values. This precludes new calls to the original function. To forestall this hazard, place functions in schemas that permit only trusted users to create objects.

Check for a function accepting exactly the input argument types. If one exists (there can be only one exact match in the set of functions considered), use it. Lack of an exact match creates a security hazard when calling, via qualified name[10], a function found in a schema that permits untrusted users to create objects. In such situations, cast arguments to force an exact match. (Cases involving unknown will never find a match at this step.)

If no exact match is found, see if the function call appears to be a special type conversion request. This happens if the function call has just one argument and the function name is the same as the (internal) name of some data type. Furthermore, the function argument must be either an unknown-type literal, or a type that is binary-coercible to the named data type, or a type that could be converted to the named data type by applying that type's I/O functions (that is, the conversion is either to or from one of the standard string types). When these conditions are met, the function call is treated as a form of CAST specification. [11]

Look for the best match.

Discard candidate functions for which the input types do not match and cannot be converted (using an implicit conversion) to match. unknown literals are assumed to be convertible to anything for this purpose. If only one candidate remains, use it; else continue to the next step.

If any input argument is of a domain type, treat it as being of the domain's base type for all subsequent steps. This ensures that domains act like their base types for purposes of ambiguous-function resolution.

Run through all candidates and keep those with the most exact matches on input types. Keep all candidates if none have exact matches. If only one candidate remains, use it; else continue to the next step.

Run through all candidates and keep those that accept preferred types (of the input data type's type category) at the most positions where type conversion will be required. Keep all candidates if none accept preferred types. If only one candidate remains, use it; else continue to the next step.

If any input arguments are unknown, check the type categories accepted at those argument positions by the remaining candidates. At each position, select the string category if any candidate accepts that category. (This bias towards string is appropriate since an unknown-type literal looks like a string.) Otherwise, if all the remaining candidates accept the same type category, select that category; otherwise fail because the correct choice cannot be deduced without more clues. Now discard candidates that do not accept the selected type category. Furthermore, if any candidate accepts a preferred type in that category, discard candidates that accept non-preferred types for that argument. Keep all candidates if none survive these tests. If only one candidate remains, use it; else continue to the next step.

If there are both unknown and known-type arguments, and all the known-type arguments have the same type, assume that the unknown arguments are also of that type, and check which candidates can accept that type at the unknown-argument positions. If exactly one candidate passes this test, use it. Otherwise, fail.

Note that the “best match” rules are identical for operator and function type resolution. Some examples follow.

Example 10.6. Rounding Function Argument Type Resolution

There is only one round function that takes two arguments; it takes a first argument of type numeric and a second argument of type integer. So the following query automatically converts the first argument of type integer to numeric:

That query is actually transformed by the parser to:

Since numeric constants with decimal points are initially assigned the type numeric, the following query will require no type conversion and therefore might be slightly more efficient:

Example 10.7. Variadic Function Resolution

This function accepts, but does not require, the VARIADIC keyword. It tolerates both integer and numeric arguments:

However, the first and second calls will prefer more-specific functions, if available:

Given the default configuration and only the first function existing, the first and second calls are insecure. Any user could intercept them by creating the second or third function. By matching the argument type exactly and using the VARIADIC keyword, the third call is secure.

Example 10.8. Substring Function Type Resolution

There are several substr functions, one of which takes types text and integer. If called with a string constant of unspecified type, the system chooses the candidate function that accepts an argument of the preferred category string (namely of type text).

If the string is declared to be of type varchar, as might be the case if it comes from a table, then the parser will try to convert it to become text:

This is transformed by the parser to effectively become:

The parser learns from the pg_cast catalog that text and varchar are binary-compatible, meaning that one can be passed to a function that accepts the other without doing any physical conversion. Therefore, no type conversion call is really inserted in this case.

And, if the function is called with an argument of type integer, the parser will try to convert that to text:

This does not work because integer does not have an implicit cast to text. An explicit cast will work, however:

[10] The hazard does not arise with a non-schema-qualified name, because a search path containing schemas that permit untrusted users to create objects is not a secure schema usage pattern.

[11] The reason for this step is to support function-style cast specifications in cases where there is not an actual cast function. If there is a cast function, it is conventionally named after its output type, and so there is no need to have a special case. See CREATE CAST for additional commentary.

Examples:

Example 1 (unknown):

SELECT round(4, 4);

 round
--------
 4.0000
(1 row)

Example 2 (unknown):

SELECT round(CAST (4 AS numeric), 4);

Example 3 (unknown):

SELECT round(4.0, 4);

Example 4 (unknown):

CREATE FUNCTION public.variadic_example(VARIADIC numeric[]) RETURNS int
  LANGUAGE sql AS 'SELECT 1';
CREATE FUNCTION

---

PostgreSQL: Documentation: 18: Appendix J. Documentation

URL: https://www.postgresql.org/docs/current/docguide.html

Contents:

• Appendix J. Documentation

PostgreSQL has four primary documentation formats:

Plain text, for pre-installation information

HTML, for on-line browsing and reference

man pages, for quick reference.

Additionally, a number of plain-text README files can be found throughout the PostgreSQL source tree, documenting various implementation issues.

HTML documentation and man pages are part of a standard distribution and are installed by default. PDF format documentation is available separately for download.

---

PostgreSQL: Documentation: 18: OPEN

URL: https://www.postgresql.org/docs/current/ecpg-sql-open.html

Contents:

• OPEN
• Synopsis
• Description
• Parameters
• Examples
• Compatibility
• See Also

OPEN — open a dynamic cursor

OPEN opens a cursor and optionally binds actual values to the placeholders in the cursor's declaration. The cursor must previously have been declared with the DECLARE command. The execution of OPEN causes the query to start executing on the server.

The name of the cursor to be opened. This can be an SQL identifier or a host variable.

A value to be bound to a placeholder in the cursor. This can be an SQL constant, a host variable, or a host variable with indicator.

The name of a descriptor containing values to be bound to the placeholders in the cursor. This can be an SQL identifier or a host variable.

OPEN is specified in the SQL standard.

Examples:

Example 1 (unknown):

OPEN cursor_name
OPEN cursor_name USING value [, ... ]
OPEN cursor_name USING SQL DESCRIPTOR descriptor_name

Example 2 (unknown):

EXEC SQL OPEN a;
EXEC SQL OPEN d USING 1, 'test';
EXEC SQL OPEN c1 USING SQL DESCRIPTOR mydesc;
EXEC SQL OPEN :curname1;

---

PostgreSQL: Documentation: 18: 7.5. Sorting Rows (ORDER BY)

URL: https://www.postgresql.org/docs/current/queries-order.html

Contents:

• 7.5. Sorting Rows (ORDER BY) #

After a query has produced an output table (after the select list has been processed) it can optionally be sorted. If sorting is not chosen, the rows will be returned in an unspecified order. The actual order in that case will depend on the scan and join plan types and the order on disk, but it must not be relied on. A particular output ordering can only be guaranteed if the sort step is explicitly chosen.

The ORDER BY clause specifies the sort order:

The sort expression(s) can be any expression that would be valid in the query's select list. An example is:

When more than one expression is specified, the later values are used to sort rows that are equal according to the earlier values. Each expression can be followed by an optional ASC or DESC keyword to set the sort direction to ascending or descending. ASC order is the default. Ascending order puts smaller values first, where “smaller” is defined in terms of the < operator. Similarly, descending order is determined with the > operator. [6]

The NULLS FIRST and NULLS LAST options can be used to determine whether nulls appear before or after non-null values in the sort ordering. By default, null values sort as if larger than any non-null value; that is, NULLS FIRST is the default for DESC order, and NULLS LAST otherwise.

Note that the ordering options are considered independently for each sort column. For example ORDER BY x, y DESC means ORDER BY x ASC, y DESC, which is not the same as ORDER BY x DESC, y DESC.

A sort_expression can also be the column label or number of an output column, as in:

both of which sort by the first output column. Note that an output column name has to stand alone, that is, it cannot be used in an expression — for example, this is not correct:

This restriction is made to reduce ambiguity. There is still ambiguity if an ORDER BY item is a simple name that could match either an output column name or a column from the table expression. The output column is used in such cases. This would only cause confusion if you use AS to rename an output column to match some other table column's name.

ORDER BY can be applied to the result of a UNION, INTERSECT, or EXCEPT combination, but in this case it is only permitted to sort by output column names or numbers, not by expressions.

[6] Actually, PostgreSQL uses the default B-tree operator class for the expression's data type to determine the sort ordering for ASC and DESC. Conventionally, data types will be set up so that the < and > operators correspond to this sort ordering, but a user-defined data type's designer could choose to do something different.

Examples:

Example 1 (unknown):

SELECT select_list
    FROM table_expression
    ORDER BY sort_expression1 [ASC | DESC] [NULLS { FIRST | LAST }]
             [, sort_expression2 [ASC | DESC] [NULLS { FIRST | LAST }] ...]

Example 2 (unknown):

SELECT a, b FROM table1 ORDER BY a + b, c;

Example 3 (unknown):

SELECT a + b AS sum, c FROM table1 ORDER BY sum;
SELECT a, max(b) FROM table1 GROUP BY a ORDER BY 1;

Example 4 (unknown):

SELECT a + b AS sum, c FROM table1 ORDER BY sum + c;          -- wrong

---

PostgreSQL: Documentation: 18: Chapter 64. Write Ahead Logging for Extensions

URL: https://www.postgresql.org/docs/current/wal-for-extensions.html

Contents:

• Chapter 64. Write Ahead Logging for Extensions

Certain extensions, principally extensions that implement custom access methods, may need to perform write-ahead logging in order to ensure crash-safety. PostgreSQL provides two ways for extensions to achieve this goal.

First, extensions can choose to use generic WAL, a special type of WAL record which describes changes to pages in a generic way. This method is simple to implement and does not require that an extension library be loaded in order to apply the records. However, generic WAL records will be ignored when performing logical decoding.

Second, extensions can choose to use a custom resource manager. This method is more flexible, supports logical decoding, and can sometimes generate much smaller write-ahead log records than would be possible with generic WAL. However, it is more complex for an extension to implement.

---

PostgreSQL: Documentation: 18: 24.2. Routine Reindexing

URL: https://www.postgresql.org/docs/current/routine-reindex.html

Contents:

• 24.2. Routine Reindexing #

In some situations it is worthwhile to rebuild indexes periodically with the REINDEX command or a series of individual rebuilding steps.

B-tree index pages that have become completely empty are reclaimed for re-use. However, there is still a possibility of inefficient use of space: if all but a few index keys on a page have been deleted, the page remains allocated. Therefore, a usage pattern in which most, but not all, keys in each range are eventually deleted will see poor use of space. For such usage patterns, periodic reindexing is recommended.

The potential for bloat in non-B-tree indexes has not been well researched. It is a good idea to periodically monitor the index's physical size when using any non-B-tree index type.

Also, for B-tree indexes, a freshly-constructed index is slightly faster to access than one that has been updated many times because logically adjacent pages are usually also physically adjacent in a newly built index. (This consideration does not apply to non-B-tree indexes.) It might be worthwhile to reindex periodically just to improve access speed.

REINDEX can be used safely and easily in all cases. This command requires an ACCESS EXCLUSIVE lock by default, hence it is often preferable to execute it with its CONCURRENTLY option, which requires only a SHARE UPDATE EXCLUSIVE lock.

---

PostgreSQL: Documentation: 18: 35.28. foreign_server_options

URL: https://www.postgresql.org/docs/current/infoschema-foreign-server-options.html

Contents:

• 35.28. foreign_server_options #

The view foreign_server_options contains all the options defined for foreign servers in the current database. Only those foreign servers are shown that the current user has access to (by way of being the owner or having some privilege).

Table 35.26. foreign_server_options Columns

foreign_server_catalog sql_identifier

Name of the database that the foreign server is defined in (always the current database)

foreign_server_name sql_identifier

Name of the foreign server

option_name sql_identifier

option_value character_data

---

PostgreSQL: Documentation: 18: 35.24. element_types

URL: https://www.postgresql.org/docs/current/infoschema-element-types.html

Contents:

• 35.24. element_types #

The view element_types contains the data type descriptors of the elements of arrays. When a table column, composite-type attribute, domain, function parameter, or function return value is defined to be of an array type, the respective information schema view only contains ARRAY in the column data_type. To obtain information on the element type of the array, you can join the respective view with this view. For example, to show the columns of a table with data types and array element types, if applicable, you could do:

This view only includes objects that the current user has access to, by way of being the owner or having some privilege.

Table 35.22. element_types Columns

object_catalog sql_identifier

Name of the database that contains the object that uses the array being described (always the current database)

object_schema sql_identifier

Name of the schema that contains the object that uses the array being described

object_name sql_identifier

Name of the object that uses the array being described

object_type character_data

The type of the object that uses the array being described: one of TABLE (the array is used by a column of that table), USER-DEFINED TYPE (the array is used by an attribute of that composite type), DOMAIN (the array is used by that domain), ROUTINE (the array is used by a parameter or the return data type of that function).

collection_type_identifier sql_identifier

The identifier of the data type descriptor of the array being described. Use this to join with the dtd_identifier columns of other information schema views.

data_type character_data

Data type of the array elements, if it is a built-in type, else USER-DEFINED (in that case, the type is identified in udt_name and associated columns).

character_maximum_length cardinal_number

Always null, since this information is not applied to array element data types in PostgreSQL

character_octet_length cardinal_number

Always null, since this information is not applied to array element data types in PostgreSQL

character_set_catalog sql_identifier

Applies to a feature not available in PostgreSQL

character_set_schema sql_identifier

Applies to a feature not available in PostgreSQL

character_set_name sql_identifier

Applies to a feature not available in PostgreSQL

collation_catalog sql_identifier

Name of the database containing the collation of the element type (always the current database), null if default or the data type of the element is not collatable

collation_schema sql_identifier

Name of the schema containing the collation of the element type, null if default or the data type of the element is not collatable

collation_name sql_identifier

Name of the collation of the element type, null if default or the data type of the element is not collatable

numeric_precision cardinal_number

Always null, since this information is not applied to array element data types in PostgreSQL

numeric_precision_radix cardinal_number

Always null, since this information is not applied to array element data types in PostgreSQL

numeric_scale cardinal_number

Always null, since this information is not applied to array element data types in PostgreSQL

datetime_precision cardinal_number

Always null, since this information is not applied to array element data types in PostgreSQL

interval_type character_data

Always null, since this information is not applied to array element data types in PostgreSQL

interval_precision cardinal_number

Always null, since this information is not applied to array element data types in PostgreSQL

udt_catalog sql_identifier

Name of the database that the data type of the elements is defined in (always the current database)

udt_schema sql_identifier

Name of the schema that the data type of the elements is defined in

udt_name sql_identifier

Name of the data type of the elements

scope_catalog sql_identifier

Applies to a feature not available in PostgreSQL

scope_schema sql_identifier

Applies to a feature not available in PostgreSQL

scope_name sql_identifier

Applies to a feature not available in PostgreSQL

maximum_cardinality cardinal_number

Always null, because arrays always have unlimited maximum cardinality in PostgreSQL

dtd_identifier sql_identifier

An identifier of the data type descriptor of the element. This is currently not useful.

Examples:

Example 1 (unknown):

SELECT c.column_name, c.data_type, e.data_type AS element_type
FROM information_schema.columns c LEFT JOIN information_schema.element_types e
     ON ((c.table_catalog, c.table_schema, c.table_name, 'TABLE', c.dtd_identifier)
       = (e.object_catalog, e.object_schema, e.object_name, e.object_type, e.collection_type_identifier))
WHERE c.table_schema = '...' AND c.table_name = '...'
ORDER BY c.ordinal_position;

---

PostgreSQL: Documentation: 18: 2. A Brief History of PostgreSQL

URL: https://www.postgresql.org/docs/current/history.html

Contents:

• 2. A Brief History of PostgreSQL #
  • 2.1. The Berkeley POSTGRES Project #
  • 2.2. Postgres95 #
  • 2.3. PostgreSQL #

The object-relational database management system now known as PostgreSQL is derived from the POSTGRES package written at the University of California at Berkeley. With decades of development behind it, PostgreSQL is now the most advanced open-source database available anywhere.

Another take on the history presented here can be found in Dr. Joe Hellerstein's paper “Looking Back at Postgres” [hell18].

The POSTGRES project, led by Professor Michael Stonebraker, was sponsored by the Defense Advanced Research Projects Agency (DARPA), the Army Research Office (ARO), the National Science Foundation (NSF), and ESL, Inc. The implementation of POSTGRES began in 1986. The initial concepts for the system were presented in [ston86], and the definition of the initial data model appeared in [rowe87]. The design of the rule system at that time was described in [ston87a]. The rationale and architecture of the storage manager were detailed in [ston87b].

POSTGRES has undergone several major releases since then. The first “demoware” system became operational in 1987 and was shown at the 1988 ACM-SIGMOD Conference. Version 1, described in [ston90a], was released to a few external users in June 1989. In response to a critique of the first rule system ([ston89]), the rule system was redesigned ([ston90b]), and Version 2 was released in June 1990 with the new rule system. Version 3 appeared in 1991 and added support for multiple storage managers, an improved query executor, and a rewritten rule system. For the most part, subsequent releases until Postgres95 (see below) focused on portability and reliability.

POSTGRES has been used to implement many different research and production applications. These include: a financial data analysis system, a jet engine performance monitoring package, an asteroid tracking database, a medical information database, and several geographic information systems. POSTGRES has also been used as an educational tool at several universities. Finally, Illustra Information Technologies (later merged into Informix, which is now owned by IBM) picked up the code and commercialized it. In late 1992, POSTGRES became the primary data manager for the Sequoia 2000 scientific computing project described in [ston92].

The size of the external user community nearly doubled during 1993. It became increasingly obvious that maintenance of the prototype code and support was taking up large amounts of time that should have been devoted to database research. In an effort to reduce this support burden, the Berkeley POSTGRES project officially ended with Version 4.2.

In 1994, Andrew Yu and Jolly Chen added an SQL language interpreter to POSTGRES. Under a new name, Postgres95 was subsequently released to the web to find its own way in the world as an open-source descendant of the original POSTGRES Berkeley code.

Postgres95 code was completely ANSI C and trimmed in size by 25%. Many internal changes improved performance and maintainability. Postgres95 release 1.0.x ran about 30–50% faster on the Wisconsin Benchmark compared to POSTGRES, Version 4.2. Apart from bug fixes, the following were the major enhancements:

The query language PostQUEL was replaced with SQL (implemented in the server). (Interface library libpq was named after PostQUEL.) Subqueries were not supported until PostgreSQL (see below), but they could be imitated in Postgres95 with user-defined SQL functions. Aggregate functions were re-implemented. Support for the GROUP BY query clause was also added.

A new program (psql) was provided for interactive SQL queries, which used GNU Readline. This largely superseded the old monitor program.

A new front-end library, libpgtcl, supported Tcl-based clients. A sample shell, pgtclsh, provided new Tcl commands to interface Tcl programs with the Postgres95 server.

The large-object interface was overhauled. The inversion large objects were the only mechanism for storing large objects. (The inversion file system was removed.)

The instance-level rule system was removed. Rules were still available as rewrite rules.

A short tutorial introducing regular SQL features as well as those of Postgres95 was distributed with the source code

GNU make (instead of BSD make) was used for the build. Also, Postgres95 could be compiled with an unpatched GCC (data alignment of doubles was fixed).

By 1996, it became clear that the name “Postgres95” would not stand the test of time. We chose a new name, PostgreSQL, to reflect the relationship between the original POSTGRES and the more recent versions with SQL capability. At the same time, we set the version numbering to start at 6.0, putting the numbers back into the sequence originally begun by the Berkeley POSTGRES project.

Postgres is still considered an official project name, both because of tradition and because people find it easier to pronounce Postgres than PostgreSQL.

The emphasis during development of Postgres95 was on identifying and understanding existing problems in the server code. With PostgreSQL, the emphasis has shifted to augmenting features and capabilities, although work continues in all areas.

Details about what has happened in each PostgreSQL release since then can be found at https://www.postgresql.org/docs/release/.

---

PostgreSQL: Documentation: 18: 18.9. Secure TCP/IP Connections with SSL

URL: https://www.postgresql.org/docs/current/ssl-tcp.html

Contents:

• 18.9. Secure TCP/IP Connections with SSL #
  • 18.9.1. Basic Setup #
  • 18.9.2. OpenSSL Configuration #
  • Note
  • 18.9.3. Using Client Certificates #
  • 18.9.4. SSL Server File Usage #
  • 18.9.5. Creating Certificates #

PostgreSQL has native support for using SSL connections to encrypt client/server communications for increased security. This requires that OpenSSL is installed on both client and server systems and that support in PostgreSQL is enabled at build time (see Chapter 17).

The terms SSL and TLS are often used interchangeably to mean a secure encrypted connection using a TLS protocol. SSL protocols are the precursors to TLS protocols, and the term SSL is still used for encrypted connections even though SSL protocols are no longer supported. SSL is used interchangeably with TLS in PostgreSQL.

With SSL support compiled in, the PostgreSQL server can be started with support for encrypted connections using TLS protocols enabled by setting the parameter ssl to on in postgresql.conf. The server will listen for both normal and SSL connections on the same TCP port, and will negotiate with any connecting client on whether to use SSL. By default, this is at the client's option; see Section 20.1 about how to set up the server to require use of SSL for some or all connections.

To start in SSL mode, files containing the server certificate and private key must exist. By default, these files are expected to be named server.crt and server.key, respectively, in the server's data directory, but other names and locations can be specified using the configuration parameters ssl_cert_file and ssl_key_file.

On Unix systems, the permissions on server.key must disallow any access to world or group; achieve this by the command chmod 0600 server.key. Alternatively, the file can be owned by root and have group read access (that is, 0640 permissions). That setup is intended for installations where certificate and key files are managed by the operating system. The user under which the PostgreSQL server runs should then be made a member of the group that has access to those certificate and key files.

If the data directory allows group read access then certificate files may need to be located outside of the data directory in order to conform to the security requirements outlined above. Generally, group access is enabled to allow an unprivileged user to backup the database, and in that case the backup software will not be able to read the certificate files and will likely error.

If the private key is protected with a passphrase, the server will prompt for the passphrase and will not start until it has been entered. Using a passphrase by default disables the ability to change the server's SSL configuration without a server restart, but see ssl_passphrase_command_supports_reload. Furthermore, passphrase-protected private keys cannot be used at all on Windows.

The first certificate in server.crt must be the server's certificate because it must match the server's private key. The certificates of “intermediate” certificate authorities can also be appended to the file. Doing this avoids the necessity of storing intermediate certificates on clients, assuming the root and intermediate certificates were created with v3_ca extensions. (This sets the certificate's basic constraint of CA to true.) This allows easier expiration of intermediate certificates.

It is not necessary to add the root certificate to server.crt. Instead, clients must have the root certificate of the server's certificate chain.

PostgreSQL reads the system-wide OpenSSL configuration file. By default, this file is named openssl.cnf and is located in the directory reported by openssl version -d. This default can be overridden by setting environment variable OPENSSL_CONF to the name of the desired configuration file.

OpenSSL supports a wide range of ciphers and authentication algorithms, of varying strength. While a list of ciphers can be specified in the OpenSSL configuration file, you can specify ciphers specifically for use by the database server by modifying ssl_ciphers in postgresql.conf.

It is possible to have authentication without encryption overhead by using NULL-SHA or NULL-MD5 ciphers. However, a man-in-the-middle could read and pass communications between client and server. Also, encryption overhead is minimal compared to the overhead of authentication. For these reasons NULL ciphers are not recommended.

To require the client to supply a trusted certificate, place certificates of the root certificate authorities (CAs) you trust in a file in the data directory, set the parameter ssl_ca_file in postgresql.conf to the new file name, and add the authentication option clientcert=verify-ca or clientcert=verify-full to the appropriate hostssl line(s) in pg_hba.conf. A certificate will then be requested from the client during SSL connection startup. (See Section 32.19 for a description of how to set up certificates on the client.)

For a hostssl entry with clientcert=verify-ca, the server will verify that the client's certificate is signed by one of the trusted certificate authorities. If clientcert=verify-full is specified, the server will not only verify the certificate chain, but it will also check whether the username or its mapping matches the cn (Common Name) of the provided certificate. Note that certificate chain validation is always ensured when the cert authentication method is used (see Section 20.12).

Intermediate certificates that chain up to existing root certificates can also appear in the ssl_ca_file file if you wish to avoid storing them on clients (assuming the root and intermediate certificates were created with v3_ca extensions). Certificate Revocation List (CRL) entries are also checked if the parameter ssl_crl_file or ssl_crl_dir is set.

The clientcert authentication option is available for all authentication methods, but only in pg_hba.conf lines specified as hostssl. When clientcert is not specified, the server verifies the client certificate against its CA file only if a client certificate is presented and the CA is configured.

There are two approaches to enforce that users provide a certificate during login.

The first approach makes use of the cert authentication method for hostssl entries in pg_hba.conf, such that the certificate itself is used for authentication while also providing ssl connection security. See Section 20.12 for details. (It is not necessary to specify any clientcert options explicitly when using the cert authentication method.) In this case, the cn (Common Name) provided in the certificate is checked against the user name or an applicable mapping.

The second approach combines any authentication method for hostssl entries with the verification of client certificates by setting the clientcert authentication option to verify-ca or verify-full. The former option only enforces that the certificate is valid, while the latter also ensures that the cn (Common Name) in the certificate matches the user name or an applicable mapping.

Table 18.2 summarizes the files that are relevant to the SSL setup on the server. (The shown file names are default names. The locally configured names could be different.)

Table 18.2. SSL Server File Usage

The server reads these files at server start and whenever the server configuration is reloaded. On Windows systems, they are also re-read whenever a new backend process is spawned for a new client connection.

If an error in these files is detected at server start, the server will refuse to start. But if an error is detected during a configuration reload, the files are ignored and the old SSL configuration continues to be used. On Windows systems, if an error in these files is detected at backend start, that backend will be unable to establish an SSL connection. In all these cases, the error condition is reported in the server log.

To create a simple self-signed certificate for the server, valid for 365 days, use the following OpenSSL command, replacing dbhost.yourdomain.com with the server's host name:

because the server will reject the file if its permissions are more liberal than this. For more details on how to create your server private key and certificate, refer to the OpenSSL documentation.

While a self-signed certificate can be used for testing, a certificate signed by a certificate authority (CA) (usually an enterprise-wide root CA) should be used in production.

To create a server certificate whose identity can be validated by clients, first create a certificate signing request (CSR) and a public/private key file:

Then, sign the request with the key to create a root certificate authority (using the default OpenSSL configuration file location on Linux):

Finally, create a server certificate signed by the new root certificate authority:

server.crt and server.key should be stored on the server, and root.crt should be stored on the client so the client can verify that the server's leaf certificate was signed by its trusted root certificate. root.key should be stored offline for use in creating future certificates.

It is also possible to create a chain of trust that includes intermediate certificates:

server.crt and intermediate.crt should be concatenated into a certificate file bundle and stored on the server. server.key should also be stored on the server. root.crt should be stored on the client so the client can verify that the server's leaf certificate was signed by a chain of certificates linked to its trusted root certificate. root.key and intermediate.key should be stored offline for use in creating future certificates.

Examples:

Example 1 (unknown):

openssl req -new -x509 -days 365 -nodes -text -out server.crt \
  -keyout server.key -subj "/CN=dbhost.yourdomain.com"

Example 2 (unknown):

chmod og-rwx server.key

Example 3 (unknown):

openssl req -new -nodes -text -out root.csr \
  -keyout root.key -subj "/CN=root.yourdomain.com"
chmod og-rwx root.key

Example 4 (unknown):

openssl x509 -req -in root.csr -text -days 3650 \
  -extfile /etc/ssl/openssl.cnf -extensions v3_ca \
  -signkey root.key -out root.crt

---

PostgreSQL: Documentation: 18: 19.15. Preset Options

URL: https://www.postgresql.org/docs/current/runtime-config-preset.html

Contents:

• 19.15. Preset Options #

The following “parameters” are read-only. As such, they have been excluded from the sample postgresql.conf file. These options report various aspects of PostgreSQL behavior that might be of interest to certain applications, particularly administrative front-ends. Most of them are determined when PostgreSQL is compiled or when it is installed.

Reports the size of a disk block. It is determined by the value of BLCKSZ when building the server. The default value is 8192 bytes. The meaning of some configuration variables (such as shared_buffers) is influenced by block_size. See Section 19.4 for information.

Reports whether data checksums are enabled for this cluster. See -k for more information.

On Unix systems this parameter reports the permissions the data directory (defined by data_directory) had at server startup. (On Microsoft Windows this parameter will always display 0700.) See the initdb -g option for more information.

Reports whether PostgreSQL has been built with assertions enabled. That is the case if the macro USE_ASSERT_CHECKING is defined when PostgreSQL is built (accomplished e.g., by the configure option --enable-cassert). By default PostgreSQL is built without assertions.

Reports the state of huge pages in the current instance: on, off, or unknown (if displayed with postgres -C). This parameter is useful to determine whether allocation of huge pages was successful under huge_pages=try. See huge_pages for more information.

Reports whether PostgreSQL was built with support for 64-bit-integer dates and times. As of PostgreSQL 10, this is always on.

Reports whether the server is currently in hot standby mode. When this is on, all transactions are forced to be read-only. Within a session, this can change only if the server is promoted to be primary. See Section 26.4 for more information.

Reports the maximum number of function arguments. It is determined by the value of FUNC_MAX_ARGS when building the server. The default value is 100 arguments.

Reports the maximum identifier length. It is determined as one less than the value of NAMEDATALEN when building the server. The default value of NAMEDATALEN is 64; therefore the default max_identifier_length is 63 bytes, which can be less than 63 characters when using multibyte encodings.

Reports the maximum number of index keys. It is determined by the value of INDEX_MAX_KEYS when building the server. The default value is 32 keys.

Reports the number of semaphores that are needed for the server based on the configured number of allowed connections (max_connections), allowed autovacuum worker processes (autovacuum_max_workers), allowed WAL sender processes (max_wal_senders), allowed background processes (max_worker_processes), etc.

Reports the number of blocks (pages) that can be stored within a file segment. It is determined by the value of RELSEG_SIZE when building the server. The maximum size of a segment file in bytes is equal to segment_size multiplied by block_size; by default this is 1GB.

Reports the database encoding (character set). It is determined when the database is created. Ordinarily, clients need only be concerned with the value of client_encoding.

Reports the version number of the server. It is determined by the value of PG_VERSION when building the server.

Reports the version number of the server as an integer. It is determined by the value of PG_VERSION_NUM when building the server.

Reports the size of the main shared memory area, rounded up to the nearest megabyte.

Reports the number of huge pages that are needed for the main shared memory area based on the specified huge_page_size. If huge pages are not supported, this will be -1.

This setting is supported only on Linux. It is always set to -1 on other platforms. For more details about using huge pages on Linux, see Section 18.4.5.

Reports the name of the SSL library that this PostgreSQL server was built with (even if SSL is not currently configured or in use on this instance), for example OpenSSL, or an empty string if none.

Reports the size of a WAL disk block. It is determined by the value of XLOG_BLCKSZ when building the server. The default value is 8192 bytes.

Reports the size of write ahead log segments. The default value is 16MB. See Section 28.5 for more information.

---

PostgreSQL: Documentation: 18: Appendix D. SQL Conformance

URL: https://www.postgresql.org/docs/current/features.html

Contents:

• Appendix D. SQL Conformance
  • Note

This section attempts to outline to what extent PostgreSQL conforms to the current SQL standard. The following information is not a full statement of conformance, but it presents the main topics in as much detail as is both reasonable and useful for users.

The formal name of the SQL standard is ISO/IEC 9075 “Database Language SQL”. A revised version of the standard is released from time to time; the most recent update appearing in 2023. The 2023 version is referred to as ISO/IEC 9075:2023, or simply as SQL:2023. The versions prior to that were SQL:2016, SQL:2011, SQL:2008, SQL:2006, SQL:2003, SQL:1999, and SQL-92. Each version replaces the previous one, so claims of conformance to earlier versions have no official merit. PostgreSQL development aims for conformance with the latest official version of the standard where such conformance does not contradict traditional features or common sense. Many of the features required by the SQL standard are supported, though sometimes with slightly differing syntax or function. Further moves towards conformance can be expected over time.

SQL-92 defined three feature sets for conformance: Entry, Intermediate, and Full. Most database management systems claiming SQL standard conformance were conforming at only the Entry level, since the entire set of features in the Intermediate and Full levels was either too voluminous or in conflict with legacy behaviors.

Starting with SQL:1999, the SQL standard defines a large set of individual features rather than the ineffectively broad three levels found in SQL-92. A large subset of these features represents the “Core” features, which every conforming SQL implementation must supply. The rest of the features are purely optional.

The standard is split into a number of parts, each also known by a shorthand name:

ISO/IEC 9075-1 Framework (SQL/Framework)

ISO/IEC 9075-2 Foundation (SQL/Foundation)

ISO/IEC 9075-3 Call Level Interface (SQL/CLI)

ISO/IEC 9075-4 Persistent Stored Modules (SQL/PSM)

ISO/IEC 9075-9 Management of External Data (SQL/MED)

ISO/IEC 9075-10 Object Language Bindings (SQL/OLB)

ISO/IEC 9075-11 Information and Definition Schemas (SQL/Schemata)

ISO/IEC 9075-13 Routines and Types using the Java Language (SQL/JRT)

ISO/IEC 9075-14 XML-related specifications (SQL/XML)

ISO/IEC 9075-15 Multi-dimensional arrays (SQL/MDA)

ISO/IEC 9075-16 Property Graph Queries (SQL/PGQ)

Note that some part numbers are not (or no longer) used.

The PostgreSQL core covers parts 1, 2, 9, 11, and 14. Part 3 is covered by the ODBC driver, and part 13 is covered by the PL/Java plug-in, but exact conformance is currently not being verified for these components. There are currently no implementations of parts 4, 10, 15, and 16 for PostgreSQL.

PostgreSQL supports most of the major features of SQL:2023. Out of 177 mandatory features required for full Core conformance, PostgreSQL conforms to at least 170. In addition, there is a long list of supported optional features. It might be worth noting that at the time of writing, no current version of any database management system claims full conformance to Core SQL:2023.

In the following two sections, we provide a list of those features that PostgreSQL supports, followed by a list of the features defined in SQL:2023 which are not yet supported in PostgreSQL. Both of these lists are approximate: There might be minor details that are nonconforming for a feature that is listed as supported, and large parts of an unsupported feature might in fact be implemented. The main body of the documentation always contains the most accurate information about what does and does not work.

Feature codes containing a hyphen are subfeatures. Therefore, if a particular subfeature is not supported, the main feature is listed as unsupported even if some other subfeatures are supported.

---

PostgreSQL: Documentation: 18: 35.2. Data Types

URL: https://www.postgresql.org/docs/current/infoschema-datatypes.html

Contents:

• 35.2. Data Types #

The columns of the information schema views use special data types that are defined in the information schema. These are defined as simple domains over ordinary built-in types. You should not use these types for work outside the information schema, but your applications must be prepared for them if they select from the information schema.

A nonnegative integer.

A character string (without specific maximum length).

A character string. This type is used for SQL identifiers, the type character_data is used for any other kind of text data.

A domain over the type timestamp with time zone

A character string domain that contains either YES or NO. This is used to represent Boolean (true/false) data in the information schema. (The information schema was invented before the type boolean was added to the SQL standard, so this convention is necessary to keep the information schema backward compatible.)

Every column in the information schema has one of these five types.

---

PostgreSQL: Documentation: 18: 6.3. Deleting Data

URL: https://www.postgresql.org/docs/current/dml-delete.html

Contents:

• 6.3. Deleting Data #

So far we have explained how to add data to tables and how to change data. What remains is to discuss how to remove data that is no longer needed. Just as adding data is only possible in whole rows, you can only remove entire rows from a table. In the previous section we explained that SQL does not provide a way to directly address individual rows. Therefore, removing rows can only be done by specifying conditions that the rows to be removed have to match. If you have a primary key in the table then you can specify the exact row. But you can also remove groups of rows matching a condition, or you can remove all rows in the table at once.

You use the DELETE command to remove rows; the syntax is very similar to the UPDATE command. For instance, to remove all rows from the products table that have a price of 10, use:

then all rows in the table will be deleted! Caveat programmer.

Examples:

Example 1 (unknown):

DELETE FROM products WHERE price = 10;

Example 2 (unknown):

DELETE FROM products;

---

PostgreSQL: Documentation: 18: 29.14. Quick Setup

URL: https://www.postgresql.org/docs/current/logical-replication-quick-setup.html

Contents:

• 29.14. Quick Setup #

First set the configuration options in postgresql.conf:

The other required settings have default values that are sufficient for a basic setup.

pg_hba.conf needs to be adjusted to allow replication (the values here depend on your actual network configuration and user you want to use for connecting):

Then on the publisher database:

And on the subscriber database:

The above will start the replication process, which synchronizes the initial table contents of the tables users and departments and then starts replicating incremental changes to those tables.

Examples:

Example 1 (unknown):

wal_level = logical

Example 2 (unknown):

host     all     repuser     0.0.0.0/0     md5

Example 3 (unknown):

CREATE PUBLICATION mypub FOR TABLE users, departments;

Example 4 (unknown):

CREATE SUBSCRIPTION mysub CONNECTION 'dbname=foo host=bar user=repuser' PUBLICATION mypub;

---

PostgreSQL: Documentation: 18: 22.4. Database Configuration

URL: https://www.postgresql.org/docs/current/manage-ag-config.html

Contents:

• 22.4. Database Configuration #

Recall from Chapter 19 that the PostgreSQL server provides a large number of run-time configuration variables. You can set database-specific default values for many of these settings.

For example, if for some reason you want to disable the GEQO optimizer for a given database, you'd ordinarily have to either disable it for all databases or make sure that every connecting client is careful to issue SET geqo TO off. To make this setting the default within a particular database, you can execute the command:

This will save the setting (but not set it immediately). In subsequent connections to this database it will appear as though SET geqo TO off; had been executed just before the session started. Note that users can still alter this setting during their sessions; it will only be the default. To undo any such setting, use ALTER DATABASE dbname RESET varname.

Examples:

Example 1 (unknown):

ALTER DATABASE mydb SET geqo TO off;

---

PostgreSQL: Documentation: 18: 20.7. SSPI Authentication

URL: https://www.postgresql.org/docs/current/sspi-auth.html

Contents:

• 20.7. SSPI Authentication #

SSPI is a Windows technology for secure authentication with single sign-on. PostgreSQL will use SSPI in negotiate mode, which will use Kerberos when possible and automatically fall back to NTLM in other cases. SSPI and GSSAPI interoperate as clients and servers, e.g., an SSPI client can authenticate to an GSSAPI server. It is recommended to use SSPI on Windows clients and servers and GSSAPI on non-Windows platforms.

When using Kerberos authentication, SSPI works the same way GSSAPI does; see Section 20.6 for details.

The following configuration options are supported for SSPI:

If set to 0, the realm name from the authenticated user principal is stripped off before being passed through the user name mapping (Section 20.2). This is discouraged and is primarily available for backwards compatibility, as it is not secure in multi-realm environments unless krb_realm is also used. It is recommended to leave include_realm set to the default (1) and to provide an explicit mapping in pg_ident.conf to convert principal names to PostgreSQL user names.

If set to 1, the domain's SAM-compatible name (also known as the NetBIOS name) is used for the include_realm option. This is the default. If set to 0, the true realm name from the Kerberos user principal name is used.

Do not disable this option unless your server runs under a domain account (this includes virtual service accounts on a domain member system) and all clients authenticating through SSPI are also using domain accounts, or authentication will fail.

If this option is enabled along with compat_realm, the user name from the Kerberos UPN is used for authentication. If it is disabled (the default), the SAM-compatible user name is used. By default, these two names are identical for new user accounts.

Note that libpq uses the SAM-compatible name if no explicit user name is specified. If you use libpq or a driver based on it, you should leave this option disabled or explicitly specify user name in the connection string.

Allows for mapping between system and database user names. See Section 20.2 for details. For an SSPI/Kerberos principal, such as username@EXAMPLE.COM (or, less commonly, username/hostbased@EXAMPLE.COM), the user name used for mapping is username@EXAMPLE.COM (or username/hostbased@EXAMPLE.COM, respectively), unless include_realm has been set to 0, in which case username (or username/hostbased) is what is seen as the system user name when mapping.

Sets the realm to match user principal names against. If this parameter is set, only users of that realm will be accepted. If it is not set, users of any realm can connect, subject to whatever user name mapping is done.

---

PostgreSQL: Documentation: 18: 32.2. Connection Status Functions

URL: https://www.postgresql.org/docs/current/libpq-status.html

Contents:

• 32.2. Connection Status Functions #
  • Tip

These functions can be used to interrogate the status of an existing database connection object.

libpq application programmers should be careful to maintain the PGconn abstraction. Use the accessor functions described below to get at the contents of PGconn. Reference to internal PGconn fields using libpq-int.h is not recommended because they are subject to change in the future.

The following functions return parameter values established at connection. These values are fixed for the life of the connection. If a multi-host connection string is used, the values of PQhost, PQport, and PQpass can change if a new connection is established using the same PGconn object. Other values are fixed for the lifetime of the PGconn object.

Returns the database name of the connection.

Returns the user name of the connection.

Returns the password of the connection.

PQpass will return either the password specified in the connection parameters, or if there was none and the password was obtained from the password file, it will return that. In the latter case, if multiple hosts were specified in the connection parameters, it is not possible to rely on the result of PQpass until the connection is established. The status of the connection can be checked using the function PQstatus.

Returns the server host name of the active connection. This can be a host name, an IP address, or a directory path if the connection is via Unix socket. (The path case can be distinguished because it will always be an absolute path, beginning with /.)

If the connection parameters specified both host and hostaddr, then PQhost will return the host information. If only hostaddr was specified, then that is returned. If multiple hosts were specified in the connection parameters, PQhost returns the host actually connected to.

PQhost returns NULL if the conn argument is NULL. Otherwise, if there is an error producing the host information (perhaps if the connection has not been fully established or there was an error), it returns an empty string.

If multiple hosts were specified in the connection parameters, it is not possible to rely on the result of PQhost until the connection is established. The status of the connection can be checked using the function PQstatus.

Returns the server IP address of the active connection. This can be the address that a host name resolved to, or an IP address provided through the hostaddr parameter.

PQhostaddr returns NULL if the conn argument is NULL. Otherwise, if there is an error producing the host information (perhaps if the connection has not been fully established or there was an error), it returns an empty string.

Returns the port of the active connection.

If multiple ports were specified in the connection parameters, PQport returns the port actually connected to.

PQport returns NULL if the conn argument is NULL. Otherwise, if there is an error producing the port information (perhaps if the connection has not been fully established or there was an error), it returns an empty string.

If multiple ports were specified in the connection parameters, it is not possible to rely on the result of PQport until the connection is established. The status of the connection can be checked using the function PQstatus.

This function no longer does anything, but it remains for backwards compatibility. The function always return an empty string, or NULL if the conn argument is NULL.

Returns the command-line options passed in the connection request.

The following functions return status data that can change as operations are executed on the PGconn object.

Returns the status of the connection.

The status can be one of a number of values. However, only two of these are seen outside of an asynchronous connection procedure: CONNECTION_OK and CONNECTION_BAD. A good connection to the database has the status CONNECTION_OK. A failed connection attempt is signaled by status CONNECTION_BAD. Ordinarily, an OK status will remain so until PQfinish, but a communications failure might result in the status changing to CONNECTION_BAD prematurely. In that case the application could try to recover by calling PQreset.

See the entry for PQconnectStartParams, PQconnectStart and PQconnectPoll with regards to other status codes that might be returned.

Returns the current in-transaction status of the server.

The status can be PQTRANS_IDLE (currently idle), PQTRANS_ACTIVE (a command is in progress), PQTRANS_INTRANS (idle, in a valid transaction block), or PQTRANS_INERROR (idle, in a failed transaction block). PQTRANS_UNKNOWN is reported if the connection is bad. PQTRANS_ACTIVE is reported only when a query has been sent to the server and not yet completed.

Looks up a current parameter setting of the server.

Certain parameter values are reported by the server automatically at connection startup or whenever their values change. PQparameterStatus can be used to interrogate these settings. It returns the current value of a parameter if known, or NULL if the parameter is not known.

Parameters reported as of the current release include:

(default_transaction_read_only and in_hot_standby were not reported by releases before 14; scram_iterations was not reported by releases before 16; search_path was not reported by releases before 18.) Note that server_version, server_encoding and integer_datetimes cannot change after startup.

If no value for standard_conforming_strings is reported, applications can assume it is off, that is, backslashes are treated as escapes in string literals. Also, the presence of this parameter can be taken as an indication that the escape string syntax (E'...') is accepted.

Although the returned pointer is declared const, it in fact points to mutable storage associated with the PGconn structure. It is unwise to assume the pointer will remain valid across queries.

Interrogates the frontend/backend protocol being used.

Applications might wish to use this function to determine whether certain features are supported. The result is formed by multiplying the server's major version number by 10000 and adding the minor version number. For example, version 3.2 would be returned as 30002, and version 4.0 would be returned as 40000. Zero is returned if the connection is bad. The 3.0 protocol is supported by PostgreSQL server versions 7.4 and above.

The protocol version will not change after connection startup is complete, but it could theoretically change during a connection reset.

Interrogates the frontend/backend protocol major version.

Unlike PQfullProtocolVersion, this returns only the major protocol version in use, but it is supported by a wider range of libpq releases back to version 7.4. Currently, the possible values are 3 (3.0 protocol), or zero (connection bad). Prior to release version 14.0, libpq could additionally return 2 (2.0 protocol).

Returns an integer representing the server version.

Applications might use this function to determine the version of the database server they are connected to. The result is formed by multiplying the server's major version number by 10000 and adding the minor version number. For example, version 10.1 will be returned as 100001, and version 11.0 will be returned as 110000. Zero is returned if the connection is bad.

Prior to major version 10, PostgreSQL used three-part version numbers in which the first two parts together represented the major version. For those versions, PQserverVersion uses two digits for each part; for example version 9.1.5 will be returned as 90105, and version 9.2.0 will be returned as 90200.

Therefore, for purposes of determining feature compatibility, applications should divide the result of PQserverVersion by 100 not 10000 to determine a logical major version number. In all release series, only the last two digits differ between minor releases (bug-fix releases).

Returns the error message most recently generated by an operation on the connection.

Nearly all libpq functions will set a message for PQerrorMessage if they fail. Note that by libpq convention, a nonempty PQerrorMessage result can consist of multiple lines, and will include a trailing newline. The caller should not free the result directly. It will be freed when the associated PGconn handle is passed to PQfinish. The result string should not be expected to remain the same across operations on the PGconn structure.

Obtains the file descriptor number of the connection socket to the server. A valid descriptor will be greater than or equal to 0; a result of -1 indicates that no server connection is currently open. (This will not change during normal operation, but could change during connection setup or reset.)

Returns the process ID (PID) of the backend process handling this connection.

The backend PID is useful for debugging purposes and for comparison to NOTIFY messages (which include the PID of the notifying backend process). Note that the PID belongs to a process executing on the database server host, not the local host!

Returns true (1) if the connection authentication method required a password, but none was available. Returns false (0) if not.

This function can be applied after a failed connection attempt to decide whether to prompt the user for a password.

Returns true (1) if the connection authentication method used a password. Returns false (0) if not.

This function can be applied after either a failed or successful connection attempt to detect whether the server demanded a password.

Returns true (1) if the connection authentication method used GSSAPI. Returns false (0) if not.

This function can be applied to detect whether the connection was authenticated with GSSAPI.

The following functions return information related to SSL. This information usually doesn't change after a connection is established.

Returns true (1) if the connection uses SSL, false (0) if not.

Returns SSL-related information about the connection.

The list of available attributes varies depending on the SSL library being used and the type of connection. Returns NULL if the connection does not use SSL or the specified attribute name is not defined for the library in use.

The following attributes are commonly available:

Name of the SSL implementation in use. (Currently, only "OpenSSL" is implemented)

SSL/TLS version in use. Common values are "TLSv1", "TLSv1.1" and "TLSv1.2", but an implementation may return other strings if some other protocol is used.

Number of key bits used by the encryption algorithm.

A short name of the ciphersuite used, e.g., "DHE-RSA-DES-CBC3-SHA". The names are specific to each SSL implementation.

Returns "on" if SSL compression is in use, else it returns "off".

Application protocol selected by the TLS Application-Layer Protocol Negotiation (ALPN) extension. The only protocol supported by libpq is postgresql, so this is mainly useful for checking whether the server supported ALPN or not. Empty string if ALPN was not used.

As a special case, the library attribute may be queried without a connection by passing NULL as the conn argument. The result will be the default SSL library name, or NULL if libpq was compiled without any SSL support. (Prior to PostgreSQL version 15, passing NULL as the conn argument always resulted in NULL. Client programs needing to differentiate between the newer and older implementations of this case may check the LIBPQ_HAS_SSL_LIBRARY_DETECTION feature macro.)

Returns an array of SSL attribute names that can be used in PQsslAttribute(). The array is terminated by a NULL pointer.

If conn is NULL, the attributes available for the default SSL library are returned, or an empty list if libpq was compiled without any SSL support. If conn is not NULL, the attributes available for the SSL library in use for the connection are returned, or an empty list if the connection is not encrypted.

Returns a pointer to an SSL-implementation-specific object describing the connection. Returns NULL if the connection is not encrypted or the requested type of object is not available from the connection's SSL implementation.

The struct(s) available depend on the SSL implementation in use. For OpenSSL, there is one struct, available under the name OpenSSL, and it returns a pointer to OpenSSL's SSL struct. To use this function, code along the following lines could be used:

This structure can be used to verify encryption levels, check server certificates, and more. Refer to the OpenSSL documentation for information about this structure.

Returns the SSL structure used in the connection, or NULL if SSL is not in use.

This function is equivalent to PQsslStruct(conn, "OpenSSL"). It should not be used in new applications, because the returned struct is specific to OpenSSL and will not be available if another SSL implementation is used. To check if a connection uses SSL, call PQsslInUse instead, and for more details about the connection, use PQsslAttribute.

Examples:

Example 1 (javascript):

char *PQdb(const PGconn *conn);

Example 2 (javascript):

char *PQuser(const PGconn *conn);

Example 3 (javascript):

char *PQpass(const PGconn *conn);

Example 4 (javascript):

char *PQhost(const PGconn *conn);

---

PostgreSQL: Documentation: 18: Chapter 25. Backup and Restore

URL: https://www.postgresql.org/docs/current/backup.html

Contents:

• Chapter 25. Backup and Restore

As with everything that contains valuable data, PostgreSQL databases should be backed up regularly. While the procedure is essentially simple, it is important to have a clear understanding of the underlying techniques and assumptions.

There are three fundamentally different approaches to backing up PostgreSQL data:

File system level backup

Each has its own strengths and weaknesses; each is discussed in turn in the following sections.

---

PostgreSQL: Documentation: 18: 35.38. role_udt_grants

URL: https://www.postgresql.org/docs/current/infoschema-role-udt-grants.html

Contents:

• 35.38. role_udt_grants #

The view role_udt_grants is intended to identify USAGE privileges granted on user-defined types where the grantor or grantee is a currently enabled role. Further information can be found under udt_privileges. The only effective difference between this view and udt_privileges is that this view omits objects that have been made accessible to the current user by way of a grant to PUBLIC. Since data types do not have real privileges in PostgreSQL, but only an implicit grant to PUBLIC, this view is empty.

Table 35.36. role_udt_grants Columns

grantor sql_identifier

The name of the role that granted the privilege

grantee sql_identifier

The name of the role that the privilege was granted to

udt_catalog sql_identifier

Name of the database containing the type (always the current database)

udt_schema sql_identifier

Name of the schema containing the type

udt_name sql_identifier

privilege_type character_data

is_grantable yes_or_no

YES if the privilege is grantable, NO if not

---

PostgreSQL: Documentation: 18: 14.3. Controlling the Planner with Explicit JOIN Clauses

URL: https://www.postgresql.org/docs/current/explicit-joins.html

Contents:

• 14.3. Controlling the Planner with Explicit JOIN Clauses #

It is possible to control the query planner to some extent by using the explicit JOIN syntax. To see why this matters, we first need some background.

In a simple join query, such as:

the planner is free to join the given tables in any order. For example, it could generate a query plan that joins A to B, using the WHERE condition a.id = b.id, and then joins C to this joined table, using the other WHERE condition. Or it could join B to C and then join A to that result. Or it could join A to C and then join them with B — but that would be inefficient, since the full Cartesian product of A and C would have to be formed, there being no applicable condition in the WHERE clause to allow optimization of the join. (All joins in the PostgreSQL executor happen between two input tables, so it's necessary to build up the result in one or another of these fashions.) The important point is that these different join possibilities give semantically equivalent results but might have hugely different execution costs. Therefore, the planner will explore all of them to try to find the most efficient query plan.

When a query only involves two or three tables, there aren't many join orders to worry about. But the number of possible join orders grows exponentially as the number of tables expands. Beyond ten or so input tables it's no longer practical to do an exhaustive search of all the possibilities, and even for six or seven tables planning might take an annoyingly long time. When there are too many input tables, the PostgreSQL planner will switch from exhaustive search to a genetic probabilistic search through a limited number of possibilities. (The switch-over threshold is set by the geqo_threshold run-time parameter.) The genetic search takes less time, but it won't necessarily find the best possible plan.

When the query involves outer joins, the planner has less freedom than it does for plain (inner) joins. For example, consider:

Although this query's restrictions are superficially similar to the previous example, the semantics are different because a row must be emitted for each row of A that has no matching row in the join of B and C. Therefore the planner has no choice of join order here: it must join B to C and then join A to that result. Accordingly, this query takes less time to plan than the previous query. In other cases, the planner might be able to determine that more than one join order is safe. For example, given:

it is valid to join A to either B or C first. Currently, only FULL JOIN completely constrains the join order. Most practical cases involving LEFT JOIN or RIGHT JOIN can be rearranged to some extent.

Explicit inner join syntax (INNER JOIN, CROSS JOIN, or unadorned JOIN) is semantically the same as listing the input relations in FROM, so it does not constrain the join order.

Even though most kinds of JOIN don't completely constrain the join order, it is possible to instruct the PostgreSQL query planner to treat all JOIN clauses as constraining the join order anyway. For example, these three queries are logically equivalent:

But if we tell the planner to honor the JOIN order, the second and third take less time to plan than the first. This effect is not worth worrying about for only three tables, but it can be a lifesaver with many tables.

To force the planner to follow the join order laid out by explicit JOINs, set the join_collapse_limit run-time parameter to 1. (Other possible values are discussed below.)

You do not need to constrain the join order completely in order to cut search time, because it's OK to use JOIN operators within items of a plain FROM list. For example, consider:

With join_collapse_limit = 1, this forces the planner to join A to B before joining them to other tables, but doesn't constrain its choices otherwise. In this example, the number of possible join orders is reduced by a factor of 5.

Constraining the planner's search in this way is a useful technique both for reducing planning time and for directing the planner to a good query plan. If the planner chooses a bad join order by default, you can force it to choose a better order via JOIN syntax — assuming that you know of a better order, that is. Experimentation is recommended.

A closely related issue that affects planning time is collapsing of subqueries into their parent query. For example, consider:

This situation might arise from use of a view that contains a join; the view's SELECT rule will be inserted in place of the view reference, yielding a query much like the above. Normally, the planner will try to collapse the subquery into the parent, yielding:

This usually results in a better plan than planning the subquery separately. (For example, the outer WHERE conditions might be such that joining X to A first eliminates many rows of A, thus avoiding the need to form the full logical output of the subquery.) But at the same time, we have increased the planning time; here, we have a five-way join problem replacing two separate three-way join problems. Because of the exponential growth of the number of possibilities, this makes a big difference. The planner tries to avoid getting stuck in huge join search problems by not collapsing a subquery if more than from_collapse_limit FROM items would result in the parent query. You can trade off planning time against quality of plan by adjusting this run-time parameter up or down.

from_collapse_limit and join_collapse_limit are similarly named because they do almost the same thing: one controls when the planner will “flatten out” subqueries, and the other controls when it will flatten out explicit joins. Typically you would either set join_collapse_limit equal to from_collapse_limit (so that explicit joins and subqueries act similarly) or set join_collapse_limit to 1 (if you want to control join order with explicit joins). But you might set them differently if you are trying to fine-tune the trade-off between planning time and run time.

Examples:

Example 1 (unknown):

SELECT * FROM a, b, c WHERE a.id = b.id AND b.ref = c.id;

Example 2 (unknown):

SELECT * FROM a LEFT JOIN (b JOIN c ON (b.ref = c.id)) ON (a.id = b.id);

Example 3 (unknown):

SELECT * FROM a LEFT JOIN b ON (a.bid = b.id) LEFT JOIN c ON (a.cid = c.id);

Example 4 (unknown):

SELECT * FROM a, b, c WHERE a.id = b.id AND b.ref = c.id;
SELECT * FROM a CROSS JOIN b CROSS JOIN c WHERE a.id = b.id AND b.ref = c.id;
SELECT * FROM a JOIN (b JOIN c ON (b.ref = c.id)) ON (a.id = b.id);

---

PostgreSQL: Documentation: 18: 36.2. The PostgreSQL Type System

URL: https://www.postgresql.org/docs/current/extend-type-system.html

Contents:

• 36.2. The PostgreSQL Type System #
  • 36.2.1. Base Types #
  • 36.2.2. Container Types #
  • 36.2.3. Domains #
  • 36.2.4. Pseudo-Types #
  • 36.2.5. Polymorphic Types #

PostgreSQL data types can be divided into base types, container types, domains, and pseudo-types.

Base types are those, like integer, that are implemented below the level of the SQL language (typically in a low-level language such as C). They generally correspond to what are often known as abstract data types. PostgreSQL can only operate on such types through functions provided by the user and only understands the behavior of such types to the extent that the user describes them. The built-in base types are described in Chapter 8.

Enumerated (enum) types can be considered as a subcategory of base types. The main difference is that they can be created using just SQL commands, without any low-level programming. Refer to Section 8.7 for more information.

PostgreSQL has three kinds of “container” types, which are types that contain multiple values of other types. These are arrays, composites, and ranges.

Arrays can hold multiple values that are all of the same type. An array type is automatically created for each base type, composite type, range type, and domain type. But there are no arrays of arrays. So far as the type system is concerned, multi-dimensional arrays are the same as one-dimensional arrays. Refer to Section 8.15 for more information.

Composite types, or row types, are created whenever the user creates a table. It is also possible to use CREATE TYPE to define a “stand-alone” composite type with no associated table. A composite type is simply a list of types with associated field names. A value of a composite type is a row or record of field values. Refer to Section 8.16 for more information.

A range type can hold two values of the same type, which are the lower and upper bounds of the range. Range types are user-created, although a few built-in ones exist. Refer to Section 8.17 for more information.

A domain is based on a particular underlying type and for many purposes is interchangeable with its underlying type. However, a domain can have constraints that restrict its valid values to a subset of what the underlying type would allow. Domains are created using the SQL command CREATE DOMAIN. Refer to Section 8.18 for more information.

There are a few “pseudo-types” for special purposes. Pseudo-types cannot appear as columns of tables or components of container types, but they can be used to declare the argument and result types of functions. This provides a mechanism within the type system to identify special classes of functions. Table 8.27 lists the existing pseudo-types.

Some pseudo-types of special interest are the polymorphic types, which are used to declare polymorphic functions. This powerful feature allows a single function definition to operate on many different data types, with the specific data type(s) being determined by the data types actually passed to it in a particular call. The polymorphic types are shown in Table 36.1. Some examples of their use appear in Section 36.5.11.

Table 36.1. Polymorphic Types

Polymorphic arguments and results are tied to each other and are resolved to specific data types when a query calling a polymorphic function is parsed. When there is more than one polymorphic argument, the actual data types of the input values must match up as described below. If the function's result type is polymorphic, or it has output parameters of polymorphic types, the types of those results are deduced from the actual types of the polymorphic inputs as described below.

For the “simple” family of polymorphic types, the matching and deduction rules work like this:

Each position (either argument or return value) declared as anyelement is allowed to have any specific actual data type, but in any given call they must all be the same actual type. Each position declared as anyarray can have any array data type, but similarly they must all be the same type. And similarly, positions declared as anyrange must all be the same range type. Likewise for anymultirange.

Furthermore, if there are positions declared anyarray and others declared anyelement, the actual array type in the anyarray positions must be an array whose elements are the same type appearing in the anyelement positions. anynonarray is treated exactly the same as anyelement, but adds the additional constraint that the actual type must not be an array type. anyenum is treated exactly the same as anyelement, but adds the additional constraint that the actual type must be an enum type.

Similarly, if there are positions declared anyrange and others declared anyelement or anyarray, the actual range type in the anyrange positions must be a range whose subtype is the same type appearing in the anyelement positions and the same as the element type of the anyarray positions. If there are positions declared anymultirange, their actual multirange type must contain ranges matching parameters declared anyrange and base elements matching parameters declared anyelement and anyarray.

Thus, when more than one argument position is declared with a polymorphic type, the net effect is that only certain combinations of actual argument types are allowed. For example, a function declared as equal(anyelement, anyelement) will take any two input values, so long as they are of the same data type.

When the return value of a function is declared as a polymorphic type, there must be at least one argument position that is also polymorphic, and the actual data type(s) supplied for the polymorphic arguments determine the actual result type for that call. For example, if there were not already an array subscripting mechanism, one could define a function that implements subscripting as subscript(anyarray, integer) returns anyelement. This declaration constrains the actual first argument to be an array type, and allows the parser to infer the correct result type from the actual first argument's type. Another example is that a function declared as f(anyarray) returns anyenum will only accept arrays of enum types.

In most cases, the parser can infer the actual data type for a polymorphic result type from arguments that are of a different polymorphic type in the same family; for example anyarray can be deduced from anyelement or vice versa. An exception is that a polymorphic result of type anyrange requires an argument of type anyrange; it cannot be deduced from anyarray or anyelement arguments. This is because there could be multiple range types with the same subtype.

Note that anynonarray and anyenum do not represent separate type variables; they are the same type as anyelement, just with an additional constraint. For example, declaring a function as f(anyelement, anyenum) is equivalent to declaring it as f(anyenum, anyenum): both actual arguments have to be the same enum type.

For the “common” family of polymorphic types, the matching and deduction rules work approximately the same as for the “simple” family, with one major difference: the actual types of the arguments need not be identical, so long as they can be implicitly cast to a single common type. The common type is selected following the same rules as for UNION and related constructs (see Section 10.5). Selection of the common type considers the actual types of anycompatible and anycompatiblenonarray inputs, the array element types of anycompatiblearray inputs, the range subtypes of anycompatiblerange inputs, and the multirange subtypes of anycompatiblemultirange inputs. If anycompatiblenonarray is present then the common type is required to be a non-array type. Once a common type is identified, arguments in anycompatible and anycompatiblenonarray positions are automatically cast to that type, and arguments in anycompatiblearray positions are automatically cast to the array type for that type.

Since there is no way to select a range type knowing only its subtype, use of anycompatiblerange and/or anycompatiblemultirange requires that all arguments declared with that type have the same actual range and/or multirange type, and that that type's subtype agree with the selected common type, so that no casting of the range values is required. As with anyrange and anymultirange, use of anycompatiblerange and anymultirange as a function result type requires that there be an anycompatiblerange or anycompatiblemultirange argument.

Notice that there is no anycompatibleenum type. Such a type would not be very useful, since there normally are not any implicit casts to enum types, meaning that there would be no way to resolve a common type for dissimilar enum inputs.

The “simple” and “common” polymorphic families represent two independent sets of type variables. Consider for example

In an actual call of this function, the first two inputs must have exactly the same type. The last two inputs must be promotable to a common type, but this type need not have anything to do with the type of the first two inputs. The result will have the common type of the last two inputs.

A variadic function (one taking a variable number of arguments, as in Section 36.5.6) can be polymorphic: this is accomplished by declaring its last parameter as VARIADIC anyarray or VARIADIC anycompatiblearray. For purposes of argument matching and determining the actual result type, such a function behaves the same as if you had written the appropriate number of anynonarray or anycompatiblenonarray parameters.

Examples:

Example 1 (unknown):

CREATE FUNCTION myfunc(a anyelement, b anyelement,
                       c anycompatible, d anycompatible)
RETURNS anycompatible AS ...

---

PostgreSQL: Documentation: 18: 18.5. Shutting Down the Server

URL: https://www.postgresql.org/docs/current/server-shutdown.html

Contents:

• 18.5. Shutting Down the Server #
  • Important

There are several ways to shut down the database server. Under the hood, they all reduce to sending a signal to the supervisor postgres process.

If you are using a pre-packaged version of PostgreSQL, and you used its provisions for starting the server, then you should also use its provisions for stopping the server. Consult the package-level documentation for details.

When managing the server directly, you can control the type of shutdown by sending different signals to the postgres process:

This is the Smart Shutdown mode. After receiving SIGTERM, the server disallows new connections, but lets existing sessions end their work normally. It shuts down only after all of the sessions terminate. If the server is in recovery when a smart shutdown is requested, recovery and streaming replication will be stopped only after all regular sessions have terminated.

This is the Fast Shutdown mode. The server disallows new connections and sends all existing server processes SIGTERM, which will cause them to abort their current transactions and exit promptly. It then waits for all server processes to exit and finally shuts down.

This is the Immediate Shutdown mode. The server will send SIGQUIT to all child processes and wait for them to terminate. If any do not terminate within 5 seconds, they will be sent SIGKILL. The supervisor server process exits as soon as all child processes have exited, without doing normal database shutdown processing. This will lead to recovery (by replaying the WAL log) upon next start-up. This is recommended only in emergencies.

The pg_ctl program provides a convenient interface for sending these signals to shut down the server. Alternatively, you can send the signal directly using kill on non-Windows systems. The PID of the postgres process can be found using the ps program, or from the file postmaster.pid in the data directory. For example, to do a fast shutdown:

It is best not to use SIGKILL to shut down the server. Doing so will prevent the server from releasing shared memory and semaphores. Furthermore, SIGKILL kills the postgres process without letting it relay the signal to its subprocesses, so it might be necessary to kill the individual subprocesses by hand as well.

To terminate an individual session while allowing other sessions to continue, use pg_terminate_backend() (see Table 9.96) or send a SIGTERM signal to the child process associated with the session.

Examples:

Example 1 (unknown):

$ kill -INT `head -1 /usr/local/pgsql/data/postmaster.pid`

---

PostgreSQL: Documentation: 18: Appendix M. Glossary

URL: https://www.postgresql.org/docs/current/glossary.html

Contents:

• Appendix M. Glossary

This is a list of terms and their meaning in the context of PostgreSQL and relational database systems in general.

Atomicity, Consistency, Isolation, and Durability. This set of properties of database transactions is intended to guarantee validity in concurrent operation and even in event of errors, power failures, etc.

A function that combines (aggregates) multiple input values, for example by counting, averaging or adding, yielding a single output value.

For more information, see Section 9.21.

See Also Window function (routine).

Interfaces which PostgreSQL use in order to access data in tables and indexes. This abstraction allows for adding support for new types of data storage.

For more information, see Chapter 62 and Chapter 63.

See Window function (routine).

The act of collecting statistics from data in tables and other relations to help the query planner to make decisions about how to execute queries.

(Don't confuse this term with the ANALYZE option to the EXPLAIN command.)

For more information, see ANALYZE.

Asynchronous I/O (AIO) describes performing I/O in a non-blocking way (asynchronously), in contrast to synchronous I/O, which blocks for the entire duration of the I/O.

With AIO, starting an I/O operation is separated from waiting for the result of the operation, allowing multiple I/O operations to be initiated concurrently, as well as performing CPU heavy operations concurrently with I/O. The price for that increased concurrency is increased complexity.

See Also Input/Output.

In reference to a datum: the fact that its value cannot be broken down into smaller components.

In reference to a database transaction: see atomicity.

The property of a transaction that either all its operations complete as a single unit or none do. In addition, if a system failure occurs during the execution of a transaction, no partial results are visible after recovery. This is one of the ACID properties.

An element with a certain name and data type found within a tuple.

A set of background processes that routinely perform vacuum and analyze operations. The auxiliary process that coordinates the work and is always present (unless autovacuum is disabled) is known as the autovacuum launcher, and the processes that carry out the tasks are known as the autovacuum workers.

For more information, see Section 24.1.6.

A process within an instance that is in charge of some specific background task for the instance. The auxiliary processes consist of the autovacuum launcher (but not the autovacuum workers), the background writer, the checkpointer, the logger, the startup process, the WAL archiver, the WAL receiver (but not the WAL senders), the WAL summarizer, and the WAL writer.

Process of an instance which acts on behalf of a client session and handles its requests.

(Don't confuse this term with the similar terms Background Worker or Background Writer).

Process within an instance, which runs system- or user-supplied code. Serves as infrastructure for several features in PostgreSQL, such as logical replication and parallel queries. In addition, Extensions can add custom background worker processes.

For more information, see Chapter 46.

An auxiliary process that writes dirty data pages from shared memory to the file system. It wakes up periodically, but works only for a short period in order to distribute its expensive I/O activity over time to avoid generating larger I/O peaks which could block other processes.

For more information, see Section 19.4.4.

A binary copy of all database cluster files. It is generated by the tool pg_basebackup. In combination with WAL files it can be used as the starting point for recovery, log shipping, or streaming replication.

Space in data pages which does not contain current row versions, such as unused (free) space or outdated row versions.

The first user initialized in a database cluster.

This user owns all system catalog tables in each database. It is also the role from which all granted permissions originate. Because of these things, this role may not be dropped.

This role also behaves as a normal database superuser, and its superuser status cannot be removed.

Some operations will access a large number of pages. A Buffer Access Strategy helps to prevent these operations from evicting too many pages from shared buffers.

A Buffer Access Strategy sets up references to a limited number of shared buffers and reuses them circularly. When the operation requires a new page, a victim buffer is chosen from the buffers in the strategy ring, which may require flushing the page's dirty data and possibly also unflushed WAL to permanent storage.

Buffer Access Strategies are used for various operations such as sequential scans of large tables, VACUUM, COPY, CREATE TABLE AS SELECT, ALTER TABLE, CREATE DATABASE, CREATE INDEX, and CLUSTER.

A conversion of a datum from its current data type to another data type.

For more information, see CREATE CAST.

The SQL standard uses this term to indicate what is called a database in PostgreSQL's terminology.

(Don't confuse this term with system catalog).

For more information, see Section 22.1.

A type of constraint defined on a relation which restricts the values allowed in one or more attributes. The check constraint can make reference to any attribute of the same row in the relation, but cannot reference other rows of the same relation or other relations.

For more information, see Section 5.5.

A point in the WAL sequence at which it is guaranteed that the heap and index data files have been updated with all information from shared memory modified before that checkpoint; a checkpoint record is written and flushed to WAL to mark that point.

A checkpoint is also the act of carrying out all the actions that are necessary to reach a checkpoint as defined above. This process is initiated when predefined conditions are met, such as a specified amount of time has passed, or a certain volume of records has been written; or it can be invoked by the user with the command CHECKPOINT.

For more information, see Section 28.5.

An auxiliary process that is responsible for executing checkpoints.

Any process, possibly remote, that establishes a session by connecting to an instance to interact with a database.

The operating system user that owns the data directory and under which the postgres process is run. It is required that this user exist prior to creating a new database cluster.

On operating systems with a root user, said user is not allowed to be the cluster owner.

An attribute found in a table or view.

The act of finalizing a transaction within the database, which makes it visible to other transactions and assures its durability.

For more information, see COMMIT.

The concept that multiple independent operations happen within the database at the same time. In PostgreSQL, concurrency is controlled by the multiversion concurrency control mechanism.

An established line of communication between a client process and a backend process, usually over a network, supporting a session. This term is sometimes used as a synonym for session.

For more information, see Section 19.3.

The property that the data in the database is always in compliance with integrity constraints. Transactions may be allowed to violate some of the constraints transiently before it commits, but if such violations are not resolved by the time it commits, such a transaction is automatically rolled back. This is one of the ACID properties.

A restriction on the values of data allowed within a table, or in attributes of a domain.

For more information, see Section 5.5.

A system which, if enabled, accumulates statistical information about the instance's activities.

For more information, see Section 27.2.

A named collection of local SQL objects.

For more information, see Section 22.1.

A collection of databases and global SQL objects, and their common static and dynamic metadata. Sometimes referred to as a cluster. A database cluster is created using the initdb program.

In PostgreSQL, the term cluster is also sometimes used to refer to an instance. (Don't confuse this term with the SQL command CLUSTER.)

See also cluster owner, the operating-system owner of a cluster, and bootstrap superuser, the PostgreSQL owner of a cluster.

A role having superuser status (see Section 21.2).

Frequently referred to as superuser.

The base directory on the file system of a server that contains all data files and subdirectories associated with a database cluster (with the exception of tablespaces, and optionally WAL). The environment variable PGDATA is commonly used to refer to the data directory.

A cluster's storage space comprises the data directory plus any additional tablespaces.

For more information, see Section 66.1.

The basic structure used to store relation data. All pages are of the same size. Data pages are typically stored on disk, each in a specific file, and can be read to shared buffers where they can be modified, becoming dirty. They become clean when written to disk. New pages, which initially exist in memory only, are also dirty until written.

The internal representation of one value of an SQL data type.

An SQL command which removes rows from a given table or relation.

For more information, see DELETE.

A user-defined data type that is based on another underlying data type. It acts the same as the underlying type except for possibly restricting the set of allowed values.

For more information, see Section 8.18.

The assurance that once a transaction has been committed, the changes remain even after a system failure or crash. This is one of the ACID properties.

A software add-on package that can be installed on an instance to get extra features.

For more information, see Section 36.17.

A physical file which stores data for a given relation. File segments are limited in size by a configuration value (typically 1 gigabyte), so if a relation exceeds that size, it is split into multiple segments.

For more information, see Section 66.1.

(Don't confuse this term with the similar term WAL segment).

A means of representing data that is not contained in the local database so that it appears as if were in local table(s). With a foreign data wrapper it is possible to define a foreign server and foreign tables.

For more information, see CREATE FOREIGN DATA WRAPPER.

A type of constraint defined on one or more columns in a table which requires the value(s) in those columns to identify zero or one row in another (or, infrequently, the same) table.

A named collection of foreign tables which all use the same foreign data wrapper and have other configuration values in common.

For more information, see CREATE SERVER.

A relation which appears to have rows and columns similar to a regular table, but will forward requests for data through its foreign data wrapper, which will return result sets structured according to the definition of the foreign table.

For more information, see CREATE FOREIGN TABLE.

Each of the separate segmented file sets in which a relation is stored. The main fork is where the actual data resides. There also exist two secondary forks for metadata: the free space map and the visibility map. Unlogged relations also have an init fork.

A storage structure that keeps metadata about each data page of a table's main fork. The free space map entry for each page stores the amount of free space that's available for future tuples, and is structured to be efficiently searched for available space for a new tuple of a given size.

For more information, see Section 66.3.

A type of routine that receives zero or more arguments, returns zero or more output values, and is constrained to run within one transaction. Functions are invoked as part of a query, for example via SELECT. Certain functions can return sets; those are called set-returning functions.

Functions can also be used for triggers to invoke.

For more information, see CREATE FUNCTION.

An SQL command that is used to allow a user or role to access specific objects within the database.

For more information, see GRANT.

Contains the values of row attributes (i.e., the data) for a relation. The heap is realized within one or more file segments in the relation's main fork.

A computer that communicates with other computers over a network. This is sometimes used as a synonym for server. It is also used to refer to a computer where client processes run.

A relation that contains data derived from a table or materialized view. Its internal structure supports fast retrieval of and access to the original data.

For more information, see CREATE INDEX.

A special base backup that for some files may contain only those pages that were modified since a previous backup, as opposed to the full contents of every file. Like base backups, it is generated by the tool pg_basebackup.

To restore incremental backups the tool pg_combinebackup is used, which combines incremental backups with a base backup. Afterwards, recovery can use WAL to bring the database cluster to a consistent state.

For more information, see Section 25.3.3.

Input/Output (I/O) describes the communication between a program and peripheral devices. In the context of database systems, I/O commonly, but not exclusively, refers to interaction with storage devices or the network.

See Also Asynchronous I/O.

An SQL command used to add new data into a table.

For more information, see INSERT.

A group of backend and auxiliary processes that communicate using a common shared memory area. One postmaster process manages the instance; one instance manages exactly one database cluster with all its databases. Many instances can run on the same server as long as their TCP ports do not conflict.

The instance handles all key features of a DBMS: read and write access to files and shared memory, assurance of the ACID properties, connections to client processes, privilege verification, crash recovery, replication, etc.

The property that the effects of a transaction are not visible to concurrent transactions before it commits. This is one of the ACID properties.

For more information, see Section 13.2.

An operation and SQL keyword used in queries for combining data from multiple relations.

A means of identifying a row within a table or other relation by values contained within one or more attributes in that relation.

A mechanism that allows a process to limit or prevent simultaneous access to a resource.

Log files contain human-readable text lines about events. Examples include login failures, long-running queries, etc.

For more information, see Section 24.3.

A table is considered logged if changes to it are sent to the WAL. By default, all regular tables are logged. A table can be specified as unlogged either at creation time or via the ALTER TABLE command.

An auxiliary process which, if enabled, writes information about database events into the current log file. When reaching certain time- or volume-dependent criteria, a new log file is created. Also called syslogger.

For more information, see Section 19.8.

A set of publisher and subscriber instances with the publisher instance replicating changes to the subscriber instance.

Archaic term for a WAL record.

Byte offset into the WAL, increasing monotonically with each new WAL record.

For more information, see pg_lsn and Section 28.6.

See Log sequence number.

See Primary (server).

The property that some information has been pre-computed and stored for later use, rather than computing it on-the-fly.

This term is used in materialized view, to mean that the data derived from the view's query is stored on disk separately from the sources of that data.

This term is also used to refer to some multi-step queries to mean that the data resulting from executing a given step is stored in memory (with the possibility of spilling to disk), so that it can be read multiple times by another step.

A relation that is defined by a SELECT statement (just like a view), but stores data in the same way that a table does. It cannot be modified via INSERT, UPDATE, DELETE, or MERGE operations.

For more information, see CREATE MATERIALIZED VIEW.

An SQL command used to conditionally add, modify, or remove rows in a given table, using data from a source relation.

For more information, see MERGE.

A mechanism designed to allow several transactions to be reading and writing the same rows without one process causing other processes to stall. In PostgreSQL, MVCC is implemented by creating copies (versions) of tuples as they are modified; after transactions that can see the old versions terminate, those old versions need to be removed.

A concept of non-existence that is a central tenet of relational database theory. It represents the absence of a definite value.

The ability to handle parts of executing a query to take advantage of parallel processes on servers with multiple CPUs.

One of several disjoint (not overlapping) subsets of a larger set.

In reference to a partitioned table: One of the tables that each contain part of the data of the partitioned table, which is said to be the parent. The partition is itself a table, so it can also be queried directly; at the same time, a partition can sometimes be a partitioned table, allowing hierarchies to be created.

In reference to a window function in a query, a partition is a user-defined criterion that identifies which neighboring rows of the query's result set can be considered by the function.

A relation that is in semantic terms the same as a table, but whose storage is distributed across several partitions.

The very first process of an instance. It starts and manages the auxiliary processes and creates backend processes on demand.

For more information, see Section 18.3.

A special case of a unique constraint defined on a table or other relation that also guarantees that all of the attributes within the primary key do not have null values. As the name implies, there can be only one primary key per table, though it is possible to have multiple unique constraints that also have no null-capable attributes.

When two or more databases are linked via replication, the server that is considered the authoritative source of information is called the primary, also known as a master.

A type of routine. Their distinctive qualities are that they do not return values, and that they are allowed to make transactional statements such as COMMIT and ROLLBACK. They are invoked via the CALL command.

For more information, see CREATE PROCEDURE.

A request sent by a client to a backend, usually to return results or to modify data on the database.

The part of PostgreSQL that is devoted to determining (planning) the most efficient way to execute queries. Also known as query optimizer, optimizer, or simply planner.

A means of restricting data in one relation by a foreign key so that it must have matching data in another relation.

The generic term for all objects in a database that have a name and a list of attributes defined in a specific order. Tables, sequences, views, foreign tables, materialized views, composite types, and indexes are all relations.

More generically, a relation is a set of tuples; for example, the result of a query is also a relation.

In PostgreSQL, Class is an archaic synonym for relation.

A database that is paired with a primary database and is maintaining a copy of some or all of the primary database's data. The foremost reasons for doing this are to allow for greater access to that data, and to maintain availability of the data in the event that the primary becomes unavailable.

The act of reproducing data on one server onto another server called a replica. This can take the form of physical replication, where all file changes from one server are copied verbatim, or logical replication where a defined subset of data changes are conveyed using a higher-level representation.

A variant of a checkpoint performed on a replica.

For more information, see Section 28.5.

A relation transmitted from a backend process to a client upon the completion of an SQL command, usually a SELECT but it can be an INSERT, UPDATE, DELETE, or MERGE command if the RETURNING clause is specified.

The fact that a result set is a relation means that a query can be used in the definition of another query, becoming a subquery.

A command to prevent access to a named set of database objects for a named list of roles.

For more information, see REVOKE.

A collection of access privileges to the instance. Roles are themselves a privilege that can be granted to other roles. This is often done for convenience or to ensure completeness when multiple users need the same privileges.

For more information, see CREATE ROLE.

A command to undo all of the operations performed since the beginning of a transaction.

For more information, see ROLLBACK.

A defined set of instructions stored in the database system that can be invoked for execution. A routine can be written in a variety of programming languages. Routines can be functions (including set-returning functions and trigger functions), aggregate functions, and procedures.

Many routines are already defined within PostgreSQL itself, but user-defined ones can also be added.

A special mark in the sequence of steps in a transaction. Data modifications after this point in time may be reverted to the time of the savepoint.

For more information, see SAVEPOINT.

A schema is a namespace for SQL objects, which all reside in the same database. Each SQL object must reside in exactly one schema.

All system-defined SQL objects reside in schema pg_catalog.

More generically, the term schema is used to mean all data descriptions (table definitions, constraints, comments, etc.) for a given database or subset thereof.

For more information, see Section 5.10.

The SQL command used to request data from a database. Normally, SELECT commands are not expected to modify the database in any way, but it is possible that functions invoked within the query could have side effects that do modify data.

For more information, see SELECT.

A type of relation that is used to generate values. Typically the generated values are sequential non-repeating numbers. They are commonly used to generate surrogate primary key values.

A computer on which PostgreSQL instances run. The term server denotes real hardware, a container, or a virtual machine.

This term is sometimes used to refer to an instance or to a host.

A state that allows a client and a backend to interact, communicating over a connection.

RAM which is used by the processes common to an instance. It mirrors parts of database files, provides a transient area for WAL records, and stores additional common information. Note that shared memory belongs to the complete instance, not to a single database.

The largest part of shared memory is known as shared buffers and is used to mirror part of data files, organized into pages. When a page is modified, it is called a dirty page until it is written back to the file system.

For more information, see Section 19.4.1.

Any object that can be created with a CREATE command. Most objects are specific to one database, and are commonly known as local objects.

Most local objects reside in a specific schema in their containing database, such as relations (all types), routines (all types), data types, etc. The names of such objects of the same type in the same schema are enforced to be unique.

There also exist local objects that do not reside in schemas; some examples are extensions, data type casts, and foreign data wrappers. The names of such objects of the same type are enforced to be unique within the database.

Other object types, such as roles, tablespaces, replication origins, subscriptions for logical replication, and databases themselves are not local SQL objects since they exist entirely outside of any specific database; they are called global objects. The names of such objects are enforced to be unique within the whole database cluster.

For more information, see Section 22.1.

A series of documents that define the SQL language.

See Replica (server).

An auxiliary process that replays WAL during crash recovery and in a physical replica.

(The name is historical: the startup process was named before replication was implemented; the name refers to its task as it relates to the server startup following a crash.)

As used in this documentation, it is a synonym for database superuser.

A collection of tables which describe the structure of all SQL objects of the instance. The system catalog resides in the schema pg_catalog. These tables contain data in internal representation and are not typically considered useful for user examination; a number of user-friendlier views, also in schema pg_catalog, offer more convenient access to some of that information, while additional tables and views exist in schema information_schema (see Chapter 35) that expose some of the same and additional information as mandated by the SQL standard.

For more information, see Section 5.10.

A collection of tuples having a common data structure (the same number of attributes, in the same order, having the same name and type per position). A table is the most common form of relation in PostgreSQL.

For more information, see CREATE TABLE.

A named location on the server file system. All SQL objects which require storage beyond their definition in the system catalog must belong to a single tablespace. Initially, a database cluster contains a single usable tablespace which is used as the default for all SQL objects, called pg_default.

For more information, see Section 22.6.

Tables that exist either for the lifetime of a session or a transaction, as specified at the time of creation. The data in them is not visible to other sessions, and is not logged. Temporary tables are often used to store intermediate data for a multi-step operation.

For more information, see CREATE TABLE.

A mechanism by which large attributes of table rows are split and stored in a secondary table, called the TOAST table. Each relation with large attributes has its own TOAST table.

For more information, see Section 66.2.

A combination of commands that must act as a single atomic command: they all succeed or all fail as a single unit, and their effects are not visible to other sessions until the transaction is complete, and possibly even later, depending on the isolation level.

For more information, see Section 13.2.

The numerical, unique, sequentially-assigned identifier that each transaction receives when it first causes a database modification. Frequently abbreviated as xid. When stored on disk, xids are only 32-bits wide, so only approximately four billion write transaction IDs can be generated; to permit the system to run for longer than that, epochs are used, also 32 bits wide. When the counter reaches the maximum xid value, it starts over at 3 (values under that are reserved) and the epoch value is incremented by one. In some contexts, the epoch and xid values are considered together as a single 64-bit value; see Section 67.1 for more details.

For more information, see Section 8.19.

Average number of transactions that are executed per second, totaled across all sessions active for a measured run. This is used as a measure of the performance characteristics of an instance.

A function which can be defined to execute whenever a certain operation (INSERT, UPDATE, DELETE, TRUNCATE) is applied to a relation. A trigger executes within the same transaction as the statement which invoked it, and if the function fails, then the invoking statement also fails.

For more information, see CREATE TRIGGER.

A collection of attributes in a fixed order. That order may be defined by the table (or other relation) where the tuple is contained, in which case the tuple is often called a row. It may also be defined by the structure of a result set, in which case it is sometimes called a record.

A type of constraint defined on a relation which restricts the values allowed in one or a combination of columns so that each value or combination of values can only appear once in the relation — that is, no other row in the relation contains values that are equal to those.

Because null values are not considered equal to each other, multiple rows with null values are allowed to exist without violating the unique constraint.

The property of certain relations that the changes to them are not reflected in the WAL. This disables replication and crash recovery for these relations.

The primary use of unlogged tables is for storing transient work data that must be shared across processes.

Temporary tables are always unlogged.

An SQL command used to modify rows that may already exist in a specified table. It cannot create or remove rows.

For more information, see UPDATE.

A role that has the login privilege (see Section 21.2).

The translation of login credentials in the local database to credentials in a remote data system defined by a foreign data wrapper.

For more information, see CREATE USER MAPPING.

Universal Coordinated Time, the primary global time reference, approximately the time prevailing at the zero meridian of longitude. Often but inaccurately referred to as GMT (Greenwich Mean Time).

The process of removing outdated tuple versions from tables or materialized views, and other closely related processing required by PostgreSQL's implementation of MVCC. This can be initiated through the use of the VACUUM command, but can also be handled automatically via autovacuum processes.

For more information, see Section 24.1 .

A relation that is defined by a SELECT statement, but has no storage of its own. Any time a query references a view, the definition of the view is substituted into the query as if the user had typed it as a subquery instead of the name of the view.

For more information, see CREATE VIEW.

A storage structure that keeps metadata about each data page of a table's main fork. The visibility map entry for each page stores two bits: the first one (all-visible) indicates that all tuples in the page are visible to all transactions. The second one (all-frozen) indicates that all tuples in the page are marked frozen.

An auxiliary process which, if enabled, saves copies of WAL files for the purpose of creating backups or keeping replicas current.

For more information, see Section 25.3.

Also known as WAL segment or WAL segment file. Each of the sequentially-numbered files that provide storage space for WAL. The files are all of the same predefined size and are written in sequential order, interspersing changes as they occur in multiple simultaneous sessions. If the system crashes, the files are read in order, and each of the changes is replayed to restore the system to the state it was in before the crash.

Each WAL file can be released after a checkpoint writes all the changes in it to the corresponding data files. Releasing the file can be done either by deleting it, or by changing its name so that it will be used in the future, which is called recycling.

For more information, see Section 28.6.

A low-level description of an individual data change. It contains sufficient information for the data change to be re-executed (replayed) in case a system failure causes the change to be lost. WAL records use a non-printable binary format.

For more information, see Section 28.6.

An auxiliary process that runs on a replica to receive WAL from the primary server for replay by the startup process.

For more information, see Section 26.2.

A special backend process that streams WAL over a network. The receiving end can be a WAL receiver in a replica, pg_receivewal, or any other client program that speaks the replication protocol.

An auxiliary process that summarizes WAL data for incremental backups.

For more information, see Section 19.5.7.

An auxiliary process that writes WAL records from shared memory to WAL files.

For more information, see Section 19.5.

A type of function used in a query that applies to a partition of the query's result set; the function's result is based on values found in rows of the same partition or frame.

All aggregate functions can be used as window functions, but window functions can also be used to, for example, give ranks to each of the rows in the partition. Also known as analytic functions.

For more information, see Section 3.5.

The journal that keeps track of the changes in the database cluster as user- and system-invoked operations take place. It comprises many individual WAL records written sequentially to WAL files.

---

PostgreSQL: Documentation: 18: 18.11. Secure TCP/IP Connections with SSH Tunnels

URL: https://www.postgresql.org/docs/current/ssh-tunnels.html

Contents:

• 18.11. Secure TCP/IP Connections with SSH Tunnels #
  • Tip

It is possible to use SSH to encrypt the network connection between clients and a PostgreSQL server. Done properly, this provides an adequately secure network connection, even for non-SSL-capable clients.

First make sure that an SSH server is running properly on the same machine as the PostgreSQL server and that you can log in using ssh as some user; you then can establish a secure tunnel to the remote server. A secure tunnel listens on a local port and forwards all traffic to a port on the remote machine. Traffic sent to the remote port can arrive on its localhost address, or different bind address if desired; it does not appear as coming from your local machine. This command creates a secure tunnel from the client machine to the remote machine foo.com:

The first number in the -L argument, 63333, is the local port number of the tunnel; it can be any unused port. (IANA reserves ports 49152 through 65535 for private use.) The name or IP address after this is the remote bind address you are connecting to, i.e., localhost, which is the default. The second number, 5432, is the remote end of the tunnel, e.g., the port number your database server is using. In order to connect to the database server using this tunnel, you connect to port 63333 on the local machine:

To the database server it will then look as though you are user joe on host foo.com connecting to the localhost bind address, and it will use whatever authentication procedure was configured for connections by that user to that bind address. Note that the server will not think the connection is SSL-encrypted, since in fact it is not encrypted between the SSH server and the PostgreSQL server. This should not pose any extra security risk because they are on the same machine.

In order for the tunnel setup to succeed you must be allowed to connect via ssh as joe@foo.com, just as if you had attempted to use ssh to create a terminal session.

You could also have set up port forwarding as

but then the database server will see the connection as coming in on its foo.com bind address, which is not opened by the default setting listen_addresses = 'localhost'. This is usually not what you want.

If you have to “hop” to the database server via some login host, one possible setup could look like this:

Note that this way the connection from shell.foo.com to db.foo.com will not be encrypted by the SSH tunnel. SSH offers quite a few configuration possibilities when the network is restricted in various ways. Please refer to the SSH documentation for details.

Several other applications exist that can provide secure tunnels using a procedure similar in concept to the one just described.

Examples:

Example 1 (unknown):

ssh -L 63333:localhost:5432 joe@foo.com

Example 2 (unknown):

psql -h localhost -p 63333 postgres

Example 3 (unknown):

ssh -L 63333:foo.com:5432 joe@foo.com

Example 4 (unknown):

ssh -L 63333:db.foo.com:5432 joe@shell.foo.com

---

PostgreSQL: Documentation: 18: 35.23. domains

URL: https://www.postgresql.org/docs/current/infoschema-domains.html

Contents:

• 35.23. domains #

The view domains contains all domains defined in the current database. Only those domains are shown that the current user has access to (by way of being the owner or having some privilege).

Table 35.21. domains Columns

domain_catalog sql_identifier

Name of the database that contains the domain (always the current database)

domain_schema sql_identifier

Name of the schema that contains the domain

domain_name sql_identifier

data_type character_data

Data type of the domain, if it is a built-in type, or ARRAY if it is some array (in that case, see the view element_types), else USER-DEFINED (in that case, the type is identified in udt_name and associated columns).

character_maximum_length cardinal_number

If the domain has a character or bit string type, the declared maximum length; null for all other data types or if no maximum length was declared.

character_octet_length cardinal_number

If the domain has a character type, the maximum possible length in octets (bytes) of a datum; null for all other data types. The maximum octet length depends on the declared character maximum length (see above) and the server encoding.

character_set_catalog sql_identifier

Applies to a feature not available in PostgreSQL

character_set_schema sql_identifier

Applies to a feature not available in PostgreSQL

character_set_name sql_identifier

Applies to a feature not available in PostgreSQL

collation_catalog sql_identifier

Name of the database containing the collation of the domain (always the current database), null if default or the data type of the domain is not collatable

collation_schema sql_identifier

Name of the schema containing the collation of the domain, null if default or the data type of the domain is not collatable

collation_name sql_identifier

Name of the collation of the domain, null if default or the data type of the domain is not collatable

numeric_precision cardinal_number

If the domain has a numeric type, this column contains the (declared or implicit) precision of the type for this domain. The precision indicates the number of significant digits. It can be expressed in decimal (base 10) or binary (base 2) terms, as specified in the column numeric_precision_radix. For all other data types, this column is null.

numeric_precision_radix cardinal_number

If the domain has a numeric type, this column indicates in which base the values in the columns numeric_precision and numeric_scale are expressed. The value is either 2 or 10. For all other data types, this column is null.

numeric_scale cardinal_number

If the domain has an exact numeric type, this column contains the (declared or implicit) scale of the type for this domain. The scale indicates the number of significant digits to the right of the decimal point. It can be expressed in decimal (base 10) or binary (base 2) terms, as specified in the column numeric_precision_radix. For all other data types, this column is null.

datetime_precision cardinal_number

If data_type identifies a date, time, timestamp, or interval type, this column contains the (declared or implicit) fractional seconds precision of the type for this domain, that is, the number of decimal digits maintained following the decimal point in the seconds value. For all other data types, this column is null.

interval_type character_data

If data_type identifies an interval type, this column contains the specification which fields the intervals include for this domain, e.g., YEAR TO MONTH, DAY TO SECOND, etc. If no field restrictions were specified (that is, the interval accepts all fields), and for all other data types, this field is null.

interval_precision cardinal_number

Applies to a feature not available in PostgreSQL (see datetime_precision for the fractional seconds precision of interval type domains)

domain_default character_data

Default expression of the domain

udt_catalog sql_identifier

Name of the database that the domain data type is defined in (always the current database)

udt_schema sql_identifier

Name of the schema that the domain data type is defined in

udt_name sql_identifier

Name of the domain data type

scope_catalog sql_identifier

Applies to a feature not available in PostgreSQL

scope_schema sql_identifier

Applies to a feature not available in PostgreSQL

scope_name sql_identifier

Applies to a feature not available in PostgreSQL

maximum_cardinality cardinal_number

Always null, because arrays always have unlimited maximum cardinality in PostgreSQL

dtd_identifier sql_identifier

An identifier of the data type descriptor of the domain, unique among the data type descriptors pertaining to the domain (which is trivial, because a domain only contains one data type descriptor). This is mainly useful for joining with other instances of such identifiers. (The specific format of the identifier is not defined and not guaranteed to remain the same in future versions.)

---

PostgreSQL: Documentation: 18: 9.30. Event Trigger Functions

URL: https://www.postgresql.org/docs/current/functions-event-triggers.html

Contents:

• 9.30. Event Trigger Functions #
  • 9.30.1. Capturing Changes at Command End #
  • 9.30.2. Processing Objects Dropped by a DDL Command #
  • 9.30.3. Handling a Table Rewrite Event #

PostgreSQL provides these helper functions to retrieve information from event triggers.

For more information about event triggers, see Chapter 38.

pg_event_trigger_ddl_commands returns a list of DDL commands executed by each user action, when invoked in a function attached to a ddl_command_end event trigger. If called in any other context, an error is raised. pg_event_trigger_ddl_commands returns one row for each base command executed; some commands that are a single SQL sentence may return more than one row. This function returns the following columns:

pg_event_trigger_dropped_objects returns a list of all objects dropped by the command in whose sql_drop event it is called. If called in any other context, an error is raised. This function returns the following columns:

The pg_event_trigger_dropped_objects function can be used in an event trigger like this:

The functions shown in Table 9.111 provide information about a table for which a table_rewrite event has just been called. If called in any other context, an error is raised.

Table 9.111. Table Rewrite Information Functions

pg_event_trigger_table_rewrite_oid () → oid

Returns the OID of the table about to be rewritten.

pg_event_trigger_table_rewrite_reason () → integer

Returns a code explaining the reason(s) for rewriting. The value is a bitmap built from the following values: 1 (the table has changed its persistence), 2 (default value of a column has changed), 4 (a column has a new data type) and 8 (the table access method has changed).

These functions can be used in an event trigger like this:

Examples:

Example 1 (unknown):

pg_event_trigger_ddl_commands () → setof record

Example 2 (unknown):

pg_event_trigger_dropped_objects () → setof record

Example 3 (unknown):

CREATE FUNCTION test_event_trigger_for_drops()
        RETURNS event_trigger LANGUAGE plpgsql AS $$
DECLARE
    obj record;
BEGIN
    FOR obj IN SELECT * FROM pg_event_trigger_dropped_objects()
    LOOP
        RAISE NOTICE '% dropped object: % %.% %',
                     tg_tag,
                     obj.object_type,
                     obj.schema_name,
                     obj.object_name,
                     obj.object_identity;
    END LOOP;
END;
$$;
CREATE EVENT TRIGGER test_event_trigger_for_drops
   ON sql_drop
   EXECUTE FUNCTION test_event_trigger_for_drops();

Example 4 (unknown):

CREATE FUNCTION test_event_trigger_table_rewrite_oid()
 RETURNS event_trigger
 LANGUAGE plpgsql AS
$$
BEGIN
  RAISE NOTICE 'rewriting table % for reason %',
                pg_event_trigger_table_rewrite_oid()::regclass,
                pg_event_trigger_table_rewrite_reason();
END;
$$;

CREATE EVENT TRIGGER test_table_rewrite_oid
                  ON table_rewrite
   EXECUTE FUNCTION test_event_trigger_table_rewrite_oid();

---

PostgreSQL: Documentation: 18: 11.7. Indexes on Expressions

URL: https://www.postgresql.org/docs/current/indexes-expressional.html

Contents:

• 11.7. Indexes on Expressions #

An index column need not be just a column of the underlying table, but can be a function or scalar expression computed from one or more columns of the table. This feature is useful to obtain fast access to tables based on the results of computations.

For example, a common way to do case-insensitive comparisons is to use the lower function:

This query can use an index if one has been defined on the result of the lower(col1) function:

If we were to declare this index UNIQUE, it would prevent creation of rows whose col1 values differ only in case, as well as rows whose col1 values are actually identical. Thus, indexes on expressions can be used to enforce constraints that are not definable as simple unique constraints.

As another example, if one often does queries like:

then it might be worth creating an index like this:

The syntax of the CREATE INDEX command normally requires writing parentheses around index expressions, as shown in the second example. The parentheses can be omitted when the expression is just a function call, as in the first example.

Index expressions are relatively expensive to maintain, because the derived expression(s) must be computed for each row insertion and non-HOT update. However, the index expressions are not recomputed during an indexed search, since they are already stored in the index. In both examples above, the system sees the query as just WHERE indexedcolumn = 'constant' and so the speed of the search is equivalent to any other simple index query. Thus, indexes on expressions are useful when retrieval speed is more important than insertion and update speed.

Examples:

Example 1 (unknown):

SELECT * FROM test1 WHERE lower(col1) = 'value';

Example 2 (unknown):

CREATE INDEX test1_lower_col1_idx ON test1 (lower(col1));

Example 3 (unknown):

SELECT * FROM people WHERE (first_name || ' ' || last_name) = 'John Smith';

Example 4 (unknown):

CREATE INDEX people_names ON people ((first_name || ' ' || last_name));

---

PostgreSQL: Documentation: 18: 26.3. Failover

URL: https://www.postgresql.org/docs/current/warm-standby-failover.html

Contents:

• 26.3. Failover #

If the primary server fails then the standby server should begin failover procedures.

If the standby server fails then no failover need take place. If the standby server can be restarted, even some time later, then the recovery process can also be restarted immediately, taking advantage of restartable recovery. If the standby server cannot be restarted, then a full new standby server instance should be created.

If the primary server fails and the standby server becomes the new primary, and then the old primary restarts, you must have a mechanism for informing the old primary that it is no longer the primary. This is sometimes known as STONITH (Shoot The Other Node In The Head), which is necessary to avoid situations where both systems think they are the primary, which will lead to confusion and ultimately data loss.

Many failover systems use just two systems, the primary and the standby, connected by some kind of heartbeat mechanism to continually verify the connectivity between the two and the viability of the primary. It is also possible to use a third system (called a witness server) to prevent some cases of inappropriate failover, but the additional complexity might not be worthwhile unless it is set up with sufficient care and rigorous testing.

PostgreSQL does not provide the system software required to identify a failure on the primary and notify the standby database server. Many such tools exist and are well integrated with the operating system facilities required for successful failover, such as IP address migration.

Once failover to the standby occurs, there is only a single server in operation. This is known as a degenerate state. The former standby is now the primary, but the former primary is down and might stay down. To return to normal operation, a standby server must be recreated, either on the former primary system when it comes up, or on a third, possibly new, system. The pg_rewind utility can be used to speed up this process on large clusters. Once complete, the primary and standby can be considered to have switched roles. Some people choose to use a third server to provide backup for the new primary until the new standby server is recreated, though clearly this complicates the system configuration and operational processes.

So, switching from primary to standby server can be fast but requires some time to re-prepare the failover cluster. Regular switching from primary to standby is useful, since it allows regular downtime on each system for maintenance. This also serves as a test of the failover mechanism to ensure that it will really work when you need it. Written administration procedures are advised.

If you have opted for logical replication slot synchronization (see Section 47.2.3), then before switching to the standby server, it is recommended to check if the logical slots synchronized on the standby server are ready for failover. This can be done by following the steps described in Section 29.3.

To trigger failover of a log-shipping standby server, run pg_ctl promote or call pg_promote(). If you're setting up reporting servers that are only used to offload read-only queries from the primary, not for high availability purposes, you don't need to promote.

---

PostgreSQL: Documentation: 18: 35.29. foreign_servers

URL: https://www.postgresql.org/docs/current/infoschema-foreign-servers.html

Contents:

• 35.29. foreign_servers #

The view foreign_servers contains all foreign servers defined in the current database. Only those foreign servers are shown that the current user has access to (by way of being the owner or having some privilege).

Table 35.27. foreign_servers Columns

foreign_server_catalog sql_identifier

Name of the database that the foreign server is defined in (always the current database)

foreign_server_name sql_identifier

Name of the foreign server

foreign_data_wrapper_catalog sql_identifier

Name of the database that contains the foreign-data wrapper used by the foreign server (always the current database)

foreign_data_wrapper_name sql_identifier

Name of the foreign-data wrapper used by the foreign server

foreign_server_type character_data

Foreign server type information, if specified upon creation

foreign_server_version character_data

Foreign server version information, if specified upon creation

authorization_identifier sql_identifier

Name of the owner of the foreign server

---

PostgreSQL: Documentation: 18: Chapter 54. Frontend/Backend Protocol

URL: https://www.postgresql.org/docs/current/protocol.html

Contents:

• Chapter 54. Frontend/Backend Protocol

PostgreSQL uses a message-based protocol for communication between frontends and backends (clients and servers). The protocol is supported over TCP/IP and also over Unix-domain sockets. Port number 5432 has been registered with IANA as the customary TCP port number for servers supporting this protocol, but in practice any non-privileged port number can be used.

This document describes version 3.2 of the protocol, introduced in PostgreSQL version 18. The server and the libpq client library are backwards compatible with protocol version 3.0, implemented in PostgreSQL 7.4 and later.

In order to serve multiple clients efficiently, the server launches a new “backend” process for each client. In the current implementation, a new child process is created immediately after an incoming connection is detected. This is transparent to the protocol, however. For purposes of the protocol, the terms “backend” and “server” are interchangeable; likewise “frontend” and “client” are interchangeable.

---

PostgreSQL: Documentation: 18: Chapter 58. Writing a Foreign Data Wrapper

URL: https://www.postgresql.org/docs/current/fdwhandler.html

Contents:

• Chapter 58. Writing a Foreign Data Wrapper
  • Note

All operations on a foreign table are handled through its foreign data wrapper, which consists of a set of functions that the core server calls. The foreign data wrapper is responsible for fetching data from the remote data source and returning it to the PostgreSQL executor. If updating foreign tables is to be supported, the wrapper must handle that, too. This chapter outlines how to write a new foreign data wrapper.

The foreign data wrappers included in the standard distribution are good references when trying to write your own. Look into the contrib subdirectory of the source tree. The CREATE FOREIGN DATA WRAPPER reference page also has some useful details.

The SQL standard specifies an interface for writing foreign data wrappers. However, PostgreSQL does not implement that API, because the effort to accommodate it into PostgreSQL would be large, and the standard API hasn't gained wide adoption anyway.

---

PostgreSQL: Documentation: 18: Appendix H. External Projects

URL: https://www.postgresql.org/docs/current/external-projects.html

Contents:

• Appendix H. External Projects

PostgreSQL is a complex software project, and managing the project is difficult. We have found that many enhancements to PostgreSQL can be more efficiently developed separately from the core project.

---

PostgreSQL: Documentation: 18: 8.1. Numeric Types

URL: https://www.postgresql.org/docs/current/datatype-numeric.html

Contents:

• 8.1. Numeric Types #
  • 8.1.1. Integer Types #
  • 8.1.2. Arbitrary Precision Numbers #
  • Note
  • Note
  • Note
  • 8.1.3. Floating-Point Types #
  • Note
  • Note
  • Note

Numeric types consist of two-, four-, and eight-byte integers, four- and eight-byte floating-point numbers, and selectable-precision decimals. Table 8.2 lists the available types.

Table 8.2. Numeric Types

The syntax of constants for the numeric types is described in Section 4.1.2. The numeric types have a full set of corresponding arithmetic operators and functions. Refer to Chapter 9 for more information. The following sections describe the types in detail.

The types smallint, integer, and bigint store whole numbers, that is, numbers without fractional components, of various ranges. Attempts to store values outside of the allowed range will result in an error.

The type integer is the common choice, as it offers the best balance between range, storage size, and performance. The smallint type is generally only used if disk space is at a premium. The bigint type is designed to be used when the range of the integer type is insufficient.

SQL only specifies the integer types integer (or int), smallint, and bigint. The type names int2, int4, and int8 are extensions, which are also used by some other SQL database systems.

The type numeric can store numbers with a very large number of digits. It is especially recommended for storing monetary amounts and other quantities where exactness is required. Calculations with numeric values yield exact results where possible, e.g., addition, subtraction, multiplication. However, calculations on numeric values are very slow compared to the integer types, or to the floating-point types described in the next section.

We use the following terms below: The precision of a numeric is the total count of significant digits in the whole number, that is, the number of digits to both sides of the decimal point. The scale of a numeric is the count of decimal digits in the fractional part, to the right of the decimal point. So the number 23.5141 has a precision of 6 and a scale of 4. Integers can be considered to have a scale of zero.

Both the maximum precision and the maximum scale of a numeric column can be configured. To declare a column of type numeric use the syntax:

The precision must be positive, while the scale may be positive or negative (see below). Alternatively:

selects a scale of 0. Specifying:

without any precision or scale creates an “unconstrained numeric” column in which numeric values of any length can be stored, up to the implementation limits. A column of this kind will not coerce input values to any particular scale, whereas numeric columns with a declared scale will coerce input values to that scale. (The SQL standard requires a default scale of 0, i.e., coercion to integer precision. We find this a bit useless. If you're concerned about portability, always specify the precision and scale explicitly.)

The maximum precision that can be explicitly specified in a numeric type declaration is 1000. An unconstrained numeric column is subject to the limits described in Table 8.2.

If the scale of a value to be stored is greater than the declared scale of the column, the system will round the value to the specified number of fractional digits. Then, if the number of digits to the left of the decimal point exceeds the declared precision minus the declared scale, an error is raised. For example, a column declared as

will round values to 1 decimal place and can store values between -99.9 and 99.9, inclusive.

Beginning in PostgreSQL 15, it is allowed to declare a numeric column with a negative scale. Then values will be rounded to the left of the decimal point. The precision still represents the maximum number of non-rounded digits. Thus, a column declared as

will round values to the nearest thousand and can store values between -99000 and 99000, inclusive. It is also allowed to declare a scale larger than the declared precision. Such a column can only hold fractional values, and it requires the number of zero digits just to the right of the decimal point to be at least the declared scale minus the declared precision. For example, a column declared as

will round values to 5 decimal places and can store values between -0.00999 and 0.00999, inclusive.

PostgreSQL permits the scale in a numeric type declaration to be any value in the range -1000 to 1000. However, the SQL standard requires the scale to be in the range 0 to precision. Using scales outside that range may not be portable to other database systems.

Numeric values are physically stored without any extra leading or trailing zeroes. Thus, the declared precision and scale of a column are maximums, not fixed allocations. (In this sense the numeric type is more akin to varchar(n) than to char(n).) The actual storage requirement is two bytes for each group of four decimal digits, plus three to eight bytes overhead.

In addition to ordinary numeric values, the numeric type has several special values:

Infinity -Infinity NaN

These are adapted from the IEEE 754 standard, and represent “infinity”, “negative infinity”, and “not-a-number”, respectively. When writing these values as constants in an SQL command, you must put quotes around them, for example UPDATE table SET x = '-Infinity'. On input, these strings are recognized in a case-insensitive manner. The infinity values can alternatively be spelled inf and -inf.

The infinity values behave as per mathematical expectations. For example, Infinity plus any finite value equals Infinity, as does Infinity plus Infinity; but Infinity minus Infinity yields NaN (not a number), because it has no well-defined interpretation. Note that an infinity can only be stored in an unconstrained numeric column, because it notionally exceeds any finite precision limit.

The NaN (not a number) value is used to represent undefined calculational results. In general, any operation with a NaN input yields another NaN. The only exception is when the operation's other inputs are such that the same output would be obtained if the NaN were to be replaced by any finite or infinite numeric value; then, that output value is used for NaN too. (An example of this principle is that NaN raised to the zero power yields one.)

In most implementations of the “not-a-number” concept, NaN is not considered equal to any other numeric value (including NaN). In order to allow numeric values to be sorted and used in tree-based indexes, PostgreSQL treats NaN values as equal, and greater than all non-NaN values.

The types decimal and numeric are equivalent. Both types are part of the SQL standard.

When rounding values, the numeric type rounds ties away from zero, while (on most machines) the real and double precision types round ties to the nearest even number. For example:

The data types real and double precision are inexact, variable-precision numeric types. On all currently supported platforms, these types are implementations of IEEE Standard 754 for Binary Floating-Point Arithmetic (single and double precision, respectively), to the extent that the underlying processor, operating system, and compiler support it.

Inexact means that some values cannot be converted exactly to the internal format and are stored as approximations, so that storing and retrieving a value might show slight discrepancies. Managing these errors and how they propagate through calculations is the subject of an entire branch of mathematics and computer science and will not be discussed here, except for the following points:

If you require exact storage and calculations (such as for monetary amounts), use the numeric type instead.

If you want to do complicated calculations with these types for anything important, especially if you rely on certain behavior in boundary cases (infinity, underflow), you should evaluate the implementation carefully.

Comparing two floating-point values for equality might not always work as expected.

On all currently supported platforms, the real type has a range of around 1E-37 to 1E+37 with a precision of at least 6 decimal digits. The double precision type has a range of around 1E-307 to 1E+308 with a precision of at least 15 digits. Values that are too large or too small will cause an error. Rounding might take place if the precision of an input number is too high. Numbers too close to zero that are not representable as distinct from zero will cause an underflow error.

By default, floating point values are output in text form in their shortest precise decimal representation; the decimal value produced is closer to the true stored binary value than to any other value representable in the same binary precision. (However, the output value is currently never exactly midway between two representable values, in order to avoid a widespread bug where input routines do not properly respect the round-to-nearest-even rule.) This value will use at most 17 significant decimal digits for float8 values, and at most 9 digits for float4 values.

This shortest-precise output format is much faster to generate than the historical rounded format.

For compatibility with output generated by older versions of PostgreSQL, and to allow the output precision to be reduced, the extra_float_digits parameter can be used to select rounded decimal output instead. Setting a value of 0 restores the previous default of rounding the value to 6 (for float4) or 15 (for float8) significant decimal digits. Setting a negative value reduces the number of digits further; for example -2 would round output to 4 or 13 digits respectively.

Any value of extra_float_digits greater than 0 selects the shortest-precise format.

Applications that wanted precise values have historically had to set extra_float_digits to 3 to obtain them. For maximum compatibility between versions, they should continue to do so.

In addition to ordinary numeric values, the floating-point types have several special values:

Infinity -Infinity NaN

These represent the IEEE 754 special values “infinity”, “negative infinity”, and “not-a-number”, respectively. When writing these values as constants in an SQL command, you must put quotes around them, for example UPDATE table SET x = '-Infinity'. On input, these strings are recognized in a case-insensitive manner. The infinity values can alternatively be spelled inf and -inf.

IEEE 754 specifies that NaN should not compare equal to any other floating-point value (including NaN). In order to allow floating-point values to be sorted and used in tree-based indexes, PostgreSQL treats NaN values as equal, and greater than all non-NaN values.

PostgreSQL also supports the SQL-standard notations float and float(p) for specifying inexact numeric types. Here, p specifies the minimum acceptable precision in binary digits. PostgreSQL accepts float(1) to float(24) as selecting the real type, while float(25) to float(53) select double precision. Values of p outside the allowed range draw an error. float with no precision specified is taken to mean double precision.

This section describes a PostgreSQL-specific way to create an autoincrementing column. Another way is to use the SQL-standard identity column feature, described at Section 5.3.

The data types smallserial, serial and bigserial are not true types, but merely a notational convenience for creating unique identifier columns (similar to the AUTO_INCREMENT property supported by some other databases). In the current implementation, specifying:

is equivalent to specifying:

Thus, we have created an integer column and arranged for its default values to be assigned from a sequence generator. A NOT NULL constraint is applied to ensure that a null value cannot be inserted. (In most cases you would also want to attach a UNIQUE or PRIMARY KEY constraint to prevent duplicate values from being inserted by accident, but this is not automatic.) Lastly, the sequence is marked as “owned by” the column, so that it will be dropped if the column or table is dropped.

Because smallserial, serial and bigserial are implemented using sequences, there may be "holes" or gaps in the sequence of values which appears in the column, even if no rows are ever deleted. A value allocated from the sequence is still "used up" even if a row containing that value is never successfully inserted into the table column. This may happen, for example, if the inserting transaction rolls back. See nextval() in Section 9.17 for details.

To insert the next value of the sequence into the serial column, specify that the serial column should be assigned its default value. This can be done either by excluding the column from the list of columns in the INSERT statement, or through the use of the DEFAULT key word.

The type names serial and serial4 are equivalent: both create integer columns. The type names bigserial and serial8 work the same way, except that they create a bigint column. bigserial should be used if you anticipate the use of more than 231 identifiers over the lifetime of the table. The type names smallserial and serial2 also work the same way, except that they create a smallint column.

The sequence created for a serial column is automatically dropped when the owning column is dropped. You can drop the sequence without dropping the column, but this will force removal of the column default expression.

Examples:

Example 1 (unknown):

NUMERIC(precision, scale)

Example 2 (unknown):

NUMERIC(precision)

Example 3 (unknown):

NUMERIC(3, 1)

Example 4 (unknown):

NUMERIC(2, -3)

---

PostgreSQL: Documentation: 18: Chapter 51. Overview of PostgreSQL Internals

URL: https://www.postgresql.org/docs/current/overview.html

Contents:

• Chapter 51. Overview of PostgreSQL Internals
  • Author

This chapter originated as part of [sim98] Stefan Simkovics' Master's Thesis prepared at Vienna University of Technology under the direction of O.Univ.Prof.Dr. Georg Gottlob and Univ.Ass. Mag. Katrin Seyr.

This chapter gives an overview of the internal structure of the backend of PostgreSQL. After having read the following sections you should have an idea of how a query is processed. This chapter is intended to help the reader understand the general sequence of operations that occur within the backend from the point at which a query is received, to the point at which the results are returned to the client.

---

PostgreSQL: Documentation: 18: Chapter 34. ECPG — Embedded SQL in C

URL: https://www.postgresql.org/docs/current/ecpg.html

Contents:

• Chapter 34. ECPG — Embedded SQL in C

This chapter describes the embedded SQL package for PostgreSQL. It was written by Linus Tolke (linus@epact.se) and Michael Meskes (meskes@postgresql.org). Originally it was written to work with C. It also works with C++, but it does not recognize all C++ constructs yet.

This documentation is quite incomplete. But since this interface is standardized, additional information can be found in many resources about SQL.

---

PostgreSQL: Documentation: 18: 9.6. Bit String Functions and Operators

URL: https://www.postgresql.org/docs/current/functions-bitstring.html

Contents:

• 9.6. Bit String Functions and Operators #

This section describes functions and operators for examining and manipulating bit strings, that is values of the types bit and bit varying. (While only type bit is mentioned in these tables, values of type bit varying can be used interchangeably.) Bit strings support the usual comparison operators shown in Table 9.1, as well as the operators shown in Table 9.14.

Table 9.14. Bit String Operators

B'10001' || B'011' → 10001011

Bitwise AND (inputs must be of equal length)

B'10001' & B'01101' → 00001

Bitwise OR (inputs must be of equal length)

B'10001' | B'01101' → 11101

Bitwise exclusive OR (inputs must be of equal length)

B'10001' # B'01101' → 11100

Bitwise shift left (string length is preserved)

B'10001' << 3 → 01000

Bitwise shift right (string length is preserved)

B'10001' >> 2 → 00100

Some of the functions available for binary strings are also available for bit strings, as shown in Table 9.15.

Table 9.15. Bit String Functions

bit_count ( bit ) → bigint

Returns the number of bits set in the bit string (also known as “popcount”).

bit_count(B'10111') → 4

bit_length ( bit ) → integer

Returns number of bits in the bit string.

bit_length(B'10111') → 5

length ( bit ) → integer

Returns number of bits in the bit string.

octet_length ( bit ) → integer

Returns number of bytes in the bit string.

octet_length(B'1011111011') → 2

overlay ( bits bit PLACING newsubstring bit FROM start integer [ FOR count integer ] ) → bit

Replaces the substring of bits that starts at the start'th bit and extends for count bits with newsubstring. If count is omitted, it defaults to the length of newsubstring.

overlay(B'01010101010101010' placing B'11111' from 2 for 3) → 0111110101010101010

position ( substring bit IN bits bit ) → integer

Returns first starting index of the specified substring within bits, or zero if it's not present.

position(B'010' in B'000001101011') → 8

substring ( bits bit [ FROM start integer ] [ FOR count integer ] ) → bit

Extracts the substring of bits starting at the start'th bit if that is specified, and stopping after count bits if that is specified. Provide at least one of start and count.

substring(B'110010111111' from 3 for 2) → 00

get_bit ( bits bit, n integer ) → integer

Extracts n'th bit from bit string; the first (leftmost) bit is bit 0.

get_bit(B'101010101010101010', 6) → 1

set_bit ( bits bit, n integer, newvalue integer ) → bit

Sets n'th bit in bit string to newvalue; the first (leftmost) bit is bit 0.

set_bit(B'101010101010101010', 6, 0) → 101010001010101010

In addition, it is possible to cast integral values to and from type bit. Casting an integer to bit(n) copies the rightmost n bits. Casting an integer to a bit string width wider than the integer itself will sign-extend on the left. Some examples:

Note that casting to just “bit” means casting to bit(1), and so will deliver only the least significant bit of the integer.

Examples:

Example 1 (unknown):

44::bit(10)                    0000101100
44::bit(3)                     100
cast(-44 as bit(12))           111111010100
'1110'::bit(4)::integer        14

---

PostgreSQL: Documentation: 18: 35.49. sql_implementation_info

URL: https://www.postgresql.org/docs/current/infoschema-sql-implementation-info.html

Contents:

• 35.49. sql_implementation_info #

The table sql_implementation_info contains information about various aspects that are left implementation-defined by the SQL standard. This information is primarily intended for use in the context of the ODBC interface; users of other interfaces will probably find this information to be of little use. For this reason, the individual implementation information items are not described here; you will find them in the description of the ODBC interface.

Table 35.47. sql_implementation_info Columns

implementation_info_id character_data

Identifier string of the implementation information item

implementation_info_name character_data

Descriptive name of the implementation information item

integer_value cardinal_number

Value of the implementation information item, or null if the value is contained in the column character_value

character_value character_data

Value of the implementation information item, or null if the value is contained in the column integer_value

comments character_data

Possibly a comment pertaining to the implementation information item

---

PostgreSQL: Documentation: 18: 23.1. Locale Support

URL: https://www.postgresql.org/docs/current/locale.html

Contents:

• 23.1. Locale Support #
  • 23.1.1. Overview #
  • Note
  • 23.1.2. Behavior #
  • 23.1.3. Selecting Locales #
  • 23.1.4. Locale Providers #
  • Note
  • Note
  • 23.1.5. ICU Locales #
  • 23.1.5.1. ICU Locale Names #

Locale support refers to an application respecting cultural preferences regarding alphabets, sorting, number formatting, etc. PostgreSQL uses the standard ISO C and POSIX locale facilities provided by the server operating system. For additional information refer to the documentation of your system.

Locale support is automatically initialized when a database cluster is created using initdb. initdb will initialize the database cluster with the locale setting of its execution environment by default, so if your system is already set to use the locale that you want in your database cluster then there is nothing else you need to do. If you want to use a different locale (or you are not sure which locale your system is set to), you can instruct initdb exactly which locale to use by specifying the --locale option. For example:

This example for Unix systems sets the locale to Swedish (sv) as spoken in Sweden (SE). Other possibilities might include en_US (U.S. English) and fr_CA (French Canadian). If more than one character set can be used for a locale then the specifications can take the form language_territory.codeset. For example, fr_BE.UTF-8 represents the French language (fr) as spoken in Belgium (BE), with a UTF-8 character set encoding.

What locales are available on your system under what names depends on what was provided by the operating system vendor and what was installed. On most Unix systems, the command locale -a will provide a list of available locales. Windows uses more verbose locale names, such as German_Germany or Swedish_Sweden.1252, but the principles are the same.

Occasionally it is useful to mix rules from several locales, e.g., use English collation rules but Spanish messages. To support that, a set of locale subcategories exist that control only certain aspects of the localization rules:

The category names translate into names of initdb options to override the locale choice for a specific category. For instance, to set the locale to French Canadian, but use U.S. rules for formatting currency, use initdb --locale=fr_CA --lc-monetary=en_US.

If you want the system to behave as if it had no locale support, use the special locale name C, or equivalently POSIX.

Some locale categories must have their values fixed when the database is created. You can use different settings for different databases, but once a database is created, you cannot change them for that database anymore. LC_COLLATE and LC_CTYPE are these categories. They affect the sort order of indexes, so they must be kept fixed, or indexes on text columns would become corrupt. (But you can alleviate this restriction using collations, as discussed in Section 23.2.) The default values for these categories are determined when initdb is run, and those values are used when new databases are created, unless specified otherwise in the CREATE DATABASE command.

The other locale categories can be changed whenever desired by setting the server configuration parameters that have the same name as the locale categories (see Section 19.11.2 for details). The values that are chosen by initdb are actually only written into the configuration file postgresql.conf to serve as defaults when the server is started. If you remove these assignments from postgresql.conf then the server will inherit the settings from its execution environment.

Note that the locale behavior of the server is determined by the environment variables seen by the server, not by the environment of any client. Therefore, be careful to configure the correct locale settings before starting the server. A consequence of this is that if client and server are set up in different locales, messages might appear in different languages depending on where they originated.

When we speak of inheriting the locale from the execution environment, this means the following on most operating systems: For a given locale category, say the collation, the following environment variables are consulted in this order until one is found to be set: LC_ALL, LC_COLLATE (or the variable corresponding to the respective category), LANG. If none of these environment variables are set then the locale defaults to C.

Some message localization libraries also look at the environment variable LANGUAGE which overrides all other locale settings for the purpose of setting the language of messages. If in doubt, please refer to the documentation of your operating system, in particular the documentation about gettext.

To enable messages to be translated to the user's preferred language, NLS must have been selected at build time (configure --enable-nls). All other locale support is built in automatically.

The locale settings influence the following SQL features:

Sort order in queries using ORDER BY or the standard comparison operators on textual data

The upper, lower, and initcap functions

Pattern matching operators (LIKE, SIMILAR TO, and POSIX-style regular expressions); locales affect both case insensitive matching and the classification of characters by character-class regular expressions

The to_char family of functions

The ability to use indexes with LIKE clauses

The drawback of using locales other than C or POSIX in PostgreSQL is its performance impact. It slows character handling and prevents ordinary indexes from being used by LIKE. For this reason use locales only if you actually need them.

As a workaround to allow PostgreSQL to use indexes with LIKE clauses under a non-C locale, several custom operator classes exist. These allow the creation of an index that performs a strict character-by-character comparison, ignoring locale comparison rules. Refer to Section 11.10 for more information. Another approach is to create indexes using the C collation, as discussed in Section 23.2.

Locales can be selected in different scopes depending on requirements. The above overview showed how locales are specified using initdb to set the defaults for the entire cluster. The following list shows where locales can be selected. Each item provides the defaults for the subsequent items, and each lower item allows overriding the defaults on a finer granularity.

As explained above, the environment of the operating system provides the defaults for the locales of a newly initialized database cluster. In many cases, this is enough: if the operating system is configured for the desired language/territory, by default PostgreSQL will also behave according to that locale.

As shown above, command-line options for initdb specify the locale settings for a newly initialized database cluster. Use this if the operating system does not have the locale configuration you want for your database system.

A locale can be selected separately for each database. The SQL command CREATE DATABASE and its command-line equivalent createdb have options for that. Use this for example if a database cluster houses databases for multiple tenants with different requirements.

Locale settings can be made for individual table columns. This uses an SQL object called collation and is explained in Section 23.2. Use this for example to sort data in different languages or customize the sort order of a particular table.

Finally, locales can be selected for an individual query. Again, this uses SQL collation objects. This could be used to change the sort order based on run-time choices or for ad-hoc experimentation.

A locale provider specifies which library defines the locale behavior for collations and character classifications.

The commands and tools that select the locale settings, as described above, each have an option to select the locale provider. Here is an example to initialize a database cluster using the ICU provider:

See the description of the respective commands and programs for details. Note that you can mix locale providers at different granularities, for example use libc by default for the cluster but have one database that uses the icu provider, and then have collation objects using either provider within those databases.

Regardless of the locale provider, the operating system is still used to provide some locale-aware behavior, such as messages (see lc_messages).

The available locale providers are listed below:

The builtin provider uses built-in operations. Only the C, C.UTF-8, and PG_UNICODE_FAST locales are supported for this provider.

The C locale behavior is identical to the C locale in the libc provider. When using this locale, the behavior may depend on the database encoding.

The C.UTF-8 locale is available only for when the database encoding is UTF-8, and the behavior is based on Unicode. The collation uses the code point values only. The regular expression character classes are based on the "POSIX Compatible" semantics, and the case mapping is the "simple" variant.

The PG_UNICODE_FAST locale is available only when the database encoding is UTF-8, and the behavior is based on Unicode. The collation uses the code point values only. The regular expression character classes are based on the "Standard" semantics, and the case mapping is the "full" variant.

The icu provider uses the external ICU library. PostgreSQL must have been configured with support.

ICU provides collation and character classification behavior that is independent of the operating system and database encoding, which is preferable if you expect to transition to other platforms without any change in results. LC_COLLATE and LC_CTYPE can be set independently of the ICU locale.

For the ICU provider, results may depend on the version of the ICU library used, as it is updated to reflect changes in natural language over time.

The libc provider uses the operating system's C library. The collation and character classification behavior is controlled by the settings LC_COLLATE and LC_CTYPE, so they cannot be set independently.

The same locale name may have different behavior on different platforms when using the libc provider.

The ICU format for the locale name is a Language Tag.

When defining a new ICU collation object or database with ICU as the provider, the given locale name is transformed ("canonicalized") into a language tag if not already in that form. For instance,

If you see this notice, ensure that the provider and locale are the expected result. For consistent results when using the ICU provider, specify the canonical language tag instead of relying on the transformation.

A locale with no language name, or the special language name root, is transformed to have the language und ("undefined").

ICU can transform most libc locale names, as well as some other formats, into language tags for easier transition to ICU. If a libc locale name is used in ICU, it may not have precisely the same behavior as in libc.

If there is a problem interpreting the locale name, or if the locale name represents a language or region that ICU does not recognize, you will see the following warning:

icu_validation_level controls how the message is reported. Unless set to ERROR, the collation will still be created, but the behavior may not be what the user intended.

A language tag, defined in BCP 47, is a standardized identifier used to identify languages, regions, and other information about a locale.

Basic language tags are simply language-region; or even just language. The language is a language code (e.g. fr for French), and region is a region code (e.g. CA for Canada). Examples: ja-JP, de, or fr-CA.

Collation settings may be included in the language tag to customize collation behavior. ICU allows extensive customization, such as sensitivity (or insensitivity) to accents, case, and punctuation; treatment of digits within text; and many other options to satisfy a variety of uses.

To include this additional collation information in a language tag, append -u, which indicates there are additional collation settings, followed by one or more -key-value pairs. The key is the key for a collation setting and value is a valid value for that setting. For boolean settings, the -key may be specified without a corresponding -value, which implies a value of true.

For example, the language tag en-US-u-kn-ks-level2 means the locale with the English language in the US region, with collation settings kn set to true and ks set to level2. Those settings mean the collation will be case-insensitive and treat a sequence of digits as a single number:

See Section 23.2.3 for details and additional examples of using language tags with custom collation information for the locale.

If locale support doesn't work according to the explanation above, check that the locale support in your operating system is correctly configured. To check what locales are installed on your system, you can use the command locale -a if your operating system provides it.

Check that PostgreSQL is actually using the locale that you think it is. The LC_COLLATE and LC_CTYPE settings are determined when a database is created, and cannot be changed except by creating a new database. Other locale settings including LC_MESSAGES and LC_MONETARY are initially determined by the environment the server is started in, but can be changed on-the-fly. You can check the active locale settings using the SHOW command.

The directory src/test/locale in the source distribution contains a test suite for PostgreSQL's locale support.

Client applications that handle server-side errors by parsing the text of the error message will obviously have problems when the server's messages are in a different language. Authors of such applications are advised to make use of the error code scheme instead.

Maintaining catalogs of message translations requires the on-going efforts of many volunteers that want to see PostgreSQL speak their preferred language well. If messages in your language are currently not available or not fully translated, your assistance would be appreciated. If you want to help, refer to Chapter 56 or write to the developers' mailing list.

Examples:

Example 1 (unknown):

initdb --locale=sv_SE

Example 2 (unknown):

initdb --locale-provider=icu --icu-locale=en

Example 3 (unknown):

CREATE COLLATION mycollation1 (provider = icu, locale = 'ja-JP');
CREATE COLLATION mycollation2 (provider = icu, locale = 'fr');

Example 4 (unknown):

CREATE COLLATION mycollation3 (provider = icu, locale = 'en-US-u-kn-true');
NOTICE:  using standard form "en-US-u-kn" for locale "en-US-u-kn-true"
CREATE COLLATION mycollation4 (provider = icu, locale = 'de_DE.utf8');
NOTICE:  using standard form "de-DE" for locale "de_DE.utf8"

---

PostgreSQL: Documentation: 18: 9.24. Subquery Expressions

URL: https://www.postgresql.org/docs/current/functions-subquery.html

Contents:

• 9.24. Subquery Expressions #
  • 9.24.1. EXISTS #
  • 9.24.2. IN #
  • 9.24.3. NOT IN #
  • 9.24.4. ANY/SOME #
  • 9.24.5. ALL #
  • 9.24.6. Single-Row Comparison #

This section describes the SQL-compliant subquery expressions available in PostgreSQL. All of the expression forms documented in this section return Boolean (true/false) results.

The argument of EXISTS is an arbitrary SELECT statement, or subquery. The subquery is evaluated to determine whether it returns any rows. If it returns at least one row, the result of EXISTS is “true”; if the subquery returns no rows, the result of EXISTS is “false”.

The subquery can refer to variables from the surrounding query, which will act as constants during any one evaluation of the subquery.

The subquery will generally only be executed long enough to determine whether at least one row is returned, not all the way to completion. It is unwise to write a subquery that has side effects (such as calling sequence functions); whether the side effects occur might be unpredictable.

Since the result depends only on whether any rows are returned, and not on the contents of those rows, the output list of the subquery is normally unimportant. A common coding convention is to write all EXISTS tests in the form EXISTS(SELECT 1 WHERE ...). There are exceptions to this rule however, such as subqueries that use INTERSECT.

This simple example is like an inner join on col2, but it produces at most one output row for each tab1 row, even if there are several matching tab2 rows:

The right-hand side is a parenthesized subquery, which must return exactly one column. The left-hand expression is evaluated and compared to each row of the subquery result. The result of IN is “true” if any equal subquery row is found. The result is “false” if no equal row is found (including the case where the subquery returns no rows).

Note that if the left-hand expression yields null, or if there are no equal right-hand values and at least one right-hand row yields null, the result of the IN construct will be null, not false. This is in accordance with SQL's normal rules for Boolean combinations of null values.

As with EXISTS, it's unwise to assume that the subquery will be evaluated completely.

The left-hand side of this form of IN is a row constructor, as described in Section 4.2.13. The right-hand side is a parenthesized subquery, which must return exactly as many columns as there are expressions in the left-hand row. The left-hand expressions are evaluated and compared row-wise to each row of the subquery result. The result of IN is “true” if any equal subquery row is found. The result is “false” if no equal row is found (including the case where the subquery returns no rows).

As usual, null values in the rows are combined per the normal rules of SQL Boolean expressions. Two rows are considered equal if all their corresponding members are non-null and equal; the rows are unequal if any corresponding members are non-null and unequal; otherwise the result of that row comparison is unknown (null). If all the per-row results are either unequal or null, with at least one null, then the result of IN is null.

The right-hand side is a parenthesized subquery, which must return exactly one column. The left-hand expression is evaluated and compared to each row of the subquery result. The result of NOT IN is “true” if only unequal subquery rows are found (including the case where the subquery returns no rows). The result is “false” if any equal row is found.

Note that if the left-hand expression yields null, or if there are no equal right-hand values and at least one right-hand row yields null, the result of the NOT IN construct will be null, not true. This is in accordance with SQL's normal rules for Boolean combinations of null values.

As with EXISTS, it's unwise to assume that the subquery will be evaluated completely.

The left-hand side of this form of NOT IN is a row constructor, as described in Section 4.2.13. The right-hand side is a parenthesized subquery, which must return exactly as many columns as there are expressions in the left-hand row. The left-hand expressions are evaluated and compared row-wise to each row of the subquery result. The result of NOT IN is “true” if only unequal subquery rows are found (including the case where the subquery returns no rows). The result is “false” if any equal row is found.

As usual, null values in the rows are combined per the normal rules of SQL Boolean expressions. Two rows are considered equal if all their corresponding members are non-null and equal; the rows are unequal if any corresponding members are non-null and unequal; otherwise the result of that row comparison is unknown (null). If all the per-row results are either unequal or null, with at least one null, then the result of NOT IN is null.

The right-hand side is a parenthesized subquery, which must return exactly one column. The left-hand expression is evaluated and compared to each row of the subquery result using the given operator, which must yield a Boolean result. The result of ANY is “true” if any true result is obtained. The result is “false” if no true result is found (including the case where the subquery returns no rows).

SOME is a synonym for ANY. IN is equivalent to = ANY.

Note that if there are no successes and at least one right-hand row yields null for the operator's result, the result of the ANY construct will be null, not false. This is in accordance with SQL's normal rules for Boolean combinations of null values.

As with EXISTS, it's unwise to assume that the subquery will be evaluated completely.

The left-hand side of this form of ANY is a row constructor, as described in Section 4.2.13. The right-hand side is a parenthesized subquery, which must return exactly as many columns as there are expressions in the left-hand row. The left-hand expressions are evaluated and compared row-wise to each row of the subquery result, using the given operator. The result of ANY is “true” if the comparison returns true for any subquery row. The result is “false” if the comparison returns false for every subquery row (including the case where the subquery returns no rows). The result is NULL if no comparison with a subquery row returns true, and at least one comparison returns NULL.

See Section 9.25.5 for details about the meaning of a row constructor comparison.

The right-hand side is a parenthesized subquery, which must return exactly one column. The left-hand expression is evaluated and compared to each row of the subquery result using the given operator, which must yield a Boolean result. The result of ALL is “true” if all rows yield true (including the case where the subquery returns no rows). The result is “false” if any false result is found. The result is NULL if no comparison with a subquery row returns false, and at least one comparison returns NULL.

NOT IN is equivalent to <> ALL.

As with EXISTS, it's unwise to assume that the subquery will be evaluated completely.

The left-hand side of this form of ALL is a row constructor, as described in Section 4.2.13. The right-hand side is a parenthesized subquery, which must return exactly as many columns as there are expressions in the left-hand row. The left-hand expressions are evaluated and compared row-wise to each row of the subquery result, using the given operator. The result of ALL is “true” if the comparison returns true for all subquery rows (including the case where the subquery returns no rows). The result is “false” if the comparison returns false for any subquery row. The result is NULL if no comparison with a subquery row returns false, and at least one comparison returns NULL.

See Section 9.25.5 for details about the meaning of a row constructor comparison.

The left-hand side is a row constructor, as described in Section 4.2.13. The right-hand side is a parenthesized subquery, which must return exactly as many columns as there are expressions in the left-hand row. Furthermore, the subquery cannot return more than one row. (If it returns zero rows, the result is taken to be null.) The left-hand side is evaluated and compared row-wise to the single subquery result row.

See Section 9.25.5 for details about the meaning of a row constructor comparison.

Examples:

Example 1 (unknown):

EXISTS (subquery)

Example 2 (unknown):

SELECT col1
FROM tab1
WHERE EXISTS (SELECT 1 FROM tab2 WHERE col2 = tab1.col2);

Example 3 (unknown):

expression IN (subquery)

Example 4 (unknown):

row_constructor IN (subquery)

---

PostgreSQL: Documentation: 18: 21.1. Database Roles

URL: https://www.postgresql.org/docs/current/database-roles.html

Contents:

• 21.1. Database Roles #

Database roles are conceptually completely separate from operating system users. In practice it might be convenient to maintain a correspondence, but this is not required. Database roles are global across a database cluster installation (and not per individual database). To create a role use the CREATE ROLE SQL command:

name follows the rules for SQL identifiers: either unadorned without special characters, or double-quoted. (In practice, you will usually want to add additional options, such as LOGIN, to the command. More details appear below.) To remove an existing role, use the analogous DROP ROLE command:

For convenience, the programs createuser and dropuser are provided as wrappers around these SQL commands that can be called from the shell command line:

To determine the set of existing roles, examine the pg_roles system catalog, for example:

or to see just those capable of logging in:

The psql program's \du meta-command is also useful for listing the existing roles.

In order to bootstrap the database system, a freshly initialized system always contains one predefined login-capable role. This role is always a “superuser”, and it will have the same name as the operating system user that initialized the database cluster with initdb unless a different name is specified. This role is often named postgres. In order to create more roles you first have to connect as this initial role.

Every connection to the database server is made using the name of some particular role, and this role determines the initial access privileges for commands issued in that connection. The role name to use for a particular database connection is indicated by the client that is initiating the connection request in an application-specific fashion. For example, the psql program uses the -U command line option to indicate the role to connect as. Many applications assume the name of the current operating system user by default (including createuser and psql). Therefore it is often convenient to maintain a naming correspondence between roles and operating system users.

The set of database roles a given client connection can connect as is determined by the client authentication setup, as explained in Chapter 20. (Thus, a client is not limited to connect as the role matching its operating system user, just as a person's login name need not match his or her real name.) Since the role identity determines the set of privileges available to a connected client, it is important to carefully configure privileges when setting up a multiuser environment.

Examples:

Example 1 (unknown):

CREATE ROLE name;

Example 2 (unknown):

DROP ROLE name;

Example 3 (unknown):

createuser name
dropuser name

Example 4 (unknown):

SELECT rolname FROM pg_roles;

---

PostgreSQL: Documentation: 18: 4.1. Lexical Structure

URL: https://www.postgresql.org/docs/current/sql-syntax-lexical.html

Contents:

• 4.1. Lexical Structure #
  • 4.1.1. Identifiers and Key Words #
  • 4.1.2. Constants #
  • 4.1.2.1. String Constants #
  • 4.1.2.2. String Constants with C-Style Escapes #
  • Caution
  • 4.1.2.3. String Constants with Unicode Escapes #
  • 4.1.2.4. Dollar-Quoted String Constants #
  • 4.1.2.5. Bit-String Constants #
  • 4.1.2.6. Numeric Constants #

SQL input consists of a sequence of commands. A command is composed of a sequence of tokens, terminated by a semicolon (“;”). The end of the input stream also terminates a command. Which tokens are valid depends on the syntax of the particular command.

A token can be a key word, an identifier, a quoted identifier, a literal (or constant), or a special character symbol. Tokens are normally separated by whitespace (space, tab, newline), but need not be if there is no ambiguity (which is generally only the case if a special character is adjacent to some other token type).

For example, the following is (syntactically) valid SQL input:

This is a sequence of three commands, one per line (although this is not required; more than one command can be on a line, and commands can usefully be split across lines).

Additionally, comments can occur in SQL input. They are not tokens, they are effectively equivalent to whitespace.

The SQL syntax is not very consistent regarding what tokens identify commands and which are operands or parameters. The first few tokens are generally the command name, so in the above example we would usually speak of a “SELECT”, an “UPDATE”, and an “INSERT” command. But for instance the UPDATE command always requires a SET token to appear in a certain position, and this particular variation of INSERT also requires a VALUES in order to be complete. The precise syntax rules for each command are described in Part VI.

Tokens such as SELECT, UPDATE, or VALUES in the example above are examples of key words, that is, words that have a fixed meaning in the SQL language. The tokens MY_TABLE and A are examples of identifiers. They identify names of tables, columns, or other database objects, depending on the command they are used in. Therefore they are sometimes simply called “names”. Key words and identifiers have the same lexical structure, meaning that one cannot know whether a token is an identifier or a key word without knowing the language. A complete list of key words can be found in Appendix C.

SQL identifiers and key words must begin with a letter (a-z, but also letters with diacritical marks and non-Latin letters) or an underscore (_). Subsequent characters in an identifier or key word can be letters, underscores, digits (0-9), or dollar signs ($). Note that dollar signs are not allowed in identifiers according to the letter of the SQL standard, so their use might render applications less portable. The SQL standard will not define a key word that contains digits or starts or ends with an underscore, so identifiers of this form are safe against possible conflict with future extensions of the standard.

The system uses no more than NAMEDATALEN-1 bytes of an identifier; longer names can be written in commands, but they will be truncated. By default, NAMEDATALEN is 64 so the maximum identifier length is 63 bytes. If this limit is problematic, it can be raised by changing the NAMEDATALEN constant in src/include/pg_config_manual.h.

Key words and unquoted identifiers are case-insensitive. Therefore:

can equivalently be written as:

A convention often used is to write key words in upper case and names in lower case, e.g.:

There is a second kind of identifier: the delimited identifier or quoted identifier. It is formed by enclosing an arbitrary sequence of characters in double-quotes ("). A delimited identifier is always an identifier, never a key word. So "select" could be used to refer to a column or table named “select”, whereas an unquoted select would be taken as a key word and would therefore provoke a parse error when used where a table or column name is expected. The example can be written with quoted identifiers like this:

Quoted identifiers can contain any character, except the character with code zero. (To include a double quote, write two double quotes.) This allows constructing table or column names that would otherwise not be possible, such as ones containing spaces or ampersands. The length limitation still applies.

Quoting an identifier also makes it case-sensitive, whereas unquoted names are always folded to lower case. For example, the identifiers FOO, foo, and "foo" are considered the same by PostgreSQL, but "Foo" and "FOO" are different from these three and each other. (The folding of unquoted names to lower case in PostgreSQL is incompatible with the SQL standard, which says that unquoted names should be folded to upper case. Thus, foo should be equivalent to "FOO" not "foo" according to the standard. If you want to write portable applications you are advised to always quote a particular name or never quote it.)

A variant of quoted identifiers allows including escaped Unicode characters identified by their code points. This variant starts with U& (upper or lower case U followed by ampersand) immediately before the opening double quote, without any spaces in between, for example U&"foo". (Note that this creates an ambiguity with the operator &. Use spaces around the operator to avoid this problem.) Inside the quotes, Unicode characters can be specified in escaped form by writing a backslash followed by the four-digit hexadecimal code point number or alternatively a backslash followed by a plus sign followed by a six-digit hexadecimal code point number. For example, the identifier "data" could be written as

The following less trivial example writes the Russian word “slon” (elephant) in Cyrillic letters:

If a different escape character than backslash is desired, it can be specified using the UESCAPE clause after the string, for example:

The escape character can be any single character other than a hexadecimal digit, the plus sign, a single quote, a double quote, or a whitespace character. Note that the escape character is written in single quotes, not double quotes, after UESCAPE.

To include the escape character in the identifier literally, write it twice.

Either the 4-digit or the 6-digit escape form can be used to specify UTF-16 surrogate pairs to compose characters with code points larger than U+FFFF, although the availability of the 6-digit form technically makes this unnecessary. (Surrogate pairs are not stored directly, but are combined into a single code point.)

If the server encoding is not UTF-8, the Unicode code point identified by one of these escape sequences is converted to the actual server encoding; an error is reported if that's not possible.

There are three kinds of implicitly-typed constants in PostgreSQL: strings, bit strings, and numbers. Constants can also be specified with explicit types, which can enable more accurate representation and more efficient handling by the system. These alternatives are discussed in the following subsections.

A string constant in SQL is an arbitrary sequence of characters bounded by single quotes ('), for example 'This is a string'. To include a single-quote character within a string constant, write two adjacent single quotes, e.g., 'Dianne''s horse'. Note that this is not the same as a double-quote character (").

Two string constants that are only separated by whitespace with at least one newline are concatenated and effectively treated as if the string had been written as one constant. For example:

is not valid syntax. (This slightly bizarre behavior is specified by SQL; PostgreSQL is following the standard.)

PostgreSQL also accepts “escape” string constants, which are an extension to the SQL standard. An escape string constant is specified by writing the letter E (upper or lower case) just before the opening single quote, e.g., E'foo'. (When continuing an escape string constant across lines, write E only before the first opening quote.) Within an escape string, a backslash character () begins a C-like backslash escape sequence, in which the combination of backslash and following character(s) represent a special byte value, as shown in Table 4.1.

Table 4.1. Backslash Escape Sequences

Any other character following a backslash is taken literally. Thus, to include a backslash character, write two backslashes (\). Also, a single quote can be included in an escape string by writing \', in addition to the normal way of ''.

It is your responsibility that the byte sequences you create, especially when using the octal or hexadecimal escapes, compose valid characters in the server character set encoding. A useful alternative is to use Unicode escapes or the alternative Unicode escape syntax, explained in Section 4.1.2.3; then the server will check that the character conversion is possible.

If the configuration parameter standard_conforming_strings is off, then PostgreSQL recognizes backslash escapes in both regular and escape string constants. However, as of PostgreSQL 9.1, the default is on, meaning that backslash escapes are recognized only in escape string constants. This behavior is more standards-compliant, but might break applications which rely on the historical behavior, where backslash escapes were always recognized. As a workaround, you can set this parameter to off, but it is better to migrate away from using backslash escapes. If you need to use a backslash escape to represent a special character, write the string constant with an E.

In addition to standard_conforming_strings, the configuration parameters escape_string_warning and backslash_quote govern treatment of backslashes in string constants.

The character with the code zero cannot be in a string constant.

PostgreSQL also supports another type of escape syntax for strings that allows specifying arbitrary Unicode characters by code point. A Unicode escape string constant starts with U& (upper or lower case letter U followed by ampersand) immediately before the opening quote, without any spaces in between, for example U&'foo'. (Note that this creates an ambiguity with the operator &. Use spaces around the operator to avoid this problem.) Inside the quotes, Unicode characters can be specified in escaped form by writing a backslash followed by the four-digit hexadecimal code point number or alternatively a backslash followed by a plus sign followed by a six-digit hexadecimal code point number. For example, the string 'data' could be written as

The following less trivial example writes the Russian word “slon” (elephant) in Cyrillic letters:

If a different escape character than backslash is desired, it can be specified using the UESCAPE clause after the string, for example:

The escape character can be any single character other than a hexadecimal digit, the plus sign, a single quote, a double quote, or a whitespace character.

To include the escape character in the string literally, write it twice.

Either the 4-digit or the 6-digit escape form can be used to specify UTF-16 surrogate pairs to compose characters with code points larger than U+FFFF, although the availability of the 6-digit form technically makes this unnecessary. (Surrogate pairs are not stored directly, but are combined into a single code point.)

If the server encoding is not UTF-8, the Unicode code point identified by one of these escape sequences is converted to the actual server encoding; an error is reported if that's not possible.

Also, the Unicode escape syntax for string constants only works when the configuration parameter standard_conforming_strings is turned on. This is because otherwise this syntax could confuse clients that parse the SQL statements to the point that it could lead to SQL injections and similar security issues. If the parameter is set to off, this syntax will be rejected with an error message.

While the standard syntax for specifying string constants is usually convenient, it can be difficult to understand when the desired string contains many single quotes, since each of those must be doubled. To allow more readable queries in such situations, PostgreSQL provides another way, called “dollar quoting”, to write string constants. A dollar-quoted string constant consists of a dollar sign ($), an optional “tag” of zero or more characters, another dollar sign, an arbitrary sequence of characters that makes up the string content, a dollar sign, the same tag that began this dollar quote, and a dollar sign. For example, here are two different ways to specify the string “Dianne's horse” using dollar quoting:

Notice that inside the dollar-quoted string, single quotes can be used without needing to be escaped. Indeed, no characters inside a dollar-quoted string are ever escaped: the string content is always written literally. Backslashes are not special, and neither are dollar signs, unless they are part of a sequence matching the opening tag.

It is possible to nest dollar-quoted string constants by choosing different tags at each nesting level. This is most commonly used in writing function definitions. For example:

Here, the sequence $q$[\t\r\n\v\]$q$ represents a dollar-quoted literal string [\t\r\n\v\], which will be recognized when the function body is executed by PostgreSQL. But since the sequence does not match the outer dollar quoting delimiter $function$, it is just some more characters within the constant so far as the outer string is concerned.

The tag, if any, of a dollar-quoted string follows the same rules as an unquoted identifier, except that it cannot contain a dollar sign. Tags are case sensitive, so $tag$String content$tag$ is correct, but $TAG$String content$tag$ is not.

A dollar-quoted string that follows a keyword or identifier must be separated from it by whitespace; otherwise the dollar quoting delimiter would be taken as part of the preceding identifier.

Dollar quoting is not part of the SQL standard, but it is often a more convenient way to write complicated string literals than the standard-compliant single quote syntax. It is particularly useful when representing string constants inside other constants, as is often needed in procedural function definitions. With single-quote syntax, each backslash in the above example would have to be written as four backslashes, which would be reduced to two backslashes in parsing the original string constant, and then to one when the inner string constant is re-parsed during function execution.

Bit-string constants look like regular string constants with a B (upper or lower case) immediately before the opening quote (no intervening whitespace), e.g., B'1001'. The only characters allowed within bit-string constants are 0 and 1.

Alternatively, bit-string constants can be specified in hexadecimal notation, using a leading X (upper or lower case), e.g., X'1FF'. This notation is equivalent to a bit-string constant with four binary digits for each hexadecimal digit.

Both forms of bit-string constant can be continued across lines in the same way as regular string constants. Dollar quoting cannot be used in a bit-string constant.

Numeric constants are accepted in these general forms:

where digits is one or more decimal digits (0 through 9). At least one digit must be before or after the decimal point, if one is used. At least one digit must follow the exponent marker (e), if one is present. There cannot be any spaces or other characters embedded in the constant, except for underscores, which can be used for visual grouping as described below. Note that any leading plus or minus sign is not actually considered part of the constant; it is an operator applied to the constant.

These are some examples of valid numeric constants:

42 3.5 4. .001 5e2 1.925e-3

Additionally, non-decimal integer constants are accepted in these forms:

where hexdigits is one or more hexadecimal digits (0-9, A-F), octdigits is one or more octal digits (0-7), and bindigits is one or more binary digits (0 or 1). Hexadecimal digits and the radix prefixes can be in upper or lower case. Note that only integers can have non-decimal forms, not numbers with fractional parts.

These are some examples of valid non-decimal integer constants:

0b100101 0B10011001 0o273 0O755 0x42f 0XFFFF

For visual grouping, underscores can be inserted between digits. These have no further effect on the value of the constant. For example:

1_500_000_000 0b10001000_00000000 0o_1_755 0xFFFF_FFFF 1.618_034

Underscores are not allowed at the start or end of a numeric constant or a group of digits (that is, immediately before or after the decimal point or the exponent marker), and more than one underscore in a row is not allowed.

A numeric constant that contains neither a decimal point nor an exponent is initially presumed to be type integer if its value fits in type integer (32 bits); otherwise it is presumed to be type bigint if its value fits in type bigint (64 bits); otherwise it is taken to be type numeric. Constants that contain decimal points and/or exponents are always initially presumed to be type numeric.

The initially assigned data type of a numeric constant is just a starting point for the type resolution algorithms. In most cases the constant will be automatically coerced to the most appropriate type depending on context. When necessary, you can force a numeric value to be interpreted as a specific data type by casting it. For example, you can force a numeric value to be treated as type real (float4) by writing:

These are actually just special cases of the general casting notations discussed next.

A constant of an arbitrary type can be entered using any one of the following notations:

The string constant's text is passed to the input conversion routine for the type called type. The result is a constant of the indicated type. The explicit type cast can be omitted if there is no ambiguity as to the type the constant must be (for example, when it is assigned directly to a table column), in which case it is automatically coerced.

The string constant can be written using either regular SQL notation or dollar-quoting.

It is also possible to specify a type coercion using a function-like syntax:

but not all type names can be used in this way; see Section 4.2.9 for details.

The ::, CAST(), and function-call syntaxes can also be used to specify run-time type conversions of arbitrary expressions, as discussed in Section 4.2.9. To avoid syntactic ambiguity, the type 'string' syntax can only be used to specify the type of a simple literal constant. Another restriction on the type 'string' syntax is that it does not work for array types; use :: or CAST() to specify the type of an array constant.

The CAST() syntax conforms to SQL. The type 'string' syntax is a generalization of the standard: SQL specifies this syntax only for a few data types, but PostgreSQL allows it for all types. The syntax with :: is historical PostgreSQL usage, as is the function-call syntax.

An operator name is a sequence of up to NAMEDATALEN-1 (63 by default) characters from the following list:

• - * / < > = ~ ! @ # % ^ & | ` ?

There are a few restrictions on operator names, however:

-- and /* cannot appear anywhere in an operator name, since they will be taken as the start of a comment.

A multiple-character operator name cannot end in + or -, unless the name also contains at least one of these characters:

For example, @- is an allowed operator name, but *- is not. This restriction allows PostgreSQL to parse SQL-compliant queries without requiring spaces between tokens.

When working with non-SQL-standard operator names, you will usually need to separate adjacent operators with spaces to avoid ambiguity. For example, if you have defined a prefix operator named @, you cannot write X@Y; you must write X @Y to ensure that PostgreSQL reads it as two operator names not one.

Some characters that are not alphanumeric have a special meaning that is different from being an operator. Details on the usage can be found at the location where the respective syntax element is described. This section only exists to advise the existence and summarize the purposes of these characters.

A dollar sign ($) followed by digits is used to represent a positional parameter in the body of a function definition or a prepared statement. In other contexts the dollar sign can be part of an identifier or a dollar-quoted string constant.

Parentheses (()) have their usual meaning to group expressions and enforce precedence. In some cases parentheses are required as part of the fixed syntax of a particular SQL command.

Brackets ([]) are used to select the elements of an array. See Section 8.15 for more information on arrays.

Commas (,) are used in some syntactical constructs to separate the elements of a list.

The semicolon (;) terminates an SQL command. It cannot appear anywhere within a command, except within a string constant or quoted identifier.

The colon (:) is used to select “slices” from arrays. (See Section 8.15.) In certain SQL dialects (such as Embedded SQL), the colon is used to prefix variable names.

The asterisk (*) is used in some contexts to denote all the fields of a table row or composite value. It also has a special meaning when used as the argument of an aggregate function, namely that the aggregate does not require any explicit parameter.

The period (.) is used in numeric constants, and to separate schema, table, and column names.

A comment is a sequence of characters beginning with double dashes and extending to the end of the line, e.g.:

Alternatively, C-style block comments can be used:

where the comment begins with /* and extends to the matching occurrence of */. These block comments nest, as specified in the SQL standard but unlike C, so that one can comment out larger blocks of code that might contain existing block comments.

A comment is removed from the input stream before further syntax analysis and is effectively replaced by whitespace.

Table 4.2 shows the precedence and associativity of the operators in PostgreSQL. Most operators have the same precedence and are left-associative. The precedence and associativity of the operators is hard-wired into the parser. Add parentheses if you want an expression with multiple operators to be parsed in some other way than what the precedence rules imply.

Table 4.2. Operator Precedence (highest to lowest)

Note that the operator precedence rules also apply to user-defined operators that have the same names as the built-in operators mentioned above. For example, if you define a “+” operator for some custom data type it will have the same precedence as the built-in “+” operator, no matter what yours does.

When a schema-qualified operator name is used in the OPERATOR syntax, as for example in:

the OPERATOR construct is taken to have the default precedence shown in Table 4.2 for “any other operator”. This is true no matter which specific operator appears inside OPERATOR().

PostgreSQL versions before 9.5 used slightly different operator precedence rules. In particular, <= >= and <> used to be treated as generic operators; IS tests used to have higher priority; and NOT BETWEEN and related constructs acted inconsistently, being taken in some cases as having the precedence of NOT rather than BETWEEN. These rules were changed for better compliance with the SQL standard and to reduce confusion from inconsistent treatment of logically equivalent constructs. In most cases, these changes will result in no behavioral change, or perhaps in “no such operator” failures which can be resolved by adding parentheses. However there are corner cases in which a query might change behavior without any parsing error being reported.

Examples:

Example 1 (unknown):

SELECT * FROM MY_TABLE;
UPDATE MY_TABLE SET A = 5;
INSERT INTO MY_TABLE VALUES (3, 'hi there');

Example 2 (unknown):

UPDATE MY_TABLE SET A = 5;

Example 3 (unknown):

uPDaTE my_TabLE SeT a = 5;

Example 4 (unknown):

UPDATE my_table SET a = 5;

---

PostgreSQL: Documentation: 18: 9.3. Mathematical Functions and Operators

URL: https://www.postgresql.org/docs/current/functions-math.html

Contents:

• 9.3. Mathematical Functions and Operators #
  • Note

Mathematical operators are provided for many PostgreSQL types. For types without standard mathematical conventions (e.g., date/time types) we describe the actual behavior in subsequent sections.

Table 9.4 shows the mathematical operators that are available for the standard numeric types. Unless otherwise noted, operators shown as accepting numeric_type are available for all the types smallint, integer, bigint, numeric, real, and double precision. Operators shown as accepting integral_type are available for the types smallint, integer, and bigint. Except where noted, each form of an operator returns the same data type as its argument(s). Calls involving multiple argument data types, such as integer + numeric, are resolved by using the type appearing later in these lists.

Table 9.4. Mathematical Operators

numeric_type + numeric_type → numeric_type

• numeric_type → numeric_type

Unary plus (no operation)

numeric_type - numeric_type → numeric_type

• numeric_type → numeric_type

numeric_type * numeric_type → numeric_type

numeric_type / numeric_type → numeric_type

Division (for integral types, division truncates the result towards zero)

5.0 / 2 → 2.5000000000000000

numeric_type % numeric_type → numeric_type

Modulo (remainder); available for smallint, integer, bigint, and numeric

numeric ^ numeric → numeric

double precision ^ double precision → double precision

Unlike typical mathematical practice, multiple uses of ^ will associate left to right by default:

2 ^ (3 ^ 3) → 134217728

|/ double precision → double precision

||/ double precision → double precision

@ numeric_type → numeric_type

integral_type & integral_type → integral_type

integral_type | integral_type → integral_type

integral_type # integral_type → integral_type

~ integral_type → integral_type

integral_type << integer → integral_type

integral_type >> integer → integral_type

Table 9.5 shows the available mathematical functions. Many of these functions are provided in multiple forms with different argument types. Except where noted, any given form of a function returns the same data type as its argument(s); cross-type cases are resolved in the same way as explained above for operators. The functions working with double precision data are mostly implemented on top of the host system's C library; accuracy and behavior in boundary cases can therefore vary depending on the host system.

Table 9.5. Mathematical Functions

abs ( numeric_type ) → numeric_type

cbrt ( double precision ) → double precision

ceil ( numeric ) → numeric

ceil ( double precision ) → double precision

Nearest integer greater than or equal to argument

ceiling ( numeric ) → numeric

ceiling ( double precision ) → double precision

Nearest integer greater than or equal to argument (same as ceil)

degrees ( double precision ) → double precision

Converts radians to degrees

degrees(0.5) → 28.64788975654116

div ( y numeric, x numeric ) → numeric

Integer quotient of y/x (truncates towards zero)

erf ( double precision ) → double precision

erf(1.0) → 0.8427007929497149

erfc ( double precision ) → double precision

Complementary error function (1 - erf(x), without loss of precision for large inputs)

erfc(1.0) → 0.15729920705028513

exp ( numeric ) → numeric

exp ( double precision ) → double precision

Exponential (e raised to the given power)

exp(1.0) → 2.7182818284590452

factorial ( bigint ) → numeric

floor ( numeric ) → numeric

floor ( double precision ) → double precision

Nearest integer less than or equal to argument

gamma ( double precision ) → double precision

gamma(0.5) → 1.772453850905516

gcd ( numeric_type, numeric_type ) → numeric_type

Greatest common divisor (the largest positive number that divides both inputs with no remainder); returns 0 if both inputs are zero; available for integer, bigint, and numeric

lcm ( numeric_type, numeric_type ) → numeric_type

Least common multiple (the smallest strictly positive number that is an integral multiple of both inputs); returns 0 if either input is zero; available for integer, bigint, and numeric

lcm(1071, 462) → 23562

lgamma ( double precision ) → double precision

Natural logarithm of the absolute value of the gamma function

lgamma(1000) → 5905.220423209181

ln ( numeric ) → numeric

ln ( double precision ) → double precision

ln(2.0) → 0.6931471805599453

log ( numeric ) → numeric

log ( double precision ) → double precision

log10 ( numeric ) → numeric

log10 ( double precision ) → double precision

Base 10 logarithm (same as log)

log ( b numeric, x numeric ) → numeric

Logarithm of x to base b

log(2.0, 64.0) → 6.0000000000

min_scale ( numeric ) → integer

Minimum scale (number of fractional decimal digits) needed to represent the supplied value precisely

min_scale(8.4100) → 2

mod ( y numeric_type, x numeric_type ) → numeric_type

Remainder of y/x; available for smallint, integer, bigint, and numeric

pi ( ) → double precision

Approximate value of π

pi() → 3.141592653589793

power ( a numeric, b numeric ) → numeric

power ( a double precision, b double precision ) → double precision

a raised to the power of b

radians ( double precision ) → double precision

Converts degrees to radians

radians(45.0) → 0.7853981633974483

round ( numeric ) → numeric

round ( double precision ) → double precision

Rounds to nearest integer. For numeric, ties are broken by rounding away from zero. For double precision, the tie-breaking behavior is platform dependent, but “round to nearest even” is the most common rule.

round ( v numeric, s integer ) → numeric

Rounds v to s decimal places. Ties are broken by rounding away from zero.

round(42.4382, 2) → 42.44

round(1234.56, -1) → 1230

scale ( numeric ) → integer

Scale of the argument (the number of decimal digits in the fractional part)

sign ( numeric ) → numeric

sign ( double precision ) → double precision

Sign of the argument (-1, 0, or +1)

sqrt ( numeric ) → numeric

sqrt ( double precision ) → double precision

sqrt(2) → 1.4142135623730951

trim_scale ( numeric ) → numeric

Reduces the value's scale (number of fractional decimal digits) by removing trailing zeroes

trim_scale(8.4100) → 8.41

trunc ( numeric ) → numeric

trunc ( double precision ) → double precision

Truncates to integer (towards zero)

trunc ( v numeric, s integer ) → numeric

Truncates v to s decimal places

trunc(42.4382, 2) → 42.43

width_bucket ( operand numeric, low numeric, high numeric, count integer ) → integer

width_bucket ( operand double precision, low double precision, high double precision, count integer ) → integer

Returns the number of the bucket in which operand falls in a histogram having count equal-width buckets spanning the range low to high. The buckets have inclusive lower bounds and exclusive upper bounds. Returns 0 for an input less than low, or count+1 for an input greater than or equal to high. If low > high, the behavior is mirror-reversed, with bucket 1 now being the one just below low, and the inclusive bounds now being on the upper side.

width_bucket(5.35, 0.024, 10.06, 5) → 3

width_bucket(9, 10, 0, 10) → 2

width_bucket ( operand anycompatible, thresholds anycompatiblearray ) → integer

Returns the number of the bucket in which operand falls given an array listing the inclusive lower bounds of the buckets. Returns 0 for an input less than the first lower bound. operand and the array elements can be of any type having standard comparison operators. The thresholds array must be sorted, smallest first, or unexpected results will be obtained.

width_bucket(now(), array['yesterday', 'today', 'tomorrow']::timestamptz[]) → 2

Table 9.6 shows functions for generating random numbers.

Table 9.6. Random Functions

random ( ) → double precision

Returns a random value in the range 0.0 <= x < 1.0

random() → 0.897124072839091

random ( min integer, max integer ) → integer

random ( min bigint, max bigint ) → bigint

random ( min numeric, max numeric ) → numeric

Returns a random value in the range min <= x <= max. For type numeric, the result will have the same number of fractional decimal digits as min or max, whichever has more.

random(-0.499, 0.499) → 0.347

random_normal ( [ mean double precision [, stddev double precision ]] ) → double precision

Returns a random value from the normal distribution with the given parameters; mean defaults to 0.0 and stddev defaults to 1.0

random_normal(0.0, 1.0) → 0.051285419

setseed ( double precision ) → void

Sets the seed for subsequent random() and random_normal() calls; argument must be between -1.0 and 1.0, inclusive

The random() and random_normal() functions listed in Table 9.6 use a deterministic pseudo-random number generator. It is fast but not suitable for cryptographic applications; see the pgcrypto module for a more secure alternative. If setseed() is called, the series of results of subsequent calls to these functions in the current session can be repeated by re-issuing setseed() with the same argument. Without any prior setseed() call in the same session, the first call to any of these functions obtains a seed from a platform-dependent source of random bits.

Table 9.7 shows the available trigonometric functions. Each of these functions comes in two variants, one that measures angles in radians and one that measures angles in degrees.

Table 9.7. Trigonometric Functions

acos ( double precision ) → double precision

Inverse cosine, result in radians

acosd ( double precision ) → double precision

Inverse cosine, result in degrees

asin ( double precision ) → double precision

Inverse sine, result in radians

asin(1) → 1.5707963267948966

asind ( double precision ) → double precision

Inverse sine, result in degrees

atan ( double precision ) → double precision

Inverse tangent, result in radians

atan(1) → 0.7853981633974483

atand ( double precision ) → double precision

Inverse tangent, result in degrees

atan2 ( y double precision, x double precision ) → double precision

Inverse tangent of y/x, result in radians

atan2(1, 0) → 1.5707963267948966

atan2d ( y double precision, x double precision ) → double precision

Inverse tangent of y/x, result in degrees

cos ( double precision ) → double precision

Cosine, argument in radians

cosd ( double precision ) → double precision

Cosine, argument in degrees

cot ( double precision ) → double precision

Cotangent, argument in radians

cot(0.5) → 1.830487721712452

cotd ( double precision ) → double precision

Cotangent, argument in degrees

sin ( double precision ) → double precision

Sine, argument in radians

sin(1) → 0.8414709848078965

sind ( double precision ) → double precision

Sine, argument in degrees

tan ( double precision ) → double precision

Tangent, argument in radians

tan(1) → 1.5574077246549023

tand ( double precision ) → double precision

Tangent, argument in degrees

Another way to work with angles measured in degrees is to use the unit transformation functions radians() and degrees() shown earlier. However, using the degree-based trigonometric functions is preferred, as that way avoids round-off error for special cases such as sind(30).

Table 9.8 shows the available hyperbolic functions.

Table 9.8. Hyperbolic Functions

sinh ( double precision ) → double precision

sinh(1) → 1.1752011936438014

cosh ( double precision ) → double precision

tanh ( double precision ) → double precision

tanh(1) → 0.7615941559557649

asinh ( double precision ) → double precision

Inverse hyperbolic sine

asinh(1) → 0.881373587019543

acosh ( double precision ) → double precision

Inverse hyperbolic cosine

atanh ( double precision ) → double precision

Inverse hyperbolic tangent

atanh(0.5) → 0.5493061443340548

---

PostgreSQL: Documentation: 18: 18.3. Starting the Database Server

URL: https://www.postgresql.org/docs/current/server-start.html

Contents:

• 18.3. Starting the Database Server #
  • 18.3.1. Server Start-up Failures #
  • 18.3.2. Client Connection Problems #

Before anyone can access the database, you must start the database server. The database server program is called postgres.

If you are using a pre-packaged version of PostgreSQL, it almost certainly includes provisions for running the server as a background task according to the conventions of your operating system. Using the package's infrastructure to start the server will be much less work than figuring out how to do this yourself. Consult the package-level documentation for details.

The bare-bones way to start the server manually is just to invoke postgres directly, specifying the location of the data directory with the -D option, for example:

which will leave the server running in the foreground. This must be done while logged into the PostgreSQL user account. Without -D, the server will try to use the data directory named by the environment variable PGDATA. If that variable is not provided either, it will fail.

Normally it is better to start postgres in the background. For this, use the usual Unix shell syntax:

It is important to store the server's stdout and stderr output somewhere, as shown above. It will help for auditing purposes and to diagnose problems. (See Section 24.3 for a more thorough discussion of log file handling.)

The postgres program also takes a number of other command-line options. For more information, see the postgres reference page and Chapter 19 below.

This shell syntax can get tedious quickly. Therefore the wrapper program pg_ctl is provided to simplify some tasks. For example:

will start the server in the background and put the output into the named log file. The -D option has the same meaning here as for postgres. pg_ctl is also capable of stopping the server.

Normally, you will want to start the database server when the computer boots. Autostart scripts are operating-system-specific. There are a few example scripts distributed with PostgreSQL in the contrib/start-scripts directory. Installing one will require root privileges.

Different systems have different conventions for starting up daemons at boot time. Many systems have a file /etc/rc.local or /etc/rc.d/rc.local. Others use init.d or rc.d directories. Whatever you do, the server must be run by the PostgreSQL user account and not by root or any other user. Therefore you probably should form your commands using su postgres -c '...'. For example:

Here are a few more operating-system-specific suggestions. (In each case be sure to use the proper installation directory and user name where we show generic values.)

For FreeBSD, look at the file contrib/start-scripts/freebsd in the PostgreSQL source distribution.

On OpenBSD, add the following lines to the file /etc/rc.local:

On Linux systems either add

to /etc/rc.d/rc.local or /etc/rc.local or look at the file contrib/start-scripts/linux in the PostgreSQL source distribution.

When using systemd, you can use the following service unit file (e.g., at /etc/systemd/system/postgresql.service):

Using Type=notify requires that the server binary was built with configure --with-systemd.

Consider carefully the timeout setting. systemd has a default timeout of 90 seconds as of this writing and will kill a process that does not report readiness within that time. But a PostgreSQL server that might have to perform crash recovery at startup could take much longer to become ready. The suggested value of infinity disables the timeout logic.

On NetBSD, use either the FreeBSD or Linux start scripts, depending on preference.

On Solaris, create a file called /etc/init.d/postgresql that contains the following line:

Then, create a symbolic link to it in /etc/rc3.d as S99postgresql.

While the server is running, its PID is stored in the file postmaster.pid in the data directory. This is used to prevent multiple server instances from running in the same data directory and can also be used for shutting down the server.

There are several common reasons the server might fail to start. Check the server's log file, or start it by hand (without redirecting standard output or standard error) and see what error messages appear. Below we explain some of the most common error messages in more detail.

This usually means just what it suggests: you tried to start another server on the same port where one is already running. However, if the kernel error message is not Address already in use or some variant of that, there might be a different problem. For example, trying to start a server on a reserved port number might draw something like:

probably means your kernel's limit on the size of shared memory is smaller than the work area PostgreSQL is trying to create (4011376640 bytes in this example). This is only likely to happen if you have set shared_memory_type to sysv. In that case, you can try starting the server with a smaller-than-normal number of buffers (shared_buffers), or reconfigure your kernel to increase the allowed shared memory size. You might also see this message when trying to start multiple servers on the same machine, if their total space requested exceeds the kernel limit.

does not mean you've run out of disk space. It means your kernel's limit on the number of System V semaphores is smaller than the number PostgreSQL wants to create. As above, you might be able to work around the problem by starting the server with a reduced number of allowed connections (max_connections), but you'll eventually want to increase the kernel limit.

Details about configuring System V IPC facilities are given in Section 18.4.1.

Although the error conditions possible on the client side are quite varied and application-dependent, a few of them might be directly related to how the server was started. Conditions other than those shown below should be documented with the respective client application.

This is the generic “I couldn't find a server to talk to” failure. It looks like the above when TCP/IP communication is attempted. A common mistake is to forget to configure listen_addresses so that the server accepts remote TCP connections.

Alternatively, you might get this when attempting Unix-domain socket communication to a local server:

If the server is indeed running, check that the client's idea of the socket path (here /tmp) agrees with the server's unix_socket_directories setting.

A connection failure message always shows the server address or socket path name, which is useful in verifying that the client is trying to connect to the right place. If there is in fact no server listening there, the kernel error message will typically be either Connection refused or No such file or directory, as illustrated. (It is important to realize that Connection refused in this context does not mean that the server got your connection request and rejected it. That case will produce a different message, as shown in Section 20.16.) Other error messages such as Connection timed out might indicate more fundamental problems, like lack of network connectivity, or a firewall blocking the connection.

Examples:

Example 1 (unknown):

$ postgres -D /usr/local/pgsql/data

Example 2 (unknown):

$ postgres -D /usr/local/pgsql/data >logfile 2>&1 &

Example 3 (unknown):

pg_ctl start -l logfile

Example 4 (unknown):

su postgres -c 'pg_ctl start -D /usr/local/pgsql/data -l serverlog'

---

PostgreSQL: Documentation: 18: Appendix A. PostgreSQL Error Codes

URL: https://www.postgresql.org/docs/current/errcodes-appendix.html

Contents:

• Appendix A. PostgreSQL Error Codes

All messages emitted by the PostgreSQL server are assigned five-character error codes that follow the SQL standard's conventions for “SQLSTATE” codes. Applications that need to know which error condition has occurred should usually test the error code, rather than looking at the textual error message. The error codes are less likely to change across PostgreSQL releases, and also are not subject to change due to localization of error messages. Note that some, but not all, of the error codes produced by PostgreSQL are defined by the SQL standard; some additional error codes for conditions not defined by the standard have been invented or borrowed from other databases.

According to the standard, the first two characters of an error code denote a class of errors, while the last three characters indicate a specific condition within that class. Thus, an application that does not recognize the specific error code might still be able to infer what to do from the error class.

Table A.1 lists all the error codes defined in PostgreSQL 18.0. (Some are not actually used at present, but are defined by the SQL standard.) The error classes are also shown. For each error class there is a “standard” error code having the last three characters 000. This code is used only for error conditions that fall within the class but do not have any more-specific code assigned.

The symbol shown in the column “Condition Name” is the condition name to use in PL/pgSQL. Condition names can be written in either upper or lower case. (Note that PL/pgSQL does not recognize warning, as opposed to error, condition names; those are classes 00, 01, and 02.)

For some types of errors, the server reports the name of a database object (a table, table column, data type, or constraint) associated with the error; for example, the name of the unique constraint that caused a unique_violation error. Such names are supplied in separate fields of the error report message so that applications need not try to extract them from the possibly-localized human-readable text of the message. As of PostgreSQL 9.3, complete coverage for this feature exists only for errors in SQLSTATE class 23 (integrity constraint violation), but this is likely to be expanded in future.

Table A.1. PostgreSQL Error Codes

---

PostgreSQL: Documentation: 18: 15.4. Parallel Safety

URL: https://www.postgresql.org/docs/current/parallel-safety.html

Contents:

• 15.4. Parallel Safety #
  • 15.4.1. Parallel Labeling for Functions and Aggregates #

The planner classifies operations involved in a query as either parallel safe, parallel restricted, or parallel unsafe. A parallel safe operation is one that does not conflict with the use of parallel query. A parallel restricted operation is one that cannot be performed in a parallel worker, but that can be performed in the leader while parallel query is in use. Therefore, parallel restricted operations can never occur below a Gather or Gather Merge node, but can occur elsewhere in a plan that contains such a node. A parallel unsafe operation is one that cannot be performed while parallel query is in use, not even in the leader. When a query contains anything that is parallel unsafe, parallel query is completely disabled for that query.

The following operations are always parallel restricted:

Scans of common table expressions (CTEs).

Scans of temporary tables.

Scans of foreign tables, unless the foreign data wrapper has an IsForeignScanParallelSafe API that indicates otherwise.

Plan nodes that reference a correlated SubPlan.

The planner cannot automatically determine whether a user-defined function or aggregate is parallel safe, parallel restricted, or parallel unsafe, because this would require predicting every operation that the function could possibly perform. In general, this is equivalent to the Halting Problem and therefore impossible. Even for simple functions where it could conceivably be done, we do not try, since this would be expensive and error-prone. Instead, all user-defined functions are assumed to be parallel unsafe unless otherwise marked. When using CREATE FUNCTION or ALTER FUNCTION, markings can be set by specifying PARALLEL SAFE, PARALLEL RESTRICTED, or PARALLEL UNSAFE as appropriate. When using CREATE AGGREGATE, the PARALLEL option can be specified with SAFE, RESTRICTED, or UNSAFE as the corresponding value.

Functions and aggregates must be marked PARALLEL UNSAFE if they write to the database, change the transaction state (other than by using a subtransaction for error recovery), access sequences, or make persistent changes to settings. Similarly, functions must be marked PARALLEL RESTRICTED if they access temporary tables, client connection state, cursors, prepared statements, or miscellaneous backend-local state that the system cannot synchronize across workers. For example, setseed and random are parallel restricted for this last reason.

In general, if a function is labeled as being safe when it is restricted or unsafe, or if it is labeled as being restricted when it is in fact unsafe, it may throw errors or produce wrong answers when used in a parallel query. C-language functions could in theory exhibit totally undefined behavior if mislabeled, since there is no way for the system to protect itself against arbitrary C code, but in most likely cases the result will be no worse than for any other function. If in doubt, it is probably best to label functions as UNSAFE.

If a function executed within a parallel worker acquires locks that are not held by the leader, for example by querying a table not referenced in the query, those locks will be released at worker exit, not end of transaction. If you write a function that does this, and this behavior difference is important to you, mark such functions as PARALLEL RESTRICTED to ensure that they execute only in the leader.

Note that the query planner does not consider deferring the evaluation of parallel-restricted functions or aggregates involved in the query in order to obtain a superior plan. So, for example, if a WHERE clause applied to a particular table is parallel restricted, the query planner will not consider performing a scan of that table in the parallel portion of a plan. In some cases, it would be possible (and perhaps even efficient) to include the scan of that table in the parallel portion of the query and defer the evaluation of the WHERE clause so that it happens above the Gather node. However, the planner does not do this.

---

PostgreSQL: Documentation: 18: 29.11. Security

URL: https://www.postgresql.org/docs/current/logical-replication-security.html

Contents:

• 29.11. Security #

The role used for the replication connection must have the REPLICATION attribute (or be a superuser). If the role lacks SUPERUSER and BYPASSRLS, publisher row security policies can execute. If the role does not trust all table owners, include options=-crow_security=off in the connection string; if a table owner then adds a row security policy, that setting will cause replication to halt rather than execute the policy. Access for the role must be configured in pg_hba.conf and it must have the LOGIN attribute.

In order to be able to copy the initial table data, the role used for the replication connection must have the SELECT privilege on a published table (or be a superuser).

To create a publication, the user must have the CREATE privilege in the database.

To add tables to a publication, the user must have ownership rights on the table. To add all tables in schema to a publication, the user must be a superuser. To create a publication that publishes all tables or all tables in schema automatically, the user must be a superuser.

There are currently no privileges on publications. Any subscription (that is able to connect) can access any publication. Thus, if you intend to hide some information from particular subscribers, such as by using row filters or column lists, or by not adding the whole table to the publication, be aware that other publications in the same database could expose the same information. Publication privileges might be added to PostgreSQL in the future to allow for finer-grained access control.

To create a subscription, the user must have the privileges of the pg_create_subscription role, as well as CREATE privileges on the database.

The subscription apply process will, at a session level, run with the privileges of the subscription owner. However, when performing an insert, update, delete, or truncate operation on a particular table, it will switch roles to the table owner and perform the operation with the table owner's privileges. This means that the subscription owner needs to be able to SET ROLE to each role that owns a replicated table.

If the subscription has been configured with run_as_owner = true, then no user switching will occur. Instead, all operations will be performed with the permissions of the subscription owner. In this case, the subscription owner only needs privileges to SELECT, INSERT, UPDATE, and DELETE from the target table, and does not need privileges to SET ROLE to the table owner. However, this also means that any user who owns a table into which replication is happening can execute arbitrary code with the privileges of the subscription owner. For example, they could do this by simply attaching a trigger to one of the tables which they own. Because it is usually undesirable to allow one role to freely assume the privileges of another, this option should be avoided unless user security within the database is of no concern.

On the publisher, privileges are only checked once at the start of a replication connection and are not re-checked as each change record is read.

On the subscriber, the subscription owner's privileges are re-checked for each transaction when applied. If a worker is in the process of applying a transaction when the ownership of the subscription is changed by a concurrent transaction, the application of the current transaction will continue under the old owner's privileges.

---

PostgreSQL: Documentation: 18: Part VI. Reference

URL: https://www.postgresql.org/docs/current/reference.html

Contents:

• Part VI. Reference

The entries in this Reference are meant to provide in reasonable length an authoritative, complete, and formal summary about their respective subjects. More information about the use of PostgreSQL, in narrative, tutorial, or example form, can be found in other parts of this book. See the cross-references listed on each reference page.

The reference entries are also available as traditional “man” pages.

---

PostgreSQL: Documentation: 18: 20.3. Authentication Methods

URL: https://www.postgresql.org/docs/current/auth-methods.html

Contents:

• 20.3. Authentication Methods #

PostgreSQL provides various methods for authenticating users:

Trust authentication, which simply trusts that users are who they say they are.

Password authentication, which requires that users send a password.

GSSAPI authentication, which relies on a GSSAPI-compatible security library. Typically this is used to access an authentication server such as a Kerberos or Microsoft Active Directory server.

SSPI authentication, which uses a Windows-specific protocol similar to GSSAPI.

Ident authentication, which relies on an “Identification Protocol” (RFC 1413) service on the client's machine. (On local Unix-socket connections, this is treated as peer authentication.)

Peer authentication, which relies on operating system facilities to identify the process at the other end of a local connection. This is not supported for remote connections.

LDAP authentication, which relies on an LDAP authentication server.

RADIUS authentication, which relies on a RADIUS authentication server.

Certificate authentication, which requires an SSL connection and authenticates users by checking the SSL certificate they send.

PAM authentication, which relies on a PAM (Pluggable Authentication Modules) library.

BSD authentication, which relies on the BSD Authentication framework (currently available only on OpenBSD).

OAuth authorization/authentication, which relies on an external OAuth 2.0 identity provider.

Peer authentication is usually recommendable for local connections, though trust authentication might be sufficient in some circumstances. Password authentication is the easiest choice for remote connections. All the other options require some kind of external security infrastructure (usually an authentication server or a certificate authority for issuing SSL certificates), or are platform-specific.

The following sections describe each of these authentication methods in more detail.

---

PostgreSQL: Documentation: 18: Chapter 59. Writing a Table Sampling Method

URL: https://www.postgresql.org/docs/current/tablesample-method.html

Contents:

• Chapter 59. Writing a Table Sampling Method

PostgreSQL's implementation of the TABLESAMPLE clause supports custom table sampling methods, in addition to the BERNOULLI and SYSTEM methods that are required by the SQL standard. The sampling method determines which rows of the table will be selected when the TABLESAMPLE clause is used.

At the SQL level, a table sampling method is represented by a single SQL function, typically implemented in C, having the signature

The name of the function is the same method name appearing in the TABLESAMPLE clause. The internal argument is a dummy (always having value zero) that simply serves to prevent this function from being called directly from an SQL command. The result of the function must be a palloc'd struct of type TsmRoutine, which contains pointers to support functions for the sampling method. These support functions are plain C functions and are not visible or callable at the SQL level. The support functions are described in Section 59.1.

In addition to function pointers, the TsmRoutine struct must provide these additional fields:

This is an OID list containing the data type OIDs of the parameter(s) that will be accepted by the TABLESAMPLE clause when this sampling method is used. For example, for the built-in methods, this list contains a single item with value FLOAT4OID, which represents the sampling percentage. Custom sampling methods can have more or different parameters.

If true, the sampling method can deliver identical samples across successive queries, if the same parameters and REPEATABLE seed value are supplied each time and the table contents have not changed. When this is false, the REPEATABLE clause is not accepted for use with the sampling method.

If true, the sampling method can deliver identical samples across successive scans in the same query (assuming unchanging parameters, seed value, and snapshot). When this is false, the planner will not select plans that would require scanning the sampled table more than once, since that might result in inconsistent query output.

The TsmRoutine struct type is declared in src/include/access/tsmapi.h, which see for additional details.

The table sampling methods included in the standard distribution are good references when trying to write your own. Look into the src/backend/access/tablesample subdirectory of the source tree for the built-in sampling methods, and into the contrib subdirectory for add-on methods.

Examples:

Example 1 (unknown):

method_name(internal) RETURNS tsm_handler

---

PostgreSQL: Documentation: 18: 31.3. Variant Comparison Files

URL: https://www.postgresql.org/docs/current/regress-variant.html

Contents:

• 31.3. Variant Comparison Files #

Since some of the tests inherently produce environment-dependent results, we have provided ways to specify alternate “expected” result files. Each regression test can have several comparison files showing possible results on different platforms. There are two independent mechanisms for determining which comparison file is used for each test.

The first mechanism allows comparison files to be selected for specific platforms. There is a mapping file, src/test/regress/resultmap, that defines which comparison file to use for each platform. To eliminate bogus test “failures” for a particular platform, you first choose or make a variant result file, and then add a line to the resultmap file.

Each line in the mapping file is of the form

The test name is just the name of the particular regression test module. The output value indicates which output file to check. For the standard regression tests, this is always out. The value corresponds to the file extension of the output file. The platform pattern is a pattern in the style of the Unix tool expr (that is, a regular expression with an implicit ^ anchor at the start). It is matched against the platform name as printed by config.guess. The comparison file name is the base name of the substitute result comparison file.

For example: some systems lack a working strtof function, for which our workaround causes rounding errors in the float4 regression test. Therefore, we provide a variant comparison file, float4-misrounded-input.out, which includes the results to be expected on these systems. To silence the bogus “failure” message on Cygwin platforms, resultmap includes:

which will trigger on any machine where the output of config.guess matches .-.-cygwin.*. Other lines in resultmap select the variant comparison file for other platforms where it's appropriate.

The second selection mechanism for variant comparison files is much more automatic: it simply uses the “best match” among several supplied comparison files. The regression test driver script considers both the standard comparison file for a test, testname.out, and variant files named testname_digit.out (where the digit is any single digit 0-9). If any such file is an exact match, the test is considered to pass; otherwise, the one that generates the shortest diff is used to create the failure report. (If resultmap includes an entry for the particular test, then the base testname is the substitute name given in resultmap.)

For example, for the char test, the comparison file char.out contains results that are expected in the C and POSIX locales, while the file char_1.out contains results sorted as they appear in many other locales.

The best-match mechanism was devised to cope with locale-dependent results, but it can be used in any situation where the test results cannot be predicted easily from the platform name alone. A limitation of this mechanism is that the test driver cannot tell which variant is actually “correct” for the current environment; it will just pick the variant that seems to work best. Therefore it is safest to use this mechanism only for variant results that you are willing to consider equally valid in all contexts.

Examples:

Example 1 (unknown):

testname:output:platformpattern=comparisonfilename

Example 2 (unknown):

float4:out:.*-.*-cygwin.*=float4-misrounded-input.out

---

PostgreSQL: Documentation: 18: 29.3. Logical Replication Failover

URL: https://www.postgresql.org/docs/current/logical-replication-failover.html

Contents:

• 29.3. Logical Replication Failover #

To allow subscriber nodes to continue replicating data from the publisher node even when the publisher node goes down, there must be a physical standby corresponding to the publisher node. The logical slots on the primary server corresponding to the subscriptions can be synchronized to the standby server by specifying failover = true when creating subscriptions. See Section 47.2.3 for details. Enabling the failover parameter ensures a seamless transition of those subscriptions after the standby is promoted. They can continue subscribing to publications on the new primary server.

Because the slot synchronization logic copies asynchronously, it is necessary to confirm that replication slots have been synced to the standby server before the failover happens. To ensure a successful failover, the standby server must be ahead of the subscriber. This can be achieved by configuring synchronized_standby_slots.

To confirm that the standby server is indeed ready for failover for a given subscriber, follow these steps to verify that all the logical replication slots required by that subscriber have been synchronized to the standby server:

On the subscriber node, use the following SQL to identify which replication slots should be synced to the standby that we plan to promote. This query will return the relevant replication slots associated with the failover-enabled subscriptions.

On the subscriber node, use the following SQL to identify which table synchronization slots should be synced to the standby that we plan to promote. This query needs to be run on each database that includes the failover-enabled subscription(s). Note that the table sync slot should be synced to the standby server only if the table copy is finished (See Section 52.55). We don't need to ensure that the table sync slots are synced in other scenarios as they will either be dropped or re-created on the new primary server in those cases.

Check that the logical replication slots identified above exist on the standby server and are ready for failover.

If all the slots are present on the standby server and the result (failover_ready) of the above SQL query is true, then existing subscriptions can continue subscribing to publications on the new primary server.

The first two steps in the above procedure are meant for a PostgreSQL subscriber. It is recommended to run these steps on each subscriber node, that will be served by the designated standby after failover, to obtain the complete list of replication slots. This list can then be verified in Step 3 to ensure failover readiness. Non-PostgreSQL subscribers, on the other hand, may use their own methods to identify the replication slots used by their respective subscriptions.

In some cases, such as during a planned failover, it is necessary to confirm that all subscribers, whether PostgreSQL or non-PostgreSQL, will be able to continue replication after failover to a given standby server. In such cases, use the following SQL, instead of performing the first two steps above, to identify which replication slots on the primary need to be synced to the standby that is intended for promotion. This query returns the relevant replication slots associated with all the failover-enabled subscriptions.

Examples:

Example 1 (unknown):

/* sub # */ SELECT
               array_agg(quote_literal(s.subslotname)) AS slots
           FROM  pg_subscription s
           WHERE s.subfailover AND
                 s.subslotname IS NOT NULL;
 slots
-------
 {'sub1','sub2','sub3'}
(1 row)

Example 2 (unknown):

/* sub # */ SELECT
               array_agg(quote_literal(slot_name)) AS slots
           FROM
           (
               SELECT CONCAT('pg_', srsubid, '_sync_', srrelid, '_', ctl.system_identifier) AS slot_name
               FROM pg_control_system() ctl, pg_subscription_rel r, pg_subscription s
               WHERE r.srsubstate = 'f' AND s.oid = r.srsubid AND s.subfailover
           );
 slots
-------
 {'pg_16394_sync_16385_7394666715149055164'}
(1 row)

Example 3 (unknown):

/* standby # */ SELECT slot_name, (synced AND NOT temporary AND invalidation_reason IS NULL) AS failover_ready
               FROM pg_replication_slots
               WHERE slot_name IN
                   ('sub1','sub2','sub3', 'pg_16394_sync_16385_7394666715149055164');
  slot_name                                 | failover_ready
--------------------------------------------+----------------
  sub1                                      | t
  sub2                                      | t
  sub3                                      | t
  pg_16394_sync_16385_7394666715149055164   | t
(4 rows)

Example 4 (unknown):

/* primary # */ SELECT array_agg(quote_literal(r.slot_name)) AS slots
               FROM pg_replication_slots r
               WHERE r.failover AND NOT r.temporary;
 slots
-------
 {'sub1','sub2','sub3', 'pg_16394_sync_16385_7394666715149055164'}
(1 row)

---

PostgreSQL: Documentation: 18: 8.12. UUID Type

URL: https://www.postgresql.org/docs/current/datatype-uuid.html

Contents:

• 8.12. UUID Type #

The data type uuid stores Universally Unique Identifiers (UUID) as defined by RFC 9562, ISO/IEC 9834-8:2005, and related standards. (Some systems refer to this data type as a globally unique identifier, or GUID, instead.) This identifier is a 128-bit quantity that is generated by an algorithm chosen to make it very unlikely that the same identifier will be generated by anyone else in the known universe using the same algorithm. Therefore, for distributed systems, these identifiers provide a better uniqueness guarantee than sequence generators, which are only unique within a single database.

RFC 9562 defines 8 different UUID versions. Each version has specific requirements for generating new UUID values, and each version provides distinct benefits and drawbacks. PostgreSQL provides native support for generating UUIDs using the UUIDv4 and UUIDv7 algorithms. Alternatively, UUID values can be generated outside of the database using any algorithm. The data type uuid can be used to store any UUID, regardless of the origin and the UUID version.

A UUID is written as a sequence of lower-case hexadecimal digits, in several groups separated by hyphens, specifically a group of 8 digits followed by three groups of 4 digits followed by a group of 12 digits, for a total of 32 digits representing the 128 bits. An example of a UUID in this standard form is:

PostgreSQL also accepts the following alternative forms for input: use of upper-case digits, the standard format surrounded by braces, omitting some or all hyphens, adding a hyphen after any group of four digits. Examples are:

Output is always in the standard form.

See Section 9.14 for how to generate a UUID in PostgreSQL.

Examples:

Example 1 (unknown):

a0eebc99-9c0b-4ef8-bb6d-6bb9bd380a11

Example 2 (unknown):

A0EEBC99-9C0B-4EF8-BB6D-6BB9BD380A11
{a0eebc99-9c0b-4ef8-bb6d-6bb9bd380a11}
a0eebc999c0b4ef8bb6d6bb9bd380a11
a0ee-bc99-9c0b-4ef8-bb6d-6bb9-bd38-0a11
{a0eebc99-9c0b4ef8-bb6d6bb9-bd380a11}

---

PostgreSQL: Documentation: 18: 34.4. Using Host Variables

URL: https://www.postgresql.org/docs/current/ecpg-variables.html

Contents:

• 34.4. Using Host Variables #
  • 34.4.1. Overview #
  • 34.4.2. Declare Sections #
  • 34.4.3. Retrieving Query Results #
  • 34.4.4. Type Mapping #
  • 34.4.4.1. Handling Character Strings #
  • 34.4.4.2. Accessing Special Data Types #
    • 34.4.4.2.1. timestamp, date #
    • 34.4.4.2.2. interval #
    • 34.4.4.2.3. numeric, decimal #

In Section 34.3 you saw how you can execute SQL statements from an embedded SQL program. Some of those statements only used fixed values and did not provide a way to insert user-supplied values into statements or have the program process the values returned by the query. Those kinds of statements are not really useful in real applications. This section explains in detail how you can pass data between your C program and the embedded SQL statements using a simple mechanism called host variables. In an embedded SQL program we consider the SQL statements to be guests in the C program code which is the host language. Therefore the variables of the C program are called host variables.

Another way to exchange values between PostgreSQL backends and ECPG applications is the use of SQL descriptors, described in Section 34.7.

Passing data between the C program and the SQL statements is particularly simple in embedded SQL. Instead of having the program paste the data into the statement, which entails various complications, such as properly quoting the value, you can simply write the name of a C variable into the SQL statement, prefixed by a colon. For example:

This statement refers to two C variables named v1 and v2 and also uses a regular SQL string literal, to illustrate that you are not restricted to use one kind of data or the other.

This style of inserting C variables in SQL statements works anywhere a value expression is expected in an SQL statement.

To pass data from the program to the database, for example as parameters in a query, or to pass data from the database back to the program, the C variables that are intended to contain this data need to be declared in specially marked sections, so the embedded SQL preprocessor is made aware of them.

This section starts with:

Between those lines, there must be normal C variable declarations, such as:

As you can see, you can optionally assign an initial value to the variable. The variable's scope is determined by the location of its declaring section within the program. You can also declare variables with the following syntax which implicitly creates a declare section:

You can have as many declare sections in a program as you like.

The declarations are also echoed to the output file as normal C variables, so there's no need to declare them again. Variables that are not intended to be used in SQL commands can be declared normally outside these special sections.

The definition of a structure or union also must be listed inside a DECLARE section. Otherwise the preprocessor cannot handle these types since it does not know the definition.

Now you should be able to pass data generated by your program into an SQL command. But how do you retrieve the results of a query? For that purpose, embedded SQL provides special variants of the usual commands SELECT and FETCH. These commands have a special INTO clause that specifies which host variables the retrieved values are to be stored in. SELECT is used for a query that returns only single row, and FETCH is used for a query that returns multiple rows, using a cursor.

So the INTO clause appears between the select list and the FROM clause. The number of elements in the select list and the list after INTO (also called the target list) must be equal.

Here is an example using the command FETCH:

Here the INTO clause appears after all the normal clauses.

When ECPG applications exchange values between the PostgreSQL server and the C application, such as when retrieving query results from the server or executing SQL statements with input parameters, the values need to be converted between PostgreSQL data types and host language variable types (C language data types, concretely). One of the main points of ECPG is that it takes care of this automatically in most cases.

In this respect, there are two kinds of data types: Some simple PostgreSQL data types, such as integer and text, can be read and written by the application directly. Other PostgreSQL data types, such as timestamp and numeric can only be accessed through special library functions; see Section 34.4.4.2.

Table 34.1 shows which PostgreSQL data types correspond to which C data types. When you wish to send or receive a value of a given PostgreSQL data type, you should declare a C variable of the corresponding C data type in the declare section.

Table 34.1. Mapping Between PostgreSQL Data Types and C Variable Types

[a] This type can only be accessed through special library functions; see Section 34.4.4.2.

[b] declared in ecpglib.h if not native

To handle SQL character string data types, such as varchar and text, there are two possible ways to declare the host variables.

One way is using char[], an array of char, which is the most common way to handle character data in C.

Note that you have to take care of the length yourself. If you use this host variable as the target variable of a query which returns a string with more than 49 characters, a buffer overflow occurs.

The other way is using the VARCHAR type, which is a special type provided by ECPG. The definition on an array of type VARCHAR is converted into a named struct for every variable. A declaration like:

The member arr hosts the string including a terminating zero byte. Thus, to store a string in a VARCHAR host variable, the host variable has to be declared with the length including the zero byte terminator. The member len holds the length of the string stored in the arr without the terminating zero byte. When a host variable is used as input for a query, if strlen(arr) and len are different, the shorter one is used.

VARCHAR can be written in upper or lower case, but not in mixed case.

char and VARCHAR host variables can also hold values of other SQL types, which will be stored in their string forms.

ECPG contains some special types that help you to interact easily with some special data types from the PostgreSQL server. In particular, it has implemented support for the numeric, decimal, date, timestamp, and interval types. These data types cannot usefully be mapped to primitive host variable types (such as int, long long int, or char[]), because they have a complex internal structure. Applications deal with these types by declaring host variables in special types and accessing them using functions in the pgtypes library. The pgtypes library, described in detail in Section 34.6 contains basic functions to deal with those types, such that you do not need to send a query to the SQL server just for adding an interval to a time stamp for example.

The follow subsections describe these special data types. For more details about pgtypes library functions, see Section 34.6.

Here is a pattern for handling timestamp variables in the ECPG host application.

First, the program has to include the header file for the timestamp type:

Next, declare a host variable as type timestamp in the declare section:

And after reading a value into the host variable, process it using pgtypes library functions. In following example, the timestamp value is converted into text (ASCII) form with the PGTYPEStimestamp_to_asc() function:

This example will show some result like following:

In addition, the DATE type can be handled in the same way. The program has to include pgtypes_date.h, declare a host variable as the date type and convert a DATE value into a text form using PGTYPESdate_to_asc() function. For more details about the pgtypes library functions, see Section 34.6.

The handling of the interval type is also similar to the timestamp and date types. It is required, however, to allocate memory for an interval type value explicitly. In other words, the memory space for the variable has to be allocated in the heap memory, not in the stack memory.

Here is an example program:

The handling of the numeric and decimal types is similar to the interval type: It requires defining a pointer, allocating some memory space on the heap, and accessing the variable using the pgtypes library functions. For more details about the pgtypes library functions, see Section 34.6.

No functions are provided specifically for the decimal type. An application has to convert it to a numeric variable using a pgtypes library function to do further processing.

Here is an example program handling numeric and decimal type variables.

The handling of the bytea type is similar to that of VARCHAR. The definition on an array of type bytea is converted into a named struct for every variable. A declaration like:

The member arr hosts binary format data. It can also handle '\0' as part of data, unlike VARCHAR. The data is converted from/to hex format and sent/received by ecpglib.

bytea variable can be used only when bytea_output is set to hex.

As a host variable you can also use arrays, typedefs, structs, and pointers.

There are two use cases for arrays as host variables. The first is a way to store some text string in char[] or VARCHAR[], as explained in Section 34.4.4.1. The second use case is to retrieve multiple rows from a query result without using a cursor. Without an array, to process a query result consisting of multiple rows, it is required to use a cursor and the FETCH command. But with array host variables, multiple rows can be received at once. The length of the array has to be defined to be able to accommodate all rows, otherwise a buffer overflow will likely occur.

Following example scans the pg_database system table and shows all OIDs and names of the available databases:

This example shows following result. (The exact values depend on local circumstances.)

A structure whose member names match the column names of a query result, can be used to retrieve multiple columns at once. The structure enables handling multiple column values in a single host variable.

The following example retrieves OIDs, names, and sizes of the available databases from the pg_database system table and using the pg_database_size() function. In this example, a structure variable dbinfo_t with members whose names match each column in the SELECT result is used to retrieve one result row without putting multiple host variables in the FETCH statement.

This example shows following result. (The exact values depend on local circumstances.)

Structure host variables “absorb” as many columns as the structure as fields. Additional columns can be assigned to other host variables. For example, the above program could also be restructured like this, with the size variable outside the structure:

Use the typedef keyword to map new types to already existing types.

Note that you could also use:

This declaration does not need to be part of a declare section; that is, you can also write typedefs as normal C statements.

Any word you declare as a typedef cannot be used as an SQL keyword in EXEC SQL commands later in the same program. For example, this won't work:

ECPG will report a syntax error for START TRANSACTION, because it no longer recognizes START as an SQL keyword, only as a typedef. (If you have such a conflict, and renaming the typedef seems impractical, you could write the SQL command using dynamic SQL.)

In PostgreSQL releases before v16, use of SQL keywords as typedef names was likely to result in syntax errors associated with use of the typedef itself, rather than use of the name as an SQL keyword. The new behavior is less likely to cause problems when an existing ECPG application is recompiled in a new PostgreSQL release with new keywords.

You can declare pointers to the most common types. Note however that you cannot use pointers as target variables of queries without auto-allocation. See Section 34.7 for more information on auto-allocation.

This section contains information on how to handle nonscalar and user-defined SQL-level data types in ECPG applications. Note that this is distinct from the handling of host variables of nonprimitive types, described in the previous section.

Multi-dimensional SQL-level arrays are not directly supported in ECPG. One-dimensional SQL-level arrays can be mapped into C array host variables and vice-versa. However, when creating a statement ecpg does not know the types of the columns, so that it cannot check if a C array is input into a corresponding SQL-level array. When processing the output of an SQL statement, ecpg has the necessary information and thus checks if both are arrays.

If a query accesses elements of an array separately, then this avoids the use of arrays in ECPG. Then, a host variable with a type that can be mapped to the element type should be used. For example, if a column type is array of integer, a host variable of type int can be used. Also if the element type is varchar or text, a host variable of type char[] or VARCHAR[] can be used.

Here is an example. Assume the following table:

The following example program retrieves the 4th element of the array and stores it into a host variable of type int:

This example shows the following result:

To map multiple array elements to the multiple elements in an array type host variables each element of array column and each element of the host variable array have to be managed separately, for example:

would not work correctly in this case, because you cannot map an array type column to an array host variable directly.

Another workaround is to store arrays in their external string representation in host variables of type char[] or VARCHAR[]. For more details about this representation, see Section 8.15.2. Note that this means that the array cannot be accessed naturally as an array in the host program (without further processing that parses the text representation).

Composite types are not directly supported in ECPG, but an easy workaround is possible. The available workarounds are similar to the ones described for arrays above: Either access each attribute separately or use the external string representation.

For the following examples, assume the following type and table:

The most obvious solution is to access each attribute separately. The following program retrieves data from the example table by selecting each attribute of the type comp_t separately:

To enhance this example, the host variables to store values in the FETCH command can be gathered into one structure. For more details about the host variable in the structure form, see Section 34.4.4.3.2. To switch to the structure, the example can be modified as below. The two host variables, intval and textval, become members of the comp_t structure, and the structure is specified on the FETCH command.

Although a structure is used in the FETCH command, the attribute names in the SELECT clause are specified one by one. This can be enhanced by using a * to ask for all attributes of the composite type value.

This way, composite types can be mapped into structures almost seamlessly, even though ECPG does not understand the composite type itself.

Finally, it is also possible to store composite type values in their external string representation in host variables of type char[] or VARCHAR[]. But that way, it is not easily possible to access the fields of the value from the host program.

New user-defined base types are not directly supported by ECPG. You can use the external string representation and host variables of type char[] or VARCHAR[], and this solution is indeed appropriate and sufficient for many types.

Here is an example using the data type complex from the example in Section 36.13. The external string representation of that type is (%f,%f), which is defined in the functions complex_in() and complex_out() functions in Section 36.13. The following example inserts the complex type values (1,1) and (3,3) into the columns a and b, and select them from the table after that.

This example shows following result:

Another workaround is avoiding the direct use of the user-defined types in ECPG and instead create a function or cast that converts between the user-defined type and a primitive type that ECPG can handle. Note, however, that type casts, especially implicit ones, should be introduced into the type system very carefully.

After this definition, the following

has the same effect as

The examples above do not handle null values. In fact, the retrieval examples will raise an error if they fetch a null value from the database. To be able to pass null values to the database or retrieve null values from the database, you need to append a second host variable specification to each host variable that contains data. This second host variable is called the indicator and contains a flag that tells whether the datum is null, in which case the value of the real host variable is ignored. Here is an example that handles the retrieval of null values correctly:

The indicator variable val_ind will be zero if the value was not null, and it will be negative if the value was null. (See Section 34.16 to enable Oracle-specific behavior.)

The indicator has another function: if the indicator value is positive, it means that the value is not null, but it was truncated when it was stored in the host variable.

If the argument -r no_indicator is passed to the preprocessor ecpg, it works in “no-indicator” mode. In no-indicator mode, if no indicator variable is specified, null values are signaled (on input and output) for character string types as empty string and for integer types as the lowest possible value for type (for example, INT_MIN for int).

Examples:

Example 1 (unknown):

EXEC SQL INSERT INTO sometable VALUES (:v1, 'foo', :v2);

Example 2 (unknown):

EXEC SQL BEGIN DECLARE SECTION;

Example 3 (unknown):

EXEC SQL END DECLARE SECTION;

Example 4 (unknown):

int   x = 4;
char  foo[16], bar[16];

---

PostgreSQL: Documentation: 18: 35.48. sql_features

URL: https://www.postgresql.org/docs/current/infoschema-sql-features.html

Contents:

• 35.48. sql_features #

The table sql_features contains information about which formal features defined in the SQL standard are supported by PostgreSQL. This is the same information that is presented in Appendix D. There you can also find some additional background information.

Table 35.46. sql_features Columns

feature_id character_data

Identifier string of the feature

feature_name character_data

Descriptive name of the feature

sub_feature_id character_data

Identifier string of the subfeature, or a zero-length string if not a subfeature

sub_feature_name character_data

Descriptive name of the subfeature, or a zero-length string if not a subfeature

is_supported yes_or_no

YES if the feature is fully supported by the current version of PostgreSQL, NO if not

is_verified_by character_data

Always null, since the PostgreSQL development group does not perform formal testing of feature conformance

comments character_data

Possibly a comment about the supported status of the feature

---

PostgreSQL: Documentation: 18: 35.45. routines

URL: https://www.postgresql.org/docs/current/infoschema-routines.html

Contents:

• 35.45. routines #

The view routines contains all functions and procedures in the current database. Only those functions and procedures are shown that the current user has access to (by way of being the owner or having some privilege).

Table 35.43. routines Columns

specific_catalog sql_identifier

Name of the database containing the function (always the current database)

specific_schema sql_identifier

Name of the schema containing the function

specific_name sql_identifier

The “specific name” of the function. This is a name that uniquely identifies the function in the schema, even if the real name of the function is overloaded. The format of the specific name is not defined, it should only be used to compare it to other instances of specific routine names.

routine_catalog sql_identifier

Name of the database containing the function (always the current database)

routine_schema sql_identifier

Name of the schema containing the function

routine_name sql_identifier

Name of the function (might be duplicated in case of overloading)

routine_type character_data

FUNCTION for a function, PROCEDURE for a procedure

module_catalog sql_identifier

Applies to a feature not available in PostgreSQL

module_schema sql_identifier

Applies to a feature not available in PostgreSQL

module_name sql_identifier

Applies to a feature not available in PostgreSQL

udt_catalog sql_identifier

Applies to a feature not available in PostgreSQL

udt_schema sql_identifier

Applies to a feature not available in PostgreSQL

udt_name sql_identifier

Applies to a feature not available in PostgreSQL

data_type character_data

Return data type of the function, if it is a built-in type, or ARRAY if it is some array (in that case, see the view element_types), else USER-DEFINED (in that case, the type is identified in type_udt_name and associated columns). Null for a procedure.

character_maximum_length cardinal_number

Always null, since this information is not applied to return data types in PostgreSQL

character_octet_length cardinal_number

Always null, since this information is not applied to return data types in PostgreSQL

character_set_catalog sql_identifier

Applies to a feature not available in PostgreSQL

character_set_schema sql_identifier

Applies to a feature not available in PostgreSQL

character_set_name sql_identifier

Applies to a feature not available in PostgreSQL

collation_catalog sql_identifier

Always null, since this information is not applied to return data types in PostgreSQL

collation_schema sql_identifier

Always null, since this information is not applied to return data types in PostgreSQL

collation_name sql_identifier

Always null, since this information is not applied to return data types in PostgreSQL

numeric_precision cardinal_number

Always null, since this information is not applied to return data types in PostgreSQL

numeric_precision_radix cardinal_number

Always null, since this information is not applied to return data types in PostgreSQL

numeric_scale cardinal_number

Always null, since this information is not applied to return data types in PostgreSQL

datetime_precision cardinal_number

Always null, since this information is not applied to return data types in PostgreSQL

interval_type character_data

Always null, since this information is not applied to return data types in PostgreSQL

interval_precision cardinal_number

Always null, since this information is not applied to return data types in PostgreSQL

type_udt_catalog sql_identifier

Name of the database that the return data type of the function is defined in (always the current database). Null for a procedure.

type_udt_schema sql_identifier

Name of the schema that the return data type of the function is defined in. Null for a procedure.

type_udt_name sql_identifier

Name of the return data type of the function. Null for a procedure.

scope_catalog sql_identifier

Applies to a feature not available in PostgreSQL

scope_schema sql_identifier

Applies to a feature not available in PostgreSQL

scope_name sql_identifier

Applies to a feature not available in PostgreSQL

maximum_cardinality cardinal_number

Always null, because arrays always have unlimited maximum cardinality in PostgreSQL

dtd_identifier sql_identifier

An identifier of the data type descriptor of the return data type of this function, unique among the data type descriptors pertaining to the function. This is mainly useful for joining with other instances of such identifiers. (The specific format of the identifier is not defined and not guaranteed to remain the same in future versions.)

routine_body character_data

If the function is an SQL function, then SQL, else EXTERNAL.

routine_definition character_data

The source text of the function (null if the function is not owned by a currently enabled role). (According to the SQL standard, this column is only applicable if routine_body is SQL, but in PostgreSQL it will contain whatever source text was specified when the function was created.)

external_name character_data

If this function is a C function, then the external name (link symbol) of the function; else null. (This works out to be the same value that is shown in routine_definition.)

external_language character_data

The language the function is written in

parameter_style character_data

Always GENERAL (The SQL standard defines other parameter styles, which are not available in PostgreSQL.)

is_deterministic yes_or_no

If the function is declared immutable (called deterministic in the SQL standard), then YES, else NO. (You cannot query the other volatility levels available in PostgreSQL through the information schema.)

sql_data_access character_data

Always MODIFIES, meaning that the function possibly modifies SQL data. This information is not useful for PostgreSQL.

is_null_call yes_or_no

If the function automatically returns null if any of its arguments are null, then YES, else NO. Null for a procedure.

sql_path character_data

Applies to a feature not available in PostgreSQL

schema_level_routine yes_or_no

Always YES (The opposite would be a method of a user-defined type, which is a feature not available in PostgreSQL.)

max_dynamic_result_sets cardinal_number

Applies to a feature not available in PostgreSQL

is_user_defined_cast yes_or_no

Applies to a feature not available in PostgreSQL

is_implicitly_invocable yes_or_no

Applies to a feature not available in PostgreSQL

security_type character_data

If the function runs with the privileges of the current user, then INVOKER, if the function runs with the privileges of the user who defined it, then DEFINER.

to_sql_specific_catalog sql_identifier

Applies to a feature not available in PostgreSQL

to_sql_specific_schema sql_identifier

Applies to a feature not available in PostgreSQL

to_sql_specific_name sql_identifier

Applies to a feature not available in PostgreSQL

Applies to a feature not available in PostgreSQL

Applies to a feature not available in PostgreSQL

last_altered time_stamp

Applies to a feature not available in PostgreSQL

new_savepoint_level yes_or_no

Applies to a feature not available in PostgreSQL

is_udt_dependent yes_or_no

Currently always NO. The alternative YES applies to a feature not available in PostgreSQL.

result_cast_from_data_type character_data

Applies to a feature not available in PostgreSQL

result_cast_as_locator yes_or_no

Applies to a feature not available in PostgreSQL

result_cast_char_max_length cardinal_number

Applies to a feature not available in PostgreSQL

result_cast_char_octet_length cardinal_number

Applies to a feature not available in PostgreSQL

result_cast_char_set_catalog sql_identifier

Applies to a feature not available in PostgreSQL

result_cast_char_set_schema sql_identifier

Applies to a feature not available in PostgreSQL

result_cast_char_set_name sql_identifier

Applies to a feature not available in PostgreSQL

result_cast_collation_catalog sql_identifier

Applies to a feature not available in PostgreSQL

result_cast_collation_schema sql_identifier

Applies to a feature not available in PostgreSQL

result_cast_collation_name sql_identifier

Applies to a feature not available in PostgreSQL

result_cast_numeric_precision cardinal_number

Applies to a feature not available in PostgreSQL

result_cast_numeric_precision_radix cardinal_number

Applies to a feature not available in PostgreSQL

result_cast_numeric_scale cardinal_number

Applies to a feature not available in PostgreSQL

result_cast_datetime_precision cardinal_number

Applies to a feature not available in PostgreSQL

result_cast_interval_type character_data

Applies to a feature not available in PostgreSQL

result_cast_interval_precision cardinal_number

Applies to a feature not available in PostgreSQL

result_cast_type_udt_catalog sql_identifier

Applies to a feature not available in PostgreSQL

result_cast_type_udt_schema sql_identifier

Applies to a feature not available in PostgreSQL

result_cast_type_udt_name sql_identifier

Applies to a feature not available in PostgreSQL

result_cast_scope_catalog sql_identifier

Applies to a feature not available in PostgreSQL

result_cast_scope_schema sql_identifier

Applies to a feature not available in PostgreSQL

result_cast_scope_name sql_identifier

Applies to a feature not available in PostgreSQL

result_cast_maximum_cardinality cardinal_number

Applies to a feature not available in PostgreSQL

result_cast_dtd_identifier sql_identifier

Applies to a feature not available in PostgreSQL

---

PostgreSQL: Documentation: 18: Chapter 6. Data Manipulation

URL: https://www.postgresql.org/docs/current/dml.html

Contents:

• Chapter 6. Data Manipulation

The previous chapter discussed how to create tables and other structures to hold your data. Now it is time to fill the tables with data. This chapter covers how to insert, update, and delete table data. The chapter after this will finally explain how to extract your long-lost data from the database.

---

PostgreSQL: Documentation: 18: Chapter 26. High Availability, Load Balancing, and Replication

URL: https://www.postgresql.org/docs/current/high-availability.html

Contents:

• Chapter 26. High Availability, Load Balancing, and Replication

Database servers can work together to allow a second server to take over quickly if the primary server fails (high availability), or to allow several computers to serve the same data (load balancing). Ideally, database servers could work together seamlessly. Web servers serving static web pages can be combined quite easily by merely load-balancing web requests to multiple machines. In fact, read-only database servers can be combined relatively easily too. Unfortunately, most database servers have a read/write mix of requests, and read/write servers are much harder to combine. This is because though read-only data needs to be placed on each server only once, a write to any server has to be propagated to all servers so that future read requests to those servers return consistent results.

This synchronization problem is the fundamental difficulty for servers working together. Because there is no single solution that eliminates the impact of the sync problem for all use cases, there are multiple solutions. Each solution addresses this problem in a different way, and minimizes its impact for a specific workload.

Some solutions deal with synchronization by allowing only one server to modify the data. Servers that can modify data are called read/write, master or primary servers. Servers that track changes in the primary are called standby or secondary servers. A standby server that cannot be connected to until it is promoted to a primary server is called a warm standby server, and one that can accept connections and serves read-only queries is called a hot standby server.

Some solutions are synchronous, meaning that a data-modifying transaction is not considered committed until all servers have committed the transaction. This guarantees that a failover will not lose any data and that all load-balanced servers will return consistent results no matter which server is queried. In contrast, asynchronous solutions allow some delay between the time of a commit and its propagation to the other servers, opening the possibility that some transactions might be lost in the switch to a backup server, and that load balanced servers might return slightly stale results. Asynchronous communication is used when synchronous would be too slow.

Solutions can also be categorized by their granularity. Some solutions can deal only with an entire database server, while others allow control at the per-table or per-database level.

Performance must be considered in any choice. There is usually a trade-off between functionality and performance. For example, a fully synchronous solution over a slow network might cut performance by more than half, while an asynchronous one might have a minimal performance impact.

The remainder of this section outlines various failover, replication, and load balancing solutions.

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PostgreSQL: Documentation: 18: 29.13. Upgrade

URL: https://www.postgresql.org/docs/current/logical-replication-upgrade.html

Contents:

• 29.13. Upgrade #
  • 29.13.1. Prepare for Publisher Upgrades #
  • 29.13.2. Prepare for Subscriber Upgrades #
  • 29.13.3. Upgrading Logical Replication Clusters #
  • Note
  • Warning
  • 29.13.3.1. Steps to Upgrade a Two-node Logical Replication Cluster #
  • Note
  • 29.13.3.2. Steps to Upgrade a Cascaded Logical Replication Cluster #
  • 29.13.3.3. Steps to Upgrade a Two-node Circular Logical Replication Cluster #

Migration of logical replication clusters is possible only when all the members of the old logical replication clusters are version 17.0 or later.

pg_upgrade attempts to migrate logical slots. This helps avoid the need for manually defining the same logical slots on the new publisher. Migration of logical slots is only supported when the old cluster is version 17.0 or later. Logical slots on clusters before version 17.0 will silently be ignored.

Before you start upgrading the publisher cluster, ensure that the subscription is temporarily disabled, by executing ALTER SUBSCRIPTION ... DISABLE. Re-enable the subscription after the upgrade.

There are some prerequisites for pg_upgrade to be able to upgrade the logical slots. If these are not met an error will be reported.

The new cluster must have wal_level as logical.

The new cluster must have max_replication_slots configured to a value greater than or equal to the number of slots present in the old cluster.

The output plugins referenced by the slots on the old cluster must be installed in the new PostgreSQL executable directory.

The old cluster has replicated all the transactions and logical decoding messages to subscribers.

All slots on the old cluster must be usable, i.e., there are no slots whose pg_replication_slots.conflicting is not true.

The new cluster must not have permanent logical slots, i.e., there must be no slots where pg_replication_slots.temporary is false.

Setup the subscriber configurations in the new subscriber. pg_upgrade attempts to migrate subscription dependencies which includes the subscription's table information present in pg_subscription_rel system catalog and also the subscription's replication origin. This allows logical replication on the new subscriber to continue from where the old subscriber was up to. Migration of subscription dependencies is only supported when the old cluster is version 17.0 or later. Subscription dependencies on clusters before version 17.0 will silently be ignored.

There are some prerequisites for pg_upgrade to be able to upgrade the subscriptions. If these are not met an error will be reported.

All the subscription tables in the old subscriber should be in state i (initialize) or r (ready). This can be verified by checking pg_subscription_rel.srsubstate.

The replication origin entry corresponding to each of the subscriptions should exist in the old cluster. This can be found by checking pg_subscription and pg_replication_origin system tables.

The new cluster must have max_active_replication_origins configured to a value greater than or equal to the number of subscriptions present in the old cluster.

While upgrading a subscriber, write operations can be performed in the publisher. These changes will be replicated to the subscriber once the subscriber upgrade is completed.

The logical replication restrictions apply to logical replication cluster upgrades also. See Section 29.8 for details.

The prerequisites of publisher upgrade apply to logical replication cluster upgrades also. See Section 29.13.1 for details.

The prerequisites of subscriber upgrade apply to logical replication cluster upgrades also. See Section 29.13.2 for details.

Upgrading logical replication cluster requires multiple steps to be performed on various nodes. Because not all operations are transactional, the user is advised to take backups as described in Section 25.3.2.

The steps to upgrade the following logical replication clusters are detailed below:

Follow the steps specified in Section 29.13.3.1 to upgrade a two-node logical replication cluster.

Follow the steps specified in Section 29.13.3.2 to upgrade a cascaded logical replication cluster.

Follow the steps specified in Section 29.13.3.3 to upgrade a two-node circular logical replication cluster.

Let's say publisher is in node1 and subscriber is in node2. The subscriber node2 has a subscription sub1_node1_node2 which is subscribing the changes from node1.

Disable all the subscriptions on node2 that are subscribing the changes from node1 by using ALTER SUBSCRIPTION ... DISABLE, e.g.:

Stop the publisher server in node1, e.g.:

Initialize data1_upgraded instance by using the required newer version.

Upgrade the publisher node1's server to the required newer version, e.g.:

Start the upgraded publisher server in node1, e.g.:

Stop the subscriber server in node2, e.g.:

Initialize data2_upgraded instance by using the required newer version.

Upgrade the subscriber node2's server to the required new version, e.g.:

Start the upgraded subscriber server in node2, e.g.:

On node2, create any tables that were created in the upgraded publisher node1 server between Step 1 and now, e.g.:

Enable all the subscriptions on node2 that are subscribing the changes from node1 by using ALTER SUBSCRIPTION ... ENABLE, e.g.:

Refresh the node2 subscription's publications using ALTER SUBSCRIPTION ... REFRESH PUBLICATION, e.g.:

In the steps described above, the publisher is upgraded first, followed by the subscriber. Alternatively, the user can use similar steps to upgrade the subscriber first, followed by the publisher.

Let's say we have a cascaded logical replication setup node1->node2->node3. Here node2 is subscribing the changes from node1 and node3 is subscribing the changes from node2. The node2 has a subscription sub1_node1_node2 which is subscribing the changes from node1. The node3 has a subscription sub1_node2_node3 which is subscribing the changes from node2.

Disable all the subscriptions on node2 that are subscribing the changes from node1 by using ALTER SUBSCRIPTION ... DISABLE, e.g.:

Stop the server in node1, e.g.:

Initialize data1_upgraded instance by using the required newer version.

Upgrade the node1's server to the required newer version, e.g.:

Start the upgraded server in node1, e.g.:

Disable all the subscriptions on node3 that are subscribing the changes from node2 by using ALTER SUBSCRIPTION ... DISABLE, e.g.:

Stop the server in node2, e.g.:

Initialize data2_upgraded instance by using the required newer version.

Upgrade the node2's server to the required new version, e.g.:

Start the upgraded server in node2, e.g.:

On node2, create any tables that were created in the upgraded publisher node1 server between Step 1 and now, e.g.:

Enable all the subscriptions on node2 that are subscribing the changes from node1 by using ALTER SUBSCRIPTION ... ENABLE, e.g.:

Refresh the node2 subscription's publications using ALTER SUBSCRIPTION ... REFRESH PUBLICATION, e.g.:

Stop the server in node3, e.g.:

Initialize data3_upgraded instance by using the required newer version.

Upgrade the node3's server to the required new version, e.g.:

Start the upgraded server in node3, e.g.:

On node3, create any tables that were created in the upgraded node2 between Step 6 and now, e.g.:

Enable all the subscriptions on node3 that are subscribing the changes from node2 by using ALTER SUBSCRIPTION ... ENABLE, e.g.:

Refresh the node3 subscription's publications using ALTER SUBSCRIPTION ... REFRESH PUBLICATION, e.g.:

Let's say we have a circular logical replication setup node1->node2 and node2->node1. Here node2 is subscribing the changes from node1 and node1 is subscribing the changes from node2. The node1 has a subscription sub1_node2_node1 which is subscribing the changes from node2. The node2 has a subscription sub1_node1_node2 which is subscribing the changes from node1.

Disable all the subscriptions on node2 that are subscribing the changes from node1 by using ALTER SUBSCRIPTION ... DISABLE, e.g.:

Stop the server in node1, e.g.:

Initialize data1_upgraded instance by using the required newer version.

Upgrade the node1's server to the required newer version, e.g.:

Start the upgraded server in node1, e.g.:

Enable all the subscriptions on node2 that are subscribing the changes from node1 by using ALTER SUBSCRIPTION ... ENABLE, e.g.:

On node1, create any tables that were created in node2 between Step 1 and now, e.g.:

Refresh the node1 subscription's publications to copy initial table data from node2 using ALTER SUBSCRIPTION ... REFRESH PUBLICATION, e.g.:

Disable all the subscriptions on node1 that are subscribing the changes from node2 by using ALTER SUBSCRIPTION ... DISABLE, e.g.:

Stop the server in node2, e.g.:

Initialize data2_upgraded instance by using the required newer version.

Upgrade the node2's server to the required new version, e.g.:

Start the upgraded server in node2, e.g.:

Enable all the subscriptions on node1 that are subscribing the changes from node2 by using ALTER SUBSCRIPTION ... ENABLE, e.g.:

On node2, create any tables that were created in the upgraded node1 between Step 9 and now, e.g.:

Refresh the node2 subscription's publications to copy initial table data from node1 using ALTER SUBSCRIPTION ... REFRESH PUBLICATION, e.g.:

Examples:

Example 1 (unknown):

/* node2 # */ ALTER SUBSCRIPTION sub1_node1_node2 DISABLE;

Example 2 (unknown):

pg_ctl -D /opt/PostgreSQL/data1 stop

Example 3 (unknown):

pg_upgrade
        --old-datadir "/opt/PostgreSQL/postgres/17/data1"
        --new-datadir "/opt/PostgreSQL/postgres/18/data1_upgraded"
        --old-bindir "/opt/PostgreSQL/postgres/17/bin"
        --new-bindir "/opt/PostgreSQL/postgres/18/bin"

Example 4 (unknown):

pg_ctl -D /opt/PostgreSQL/data1_upgraded start -l logfile

---

PostgreSQL: Documentation: 18: 18.7. Preventing Server Spoofing

URL: https://www.postgresql.org/docs/current/preventing-server-spoofing.html

Contents:

• 18.7. Preventing Server Spoofing #

While the server is running, it is not possible for a malicious user to take the place of the normal database server. However, when the server is down, it is possible for a local user to spoof the normal server by starting their own server. The spoof server could read passwords and queries sent by clients, but could not return any data because the PGDATA directory would still be secure because of directory permissions. Spoofing is possible because any user can start a database server; a client cannot identify an invalid server unless it is specially configured.

One way to prevent spoofing of local connections is to use a Unix domain socket directory (unix_socket_directories) that has write permission only for a trusted local user. This prevents a malicious user from creating their own socket file in that directory. If you are concerned that some applications might still reference /tmp for the socket file and hence be vulnerable to spoofing, during operating system startup create a symbolic link /tmp/.s.PGSQL.5432 that points to the relocated socket file. You also might need to modify your /tmp cleanup script to prevent removal of the symbolic link.

Another option for local connections is for clients to use requirepeer to specify the required owner of the server process connected to the socket.

To prevent spoofing on TCP connections, either use SSL certificates and make sure that clients check the server's certificate, or use GSSAPI encryption (or both, if they're on separate connections).

To prevent spoofing with SSL, the server must be configured to accept only hostssl connections (Section 20.1) and have SSL key and certificate files (Section 18.9). The TCP client must connect using sslmode=verify-ca or verify-full and have the appropriate root certificate file installed (Section 32.19.1). Alternatively the system CA pool, as defined by the SSL implementation, can be used using sslrootcert=system; in this case, sslmode=verify-full is forced for safety, since it is generally trivial to obtain certificates which are signed by a public CA.

To prevent server spoofing from occurring when using scram-sha-256 password authentication over a network, you should ensure that you connect to the server using SSL and with one of the anti-spoofing methods described in the previous paragraph. Additionally, the SCRAM implementation in libpq cannot protect the entire authentication exchange, but using the channel_binding=require connection parameter provides a mitigation against server spoofing. An attacker that uses a rogue server to intercept a SCRAM exchange can use offline analysis to potentially determine the hashed password from the client.

To prevent spoofing with GSSAPI, the server must be configured to accept only hostgssenc connections (Section 20.1) and use gss authentication with them. The TCP client must connect using gssencmode=require.

---

PostgreSQL: Documentation: 18: 35.3. information_schema_catalog_name

URL: https://www.postgresql.org/docs/current/infoschema-information-schema-catalog-name.html

Contents:

• 35.3. information_schema_catalog_name #

information_schema_catalog_name is a table that always contains one row and one column containing the name of the current database (current catalog, in SQL terminology).

Table 35.1. information_schema_catalog_name Columns

catalog_name sql_identifier

Name of the database that contains this information schema

---

PostgreSQL: Documentation: 18: 34.3. Running SQL Commands

URL: https://www.postgresql.org/docs/current/ecpg-commands.html

Contents:

• 34.3. Running SQL Commands #
  • 34.3.1. Executing SQL Statements #
  • 34.3.2. Using Cursors #
  • Note
  • 34.3.3. Managing Transactions #
  • 34.3.4. Prepared Statements #

Any SQL command can be run from within an embedded SQL application. Below are some examples of how to do that.

SELECT statements that return a single result row can also be executed using EXEC SQL directly. To handle result sets with multiple rows, an application has to use a cursor; see Section 34.3.2 below. (As a special case, an application can fetch multiple rows at once into an array host variable; see Section 34.4.4.3.1.)

Also, a configuration parameter can be retrieved with the SHOW command:

The tokens of the form :something are host variables, that is, they refer to variables in the C program. They are explained in Section 34.4.

To retrieve a result set holding multiple rows, an application has to declare a cursor and fetch each row from the cursor. The steps to use a cursor are the following: declare a cursor, open it, fetch a row from the cursor, repeat, and finally close it.

Select using cursors:

For more details about declaring a cursor, see DECLARE; for more details about fetching rows from a cursor, see FETCH.

The ECPG DECLARE command does not actually cause a statement to be sent to the PostgreSQL backend. The cursor is opened in the backend (using the backend's DECLARE command) at the point when the OPEN command is executed.

In the default mode, statements are committed only when EXEC SQL COMMIT is issued. The embedded SQL interface also supports autocommit of transactions (similar to psql's default behavior) via the -t command-line option to ecpg (see ecpg) or via the EXEC SQL SET AUTOCOMMIT TO ON statement. In autocommit mode, each command is automatically committed unless it is inside an explicit transaction block. This mode can be explicitly turned off using EXEC SQL SET AUTOCOMMIT TO OFF.

The following transaction management commands are available:

Commit an in-progress transaction.

Roll back an in-progress transaction.

Prepare the current transaction for two-phase commit.

Commit a transaction that is in prepared state.

Roll back a transaction that is in prepared state.

Enable autocommit mode.

Disable autocommit mode. This is the default.

When the values to be passed to an SQL statement are not known at compile time, or the same statement is going to be used many times, then prepared statements can be useful.

The statement is prepared using the command PREPARE. For the values that are not known yet, use the placeholder “?”:

If a statement returns a single row, the application can call EXECUTE after PREPARE to execute the statement, supplying the actual values for the placeholders with a USING clause:

If a statement returns multiple rows, the application can use a cursor declared based on the prepared statement. To bind input parameters, the cursor must be opened with a USING clause:

When you don't need the prepared statement anymore, you should deallocate it:

For more details about PREPARE, see PREPARE. Also see Section 34.5 for more details about using placeholders and input parameters.

Examples:

Example 1 (unknown):

EXEC SQL CREATE TABLE foo (number integer, ascii char(16));
EXEC SQL CREATE UNIQUE INDEX num1 ON foo(number);
EXEC SQL COMMIT;

Example 2 (unknown):

EXEC SQL INSERT INTO foo (number, ascii) VALUES (9999, 'doodad');
EXEC SQL COMMIT;

Example 3 (unknown):

EXEC SQL DELETE FROM foo WHERE number = 9999;
EXEC SQL COMMIT;

Example 4 (unknown):

EXEC SQL UPDATE foo
    SET ascii = 'foobar'
    WHERE number = 9999;
EXEC SQL COMMIT;

---

PostgreSQL: Documentation: 18: 11.10. Operator Classes and Operator Families

URL: https://www.postgresql.org/docs/current/indexes-opclass.html

Contents:

• 11.10. Operator Classes and Operator Families #
  • Tip

An index definition can specify an operator class for each column of an index.

The operator class identifies the operators to be used by the index for that column. For example, a B-tree index on the type int4 would use the int4_ops class; this operator class includes comparison functions for values of type int4. In practice the default operator class for the column's data type is usually sufficient. The main reason for having operator classes is that for some data types, there could be more than one meaningful index behavior. For example, we might want to sort a complex-number data type either by absolute value or by real part. We could do this by defining two operator classes for the data type and then selecting the proper class when making an index. The operator class determines the basic sort ordering (which can then be modified by adding sort options COLLATE, ASC/DESC and/or NULLS FIRST/NULLS LAST).

There are also some built-in operator classes besides the default ones:

The operator classes text_pattern_ops, varchar_pattern_ops, and bpchar_pattern_ops support B-tree indexes on the types text, varchar, and char respectively. The difference from the default operator classes is that the values are compared strictly character by character rather than according to the locale-specific collation rules. This makes these operator classes suitable for use by queries involving pattern matching expressions (LIKE or POSIX regular expressions) when the database does not use the standard “C” locale. As an example, you might index a varchar column like this:

Note that you should also create an index with the default operator class if you want queries involving ordinary <, <=, >, or >= comparisons to use an index. Such queries cannot use the xxx_pattern_ops operator classes. (Ordinary equality comparisons can use these operator classes, however.) It is possible to create multiple indexes on the same column with different operator classes. If you do use the C locale, you do not need the xxx_pattern_ops operator classes, because an index with the default operator class is usable for pattern-matching queries in the C locale.

The following query shows all defined operator classes:

An operator class is actually just a subset of a larger structure called an operator family. In cases where several data types have similar behaviors, it is frequently useful to define cross-data-type operators and allow these to work with indexes. To do this, the operator classes for each of the types must be grouped into the same operator family. The cross-type operators are members of the family, but are not associated with any single class within the family.

This expanded version of the previous query shows the operator family each operator class belongs to:

This query shows all defined operator families and all the operators included in each family:

psql has commands \dAc, \dAf, and \dAo, which provide slightly more sophisticated versions of these queries.

Examples:

Example 1 (unknown):

CREATE INDEX name ON table (column opclass [ ( opclass_options ) ] [sort options] [, ...]);

Example 2 (unknown):

CREATE INDEX test_index ON test_table (col varchar_pattern_ops);

Example 3 (unknown):

SELECT am.amname AS index_method,
       opc.opcname AS opclass_name,
       opc.opcintype::regtype AS indexed_type,
       opc.opcdefault AS is_default
    FROM pg_am am, pg_opclass opc
    WHERE opc.opcmethod = am.oid
    ORDER BY index_method, opclass_name;

Example 4 (unknown):

SELECT am.amname AS index_method,
       opc.opcname AS opclass_name,
       opf.opfname AS opfamily_name,
       opc.opcintype::regtype AS indexed_type,
       opc.opcdefault AS is_default
    FROM pg_am am, pg_opclass opc, pg_opfamily opf
    WHERE opc.opcmethod = am.oid AND
          opc.opcfamily = opf.oid
    ORDER BY index_method, opclass_name;

---

PostgreSQL: Documentation: 18: 35.52. table_constraints

URL: https://www.postgresql.org/docs/current/infoschema-table-constraints.html

Contents:

• 35.52. table_constraints #

The view table_constraints contains all constraints belonging to tables that the current user owns or has some privilege other than SELECT on.

Table 35.50. table_constraints Columns

constraint_catalog sql_identifier

Name of the database that contains the constraint (always the current database)

constraint_schema sql_identifier

Name of the schema that contains the constraint

constraint_name sql_identifier

Name of the constraint

table_catalog sql_identifier

Name of the database that contains the table (always the current database)

table_schema sql_identifier

Name of the schema that contains the table

table_name sql_identifier

constraint_type character_data

Type of the constraint: CHECK (includes not-null constraints), FOREIGN KEY, PRIMARY KEY, or UNIQUE

is_deferrable yes_or_no

YES if the constraint is deferrable, NO if not

initially_deferred yes_or_no

YES if the constraint is deferrable and initially deferred, NO if not

YES if the constraint is enforced, NO if not

nulls_distinct yes_or_no

If the constraint is a unique constraint, then YES if the constraint treats nulls as distinct or NO if it treats nulls as not distinct, otherwise null for other types of constraints.

---

PostgreSQL: Documentation: 18: 22.6. Tablespaces

URL: https://www.postgresql.org/docs/current/manage-ag-tablespaces.html

Contents:

• 22.6. Tablespaces #
  • Warning
  • Note

Tablespaces in PostgreSQL allow database administrators to define locations in the file system where the files representing database objects can be stored. Once created, a tablespace can be referred to by name when creating database objects.

By using tablespaces, an administrator can control the disk layout of a PostgreSQL installation. This is useful in at least two ways. First, if the partition or volume on which the cluster was initialized runs out of space and cannot be extended, a tablespace can be created on a different partition and used until the system can be reconfigured.

Second, tablespaces allow an administrator to use knowledge of the usage pattern of database objects to optimize performance. For example, an index which is very heavily used can be placed on a very fast, highly available disk, such as an expensive solid state device. At the same time a table storing archived data which is rarely used or not performance critical could be stored on a less expensive, slower disk system.

Even though located outside the main PostgreSQL data directory, tablespaces are an integral part of the database cluster and cannot be treated as an autonomous collection of data files. They are dependent on metadata contained in the main data directory, and therefore cannot be attached to a different database cluster or backed up individually. Similarly, if you lose a tablespace (file deletion, disk failure, etc.), the database cluster might become unreadable or unable to start. Placing a tablespace on a temporary file system like a RAM disk risks the reliability of the entire cluster.

To define a tablespace, use the CREATE TABLESPACE command, for example::

The location must be an existing, empty directory that is owned by the PostgreSQL operating system user. All objects subsequently created within the tablespace will be stored in files underneath this directory. The location must not be on removable or transient storage, as the cluster might fail to function if the tablespace is missing or lost.

There is usually not much point in making more than one tablespace per logical file system, since you cannot control the location of individual files within a logical file system. However, PostgreSQL does not enforce any such limitation, and indeed it is not directly aware of the file system boundaries on your system. It just stores files in the directories you tell it to use.

Creation of the tablespace itself must be done as a database superuser, but after that you can allow ordinary database users to use it. To do that, grant them the CREATE privilege on it.

Tables, indexes, and entire databases can be assigned to particular tablespaces. To do so, a user with the CREATE privilege on a given tablespace must pass the tablespace name as a parameter to the relevant command. For example, the following creates a table in the tablespace space1:

Alternatively, use the default_tablespace parameter:

When default_tablespace is set to anything but an empty string, it supplies an implicit TABLESPACE clause for CREATE TABLE and CREATE INDEX commands that do not have an explicit one.

There is also a temp_tablespaces parameter, which determines the placement of temporary tables and indexes, as well as temporary files that are used for purposes such as sorting large data sets. This can be a list of tablespace names, rather than only one, so that the load associated with temporary objects can be spread over multiple tablespaces. A random member of the list is picked each time a temporary object is to be created.

The tablespace associated with a database is used to store the system catalogs of that database. Furthermore, it is the default tablespace used for tables, indexes, and temporary files created within the database, if no TABLESPACE clause is given and no other selection is specified by default_tablespace or temp_tablespaces (as appropriate). If a database is created without specifying a tablespace for it, it uses the same tablespace as the template database it is copied from.

Two tablespaces are automatically created when the database cluster is initialized. The pg_global tablespace is used only for shared system catalogs. The pg_default tablespace is the default tablespace of the template1 and template0 databases (and, therefore, will be the default tablespace for other databases as well, unless overridden by a TABLESPACE clause in CREATE DATABASE).

Once created, a tablespace can be used from any database, provided the requesting user has sufficient privilege. This means that a tablespace cannot be dropped until all objects in all databases using the tablespace have been removed.

To remove an empty tablespace, use the DROP TABLESPACE command.

To determine the set of existing tablespaces, examine the pg_tablespace system catalog, for example

It is possible to find which databases use which tablespaces; see Table 9.76. The psql program's \db meta-command is also useful for listing the existing tablespaces.

The directory $PGDATA/pg_tblspc contains symbolic links that point to each of the non-built-in tablespaces defined in the cluster. Although not recommended, it is possible to adjust the tablespace layout by hand by redefining these links. Under no circumstances perform this operation while the server is running.

Examples:

Example 1 (unknown):

CREATE TABLESPACE fastspace LOCATION '/ssd1/postgresql/data';

Example 2 (unknown):

CREATE TABLE foo(i int) TABLESPACE space1;

Example 3 (unknown):

SET default_tablespace = space1;
CREATE TABLE foo(i int);

Example 4 (unknown):

SELECT spcname, spcowner::regrole, pg_tablespace_location(oid) FROM pg_tablespace;

---

PostgreSQL: Documentation: 18: 9.31. Statistics Information Functions

URL: https://www.postgresql.org/docs/current/functions-statistics.html

Contents:

• 9.31. Statistics Information Functions #
  • 9.31.1. Inspecting MCV Lists #

PostgreSQL provides a function to inspect complex statistics defined using the CREATE STATISTICS command.

pg_mcv_list_items returns a set of records describing all items stored in a multi-column MCV list. It returns the following columns:

The pg_mcv_list_items function can be used like this:

Values of the pg_mcv_list type can be obtained only from the pg_statistic_ext_data.stxdmcv column.

Examples:

Example 1 (unknown):

pg_mcv_list_items ( pg_mcv_list ) → setof record

Example 2 (unknown):

SELECT m.* FROM pg_statistic_ext join pg_statistic_ext_data on (oid = stxoid),
                pg_mcv_list_items(stxdmcv) m WHERE stxname = 'stts';

---

PostgreSQL: Documentation: 18: 30.3. Configuration

URL: https://www.postgresql.org/docs/current/jit-configuration.html

Contents:

• 30.3. Configuration #

The configuration variable jit determines whether JIT compilation is enabled or disabled. If it is enabled, the configuration variables jit_above_cost, jit_inline_above_cost, and jit_optimize_above_cost determine whether JIT compilation is performed for a query, and how much effort is spent doing so.

jit_provider determines which JIT implementation is used. It is rarely required to be changed. See Section 30.4.2.

For development and debugging purposes a few additional configuration parameters exist, as described in Section 19.17.

---

PostgreSQL: Documentation: 18: 32.15. Environment Variables

URL: https://www.postgresql.org/docs/current/libpq-envars.html

Contents:

• 32.15. Environment Variables #

The following environment variables can be used to select default connection parameter values, which will be used by PQconnectdb, PQsetdbLogin and PQsetdb if no value is directly specified by the calling code. These are useful to avoid hard-coding database connection information into simple client applications, for example.

PGHOST behaves the same as the host connection parameter.

PGSSLNEGOTIATION behaves the same as the sslnegotiation connection parameter.

PGHOSTADDR behaves the same as the hostaddr connection parameter. This can be set instead of or in addition to PGHOST to avoid DNS lookup overhead.

PGPORT behaves the same as the port connection parameter.

PGDATABASE behaves the same as the dbname connection parameter.

PGUSER behaves the same as the user connection parameter.

PGPASSWORD behaves the same as the password connection parameter. Use of this environment variable is not recommended for security reasons, as some operating systems allow non-root users to see process environment variables via ps; instead consider using a password file (see Section 32.16).

PGPASSFILE behaves the same as the passfile connection parameter.

PGREQUIREAUTH behaves the same as the require_auth connection parameter.

PGCHANNELBINDING behaves the same as the channel_binding connection parameter.

PGSERVICE behaves the same as the service connection parameter.

PGSERVICEFILE specifies the name of the per-user connection service file (see Section 32.17). Defaults to ~/.pg_service.conf, or %APPDATA%\postgresql.pg_service.conf on Microsoft Windows.

PGOPTIONS behaves the same as the options connection parameter.

PGAPPNAME behaves the same as the application_name connection parameter.

PGSSLMODE behaves the same as the sslmode connection parameter.

PGREQUIRESSL behaves the same as the requiressl connection parameter. This environment variable is deprecated in favor of the PGSSLMODE variable; setting both variables suppresses the effect of this one.

PGSSLCOMPRESSION behaves the same as the sslcompression connection parameter.

PGSSLCERT behaves the same as the sslcert connection parameter.

PGSSLKEY behaves the same as the sslkey connection parameter.

PGSSLCERTMODE behaves the same as the sslcertmode connection parameter.

PGSSLROOTCERT behaves the same as the sslrootcert connection parameter.

PGSSLCRL behaves the same as the sslcrl connection parameter.

PGSSLCRLDIR behaves the same as the sslcrldir connection parameter.

PGSSLSNI behaves the same as the sslsni connection parameter.

PGREQUIREPEER behaves the same as the requirepeer connection parameter.

PGSSLMINPROTOCOLVERSION behaves the same as the ssl_min_protocol_version connection parameter.

PGSSLMAXPROTOCOLVERSION behaves the same as the ssl_max_protocol_version connection parameter.

PGGSSENCMODE behaves the same as the gssencmode connection parameter.

PGKRBSRVNAME behaves the same as the krbsrvname connection parameter.

PGGSSLIB behaves the same as the gsslib connection parameter.

PGGSSDELEGATION behaves the same as the gssdelegation connection parameter.

PGCONNECT_TIMEOUT behaves the same as the connect_timeout connection parameter.

PGCLIENTENCODING behaves the same as the client_encoding connection parameter.

PGTARGETSESSIONATTRS behaves the same as the target_session_attrs connection parameter.

PGLOADBALANCEHOSTS behaves the same as the load_balance_hosts connection parameter.

PGMINPROTOCOLVERSION behaves the same as the min_protocol_version connection parameter.

PGMAXPROTOCOLVERSION behaves the same as the max_protocol_version connection parameter.

The following environment variables can be used to specify default behavior for each PostgreSQL session. (See also the ALTER ROLE and ALTER DATABASE commands for ways to set default behavior on a per-user or per-database basis.)

PGDATESTYLE sets the default style of date/time representation. (Equivalent to SET datestyle TO ....)

PGTZ sets the default time zone. (Equivalent to SET timezone TO ....)

PGGEQO sets the default mode for the genetic query optimizer. (Equivalent to SET geqo TO ....)

Refer to the SQL command SET for information on correct values for these environment variables.

The following environment variables determine internal behavior of libpq; they override compiled-in defaults.

PGSYSCONFDIR sets the directory containing the pg_service.conf file and in a future version possibly other system-wide configuration files.

PGLOCALEDIR sets the directory containing the locale files for message localization.

---

PostgreSQL: Documentation: 18: 11.4. Indexes and ORDER BY

URL: https://www.postgresql.org/docs/current/indexes-ordering.html

Contents:

• 11.4. Indexes and ORDER BY #

In addition to simply finding the rows to be returned by a query, an index may be able to deliver them in a specific sorted order. This allows a query's ORDER BY specification to be honored without a separate sorting step. Of the index types currently supported by PostgreSQL, only B-tree can produce sorted output — the other index types return matching rows in an unspecified, implementation-dependent order.

The planner will consider satisfying an ORDER BY specification either by scanning an available index that matches the specification, or by scanning the table in physical order and doing an explicit sort. For a query that requires scanning a large fraction of the table, an explicit sort is likely to be faster than using an index because it requires less disk I/O due to following a sequential access pattern. Indexes are more useful when only a few rows need be fetched. An important special case is ORDER BY in combination with LIMIT n: an explicit sort will have to process all the data to identify the first n rows, but if there is an index matching the ORDER BY, the first n rows can be retrieved directly, without scanning the remainder at all.

By default, B-tree indexes store their entries in ascending order with nulls last (table TID is treated as a tiebreaker column among otherwise equal entries). This means that a forward scan of an index on column x produces output satisfying ORDER BY x (or more verbosely, ORDER BY x ASC NULLS LAST). The index can also be scanned backward, producing output satisfying ORDER BY x DESC (or more verbosely, ORDER BY x DESC NULLS FIRST, since NULLS FIRST is the default for ORDER BY DESC).

You can adjust the ordering of a B-tree index by including the options ASC, DESC, NULLS FIRST, and/or NULLS LAST when creating the index; for example:

An index stored in ascending order with nulls first can satisfy either ORDER BY x ASC NULLS FIRST or ORDER BY x DESC NULLS LAST depending on which direction it is scanned in.

You might wonder why bother providing all four options, when two options together with the possibility of backward scan would cover all the variants of ORDER BY. In single-column indexes the options are indeed redundant, but in multicolumn indexes they can be useful. Consider a two-column index on (x, y): this can satisfy ORDER BY x, y if we scan forward, or ORDER BY x DESC, y DESC if we scan backward. But it might be that the application frequently needs to use ORDER BY x ASC, y DESC. There is no way to get that ordering from a plain index, but it is possible if the index is defined as (x ASC, y DESC) or (x DESC, y ASC).

Obviously, indexes with non-default sort orderings are a fairly specialized feature, but sometimes they can produce tremendous speedups for certain queries. Whether it's worth maintaining such an index depends on how often you use queries that require a special sort ordering.

Examples:

Example 1 (unknown):

CREATE INDEX test2_info_nulls_low ON test2 (info NULLS FIRST);
CREATE INDEX test3_desc_index ON test3 (id DESC NULLS LAST);

---

PostgreSQL: Documentation: 18: 32.3. Command Execution Functions

URL: https://www.postgresql.org/docs/current/libpq-exec.html

Contents:

• 32.3. Command Execution Functions #
  • 32.3.1. Main Functions #
  • Tip
  • Note
  • 32.3.2. Retrieving Query Result Information #
  • 32.3.3. Retrieving Other Result Information #
  • 32.3.4. Escaping Strings for Inclusion in SQL Commands #
  • Tip
  • Tip

Once a connection to a database server has been successfully established, the functions described here are used to perform SQL queries and commands.

Submits a command to the server and waits for the result.

Returns a PGresult pointer or possibly a null pointer. A non-null pointer will generally be returned except in out-of-memory conditions or serious errors such as inability to send the command to the server. The PQresultStatus function should be called to check the return value for any errors (including the value of a null pointer, in which case it will return PGRES_FATAL_ERROR). Use PQerrorMessage to get more information about such errors.

The command string can include multiple SQL commands (separated by semicolons). Multiple queries sent in a single PQexec call are processed in a single transaction, unless there are explicit BEGIN/COMMIT commands included in the query string to divide it into multiple transactions. (See Section 54.2.2.1 for more details about how the server handles multi-query strings.) Note however that the returned PGresult structure describes only the result of the last command executed from the string. Should one of the commands fail, processing of the string stops with it and the returned PGresult describes the error condition.

Submits a command to the server and waits for the result, with the ability to pass parameters separately from the SQL command text.

PQexecParams is like PQexec, but offers additional functionality: parameter values can be specified separately from the command string proper, and query results can be requested in either text or binary format.

The function arguments are:

The connection object to send the command through.

The SQL command string to be executed. If parameters are used, they are referred to in the command string as $1, $2, etc.

The number of parameters supplied; it is the length of the arrays paramTypes[], paramValues[], paramLengths[], and paramFormats[]. (The array pointers can be NULL when nParams is zero.)

Specifies, by OID, the data types to be assigned to the parameter symbols. If paramTypes is NULL, or any particular element in the array is zero, the server infers a data type for the parameter symbol in the same way it would do for an untyped literal string.

Specifies the actual values of the parameters. A null pointer in this array means the corresponding parameter is null; otherwise the pointer points to a zero-terminated text string (for text format) or binary data in the format expected by the server (for binary format).

Specifies the actual data lengths of binary-format parameters. It is ignored for null parameters and text-format parameters. The array pointer can be null when there are no binary parameters.

Specifies whether parameters are text (put a zero in the array entry for the corresponding parameter) or binary (put a one in the array entry for the corresponding parameter). If the array pointer is null then all parameters are presumed to be text strings.

Values passed in binary format require knowledge of the internal representation expected by the backend. For example, integers must be passed in network byte order. Passing numeric values requires knowledge of the server storage format, as implemented in src/backend/utils/adt/numeric.c::numeric_send() and src/backend/utils/adt/numeric.c::numeric_recv().

Specify zero to obtain results in text format, or one to obtain results in binary format. (There is not currently a provision to obtain different result columns in different formats, although that is possible in the underlying protocol.)

The primary advantage of PQexecParams over PQexec is that parameter values can be separated from the command string, thus avoiding the need for tedious and error-prone quoting and escaping.

Unlike PQexec, PQexecParams allows at most one SQL command in the given string. (There can be semicolons in it, but not more than one nonempty command.) This is a limitation of the underlying protocol, but has some usefulness as an extra defense against SQL-injection attacks.

Specifying parameter types via OIDs is tedious, particularly if you prefer not to hard-wire particular OID values into your program. However, you can avoid doing so even in cases where the server by itself cannot determine the type of the parameter, or chooses a different type than you want. In the SQL command text, attach an explicit cast to the parameter symbol to show what data type you will send. For example:

This forces parameter $1 to be treated as bigint, whereas by default it would be assigned the same type as x. Forcing the parameter type decision, either this way or by specifying a numeric type OID, is strongly recommended when sending parameter values in binary format, because binary format has less redundancy than text format and so there is less chance that the server will detect a type mismatch mistake for you.

Submits a request to create a prepared statement with the given parameters, and waits for completion.

PQprepare creates a prepared statement for later execution with PQexecPrepared. This feature allows commands to be executed repeatedly without being parsed and planned each time; see PREPARE for details.

The function creates a prepared statement named stmtName from the query string, which must contain a single SQL command. stmtName can be "" to create an unnamed statement, in which case any pre-existing unnamed statement is automatically replaced; otherwise it is an error if the statement name is already defined in the current session. If any parameters are used, they are referred to in the query as $1, $2, etc. nParams is the number of parameters for which types are pre-specified in the array paramTypes[]. (The array pointer can be NULL when nParams is zero.) paramTypes[] specifies, by OID, the data types to be assigned to the parameter symbols. If paramTypes is NULL, or any particular element in the array is zero, the server assigns a data type to the parameter symbol in the same way it would do for an untyped literal string. Also, the query can use parameter symbols with numbers higher than nParams; data types will be inferred for these symbols as well. (See PQdescribePrepared for a means to find out what data types were inferred.)

As with PQexec, the result is normally a PGresult object whose contents indicate server-side success or failure. A null result indicates out-of-memory or inability to send the command at all. Use PQerrorMessage to get more information about such errors.

Prepared statements for use with PQexecPrepared can also be created by executing SQL PREPARE statements.

Sends a request to execute a prepared statement with given parameters, and waits for the result.

PQexecPrepared is like PQexecParams, but the command to be executed is specified by naming a previously-prepared statement, instead of giving a query string. This feature allows commands that will be used repeatedly to be parsed and planned just once, rather than each time they are executed. The statement must have been prepared previously in the current session.

The parameters are identical to PQexecParams, except that the name of a prepared statement is given instead of a query string, and the paramTypes[] parameter is not present (it is not needed since the prepared statement's parameter types were determined when it was created).

Submits a request to obtain information about the specified prepared statement, and waits for completion.

PQdescribePrepared allows an application to obtain information about a previously prepared statement.

stmtName can be "" or NULL to reference the unnamed statement, otherwise it must be the name of an existing prepared statement. On success, a PGresult with status PGRES_COMMAND_OK is returned. The functions PQnparams and PQparamtype can be applied to this PGresult to obtain information about the parameters of the prepared statement, and the functions PQnfields, PQfname, PQftype, etc. provide information about the result columns (if any) of the statement.

Submits a request to obtain information about the specified portal, and waits for completion.

PQdescribePortal allows an application to obtain information about a previously created portal. (libpq does not provide any direct access to portals, but you can use this function to inspect the properties of a cursor created with a DECLARE CURSOR SQL command.)

portalName can be "" or NULL to reference the unnamed portal, otherwise it must be the name of an existing portal. On success, a PGresult with status PGRES_COMMAND_OK is returned. The functions PQnfields, PQfname, PQftype, etc. can be applied to the PGresult to obtain information about the result columns (if any) of the portal.

Submits a request to close the specified prepared statement, and waits for completion.

PQclosePrepared allows an application to close a previously prepared statement. Closing a statement releases all of its associated resources on the server and allows its name to be reused.

stmtName can be "" or NULL to reference the unnamed statement. It is fine if no statement exists with this name, in that case the operation is a no-op. On success, a PGresult with status PGRES_COMMAND_OK is returned.

Submits a request to close the specified portal, and waits for completion.

PQclosePortal allows an application to trigger a close of a previously created portal. Closing a portal releases all of its associated resources on the server and allows its name to be reused. (libpq does not provide any direct access to portals, but you can use this function to close a cursor created with a DECLARE CURSOR SQL command.)

portalName can be "" or NULL to reference the unnamed portal. It is fine if no portal exists with this name, in that case the operation is a no-op. On success, a PGresult with status PGRES_COMMAND_OK is returned.

The PGresult structure encapsulates the result returned by the server. libpq application programmers should be careful to maintain the PGresult abstraction. Use the accessor functions below to get at the contents of PGresult. Avoid directly referencing the fields of the PGresult structure because they are subject to change in the future.

Returns the result status of the command.

PQresultStatus can return one of the following values:

The string sent to the server was empty.

Successful completion of a command returning no data.

Successful completion of a command returning data (such as a SELECT or SHOW).

Copy Out (from server) data transfer started.

Copy In (to server) data transfer started.

The server's response was not understood.

A nonfatal error (a notice or warning) occurred.

A fatal error occurred.

Copy In/Out (to and from server) data transfer started. This feature is currently used only for streaming replication, so this status should not occur in ordinary applications.

The PGresult contains a single result tuple from the current command. This status occurs only when single-row mode has been selected for the query (see Section 32.6).

The PGresult contains several result tuples from the current command. This status occurs only when chunked mode has been selected for the query (see Section 32.6). The number of tuples will not exceed the limit passed to PQsetChunkedRowsMode.

The PGresult represents a synchronization point in pipeline mode, requested by either PQpipelineSync or PQsendPipelineSync. This status occurs only when pipeline mode has been selected.

The PGresult represents a pipeline that has received an error from the server. PQgetResult must be called repeatedly, and each time it will return this status code until the end of the current pipeline, at which point it will return PGRES_PIPELINE_SYNC and normal processing can resume.

If the result status is PGRES_TUPLES_OK, PGRES_SINGLE_TUPLE, or PGRES_TUPLES_CHUNK, then the functions described below can be used to retrieve the rows returned by the query. Note that a SELECT command that happens to retrieve zero rows still shows PGRES_TUPLES_OK. PGRES_COMMAND_OK is for commands that can never return rows (INSERT or UPDATE without a RETURNING clause, etc.). A response of PGRES_EMPTY_QUERY might indicate a bug in the client software.

A result of status PGRES_NONFATAL_ERROR will never be returned directly by PQexec or other query execution functions; results of this kind are instead passed to the notice processor (see Section 32.13).

Converts the enumerated type returned by PQresultStatus into a string constant describing the status code. The caller should not free the result.

Returns the error message associated with the command, or an empty string if there was no error.

If there was an error, the returned string will include a trailing newline. The caller should not free the result directly. It will be freed when the associated PGresult handle is passed to PQclear.

Immediately following a PQexec or PQgetResult call, PQerrorMessage (on the connection) will return the same string as PQresultErrorMessage (on the result). However, a PGresult will retain its error message until destroyed, whereas the connection's error message will change when subsequent operations are done. Use PQresultErrorMessage when you want to know the status associated with a particular PGresult; use PQerrorMessage when you want to know the status from the latest operation on the connection.

Returns a reformatted version of the error message associated with a PGresult object.

In some situations a client might wish to obtain a more detailed version of a previously-reported error. PQresultVerboseErrorMessage addresses this need by computing the message that would have been produced by PQresultErrorMessage if the specified verbosity settings had been in effect for the connection when the given PGresult was generated. If the PGresult is not an error result, “PGresult is not an error result” is reported instead. The returned string includes a trailing newline.

Unlike most other functions for extracting data from a PGresult, the result of this function is a freshly allocated string. The caller must free it using PQfreemem() when the string is no longer needed.

A NULL return is possible if there is insufficient memory.

Returns an individual field of an error report.

fieldcode is an error field identifier; see the symbols listed below. NULL is returned if the PGresult is not an error or warning result, or does not include the specified field. Field values will normally not include a trailing newline. The caller should not free the result directly. It will be freed when the associated PGresult handle is passed to PQclear.

The following field codes are available:

The severity; the field contents are ERROR, FATAL, or PANIC (in an error message), or WARNING, NOTICE, DEBUG, INFO, or LOG (in a notice message), or a localized translation of one of these. Always present.

The severity; the field contents are ERROR, FATAL, or PANIC (in an error message), or WARNING, NOTICE, DEBUG, INFO, or LOG (in a notice message). This is identical to the PG_DIAG_SEVERITY field except that the contents are never localized. This is present only in reports generated by PostgreSQL versions 9.6 and later.

The SQLSTATE code for the error. The SQLSTATE code identifies the type of error that has occurred; it can be used by front-end applications to perform specific operations (such as error handling) in response to a particular database error. For a list of the possible SQLSTATE codes, see Appendix A. This field is not localizable, and is always present.

The primary human-readable error message (typically one line). Always present.

Detail: an optional secondary error message carrying more detail about the problem. Might run to multiple lines.

Hint: an optional suggestion what to do about the problem. This is intended to differ from detail in that it offers advice (potentially inappropriate) rather than hard facts. Might run to multiple lines.

A string containing a decimal integer indicating an error cursor position as an index into the original statement string. The first character has index 1, and positions are measured in characters not bytes.

This is defined the same as the PG_DIAG_STATEMENT_POSITION field, but it is used when the cursor position refers to an internally generated command rather than the one submitted by the client. The PG_DIAG_INTERNAL_QUERY field will always appear when this field appears.

The text of a failed internally-generated command. This could be, for example, an SQL query issued by a PL/pgSQL function.

An indication of the context in which the error occurred. Presently this includes a call stack traceback of active procedural language functions and internally-generated queries. The trace is one entry per line, most recent first.

If the error was associated with a specific database object, the name of the schema containing that object, if any.

If the error was associated with a specific table, the name of the table. (Refer to the schema name field for the name of the table's schema.)

If the error was associated with a specific table column, the name of the column. (Refer to the schema and table name fields to identify the table.)

If the error was associated with a specific data type, the name of the data type. (Refer to the schema name field for the name of the data type's schema.)

If the error was associated with a specific constraint, the name of the constraint. Refer to fields listed above for the associated table or domain. (For this purpose, indexes are treated as constraints, even if they weren't created with constraint syntax.)

The file name of the source-code location where the error was reported.

The line number of the source-code location where the error was reported.

The name of the source-code function reporting the error.

The fields for schema name, table name, column name, data type name, and constraint name are supplied only for a limited number of error types; see Appendix A. Do not assume that the presence of any of these fields guarantees the presence of another field. Core error sources observe the interrelationships noted above, but user-defined functions may use these fields in other ways. In the same vein, do not assume that these fields denote contemporary objects in the current database.

The client is responsible for formatting displayed information to meet its needs; in particular it should break long lines as needed. Newline characters appearing in the error message fields should be treated as paragraph breaks, not line breaks.

Errors generated internally by libpq will have severity and primary message, but typically no other fields.

Note that error fields are only available from PGresult objects, not PGconn objects; there is no PQerrorField function.

Frees the storage associated with a PGresult. Every command result should be freed via PQclear when it is no longer needed.

If the argument is a NULL pointer, no operation is performed.

You can keep a PGresult object around for as long as you need it; it does not go away when you issue a new command, nor even if you close the connection. To get rid of it, you must call PQclear. Failure to do this will result in memory leaks in your application.

These functions are used to extract information from a PGresult object that represents a successful query result (that is, one that has status PGRES_TUPLES_OK, PGRES_SINGLE_TUPLE, or PGRES_TUPLES_CHUNK). They can also be used to extract information from a successful Describe operation: a Describe's result has all the same column information that actual execution of the query would provide, but it has zero rows. For objects with other status values, these functions will act as though the result has zero rows and zero columns.

Returns the number of rows (tuples) in the query result. (Note that PGresult objects are limited to no more than INT_MAX rows, so an int result is sufficient.)

Returns the number of columns (fields) in each row of the query result.

Returns the column name associated with the given column number. Column numbers start at 0. The caller should not free the result directly. It will be freed when the associated PGresult handle is passed to PQclear.

NULL is returned if the column number is out of range.

Returns the column number associated with the given column name.

-1 is returned if the given name does not match any column.

The given name is treated like an identifier in an SQL command, that is, it is downcased unless double-quoted. For example, given a query result generated from the SQL command:

we would have the results:

Returns the OID of the table from which the given column was fetched. Column numbers start at 0.

InvalidOid is returned if the column number is out of range, or if the specified column is not a simple reference to a table column. You can query the system table pg_class to determine exactly which table is referenced.

The type Oid and the constant InvalidOid will be defined when you include the libpq header file. They will both be some integer type.

Returns the column number (within its table) of the column making up the specified query result column. Query-result column numbers start at 0, but table columns have nonzero numbers.

Zero is returned if the column number is out of range, or if the specified column is not a simple reference to a table column.

Returns the format code indicating the format of the given column. Column numbers start at 0.

Format code zero indicates textual data representation, while format code one indicates binary representation. (Other codes are reserved for future definition.)

Returns the data type associated with the given column number. The integer returned is the internal OID number of the type. Column numbers start at 0.

You can query the system table pg_type to obtain the names and properties of the various data types. The OIDs of the built-in data types are defined in the file catalog/pg_type_d.h in the PostgreSQL installation's include directory.

Returns the type modifier of the column associated with the given column number. Column numbers start at 0.

The interpretation of modifier values is type-specific; they typically indicate precision or size limits. The value -1 is used to indicate “no information available”. Most data types do not use modifiers, in which case the value is always -1.

Returns the size in bytes of the column associated with the given column number. Column numbers start at 0.

PQfsize returns the space allocated for this column in a database row, in other words the size of the server's internal representation of the data type. (Accordingly, it is not really very useful to clients.) A negative value indicates the data type is variable-length.

Returns 1 if the PGresult contains binary data and 0 if it contains text data.

This function is deprecated (except for its use in connection with COPY), because it is possible for a single PGresult to contain text data in some columns and binary data in others. PQfformat is preferred. PQbinaryTuples returns 1 only if all columns of the result are binary (format 1).

Returns a single field value of one row of a PGresult. Row and column numbers start at 0. The caller should not free the result directly. It will be freed when the associated PGresult handle is passed to PQclear.

For data in text format, the value returned by PQgetvalue is a null-terminated character string representation of the field value. For data in binary format, the value is in the binary representation determined by the data type's typsend and typreceive functions. (The value is actually followed by a zero byte in this case too, but that is not ordinarily useful, since the value is likely to contain embedded nulls.)

An empty string is returned if the field value is null. See PQgetisnull to distinguish null values from empty-string values.

The pointer returned by PQgetvalue points to storage that is part of the PGresult structure. One should not modify the data it points to, and one must explicitly copy the data into other storage if it is to be used past the lifetime of the PGresult structure itself.

Tests a field for a null value. Row and column numbers start at 0.

This function returns 1 if the field is null and 0 if it contains a non-null value. (Note that PQgetvalue will return an empty string, not a null pointer, for a null field.)

Returns the actual length of a field value in bytes. Row and column numbers start at 0.

This is the actual data length for the particular data value, that is, the size of the object pointed to by PQgetvalue. For text data format this is the same as strlen(). For binary format this is essential information. Note that one should not rely on PQfsize to obtain the actual data length.

Returns the number of parameters of a prepared statement.

This function is only useful when inspecting the result of PQdescribePrepared. For other types of results it will return zero.

Returns the data type of the indicated statement parameter. Parameter numbers start at 0.

This function is only useful when inspecting the result of PQdescribePrepared. For other types of results it will return zero.

Prints out all the rows and, optionally, the column names to the specified output stream.

This function was formerly used by psql to print query results, but this is no longer the case. Note that it assumes all the data is in text format.

These functions are used to extract other information from PGresult objects.

Returns the command status tag from the SQL command that generated the PGresult.

Commonly this is just the name of the command, but it might include additional data such as the number of rows processed. The caller should not free the result directly. It will be freed when the associated PGresult handle is passed to PQclear.

Returns the number of rows affected by the SQL command.

This function returns a string containing the number of rows affected by the SQL statement that generated the PGresult. This function can only be used following the execution of a SELECT, CREATE TABLE AS, INSERT, UPDATE, DELETE, MERGE, MOVE, FETCH, or COPY statement, or an EXECUTE of a prepared query that contains an INSERT, UPDATE, DELETE, or MERGE statement. If the command that generated the PGresult was anything else, PQcmdTuples returns an empty string. The caller should not free the return value directly. It will be freed when the associated PGresult handle is passed to PQclear.

Returns the OID of the inserted row, if the SQL command was an INSERT that inserted exactly one row into a table that has OIDs, or a EXECUTE of a prepared query containing a suitable INSERT statement. Otherwise, this function returns InvalidOid. This function will also return InvalidOid if the table affected by the INSERT statement does not contain OIDs.

This function is deprecated in favor of PQoidValue and is not thread-safe. It returns a string with the OID of the inserted row, while PQoidValue returns the OID value.

PQescapeLiteral escapes a string for use within an SQL command. This is useful when inserting data values as literal constants in SQL commands. Certain characters (such as quotes and backslashes) must be escaped to prevent them from being interpreted specially by the SQL parser. PQescapeLiteral performs this operation.

PQescapeLiteral returns an escaped version of the str parameter in memory allocated with malloc(). This memory should be freed using PQfreemem() when the result is no longer needed. A terminating zero byte is not required, and should not be counted in length. (If a terminating zero byte is found before length bytes are processed, PQescapeLiteral stops at the zero; the behavior is thus rather like strncpy.) The return string has all special characters replaced so that they can be properly processed by the PostgreSQL string literal parser. A terminating zero byte is also added. The single quotes that must surround PostgreSQL string literals are included in the result string.

On error, PQescapeLiteral returns NULL and a suitable message is stored in the conn object.

It is especially important to do proper escaping when handling strings that were received from an untrustworthy source. Otherwise there is a security risk: you are vulnerable to “SQL injection” attacks wherein unwanted SQL commands are fed to your database.

Note that it is neither necessary nor correct to do escaping when a data value is passed as a separate parameter in PQexecParams or its sibling routines.

PQescapeIdentifier escapes a string for use as an SQL identifier, such as a table, column, or function name. This is useful when a user-supplied identifier might contain special characters that would otherwise not be interpreted as part of the identifier by the SQL parser, or when the identifier might contain upper case characters whose case should be preserved.

PQescapeIdentifier returns a version of the str parameter escaped as an SQL identifier in memory allocated with malloc(). This memory must be freed using PQfreemem() when the result is no longer needed. A terminating zero byte is not required, and should not be counted in length. (If a terminating zero byte is found before length bytes are processed, PQescapeIdentifier stops at the zero; the behavior is thus rather like strncpy.) The return string has all special characters replaced so that it will be properly processed as an SQL identifier. A terminating zero byte is also added. The return string will also be surrounded by double quotes.

On error, PQescapeIdentifier returns NULL and a suitable message is stored in the conn object.

As with string literals, to prevent SQL injection attacks, SQL identifiers must be escaped when they are received from an untrustworthy source.

PQescapeStringConn escapes string literals, much like PQescapeLiteral. Unlike PQescapeLiteral, the caller is responsible for providing an appropriately sized buffer. Furthermore, PQescapeStringConn does not generate the single quotes that must surround PostgreSQL string literals; they should be provided in the SQL command that the result is inserted into. The parameter from points to the first character of the string that is to be escaped, and the length parameter gives the number of bytes in this string. A terminating zero byte is not required, and should not be counted in length. (If a terminating zero byte is found before length bytes are processed, PQescapeStringConn stops at the zero; the behavior is thus rather like strncpy.) to shall point to a buffer that is able to hold at least one more byte than twice the value of length, otherwise the behavior is undefined. Behavior is likewise undefined if the to and from strings overlap.

If the error parameter is not NULL, then *error is set to zero on success, nonzero on error. Presently the only possible error conditions involve invalid multibyte encoding in the source string. The output string is still generated on error, but it can be expected that the server will reject it as malformed. On error, a suitable message is stored in the conn object, whether or not error is NULL.

PQescapeStringConn returns the number of bytes written to to, not including the terminating zero byte.

PQescapeString is an older, deprecated version of PQescapeStringConn.

The only difference from PQescapeStringConn is that PQescapeString does not take PGconn or error parameters. Because of this, it cannot adjust its behavior depending on the connection properties (such as character encoding) and therefore it might give the wrong results. Also, it has no way to report error conditions.

PQescapeString can be used safely in client programs that work with only one PostgreSQL connection at a time (in this case it can find out what it needs to know “behind the scenes”). In other contexts it is a security hazard and should be avoided in favor of PQescapeStringConn.

Escapes binary data for use within an SQL command with the type bytea. As with PQescapeStringConn, this is only used when inserting data directly into an SQL command string.

Certain byte values must be escaped when used as part of a bytea literal in an SQL statement. PQescapeByteaConn escapes bytes using either hex encoding or backslash escaping. See Section 8.4 for more information.

The from parameter points to the first byte of the string that is to be escaped, and the from_length parameter gives the number of bytes in this binary string. (A terminating zero byte is neither necessary nor counted.) The to_length parameter points to a variable that will hold the resultant escaped string length. This result string length includes the terminating zero byte of the result.

PQescapeByteaConn returns an escaped version of the from parameter binary string in memory allocated with malloc(). This memory should be freed using PQfreemem() when the result is no longer needed. The return string has all special characters replaced so that they can be properly processed by the PostgreSQL string literal parser, and the bytea input function. A terminating zero byte is also added. The single quotes that must surround PostgreSQL string literals are not part of the result string.

On error, a null pointer is returned, and a suitable error message is stored in the conn object. Currently, the only possible error is insufficient memory for the result string.

PQescapeBytea is an older, deprecated version of PQescapeByteaConn.

The only difference from PQescapeByteaConn is that PQescapeBytea does not take a PGconn parameter. Because of this, PQescapeBytea can only be used safely in client programs that use a single PostgreSQL connection at a time (in this case it can find out what it needs to know “behind the scenes”). It might give the wrong results if used in programs that use multiple database connections (use PQescapeByteaConn in such cases).

Converts a string representation of binary data into binary data — the reverse of PQescapeBytea. This is needed when retrieving bytea data in text format, but not when retrieving it in binary format.

The from parameter points to a string such as might be returned by PQgetvalue when applied to a bytea column. PQunescapeBytea converts this string representation into its binary representation. It returns a pointer to a buffer allocated with malloc(), or NULL on error, and puts the size of the buffer in to_length. The result must be freed using PQfreemem when it is no longer needed.

This conversion is not exactly the inverse of PQescapeBytea, because the string is not expected to be “escaped” when received from PQgetvalue. In particular this means there is no need for string quoting considerations, and so no need for a PGconn parameter.

Examples:

Example 1 (javascript):

PGresult *PQexec(PGconn *conn, const char *command);

Example 2 (javascript):

PGresult *PQexecParams(PGconn *conn,
                       const char *command,
                       int nParams,
                       const Oid *paramTypes,
                       const char * const *paramValues,
                       const int *paramLengths,
                       const int *paramFormats,
                       int resultFormat);

Example 3 (unknown):

SELECT * FROM mytable WHERE x = $1::bigint;

Example 4 (javascript):

PGresult *PQprepare(PGconn *conn,
                    const char *stmtName,
                    const char *query,
                    int nParams,
                    const Oid *paramTypes);

---

PostgreSQL: Documentation: 18: 35.19. constraint_table_usage

URL: https://www.postgresql.org/docs/current/infoschema-constraint-table-usage.html

Contents:

• 35.19. constraint_table_usage #

The view constraint_table_usage identifies all tables in the current database that are used by some constraint and are owned by a currently enabled role. (This is different from the view table_constraints, which identifies all table constraints along with the table they are defined on.) For a foreign key constraint, this view identifies the table that the foreign key references. For a unique or primary key constraint, this view simply identifies the table the constraint belongs to. Check constraints and not-null constraints are not included in this view.

Table 35.17. constraint_table_usage Columns

table_catalog sql_identifier

Name of the database that contains the table that is used by some constraint (always the current database)

table_schema sql_identifier

Name of the schema that contains the table that is used by some constraint

table_name sql_identifier

Name of the table that is used by some constraint

constraint_catalog sql_identifier

Name of the database that contains the constraint (always the current database)

constraint_schema sql_identifier

Name of the schema that contains the constraint

constraint_name sql_identifier

Name of the constraint

---

PostgreSQL: Documentation: 18: 35.17. columns

URL: https://www.postgresql.org/docs/current/infoschema-columns.html

Contents:

• 35.17. columns #

The view columns contains information about all table columns (or view columns) in the database. System columns (ctid, etc.) are not included. Only those columns are shown that the current user has access to (by way of being the owner or having some privilege).

Table 35.15. columns Columns

table_catalog sql_identifier

Name of the database containing the table (always the current database)

table_schema sql_identifier

Name of the schema containing the table

table_name sql_identifier

column_name sql_identifier

ordinal_position cardinal_number

Ordinal position of the column within the table (count starts at 1)

column_default character_data

Default expression of the column

is_nullable yes_or_no

YES if the column is possibly nullable, NO if it is known not nullable. A not-null constraint is one way a column can be known not nullable, but there can be others.

data_type character_data

Data type of the column, if it is a built-in type, or ARRAY if it is some array (in that case, see the view element_types), else USER-DEFINED (in that case, the type is identified in udt_name and associated columns). If the column is based on a domain, this column refers to the type underlying the domain (and the domain is identified in domain_name and associated columns).

character_maximum_length cardinal_number

If data_type identifies a character or bit string type, the declared maximum length; null for all other data types or if no maximum length was declared.

character_octet_length cardinal_number

If data_type identifies a character type, the maximum possible length in octets (bytes) of a datum; null for all other data types. The maximum octet length depends on the declared character maximum length (see above) and the server encoding.

numeric_precision cardinal_number

If data_type identifies a numeric type, this column contains the (declared or implicit) precision of the type for this column. The precision indicates the number of significant digits. It can be expressed in decimal (base 10) or binary (base 2) terms, as specified in the column numeric_precision_radix. For all other data types, this column is null.

numeric_precision_radix cardinal_number

If data_type identifies a numeric type, this column indicates in which base the values in the columns numeric_precision and numeric_scale are expressed. The value is either 2 or 10. For all other data types, this column is null.

numeric_scale cardinal_number

If data_type identifies an exact numeric type, this column contains the (declared or implicit) scale of the type for this column. The scale indicates the number of significant digits to the right of the decimal point. It can be expressed in decimal (base 10) or binary (base 2) terms, as specified in the column numeric_precision_radix. For all other data types, this column is null.

datetime_precision cardinal_number

If data_type identifies a date, time, timestamp, or interval type, this column contains the (declared or implicit) fractional seconds precision of the type for this column, that is, the number of decimal digits maintained following the decimal point in the seconds value. For all other data types, this column is null.

interval_type character_data

If data_type identifies an interval type, this column contains the specification which fields the intervals include for this column, e.g., YEAR TO MONTH, DAY TO SECOND, etc. If no field restrictions were specified (that is, the interval accepts all fields), and for all other data types, this field is null.

interval_precision cardinal_number

Applies to a feature not available in PostgreSQL (see datetime_precision for the fractional seconds precision of interval type columns)

character_set_catalog sql_identifier

Applies to a feature not available in PostgreSQL

character_set_schema sql_identifier

Applies to a feature not available in PostgreSQL

character_set_name sql_identifier

Applies to a feature not available in PostgreSQL

collation_catalog sql_identifier

Name of the database containing the collation of the column (always the current database), null if default or the data type of the column is not collatable

collation_schema sql_identifier

Name of the schema containing the collation of the column, null if default or the data type of the column is not collatable

collation_name sql_identifier

Name of the collation of the column, null if default or the data type of the column is not collatable

domain_catalog sql_identifier

If the column has a domain type, the name of the database that the domain is defined in (always the current database), else null.

domain_schema sql_identifier

If the column has a domain type, the name of the schema that the domain is defined in, else null.

domain_name sql_identifier

If the column has a domain type, the name of the domain, else null.

udt_catalog sql_identifier

Name of the database that the column data type (the underlying type of the domain, if applicable) is defined in (always the current database)

udt_schema sql_identifier

Name of the schema that the column data type (the underlying type of the domain, if applicable) is defined in

udt_name sql_identifier

Name of the column data type (the underlying type of the domain, if applicable)

scope_catalog sql_identifier

Applies to a feature not available in PostgreSQL

scope_schema sql_identifier

Applies to a feature not available in PostgreSQL

scope_name sql_identifier

Applies to a feature not available in PostgreSQL

maximum_cardinality cardinal_number

Always null, because arrays always have unlimited maximum cardinality in PostgreSQL

dtd_identifier sql_identifier

An identifier of the data type descriptor of the column, unique among the data type descriptors pertaining to the table. This is mainly useful for joining with other instances of such identifiers. (The specific format of the identifier is not defined and not guaranteed to remain the same in future versions.)

is_self_referencing yes_or_no

Applies to a feature not available in PostgreSQL

is_identity yes_or_no

If the column is an identity column, then YES, else NO.

identity_generation character_data

If the column is an identity column, then ALWAYS or BY DEFAULT, reflecting the definition of the column.

identity_start character_data

If the column is an identity column, then the start value of the internal sequence, else null.

identity_increment character_data

If the column is an identity column, then the increment of the internal sequence, else null.

identity_maximum character_data

If the column is an identity column, then the maximum value of the internal sequence, else null.

identity_minimum character_data

If the column is an identity column, then the minimum value of the internal sequence, else null.

identity_cycle yes_or_no

If the column is an identity column, then YES if the internal sequence cycles or NO if it does not; otherwise null.

is_generated character_data

If the column is a generated column, then ALWAYS, else NEVER.

generation_expression character_data

If the column is a generated column, then the generation expression, else null.

is_updatable yes_or_no

YES if the column is updatable, NO if not (Columns in base tables are always updatable, columns in views not necessarily)

Since data types can be defined in a variety of ways in SQL, and PostgreSQL contains additional ways to define data types, their representation in the information schema can be somewhat difficult. The column data_type is supposed to identify the underlying built-in type of the column. In PostgreSQL, this means that the type is defined in the system catalog schema pg_catalog. This column might be useful if the application can handle the well-known built-in types specially (for example, format the numeric types differently or use the data in the precision columns). The columns udt_name, udt_schema, and udt_catalog always identify the underlying data type of the column, even if the column is based on a domain. (Since PostgreSQL treats built-in types like user-defined types, built-in types appear here as well. This is an extension of the SQL standard.) These columns should be used if an application wants to process data differently according to the type, because in that case it wouldn't matter if the column is really based on a domain. If the column is based on a domain, the identity of the domain is stored in the columns domain_name, domain_schema, and domain_catalog. If you want to pair up columns with their associated data types and treat domains as separate types, you could write coalesce(domain_name, udt_name), etc.

---

PostgreSQL: Documentation: 18: Chapter 11. Indexes

URL: https://www.postgresql.org/docs/current/indexes.html

Contents:

• Chapter 11. Indexes

Indexes are a common way to enhance database performance. An index allows the database server to find and retrieve specific rows much faster than it could do without an index. But indexes also add overhead to the database system as a whole, so they should be used sensibly.

---

PostgreSQL: Documentation: 18: 8.16. Composite Types

URL: https://www.postgresql.org/docs/current/rowtypes.html

Contents:

• 8.16. Composite Types #
  • 8.16.1. Declaration of Composite Types #
  • 8.16.2. Constructing Composite Values #
  • 8.16.3. Accessing Composite Types #
  • 8.16.4. Modifying Composite Types #
  • 8.16.5. Using Composite Types in Queries #
  • Tip
  • Tip
  • 8.16.6. Composite Type Input and Output Syntax #
  • Note

A composite type represents the structure of a row or record; it is essentially just a list of field names and their data types. PostgreSQL allows composite types to be used in many of the same ways that simple types can be used. For example, a column of a table can be declared to be of a composite type.

Here are two simple examples of defining composite types:

The syntax is comparable to CREATE TABLE, except that only field names and types can be specified; no constraints (such as NOT NULL) can presently be included. Note that the AS keyword is essential; without it, the system will think a different kind of CREATE TYPE command is meant, and you will get odd syntax errors.

Having defined the types, we can use them to create tables:

Whenever you create a table, a composite type is also automatically created, with the same name as the table, to represent the table's row type. For example, had we said:

then the same inventory_item composite type shown above would come into being as a byproduct, and could be used just as above. Note however an important restriction of the current implementation: since no constraints are associated with a composite type, the constraints shown in the table definition do not apply to values of the composite type outside the table. (To work around this, create a domain over the composite type, and apply the desired constraints as CHECK constraints of the domain.)

To write a composite value as a literal constant, enclose the field values within parentheses and separate them by commas. You can put double quotes around any field value, and must do so if it contains commas or parentheses. (More details appear below.) Thus, the general format of a composite constant is the following:

which would be a valid value of the inventory_item type defined above. To make a field be NULL, write no characters at all in its position in the list. For example, this constant specifies a NULL third field:

If you want an empty string rather than NULL, write double quotes:

Here the first field is a non-NULL empty string, the third is NULL.

(These constants are actually only a special case of the generic type constants discussed in Section 4.1.2.7. The constant is initially treated as a string and passed to the composite-type input conversion routine. An explicit type specification might be necessary to tell which type to convert the constant to.)

The ROW expression syntax can also be used to construct composite values. In most cases this is considerably simpler to use than the string-literal syntax since you don't have to worry about multiple layers of quoting. We already used this method above:

The ROW keyword is actually optional as long as you have more than one field in the expression, so these can be simplified to:

The ROW expression syntax is discussed in more detail in Section 4.2.13.

To access a field of a composite column, one writes a dot and the field name, much like selecting a field from a table name. In fact, it's so much like selecting from a table name that you often have to use parentheses to keep from confusing the parser. For example, you might try to select some subfields from our on_hand example table with something like:

This will not work since the name item is taken to be a table name, not a column name of on_hand, per SQL syntax rules. You must write it like this:

or if you need to use the table name as well (for instance in a multitable query), like this:

Now the parenthesized object is correctly interpreted as a reference to the item column, and then the subfield can be selected from it.

Similar syntactic issues apply whenever you select a field from a composite value. For instance, to select just one field from the result of a function that returns a composite value, you'd need to write something like:

Without the extra parentheses, this will generate a syntax error.

The special field name * means “all fields”, as further explained in Section 8.16.5.

Here are some examples of the proper syntax for inserting and updating composite columns. First, inserting or updating a whole column:

The first example omits ROW, the second uses it; we could have done it either way.

We can update an individual subfield of a composite column:

Notice here that we don't need to (and indeed cannot) put parentheses around the column name appearing just after SET, but we do need parentheses when referencing the same column in the expression to the right of the equal sign.

And we can specify subfields as targets for INSERT, too:

Had we not supplied values for all the subfields of the column, the remaining subfields would have been filled with null values.

There are various special syntax rules and behaviors associated with composite types in queries. These rules provide useful shortcuts, but can be confusing if you don't know the logic behind them.

In PostgreSQL, a reference to a table name (or alias) in a query is effectively a reference to the composite value of the table's current row. For example, if we had a table inventory_item as shown above, we could write:

This query produces a single composite-valued column, so we might get output like:

Note however that simple names are matched to column names before table names, so this example works only because there is no column named c in the query's tables.

The ordinary qualified-column-name syntax table_name.column_name can be understood as applying field selection to the composite value of the table's current row. (For efficiency reasons, it's not actually implemented that way.)

then, according to the SQL standard, we should get the contents of the table expanded into separate columns:

PostgreSQL will apply this expansion behavior to any composite-valued expression, although as shown above, you need to write parentheses around the value that .* is applied to whenever it's not a simple table name. For example, if myfunc() is a function returning a composite type with columns a, b, and c, then these two queries have the same result:

PostgreSQL handles column expansion by actually transforming the first form into the second. So, in this example, myfunc() would get invoked three times per row with either syntax. If it's an expensive function you may wish to avoid that, which you can do with a query like:

Placing the function in a LATERAL FROM item keeps it from being invoked more than once per row. m.* is still expanded into m.a, m.b, m.c, but now those variables are just references to the output of the FROM item. (The LATERAL keyword is optional here, but we show it to clarify that the function is getting x from some_table.)

The composite_value.* syntax results in column expansion of this kind when it appears at the top level of a SELECT output list, a RETURNING list in INSERT/UPDATE/DELETE/MERGE, a VALUES clause, or a row constructor. In all other contexts (including when nested inside one of those constructs), attaching .* to a composite value does not change the value, since it means “all columns” and so the same composite value is produced again. For example, if somefunc() accepts a composite-valued argument, these queries are the same:

In both cases, the current row of inventory_item is passed to the function as a single composite-valued argument. Even though .* does nothing in such cases, using it is good style, since it makes clear that a composite value is intended. In particular, the parser will consider c in c.* to refer to a table name or alias, not to a column name, so that there is no ambiguity; whereas without .*, it is not clear whether c means a table name or a column name, and in fact the column-name interpretation will be preferred if there is a column named c.

Another example demonstrating these concepts is that all these queries mean the same thing:

All of these ORDER BY clauses specify the row's composite value, resulting in sorting the rows according to the rules described in Section 9.25.6. However, if inventory_item contained a column named c, the first case would be different from the others, as it would mean to sort by that column only. Given the column names previously shown, these queries are also equivalent to those above:

(The last case uses a row constructor with the key word ROW omitted.)

Another special syntactical behavior associated with composite values is that we can use functional notation for extracting a field of a composite value. The simple way to explain this is that the notations field(table) and table.field are interchangeable. For example, these queries are equivalent:

Moreover, if we have a function that accepts a single argument of a composite type, we can call it with either notation. These queries are all equivalent:

This equivalence between functional notation and field notation makes it possible to use functions on composite types to implement “computed fields”. An application using the last query above wouldn't need to be directly aware that somefunc isn't a real column of the table.

Because of this behavior, it's unwise to give a function that takes a single composite-type argument the same name as any of the fields of that composite type. If there is ambiguity, the field-name interpretation will be chosen if field-name syntax is used, while the function will be chosen if function-call syntax is used. However, PostgreSQL versions before 11 always chose the field-name interpretation, unless the syntax of the call required it to be a function call. One way to force the function interpretation in older versions is to schema-qualify the function name, that is, write schema.func(compositevalue).

The external text representation of a composite value consists of items that are interpreted according to the I/O conversion rules for the individual field types, plus decoration that indicates the composite structure. The decoration consists of parentheses (( and )) around the whole value, plus commas (,) between adjacent items. Whitespace outside the parentheses is ignored, but within the parentheses it is considered part of the field value, and might or might not be significant depending on the input conversion rules for the field data type. For example, in:

the whitespace will be ignored if the field type is integer, but not if it is text.

As shown previously, when writing a composite value you can write double quotes around any individual field value. You must do so if the field value would otherwise confuse the composite-value parser. In particular, fields containing parentheses, commas, double quotes, or backslashes must be double-quoted. To put a double quote or backslash in a quoted composite field value, precede it with a backslash. (Also, a pair of double quotes within a double-quoted field value is taken to represent a double quote character, analogously to the rules for single quotes in SQL literal strings.) Alternatively, you can avoid quoting and use backslash-escaping to protect all data characters that would otherwise be taken as composite syntax.

A completely empty field value (no characters at all between the commas or parentheses) represents a NULL. To write a value that is an empty string rather than NULL, write "".

The composite output routine will put double quotes around field values if they are empty strings or contain parentheses, commas, double quotes, backslashes, or white space. (Doing so for white space is not essential, but aids legibility.) Double quotes and backslashes embedded in field values will be doubled.

Remember that what you write in an SQL command will first be interpreted as a string literal, and then as a composite. This doubles the number of backslashes you need (assuming escape string syntax is used). For example, to insert a text field containing a double quote and a backslash in a composite value, you'd need to write:

The string-literal processor removes one level of backslashes, so that what arrives at the composite-value parser looks like ("\"\"). In turn, the string fed to the text data type's input routine becomes ". (If we were working with a data type whose input routine also treated backslashes specially, bytea for example, we might need as many as eight backslashes in the command to get one backslash into the stored composite field.) Dollar quoting (see Section 4.1.2.4) can be used to avoid the need to double backslashes.

The ROW constructor syntax is usually easier to work with than the composite-literal syntax when writing composite values in SQL commands. In ROW, individual field values are written the same way they would be written when not members of a composite.

Examples:

Example 1 (unknown):

CREATE TYPE complex AS (
    r       double precision,
    i       double precision
);

CREATE TYPE inventory_item AS (
    name            text,
    supplier_id     integer,
    price           numeric
);

Example 2 (unknown):

CREATE TABLE on_hand (
    item      inventory_item,
    count     integer
);

INSERT INTO on_hand VALUES (ROW('fuzzy dice', 42, 1.99), 1000);

Example 3 (unknown):

CREATE FUNCTION price_extension(inventory_item, integer) RETURNS numeric
AS 'SELECT $1.price * $2' LANGUAGE SQL;

SELECT price_extension(item, 10) FROM on_hand;

Example 4 (unknown):

CREATE TABLE inventory_item (
    name            text,
    supplier_id     integer REFERENCES suppliers,
    price           numeric CHECK (price > 0)
);

---

PostgreSQL: Documentation: 18: 32.5. Pipeline Mode

URL: https://www.postgresql.org/docs/current/libpq-pipeline-mode.html

Contents:

• 32.5. Pipeline Mode #
  • 32.5.1. Using Pipeline Mode #
  • Note
  • 32.5.1.1. Issuing Queries #
  • 32.5.1.2. Processing Results #
  • 32.5.1.3. Error Handling #
  • Note
  • 32.5.1.4. Interleaving Result Processing and Query Dispatch #
  • 32.5.2. Functions Associated with Pipeline Mode #
  • 32.5.3. When to Use Pipeline Mode #

libpq pipeline mode allows applications to send a query without having to read the result of the previously sent query. Taking advantage of the pipeline mode, a client will wait less for the server, since multiple queries/results can be sent/received in a single network transaction.

While pipeline mode provides a significant performance boost, writing clients using the pipeline mode is more complex because it involves managing a queue of pending queries and finding which result corresponds to which query in the queue.

Pipeline mode also generally consumes more memory on both the client and server, though careful and aggressive management of the send/receive queue can mitigate this. This applies whether or not the connection is in blocking or non-blocking mode.

While libpq's pipeline API was introduced in PostgreSQL 14, it is a client-side feature which doesn't require special server support and works on any server that supports the v3 extended query protocol. For more information see Section 54.2.4.

To issue pipelines, the application must switch the connection into pipeline mode, which is done with PQenterPipelineMode. PQpipelineStatus can be used to test whether pipeline mode is active. In pipeline mode, only asynchronous operations that utilize the extended query protocol are permitted, command strings containing multiple SQL commands are disallowed, and so is COPY. Using synchronous command execution functions such as PQfn, PQexec, PQexecParams, PQprepare, PQexecPrepared, PQdescribePrepared, PQdescribePortal, PQclosePrepared, PQclosePortal, is an error condition. PQsendQuery is also disallowed, because it uses the simple query protocol. Once all dispatched commands have had their results processed, and the end pipeline result has been consumed, the application may return to non-pipelined mode with PQexitPipelineMode.

It is best to use pipeline mode with libpq in non-blocking mode. If used in blocking mode it is possible for a client/server deadlock to occur. [15]

After entering pipeline mode, the application dispatches requests using PQsendQueryParams or its prepared-query sibling PQsendQueryPrepared. These requests are queued on the client-side until flushed to the server; this occurs when PQpipelineSync is used to establish a synchronization point in the pipeline, or when PQflush is called. The functions PQsendPrepare, PQsendDescribePrepared, PQsendDescribePortal, PQsendClosePrepared, and PQsendClosePortal also work in pipeline mode. Result processing is described below.

The server executes statements, and returns results, in the order the client sends them. The server will begin executing the commands in the pipeline immediately, not waiting for the end of the pipeline. Note that results are buffered on the server side; the server flushes that buffer when a synchronization point is established with either PQpipelineSync or PQsendPipelineSync, or when PQsendFlushRequest is called. If any statement encounters an error, the server aborts the current transaction and does not execute any subsequent command in the queue until the next synchronization point; a PGRES_PIPELINE_ABORTED result is produced for each such command. (This remains true even if the commands in the pipeline would rollback the transaction.) Query processing resumes after the synchronization point.

It's fine for one operation to depend on the results of a prior one; for example, one query may define a table that the next query in the same pipeline uses. Similarly, an application may create a named prepared statement and execute it with later statements in the same pipeline.

To process the result of one query in a pipeline, the application calls PQgetResult repeatedly and handles each result until PQgetResult returns null. The result from the next query in the pipeline may then be retrieved using PQgetResult again and the cycle repeated. The application handles individual statement results as normal. When the results of all the queries in the pipeline have been returned, PQgetResult returns a result containing the status value PGRES_PIPELINE_SYNC

The client may choose to defer result processing until the complete pipeline has been sent, or interleave that with sending further queries in the pipeline; see Section 32.5.1.4.

PQgetResult behaves the same as for normal asynchronous processing except that it may contain the new PGresult types PGRES_PIPELINE_SYNC and PGRES_PIPELINE_ABORTED. PGRES_PIPELINE_SYNC is reported exactly once for each PQpipelineSync or PQsendPipelineSync at the corresponding point in the pipeline. PGRES_PIPELINE_ABORTED is emitted in place of a normal query result for the first error and all subsequent results until the next PGRES_PIPELINE_SYNC; see Section 32.5.1.3.

PQisBusy, PQconsumeInput, etc operate as normal when processing pipeline results. In particular, a call to PQisBusy in the middle of a pipeline returns 0 if the results for all the queries issued so far have been consumed.

libpq does not provide any information to the application about the query currently being processed (except that PQgetResult returns null to indicate that we start returning the results of next query). The application must keep track of the order in which it sent queries, to associate them with their corresponding results. Applications will typically use a state machine or a FIFO queue for this.

From the client's perspective, after PQresultStatus returns PGRES_FATAL_ERROR, the pipeline is flagged as aborted. PQresultStatus will report a PGRES_PIPELINE_ABORTED result for each remaining queued operation in an aborted pipeline. The result for PQpipelineSync or PQsendPipelineSync is reported as PGRES_PIPELINE_SYNC to signal the end of the aborted pipeline and resumption of normal result processing.

The client must process results with PQgetResult during error recovery.

If the pipeline used an implicit transaction, then operations that have already executed are rolled back and operations that were queued to follow the failed operation are skipped entirely. The same behavior holds if the pipeline starts and commits a single explicit transaction (i.e. the first statement is BEGIN and the last is COMMIT) except that the session remains in an aborted transaction state at the end of the pipeline. If a pipeline contains multiple explicit transactions, all transactions that committed prior to the error remain committed, the currently in-progress transaction is aborted, and all subsequent operations are skipped completely, including subsequent transactions. If a pipeline synchronization point occurs with an explicit transaction block in aborted state, the next pipeline will become aborted immediately unless the next command puts the transaction in normal mode with ROLLBACK.

The client must not assume that work is committed when it sends a COMMIT — only when the corresponding result is received to confirm the commit is complete. Because errors arrive asynchronously, the application needs to be able to restart from the last received committed change and resend work done after that point if something goes wrong.

To avoid deadlocks on large pipelines the client should be structured around a non-blocking event loop using operating system facilities such as select, poll, WaitForMultipleObjectEx, etc.

The client application should generally maintain a queue of work remaining to be dispatched and a queue of work that has been dispatched but not yet had its results processed. When the socket is writable it should dispatch more work. When the socket is readable it should read results and process them, matching them up to the next entry in its corresponding results queue. Based on available memory, results from the socket should be read frequently: there's no need to wait until the pipeline end to read the results. Pipelines should be scoped to logical units of work, usually (but not necessarily) one transaction per pipeline. There's no need to exit pipeline mode and re-enter it between pipelines, or to wait for one pipeline to finish before sending the next.

An example using select() and a simple state machine to track sent and received work is in src/test/modules/libpq_pipeline/libpq_pipeline.c in the PostgreSQL source distribution.

Returns the current pipeline mode status of the libpq connection.

PQpipelineStatus can return one of the following values:

The libpq connection is in pipeline mode.

The libpq connection is not in pipeline mode.

The libpq connection is in pipeline mode and an error occurred while processing the current pipeline. The aborted flag is cleared when PQgetResult returns a result of type PGRES_PIPELINE_SYNC.

Causes a connection to enter pipeline mode if it is currently idle or already in pipeline mode.

Returns 1 for success. Returns 0 and has no effect if the connection is not currently idle, i.e., it has a result ready, or it is waiting for more input from the server, etc. This function does not actually send anything to the server, it just changes the libpq connection state.

Causes a connection to exit pipeline mode if it is currently in pipeline mode with an empty queue and no pending results.

Returns 1 for success. Returns 1 and takes no action if not in pipeline mode. If the current statement isn't finished processing, or PQgetResult has not been called to collect results from all previously sent query, returns 0 (in which case, use PQerrorMessage to get more information about the failure).

Marks a synchronization point in a pipeline by sending a sync message and flushing the send buffer. This serves as the delimiter of an implicit transaction and an error recovery point; see Section 32.5.1.3.

Returns 1 for success. Returns 0 if the connection is not in pipeline mode or sending a sync message failed.

Marks a synchronization point in a pipeline by sending a sync message without flushing the send buffer. This serves as the delimiter of an implicit transaction and an error recovery point; see Section 32.5.1.3.

Returns 1 for success. Returns 0 if the connection is not in pipeline mode or sending a sync message failed. Note that the message is not itself flushed to the server automatically; use PQflush if necessary.

Sends a request for the server to flush its output buffer.

Returns 1 for success. Returns 0 on any failure.

The server flushes its output buffer automatically as a result of PQpipelineSync being called, or on any request when not in pipeline mode; this function is useful to cause the server to flush its output buffer in pipeline mode without establishing a synchronization point. Note that the request is not itself flushed to the server automatically; use PQflush if necessary.

Much like asynchronous query mode, there is no meaningful performance overhead when using pipeline mode. It increases client application complexity, and extra caution is required to prevent client/server deadlocks, but pipeline mode can offer considerable performance improvements, in exchange for increased memory usage from leaving state around longer.

Pipeline mode is most useful when the server is distant, i.e., network latency (“ping time”) is high, and also when many small operations are being performed in rapid succession. There is usually less benefit in using pipelined commands when each query takes many multiples of the client/server round-trip time to execute. A 100-statement operation run on a server 300 ms round-trip-time away would take 30 seconds in network latency alone without pipelining; with pipelining it may spend as little as 0.3 s waiting for results from the server.

Use pipelined commands when your application does lots of small INSERT, UPDATE and DELETE operations that can't easily be transformed into operations on sets, or into a COPY operation.

Pipeline mode is not useful when information from one operation is required by the client to produce the next operation. In such cases, the client would have to introduce a synchronization point and wait for a full client/server round-trip to get the results it needs. However, it's often possible to adjust the client design to exchange the required information server-side. Read-modify-write cycles are especially good candidates; for example:

could be much more efficiently done with:

Pipelining is less useful, and more complex, when a single pipeline contains multiple transactions (see Section 32.5.1.3).

[15] The client will block trying to send queries to the server, but the server will block trying to send results to the client from queries it has already processed. This only occurs when the client sends enough queries to fill both its output buffer and the server's receive buffer before it switches to processing input from the server, but it's hard to predict exactly when that will happen.

Examples:

Example 1 (javascript):

PGpipelineStatus PQpipelineStatus(const PGconn *conn);

Example 2 (unknown):

int PQenterPipelineMode(PGconn *conn);

Example 3 (unknown):

int PQexitPipelineMode(PGconn *conn);

Example 4 (unknown):

int PQpipelineSync(PGconn *conn);

---

PostgreSQL: Documentation: 18: 5.12. Table Partitioning

URL: https://www.postgresql.org/docs/current/ddl-partitioning.html

Contents:

• 5.12. Table Partitioning #
  • 5.12.1. Overview #
  • 5.12.2. Declarative Partitioning #
  • 5.12.2.1. Example #
  • 5.12.2.2. Partition Maintenance #
  • 5.12.2.3. Limitations #
  • 5.12.3. Partitioning Using Inheritance #
  • 5.12.3.1. Example #
  • Note
  • 5.12.3.2. Maintenance for Inheritance Partitioning #

PostgreSQL supports basic table partitioning. This section describes why and how to implement partitioning as part of your database design.

Partitioning refers to splitting what is logically one large table into smaller physical pieces. Partitioning can provide several benefits:

Query performance can be improved dramatically in certain situations, particularly when most of the heavily accessed rows of the table are in a single partition or a small number of partitions. Partitioning effectively substitutes for the upper tree levels of indexes, making it more likely that the heavily-used parts of the indexes fit in memory.

When queries or updates access a large percentage of a single partition, performance can be improved by using a sequential scan of that partition instead of using an index, which would require random-access reads scattered across the whole table.

Bulk loads and deletes can be accomplished by adding or removing partitions, if the usage pattern is accounted for in the partitioning design. Dropping an individual partition using DROP TABLE, or doing ALTER TABLE DETACH PARTITION, is far faster than a bulk operation. These commands also entirely avoid the VACUUM overhead caused by a bulk DELETE.

Seldom-used data can be migrated to cheaper and slower storage media.

These benefits will normally be worthwhile only when a table would otherwise be very large. The exact point at which a table will benefit from partitioning depends on the application, although a rule of thumb is that the size of the table should exceed the physical memory of the database server.

PostgreSQL offers built-in support for the following forms of partitioning:

The table is partitioned into “ranges” defined by a key column or set of columns, with no overlap between the ranges of values assigned to different partitions. For example, one might partition by date ranges, or by ranges of identifiers for particular business objects. Each range's bounds are understood as being inclusive at the lower end and exclusive at the upper end. For example, if one partition's range is from 1 to 10, and the next one's range is from 10 to 20, then value 10 belongs to the second partition not the first.

The table is partitioned by explicitly listing which key value(s) appear in each partition.

The table is partitioned by specifying a modulus and a remainder for each partition. Each partition will hold the rows for which the hash value of the partition key divided by the specified modulus will produce the specified remainder.

If your application needs to use other forms of partitioning not listed above, alternative methods such as inheritance and UNION ALL views can be used instead. Such methods offer flexibility but do not have some of the performance benefits of built-in declarative partitioning.

PostgreSQL allows you to declare that a table is divided into partitions. The table that is divided is referred to as a partitioned table. The declaration includes the partitioning method as described above, plus a list of columns or expressions to be used as the partition key.

The partitioned table itself is a “virtual” table having no storage of its own. Instead, the storage belongs to partitions, which are otherwise-ordinary tables associated with the partitioned table. Each partition stores a subset of the data as defined by its partition bounds. All rows inserted into a partitioned table will be routed to the appropriate one of the partitions based on the values of the partition key column(s). Updating the partition key of a row will cause it to be moved into a different partition if it no longer satisfies the partition bounds of its original partition.

Partitions may themselves be defined as partitioned tables, resulting in sub-partitioning. Although all partitions must have the same columns as their partitioned parent, partitions may have their own indexes, constraints and default values, distinct from those of other partitions. See CREATE TABLE for more details on creating partitioned tables and partitions.

It is not possible to turn a regular table into a partitioned table or vice versa. However, it is possible to add an existing regular or partitioned table as a partition of a partitioned table, or remove a partition from a partitioned table turning it into a standalone table; this can simplify and speed up many maintenance processes. See ALTER TABLE to learn more about the ATTACH PARTITION and DETACH PARTITION sub-commands.

Partitions can also be foreign tables, although considerable care is needed because it is then the user's responsibility that the contents of the foreign table satisfy the partitioning rule. There are some other restrictions as well. See CREATE FOREIGN TABLE for more information.

Suppose we are constructing a database for a large ice cream company. The company measures peak temperatures every day as well as ice cream sales in each region. Conceptually, we want a table like:

We know that most queries will access just the last week's, month's or quarter's data, since the main use of this table will be to prepare online reports for management. To reduce the amount of old data that needs to be stored, we decide to keep only the most recent 3 years worth of data. At the beginning of each month we will remove the oldest month's data. In this situation we can use partitioning to help us meet all of our different requirements for the measurements table.

To use declarative partitioning in this case, use the following steps:

Create the measurement table as a partitioned table by specifying the PARTITION BY clause, which includes the partitioning method (RANGE in this case) and the list of column(s) to use as the partition key.

Create partitions. Each partition's definition must specify bounds that correspond to the partitioning method and partition key of the parent. Note that specifying bounds such that the new partition's values would overlap with those in one or more existing partitions will cause an error.

Partitions thus created are in every way normal PostgreSQL tables (or, possibly, foreign tables). It is possible to specify a tablespace and storage parameters for each partition separately.

For our example, each partition should hold one month's worth of data, to match the requirement of deleting one month's data at a time. So the commands might look like:

(Recall that adjacent partitions can share a bound value, since range upper bounds are treated as exclusive bounds.)

If you wish to implement sub-partitioning, again specify the PARTITION BY clause in the commands used to create individual partitions, for example:

After creating partitions of measurement_y2006m02, any data inserted into measurement that is mapped to measurement_y2006m02 (or data that is directly inserted into measurement_y2006m02, which is allowed provided its partition constraint is satisfied) will be further redirected to one of its partitions based on the peaktemp column. The partition key specified may overlap with the parent's partition key, although care should be taken when specifying the bounds of a sub-partition such that the set of data it accepts constitutes a subset of what the partition's own bounds allow; the system does not try to check whether that's really the case.

Inserting data into the parent table that does not map to one of the existing partitions will cause an error; an appropriate partition must be added manually.

It is not necessary to manually create table constraints describing the partition boundary conditions for partitions. Such constraints will be created automatically.

Create an index on the key column(s), as well as any other indexes you might want, on the partitioned table. (The key index is not strictly necessary, but in most scenarios it is helpful.) This automatically creates a matching index on each partition, and any partitions you create or attach later will also have such an index. An index or unique constraint declared on a partitioned table is “virtual” in the same way that the partitioned table is: the actual data is in child indexes on the individual partition tables.

Ensure that the enable_partition_pruning configuration parameter is not disabled in postgresql.conf. If it is, queries will not be optimized as desired.

In the above example we would be creating a new partition each month, so it might be wise to write a script that generates the required DDL automatically.

Normally the set of partitions established when initially defining the table is not intended to remain static. It is common to want to remove partitions holding old data and periodically add new partitions for new data. One of the most important advantages of partitioning is precisely that it allows this otherwise painful task to be executed nearly instantaneously by manipulating the partition structure, rather than physically moving large amounts of data around.

The simplest option for removing old data is to drop the partition that is no longer necessary:

This can very quickly delete millions of records because it doesn't have to individually delete every record. Note however that the above command requires taking an ACCESS EXCLUSIVE lock on the parent table.

Another option that is often preferable is to remove the partition from the partitioned table but retain access to it as a table in its own right. This has two forms:

These allow further operations to be performed on the data before it is dropped. For example, this is often a useful time to back up the data using COPY, pg_dump, or similar tools. It might also be a useful time to aggregate data into smaller formats, perform other data manipulations, or run reports. The first form of the command requires an ACCESS EXCLUSIVE lock on the parent table. Adding the CONCURRENTLY qualifier as in the second form allows the detach operation to require only SHARE UPDATE EXCLUSIVE lock on the parent table, but see ALTER TABLE ... DETACH PARTITION for details on the restrictions.

Similarly we can add a new partition to handle new data. We can create an empty partition in the partitioned table just as the original partitions were created above:

As an alternative to creating a new partition, it is sometimes more convenient to create a new table separate from the partition structure and attach it as a partition later. This allows new data to be loaded, checked, and transformed prior to it appearing in the partitioned table. Moreover, the ATTACH PARTITION operation requires only a SHARE UPDATE EXCLUSIVE lock on the partitioned table rather than the ACCESS EXCLUSIVE lock required by CREATE TABLE ... PARTITION OF, so it is more friendly to concurrent operations on the partitioned table; see ALTER TABLE ... ATTACH PARTITION for additional details. The CREATE TABLE ... LIKE option can be helpful to avoid tediously repeating the parent table's definition; for example:

Note that when running the ATTACH PARTITION command, the table will be scanned to validate the partition constraint while holding an ACCESS EXCLUSIVE lock on that partition. As shown above, it is recommended to avoid this scan by creating a CHECK constraint matching the expected partition constraint on the table prior to attaching it. Once the ATTACH PARTITION is complete, it is recommended to drop the now-redundant CHECK constraint. If the table being attached is itself a partitioned table, then each of its sub-partitions will be recursively locked and scanned until either a suitable CHECK constraint is encountered or the leaf partitions are reached.

Similarly, if the partitioned table has a DEFAULT partition, it is recommended to create a CHECK constraint which excludes the to-be-attached partition's constraint. If this is not done, the DEFAULT partition will be scanned to verify that it contains no records which should be located in the partition being attached. This operation will be performed whilst holding an ACCESS EXCLUSIVE lock on the DEFAULT partition. If the DEFAULT partition is itself a partitioned table, then each of its partitions will be recursively checked in the same way as the table being attached, as mentioned above.

As mentioned earlier, it is possible to create indexes on partitioned tables so that they are applied automatically to the entire hierarchy. This can be very convenient as not only will all existing partitions be indexed, but any future partitions will be as well. However, one limitation when creating new indexes on partitioned tables is that it is not possible to use the CONCURRENTLY qualifier, which could lead to long lock times. To avoid this, you can use CREATE INDEX ON ONLY the partitioned table, which creates the new index marked as invalid, preventing automatic application to existing partitions. Instead, indexes can then be created individually on each partition using CONCURRENTLY and attached to the partitioned index on the parent using ALTER INDEX ... ATTACH PARTITION. Once indexes for all the partitions are attached to the parent index, the parent index will be marked valid automatically. Example:

This technique can be used with UNIQUE and PRIMARY KEY constraints too; the indexes are created implicitly when the constraint is created. Example:

The following limitations apply to partitioned tables:

To create a unique or primary key constraint on a partitioned table, the partition keys must not include any expressions or function calls and the constraint's columns must include all of the partition key columns. This limitation exists because the individual indexes making up the constraint can only directly enforce uniqueness within their own partitions; therefore, the partition structure itself must guarantee that there are not duplicates in different partitions.

Similarly an exclusion constraint must include all the partition key columns. Furthermore the constraint must compare those columns for equality (not e.g. &&). Again, this limitation stems from not being able to enforce cross-partition restrictions. The constraint may include additional columns that aren't part of the partition key, and it may compare those with any operators you like.

BEFORE ROW triggers on INSERT cannot change which partition is the final destination for a new row.

Mixing temporary and permanent relations in the same partition tree is not allowed. Hence, if the partitioned table is permanent, so must be its partitions and likewise if the partitioned table is temporary. When using temporary relations, all members of the partition tree have to be from the same session.

Individual partitions are linked to their partitioned table using inheritance behind-the-scenes. However, it is not possible to use all of the generic features of inheritance with declaratively partitioned tables or their partitions, as discussed below. Notably, a partition cannot have any parents other than the partitioned table it is a partition of, nor can a table inherit from both a partitioned table and a regular table. That means partitioned tables and their partitions never share an inheritance hierarchy with regular tables.

Since a partition hierarchy consisting of the partitioned table and its partitions is still an inheritance hierarchy, tableoid and all the normal rules of inheritance apply as described in Section 5.11, with a few exceptions:

Partitions cannot have columns that are not present in the parent. It is not possible to specify columns when creating partitions with CREATE TABLE, nor is it possible to add columns to partitions after-the-fact using ALTER TABLE. Tables may be added as a partition with ALTER TABLE ... ATTACH PARTITION only if their columns exactly match the parent.

Both CHECK and NOT NULL constraints of a partitioned table are always inherited by all its partitions; it is not allowed to create NO INHERIT constraints of those types. You cannot drop a constraint of those types if the same constraint is present in the parent table.

Using ONLY to add or drop a constraint on only the partitioned table is supported as long as there are no partitions. Once partitions exist, using ONLY will result in an error for any constraints other than UNIQUE and PRIMARY KEY. Instead, constraints on the partitions themselves can be added and (if they are not present in the parent table) dropped.

As a partitioned table does not have any data itself, attempts to use TRUNCATE ONLY on a partitioned table will always return an error.

While the built-in declarative partitioning is suitable for most common use cases, there are some circumstances where a more flexible approach may be useful. Partitioning can be implemented using table inheritance, which allows for several features not supported by declarative partitioning, such as:

For declarative partitioning, partitions must have exactly the same set of columns as the partitioned table, whereas with table inheritance, child tables may have extra columns not present in the parent.

Table inheritance allows for multiple inheritance.

Declarative partitioning only supports range, list and hash partitioning, whereas table inheritance allows data to be divided in a manner of the user's choosing. (Note, however, that if constraint exclusion is unable to prune child tables effectively, query performance might be poor.)

This example builds a partitioning structure equivalent to the declarative partitioning example above. Use the following steps:

Create the “root” table, from which all of the “child” tables will inherit. This table will contain no data. Do not define any check constraints on this table, unless you intend them to be applied equally to all child tables. There is no point in defining any indexes or unique constraints on it, either. For our example, the root table is the measurement table as originally defined:

Create several “child” tables that each inherit from the root table. Normally, these tables will not add any columns to the set inherited from the root. Just as with declarative partitioning, these tables are in every way normal PostgreSQL tables (or foreign tables).

Add non-overlapping table constraints to the child tables to define the allowed key values in each.

Typical examples would be:

Ensure that the constraints guarantee that there is no overlap between the key values permitted in different child tables. A common mistake is to set up range constraints like:

This is wrong since it is not clear which child table the key value 200 belongs in. Instead, ranges should be defined in this style:

For each child table, create an index on the key column(s), as well as any other indexes you might want.

We want our application to be able to say INSERT INTO measurement ... and have the data be redirected into the appropriate child table. We can arrange that by attaching a suitable trigger function to the root table. If data will be added only to the latest child, we can use a very simple trigger function:

After creating the function, we create a trigger which calls the trigger function:

We must redefine the trigger function each month so that it always inserts into the current child table. The trigger definition does not need to be updated, however.

We might want to insert data and have the server automatically locate the child table into which the row should be added. We could do this with a more complex trigger function, for example:

The trigger definition is the same as before. Note that each IF test must exactly match the CHECK constraint for its child table.

While this function is more complex than the single-month case, it doesn't need to be updated as often, since branches can be added in advance of being needed.

In practice, it might be best to check the newest child first, if most inserts go into that child. For simplicity, we have shown the trigger's tests in the same order as in other parts of this example.

A different approach to redirecting inserts into the appropriate child table is to set up rules, instead of a trigger, on the root table. For example:

A rule has significantly more overhead than a trigger, but the overhead is paid once per query rather than once per row, so this method might be advantageous for bulk-insert situations. In most cases, however, the trigger method will offer better performance.

Be aware that COPY ignores rules. If you want to use COPY to insert data, you'll need to copy into the correct child table rather than directly into the root. COPY does fire triggers, so you can use it normally if you use the trigger approach.

Another disadvantage of the rule approach is that there is no simple way to force an error if the set of rules doesn't cover the insertion date; the data will silently go into the root table instead.

Ensure that the constraint_exclusion configuration parameter is not disabled in postgresql.conf; otherwise child tables may be accessed unnecessarily.

As we can see, a complex table hierarchy could require a substantial amount of DDL. In the above example we would be creating a new child table each month, so it might be wise to write a script that generates the required DDL automatically.

To remove old data quickly, simply drop the child table that is no longer necessary:

To remove the child table from the inheritance hierarchy table but retain access to it as a table in its own right:

To add a new child table to handle new data, create an empty child table just as the original children were created above:

Alternatively, one may want to create and populate the new child table before adding it to the table hierarchy. This could allow data to be loaded, checked, and transformed before being made visible to queries on the parent table.

The following caveats apply to partitioning implemented using inheritance:

There is no automatic way to verify that all of the CHECK constraints are mutually exclusive. It is safer to create code that generates child tables and creates and/or modifies associated objects than to write each by hand.

Indexes and foreign key constraints apply to single tables and not to their inheritance children, hence they have some caveats to be aware of.

The schemes shown here assume that the values of a row's key column(s) never change, or at least do not change enough to require it to move to another partition. An UPDATE that attempts to do that will fail because of the CHECK constraints. If you need to handle such cases, you can put suitable update triggers on the child tables, but it makes management of the structure much more complicated.

Manual VACUUM and ANALYZE commands will automatically process all inheritance child tables. If this is undesirable, you can use the ONLY keyword. A command like:

will only process the root table.

INSERT statements with ON CONFLICT clauses are unlikely to work as expected, as the ON CONFLICT action is only taken in case of unique violations on the specified target relation, not its child relations.

Triggers or rules will be needed to route rows to the desired child table, unless the application is explicitly aware of the partitioning scheme. Triggers may be complicated to write, and will be much slower than the tuple routing performed internally by declarative partitioning.

Partition pruning is a query optimization technique that improves performance for declaratively partitioned tables. As an example:

Without partition pruning, the above query would scan each of the partitions of the measurement table. With partition pruning enabled, the planner will examine the definition of each partition and prove that the partition need not be scanned because it could not contain any rows meeting the query's WHERE clause. When the planner can prove this, it excludes (prunes) the partition from the query plan.

By using the EXPLAIN command and the enable_partition_pruning configuration parameter, it's possible to show the difference between a plan for which partitions have been pruned and one for which they have not. A typical unoptimized plan for this type of table setup is:

Some or all of the partitions might use index scans instead of full-table sequential scans, but the point here is that there is no need to scan the older partitions at all to answer this query. When we enable partition pruning, we get a significantly cheaper plan that will deliver the same answer:

Note that partition pruning is driven only by the constraints defined implicitly by the partition keys, not by the presence of indexes. Therefore it isn't necessary to define indexes on the key columns. Whether an index needs to be created for a given partition depends on whether you expect that queries that scan the partition will generally scan a large part of the partition or just a small part. An index will be helpful in the latter case but not the former.

Partition pruning can be performed not only during the planning of a given query, but also during its execution. This is useful as it can allow more partitions to be pruned when clauses contain expressions whose values are not known at query planning time, for example, parameters defined in a PREPARE statement, using a value obtained from a subquery, or using a parameterized value on the inner side of a nested loop join. Partition pruning during execution can be performed at any of the following times:

During initialization of the query plan. Partition pruning can be performed here for parameter values which are known during the initialization phase of execution. Partitions which are pruned during this stage will not show up in the query's EXPLAIN or EXPLAIN ANALYZE. It is possible to determine the number of partitions which were removed during this phase by observing the “Subplans Removed” property in the EXPLAIN output. The query planner obtains locks for all partitions which are part of the plan. However, when the executor uses a cached plan, locks are only obtained on the partitions which remain after partition pruning done during the initialization phase of execution, i.e., the ones shown in the EXPLAIN output and not the ones referred to by the “Subplans Removed” property.

During actual execution of the query plan. Partition pruning may also be performed here to remove partitions using values which are only known during actual query execution. This includes values from subqueries and values from execution-time parameters such as those from parameterized nested loop joins. Since the value of these parameters may change many times during the execution of the query, partition pruning is performed whenever one of the execution parameters being used by partition pruning changes. Determining if partitions were pruned during this phase requires careful inspection of the loops property in the EXPLAIN ANALYZE output. Subplans corresponding to different partitions may have different values for it depending on how many times each of them was pruned during execution. Some may be shown as (never executed) if they were pruned every time.

Partition pruning can be disabled using the enable_partition_pruning setting.

Constraint exclusion is a query optimization technique similar to partition pruning. While it is primarily used for partitioning implemented using the legacy inheritance method, it can be used for other purposes, including with declarative partitioning.

Constraint exclusion works in a very similar way to partition pruning, except that it uses each table's CHECK constraints — which gives it its name — whereas partition pruning uses the table's partition bounds, which exist only in the case of declarative partitioning. Another difference is that constraint exclusion is only applied at plan time; there is no attempt to remove partitions at execution time.

The fact that constraint exclusion uses CHECK constraints, which makes it slow compared to partition pruning, can sometimes be used as an advantage: because constraints can be defined even on declaratively-partitioned tables, in addition to their internal partition bounds, constraint exclusion may be able to elide additional partitions from the query plan.

The default (and recommended) setting of constraint_exclusion is neither on nor off, but an intermediate setting called partition, which causes the technique to be applied only to queries that are likely to be working on inheritance partitioned tables. The on setting causes the planner to examine CHECK constraints in all queries, even simple ones that are unlikely to benefit.

The following caveats apply to constraint exclusion:

Constraint exclusion is only applied during query planning, unlike partition pruning, which can also be applied during query execution.

Constraint exclusion only works when the query's WHERE clause contains constants (or externally supplied parameters). For example, a comparison against a non-immutable function such as CURRENT_TIMESTAMP cannot be optimized, since the planner cannot know which child table the function's value might fall into at run time.

Keep the partitioning constraints simple, else the planner may not be able to prove that child tables might not need to be visited. Use simple equality conditions for list partitioning, or simple range tests for range partitioning, as illustrated in the preceding examples. A good rule of thumb is that partitioning constraints should contain only comparisons of the partitioning column(s) to constants using B-tree-indexable operators, because only B-tree-indexable column(s) are allowed in the partition key.

All constraints on all children of the parent table are examined during constraint exclusion, so large numbers of children are likely to increase query planning time considerably. So the legacy inheritance based partitioning will work well with up to perhaps a hundred child tables; don't try to use many thousands of children.

The choice of how to partition a table should be made carefully, as the performance of query planning and execution can be negatively affected by poor design.

One of the most critical design decisions will be the column or columns by which you partition your data. Often the best choice will be to partition by the column or set of columns which most commonly appear in WHERE clauses of queries being executed on the partitioned table. WHERE clauses that are compatible with the partition bound constraints can be used to prune unneeded partitions. However, you may be forced into making other decisions by requirements for the PRIMARY KEY or a UNIQUE constraint. Removal of unwanted data is also a factor to consider when planning your partitioning strategy. An entire partition can be detached fairly quickly, so it may be beneficial to design the partition strategy in such a way that all data to be removed at once is located in a single partition.

Choosing the target number of partitions that the table should be divided into is also a critical decision to make. Not having enough partitions may mean that indexes remain too large and that data locality remains poor which could result in low cache hit ratios. However, dividing the table into too many partitions can also cause issues. Too many partitions can mean longer query planning times and higher memory consumption during both query planning and execution, as further described below. When choosing how to partition your table, it's also important to consider what changes may occur in the future. For example, if you choose to have one partition per customer and you currently have a small number of large customers, consider the implications if in several years you instead find yourself with a large number of small customers. In this case, it may be better to choose to partition by HASH and choose a reasonable number of partitions rather than trying to partition by LIST and hoping that the number of customers does not increase beyond what it is practical to partition the data by.

Sub-partitioning can be useful to further divide partitions that are expected to become larger than other partitions. Another option is to use range partitioning with multiple columns in the partition key. Either of these can easily lead to excessive numbers of partitions, so restraint is advisable.

It is important to consider the overhead of partitioning during query planning and execution. The query planner is generally able to handle partition hierarchies with up to a few thousand partitions fairly well, provided that typical queries allow the query planner to prune all but a small number of partitions. Planning times become longer and memory consumption becomes higher when more partitions remain after the planner performs partition pruning. Another reason to be concerned about having a large number of partitions is that the server's memory consumption may grow significantly over time, especially if many sessions touch large numbers of partitions. That's because each partition requires its metadata to be loaded into the local memory of each session that touches it.

With data warehouse type workloads, it can make sense to use a larger number of partitions than with an OLTP type workload. Generally, in data warehouses, query planning time is less of a concern as the majority of processing time is spent during query execution. With either of these two types of workload, it is important to make the right decisions early, as re-partitioning large quantities of data can be painfully slow. Simulations of the intended workload are often beneficial for optimizing the partitioning strategy. Never just assume that more partitions are better than fewer partitions, nor vice-versa.

Examples:

Example 1 (unknown):

CREATE TABLE measurement (
    city_id         int not null,
    logdate         date not null,
    peaktemp        int,
    unitsales       int
);

Example 2 (unknown):

CREATE TABLE measurement (
    city_id         int not null,
    logdate         date not null,
    peaktemp        int,
    unitsales       int
) PARTITION BY RANGE (logdate);

Example 3 (unknown):

CREATE TABLE measurement_y2006m02 PARTITION OF measurement
    FOR VALUES FROM ('2006-02-01') TO ('2006-03-01');

CREATE TABLE measurement_y2006m03 PARTITION OF measurement
    FOR VALUES FROM ('2006-03-01') TO ('2006-04-01');

...
CREATE TABLE measurement_y2007m11 PARTITION OF measurement
    FOR VALUES FROM ('2007-11-01') TO ('2007-12-01');

CREATE TABLE measurement_y2007m12 PARTITION OF measurement
    FOR VALUES FROM ('2007-12-01') TO ('2008-01-01')
    TABLESPACE fasttablespace;

CREATE TABLE measurement_y2008m01 PARTITION OF measurement
    FOR VALUES FROM ('2008-01-01') TO ('2008-02-01')
    WITH (parallel_workers = 4)
    TABLESPACE fasttablespace;

Example 4 (unknown):

CREATE TABLE measurement_y2006m02 PARTITION OF measurement
    FOR VALUES FROM ('2006-02-01') TO ('2006-03-01')
    PARTITION BY RANGE (peaktemp);

---

PostgreSQL: Documentation: 18: Chapter 40. Procedural Languages

URL: https://www.postgresql.org/docs/current/xplang.html

Contents:

• Chapter 40. Procedural Languages

PostgreSQL allows user-defined functions to be written in other languages besides SQL and C. These other languages are generically called procedural languages (PLs). For a function written in a procedural language, the database server has no built-in knowledge about how to interpret the function's source text. Instead, the task is passed to a special handler that knows the details of the language. The handler could either do all the work of parsing, syntax analysis, execution, etc. itself, or it could serve as “glue” between PostgreSQL and an existing implementation of a programming language. The handler itself is a C language function compiled into a shared object and loaded on demand, just like any other C function.

There are currently four procedural languages available in the standard PostgreSQL distribution: PL/pgSQL (Chapter 41), PL/Tcl (Chapter 42), PL/Perl (Chapter 43), and PL/Python (PL/Python). There are additional procedural languages available that are not included in the core distribution. Appendix H has information about finding them. In addition other languages can be defined by users; the basics of developing a new procedural language are covered in Chapter 57.

---

PostgreSQL: Documentation: 18: 32.17. The Connection Service File

URL: https://www.postgresql.org/docs/current/libpq-pgservice.html

Contents:

• 32.17. The Connection Service File #

The connection service file allows libpq connection parameters to be associated with a single service name. That service name can then be specified using the service key word in a libpq connection string, and the associated settings will be used. This allows connection parameters to be modified without requiring a recompile of the libpq-using application. The service name can also be specified using the PGSERVICE environment variable.

Service names can be defined in either a per-user service file or a system-wide file. If the same service name exists in both the user and the system file, the user file takes precedence. By default, the per-user service file is named ~/.pg_service.conf. On Microsoft Windows, it is named %APPDATA%\postgresql.pg_service.conf (where %APPDATA% refers to the Application Data subdirectory in the user's profile). A different file name can be specified by setting the environment variable PGSERVICEFILE. The system-wide file is named pg_service.conf. By default it is sought in the etc directory of the PostgreSQL installation (use pg_config --sysconfdir to identify this directory precisely). Another directory, but not a different file name, can be specified by setting the environment variable PGSYSCONFDIR.

Either service file uses an “INI file” format where the section name is the service name and the parameters are connection parameters; see Section 32.1.2 for a list. For example:

An example file is provided in the PostgreSQL installation at share/pg_service.conf.sample.

Connection parameters obtained from a service file are combined with parameters obtained from other sources. A service file setting overrides the corresponding environment variable, and in turn can be overridden by a value given directly in the connection string. For example, using the above service file, a connection string service=mydb port=5434 will use host somehost, port 5434, user admin, and other parameters as set by environment variables or built-in defaults.

Examples:

Example 1 (unknown):

# comment
[mydb]
host=somehost
port=5433
user=admin

---

PostgreSQL: Documentation: 18: 22.2. Creating a Database

URL: https://www.postgresql.org/docs/current/manage-ag-createdb.html

Contents:

• 22.2. Creating a Database #
  • Note

In order to create a database, the PostgreSQL server must be up and running (see Section 18.3).

Databases are created with the SQL command CREATE DATABASE:

where name follows the usual rules for SQL identifiers. The current role automatically becomes the owner of the new database. It is the privilege of the owner of a database to remove it later (which also removes all the objects in it, even if they have a different owner).

The creation of databases is a restricted operation. See Section 21.2 for how to grant permission.

Since you need to be connected to the database server in order to execute the CREATE DATABASE command, the question remains how the first database at any given site can be created. The first database is always created by the initdb command when the data storage area is initialized. (See Section 18.2.) This database is called postgres. So to create the first “ordinary” database you can connect to postgres.

Two additional databases, template1 and template0, are also created during database cluster initialization. Whenever a new database is created within the cluster, template1 is essentially cloned. This means that any changes you make in template1 are propagated to all subsequently created databases. Because of this, avoid creating objects in template1 unless you want them propagated to every newly created database. template0 is meant as a pristine copy of the original contents of template1. It can be cloned instead of template1 when it is important to make a database without any such site-local additions. More details appear in Section 22.3.

As a convenience, there is a program you can execute from the shell to create new databases, createdb.

createdb does no magic. It connects to the postgres database and issues the CREATE DATABASE command, exactly as described above. The createdb reference page contains the invocation details. Note that createdb without any arguments will create a database with the current user name.

Chapter 20 contains information about how to restrict who can connect to a given database.

Sometimes you want to create a database for someone else, and have them become the owner of the new database, so they can configure and manage it themselves. To achieve that, use one of the following commands:

from the SQL environment, or:

from the shell. Only the superuser is allowed to create a database for someone else (that is, for a role you are not a member of).

Examples:

Example 1 (unknown):

CREATE DATABASE name;

Example 2 (unknown):

createdb dbname

Example 3 (unknown):

CREATE DATABASE dbname OWNER rolename;

Example 4 (unknown):

createdb -O rolename dbname

---

PostgreSQL: Documentation: 18: 29.8. Restrictions

URL: https://www.postgresql.org/docs/current/logical-replication-restrictions.html

Contents:

• 29.8. Restrictions #

Logical replication currently has the following restrictions or missing functionality. These might be addressed in future releases.

The database schema and DDL commands are not replicated. The initial schema can be copied by hand using pg_dump --schema-only. Subsequent schema changes would need to be kept in sync manually. (Note, however, that there is no need for the schemas to be absolutely the same on both sides.) Logical replication is robust when schema definitions change in a live database: When the schema is changed on the publisher and replicated data starts arriving at the subscriber but does not fit into the table schema, replication will error until the schema is updated. In many cases, intermittent errors can be avoided by applying additive schema changes to the subscriber first.

Sequence data is not replicated. The data in serial or identity columns backed by sequences will of course be replicated as part of the table, but the sequence itself would still show the start value on the subscriber. If the subscriber is used as a read-only database, then this should typically not be a problem. If, however, some kind of switchover or failover to the subscriber database is intended, then the sequences would need to be updated to the latest values, either by copying the current data from the publisher (perhaps using pg_dump) or by determining a sufficiently high value from the tables themselves.

Replication of TRUNCATE commands is supported, but some care must be taken when truncating groups of tables connected by foreign keys. When replicating a truncate action, the subscriber will truncate the same group of tables that was truncated on the publisher, either explicitly specified or implicitly collected via CASCADE, minus tables that are not part of the subscription. This will work correctly if all affected tables are part of the same subscription. But if some tables to be truncated on the subscriber have foreign-key links to tables that are not part of the same (or any) subscription, then the application of the truncate action on the subscriber will fail.

Large objects (see Chapter 33) are not replicated. There is no workaround for that, other than storing data in normal tables.

Replication is only supported by tables, including partitioned tables. Attempts to replicate other types of relations, such as views, materialized views, or foreign tables, will result in an error.

When replicating between partitioned tables, the actual replication originates, by default, from the leaf partitions on the publisher, so partitions on the publisher must also exist on the subscriber as valid target tables. (They could either be leaf partitions themselves, or they could be further subpartitioned, or they could even be independent tables.) Publications can also specify that changes are to be replicated using the identity and schema of the partitioned root table instead of that of the individual leaf partitions in which the changes actually originate (see publish_via_partition_root parameter of CREATE PUBLICATION).

When using REPLICA IDENTITY FULL on published tables, it is important to note that the UPDATE and DELETE operations cannot be applied to subscribers if the tables include attributes with datatypes (such as point or box) that do not have a default operator class for B-tree or Hash. However, this limitation can be overcome by ensuring that the table has a primary key or replica identity defined for it.

---

PostgreSQL: Documentation: 18: 9.26. Set Returning Functions

URL: https://www.postgresql.org/docs/current/functions-srf.html

Contents:

• 9.26. Set Returning Functions #

This section describes functions that possibly return more than one row. The most widely used functions in this class are series generating functions, as detailed in Table 9.69 and Table 9.70. Other, more specialized set-returning functions are described elsewhere in this manual. See Section 7.2.1.4 for ways to combine multiple set-returning functions.

Table 9.69. Series Generating Functions

generate_series ( start integer, stop integer [, step integer ] ) → setof integer

generate_series ( start bigint, stop bigint [, step bigint ] ) → setof bigint

generate_series ( start numeric, stop numeric [, step numeric ] ) → setof numeric

Generates a series of values from start to stop, with a step size of step. step defaults to 1.

generate_series ( start timestamp, stop timestamp, step interval ) → setof timestamp

generate_series ( start timestamp with time zone, stop timestamp with time zone, step interval [, timezone text ] ) → setof timestamp with time zone

Generates a series of values from start to stop, with a step size of step. In the timezone-aware form, times of day and daylight-savings adjustments are computed according to the time zone named by the timezone argument, or the current TimeZone setting if that is omitted.

When step is positive, zero rows are returned if start is greater than stop. Conversely, when step is negative, zero rows are returned if start is less than stop. Zero rows are also returned if any input is NULL. It is an error for step to be zero. Some examples follow:

Table 9.70. Subscript Generating Functions

generate_subscripts ( array anyarray, dim integer ) → setof integer

Generates a series comprising the valid subscripts of the dim'th dimension of the given array.

generate_subscripts ( array anyarray, dim integer, reverse boolean ) → setof integer

Generates a series comprising the valid subscripts of the dim'th dimension of the given array. When reverse is true, returns the series in reverse order.

generate_subscripts is a convenience function that generates the set of valid subscripts for the specified dimension of the given array. Zero rows are returned for arrays that do not have the requested dimension, or if any input is NULL. Some examples follow:

When a function in the FROM clause is suffixed by WITH ORDINALITY, a bigint column is appended to the function's output column(s), which starts from 1 and increments by 1 for each row of the function's output. This is most useful in the case of set returning functions such as unnest().

Examples:

Example 1 (unknown):

SELECT * FROM generate_series(2,4);
 generate_series
-----------------
               2
               3
               4
(3 rows)

SELECT * FROM generate_series(5,1,-2);
 generate_series
-----------------
               5
               3
               1
(3 rows)

SELECT * FROM generate_series(4,3);
 generate_series
-----------------
(0 rows)

SELECT generate_series(1.1, 4, 1.3);
 generate_series
-----------------
             1.1
             2.4
             3.7
(3 rows)

-- this example relies on the date-plus-integer operator:
SELECT current_date + s.a AS dates FROM generate_series(0,14,7) AS s(a);
   dates
------------
 2004-02-05
 2004-02-12
 2004-02-19
(3 rows)

SELECT * FROM generate_series('2008-03-01 00:00'::timestamp,
                              '2008-03-04 12:00', '10 hours');
   generate_series
---------------------
 2008-03-01 00:00:00
 2008-03-01 10:00:00
 2008-03-01 20:00:00
 2008-03-02 06:00:00
 2008-03-02 16:00:00
 2008-03-03 02:00:00
 2008-03-03 12:00:00
 2008-03-03 22:00:00
 2008-03-04 08:00:00
(9 rows)

-- this example assumes that TimeZone is set to UTC; note the DST transition:
SELECT * FROM generate_series('2001-10-22 00:00 -04:00'::timestamptz,
                              '2001-11-01 00:00 -05:00'::timestamptz,
                              '1 day'::interval, 'America/New_York');
    generate_series
------------------------
 2001-10-22 04:00:00+00
 2001-10-23 04:00:00+00
 2001-10-24 04:00:00+00
 2001-10-25 04:00:00+00
 2001-10-26 04:00:00+00
 2001-10-27 04:00:00+00
 2001-10-28 04:00:00+00
 2001-10-29 05:00:00+00
 2001-10-30 05:00:00+00
 2001-10-31 05:00:00+00
 2001-11-01 05:00:00+00
(11 rows)

Example 2 (unknown):

-- basic usage:
SELECT generate_subscripts('{NULL,1,NULL,2}'::int[], 1) AS s;
 s
---
 1
 2
 3
 4
(4 rows)

-- presenting an array, the subscript and the subscripted
-- value requires a subquery:
SELECT * FROM arrays;
         a
--------------------
 {-1,-2}
 {100,200,300}
(2 rows)

SELECT a AS array, s AS subscript, a[s] AS value
FROM (SELECT generate_subscripts(a, 1) AS s, a FROM arrays) foo;
     array     | subscript | value
---------------+-----------+-------
 {-1,-2}       |         1 |    -1
 {-1,-2}       |         2 |    -2
 {100,200,300} |         1 |   100
 {100,200,300} |         2 |   200
 {100,200,300} |         3 |   300
(5 rows)

-- unnest a 2D array:
CREATE OR REPLACE FUNCTION unnest2(anyarray)
RETURNS SETOF anyelement AS $$
select $1[i][j]
   from generate_subscripts($1,1) g1(i),
        generate_subscripts($1,2) g2(j);
$$ LANGUAGE sql IMMUTABLE;
CREATE FUNCTION
SELECT * FROM unnest2(ARRAY[[1,2],[3,4]]);
 unnest2
---------
       1
       2
       3
       4
(4 rows)

Example 3 (unknown):

-- set returning function WITH ORDINALITY:
SELECT * FROM pg_ls_dir('.') WITH ORDINALITY AS t(ls,n);
       ls        | n
-----------------+----
 pg_serial       |  1
 pg_twophase     |  2
 postmaster.opts |  3
 pg_notify       |  4
 postgresql.conf |  5
 pg_tblspc       |  6
 logfile         |  7
 base            |  8
 postmaster.pid  |  9
 pg_ident.conf   | 10
 global          | 11
 pg_xact         | 12
 pg_snapshots    | 13
 pg_multixact    | 14
 PG_VERSION      | 15
 pg_wal          | 16
 pg_hba.conf     | 17
 pg_stat_tmp     | 18
 pg_subtrans     | 19
(19 rows)

---

PostgreSQL: Documentation: 18: 20.14. BSD Authentication

URL: https://www.postgresql.org/docs/current/auth-bsd.html

Contents:

• 20.14. BSD Authentication #
  • Note

This authentication method operates similarly to password except that it uses BSD Authentication to verify the password. BSD Authentication is used only to validate user name/password pairs. Therefore the user's role must already exist in the database before BSD Authentication can be used for authentication. The BSD Authentication framework is currently only available on OpenBSD.

BSD Authentication in PostgreSQL uses the auth-postgresql login type and authenticates with the postgresql login class if that's defined in login.conf. By default that login class does not exist, and PostgreSQL will use the default login class.

To use BSD Authentication, the PostgreSQL user account (that is, the operating system user running the server) must first be added to the auth group. The auth group exists by default on OpenBSD systems.

---

PostgreSQL: Documentation: 18: Chapter 70. Backup Manifest Format

URL: https://www.postgresql.org/docs/current/backup-manifest-format.html

Contents:

• Chapter 70. Backup Manifest Format

The backup manifest generated by pg_basebackup is primarily intended to permit the backup to be verified using pg_verifybackup. However, it is also possible for other tools to read the backup manifest file and use the information contained therein for their own purposes. To that end, this chapter describes the format of the backup manifest file.

A backup manifest is a JSON document encoded as UTF-8. (Although in general JSON documents are required to be Unicode, PostgreSQL permits the json and jsonb data types to be used with any supported server encoding. There is no similar exception for backup manifests.) The JSON document is always an object; the keys that are present in this object are described in the next section.

---

PostgreSQL: Documentation: 18: 32.12. Miscellaneous Functions

URL: https://www.postgresql.org/docs/current/libpq-misc.html

Contents:

• 32.12. Miscellaneous Functions #
  • Note

As always, there are some functions that just don't fit anywhere.

Frees memory allocated by libpq.

Frees memory allocated by libpq, particularly PQescapeByteaConn, PQescapeBytea, PQunescapeBytea, and PQnotifies. It is particularly important that this function, rather than free(), be used on Microsoft Windows. This is because allocating memory in a DLL and releasing it in the application works only if multithreaded/single-threaded, release/debug, and static/dynamic flags are the same for the DLL and the application. On non-Microsoft Windows platforms, this function is the same as the standard library function free().

Frees the data structures allocated by PQconndefaults or PQconninfoParse.

If the argument is a NULL pointer, no operation is performed.

A simple PQfreemem will not do for this, since the array contains references to subsidiary strings.

Prepares the encrypted form of a PostgreSQL password.

This function is intended to be used by client applications that wish to send commands like ALTER USER joe PASSWORD 'pwd'. It is good practice not to send the original cleartext password in such a command, because it might be exposed in command logs, activity displays, and so on. Instead, use this function to convert the password to encrypted form before it is sent.

The passwd and user arguments are the cleartext password, and the SQL name of the user it is for. algorithm specifies the encryption algorithm to use to encrypt the password. Currently supported algorithms are md5 and scram-sha-256 (on and off are also accepted as aliases for md5, for compatibility with older server versions). Note that support for scram-sha-256 was introduced in PostgreSQL version 10, and will not work correctly with older server versions. If algorithm is NULL, this function will query the server for the current value of the password_encryption setting. That can block, and will fail if the current transaction is aborted, or if the connection is busy executing another query. If you wish to use the default algorithm for the server but want to avoid blocking, query password_encryption yourself before calling PQencryptPasswordConn, and pass that value as the algorithm.

The return value is a string allocated by malloc. The caller can assume the string doesn't contain any special characters that would require escaping. Use PQfreemem to free the result when done with it. On error, returns NULL, and a suitable message is stored in the connection object.

Changes a PostgreSQL password.

This function uses PQencryptPasswordConn to build and execute the command ALTER USER ... PASSWORD '...', thereby changing the user's password. It exists for the same reason as PQencryptPasswordConn, but is more convenient as it both builds and runs the command for you. PQencryptPasswordConn is passed a NULL for the algorithm argument, hence encryption is done according to the server's password_encryption setting.

The user and passwd arguments are the SQL name of the target user, and the new cleartext password.

Returns a PGresult pointer representing the result of the ALTER USER command, or a null pointer if the routine failed before issuing any command. The PQresultStatus function should be called to check the return value for any errors (including the value of a null pointer, in which case it will return PGRES_FATAL_ERROR). Use PQerrorMessage to get more information about such errors.

Prepares the md5-encrypted form of a PostgreSQL password.

PQencryptPassword is an older, deprecated version of PQencryptPasswordConn. The difference is that PQencryptPassword does not require a connection object, and md5 is always used as the encryption algorithm.

Constructs an empty PGresult object with the given status.

This is libpq's internal function to allocate and initialize an empty PGresult object. This function returns NULL if memory could not be allocated. It is exported because some applications find it useful to generate result objects (particularly objects with error status) themselves. If conn is not null and status indicates an error, the current error message of the specified connection is copied into the PGresult. Also, if conn is not null, any event procedures registered in the connection are copied into the PGresult. (They do not get PGEVT_RESULTCREATE calls, but see PQfireResultCreateEvents.) Note that PQclear should eventually be called on the object, just as with a PGresult returned by libpq itself.

Fires a PGEVT_RESULTCREATE event (see Section 32.14) for each event procedure registered in the PGresult object. Returns non-zero for success, zero if any event procedure fails.

The conn argument is passed through to event procedures but not used directly. It can be NULL if the event procedures won't use it.

Event procedures that have already received a PGEVT_RESULTCREATE or PGEVT_RESULTCOPY event for this object are not fired again.

The main reason that this function is separate from PQmakeEmptyPGresult is that it is often appropriate to create a PGresult and fill it with data before invoking the event procedures.

Makes a copy of a PGresult object. The copy is not linked to the source result in any way and PQclear must be called when the copy is no longer needed. If the function fails, NULL is returned.

This is not intended to make an exact copy. The returned result is always put into PGRES_TUPLES_OK status, and does not copy any error message in the source. (It does copy the command status string, however.) The flags argument determines what else is copied. It is a bitwise OR of several flags. PG_COPYRES_ATTRS specifies copying the source result's attributes (column definitions). PG_COPYRES_TUPLES specifies copying the source result's tuples. (This implies copying the attributes, too.) PG_COPYRES_NOTICEHOOKS specifies copying the source result's notify hooks. PG_COPYRES_EVENTS specifies copying the source result's events. (But any instance data associated with the source is not copied.) The event procedures receive PGEVT_RESULTCOPY events.

Sets the attributes of a PGresult object.

The provided attDescs are copied into the result. If the attDescs pointer is NULL or numAttributes is less than one, the request is ignored and the function succeeds. If res already contains attributes, the function will fail. If the function fails, the return value is zero. If the function succeeds, the return value is non-zero.

Sets a tuple field value of a PGresult object.

The function will automatically grow the result's internal tuples array as needed. However, the tup_num argument must be less than or equal to PQntuples, meaning this function can only grow the tuples array one tuple at a time. But any field of any existing tuple can be modified in any order. If a value at field_num already exists, it will be overwritten. If len is -1 or value is NULL, the field value will be set to an SQL null value. The value is copied into the result's private storage, thus is no longer needed after the function returns. If the function fails, the return value is zero. If the function succeeds, the return value is non-zero.

Allocate subsidiary storage for a PGresult object.

Any memory allocated with this function will be freed when res is cleared. If the function fails, the return value is NULL. The result is guaranteed to be adequately aligned for any type of data, just as for malloc.

Retrieves the number of bytes allocated for a PGresult object.

This value is the sum of all malloc requests associated with the PGresult object, that is, all the memory that will be freed by PQclear. This information can be useful for managing memory consumption.

Return the version of libpq that is being used.

The result of this function can be used to determine, at run time, whether specific functionality is available in the currently loaded version of libpq. The function can be used, for example, to determine which connection options are available in PQconnectdb.

The result is formed by multiplying the library's major version number by 10000 and adding the minor version number. For example, version 10.1 will be returned as 100001, and version 11.0 will be returned as 110000.

Prior to major version 10, PostgreSQL used three-part version numbers in which the first two parts together represented the major version. For those versions, PQlibVersion uses two digits for each part; for example version 9.1.5 will be returned as 90105, and version 9.2.0 will be returned as 90200.

Therefore, for purposes of determining feature compatibility, applications should divide the result of PQlibVersion by 100 not 10000 to determine a logical major version number. In all release series, only the last two digits differ between minor releases (bug-fix releases).

This function appeared in PostgreSQL version 9.1, so it cannot be used to detect required functionality in earlier versions, since calling it will create a link dependency on version 9.1 or later.

Retrieves the current time, expressed as the number of microseconds since the Unix epoch (that is, time_t times 1 million).

This is primarily useful for calculating timeout values to use with PQsocketPoll.

Examples:

Example 1 (unknown):

void PQfreemem(void *ptr);

Example 2 (unknown):

void PQconninfoFree(PQconninfoOption *connOptions);

Example 3 (javascript):

char *PQencryptPasswordConn(PGconn *conn, const char *passwd, const char *user, const char *algorithm);

Example 4 (javascript):

PGresult *PQchangePassword(PGconn *conn, const char *user, const char *passwd);

---

PostgreSQL: Documentation: 18: 4. Further Information

URL: https://www.postgresql.org/docs/current/resources.html

Contents:

• 4. Further Information #

Besides the documentation, that is, this book, there are other resources about PostgreSQL:

The PostgreSQL wiki contains the project's FAQ (Frequently Asked Questions) list, TODO list, and detailed information about many more topics.

The PostgreSQL web site carries details on the latest release and other information to make your work or play with PostgreSQL more productive.

The mailing lists are a good place to have your questions answered, to share experiences with other users, and to contact the developers. Consult the PostgreSQL web site for details.

PostgreSQL is an open-source project. As such, it depends on the user community for ongoing support. As you begin to use PostgreSQL, you will rely on others for help, either through the documentation or through the mailing lists. Consider contributing your knowledge back. Read the mailing lists and answer questions. If you learn something which is not in the documentation, write it up and contribute it. If you add features to the code, contribute them.

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PostgreSQL: Documentation: 18: 36.6. Function Overloading

URL: https://www.postgresql.org/docs/current/xfunc-overload.html

Contents:

• 36.6. Function Overloading #

More than one function can be defined with the same SQL name, so long as the arguments they take are different. In other words, function names can be overloaded. Whether or not you use it, this capability entails security precautions when calling functions in databases where some users mistrust other users; see Section 10.3. When a query is executed, the server will determine which function to call from the data types and the number of the provided arguments. Overloading can also be used to simulate functions with a variable number of arguments, up to a finite maximum number.

When creating a family of overloaded functions, one should be careful not to create ambiguities. For instance, given the functions:

it is not immediately clear which function would be called with some trivial input like test(1, 1.5). The currently implemented resolution rules are described in Chapter 10, but it is unwise to design a system that subtly relies on this behavior.

A function that takes a single argument of a composite type should generally not have the same name as any attribute (field) of that type. Recall that attribute(table) is considered equivalent to table.attribute. In the case that there is an ambiguity between a function on a composite type and an attribute of the composite type, the attribute will always be used. It is possible to override that choice by schema-qualifying the function name (that is, schema.func(table) ) but it's better to avoid the problem by not choosing conflicting names.

Another possible conflict is between variadic and non-variadic functions. For instance, it is possible to create both foo(numeric) and foo(VARIADIC numeric[]). In this case it is unclear which one should be matched to a call providing a single numeric argument, such as foo(10.1). The rule is that the function appearing earlier in the search path is used, or if the two functions are in the same schema, the non-variadic one is preferred.

When overloading C-language functions, there is an additional constraint: The C name of each function in the family of overloaded functions must be different from the C names of all other functions, either internal or dynamically loaded. If this rule is violated, the behavior is not portable. You might get a run-time linker error, or one of the functions will get called (usually the internal one). The alternative form of the AS clause for the SQL CREATE FUNCTION command decouples the SQL function name from the function name in the C source code. For instance:

The names of the C functions here reflect one of many possible conventions.

Examples:

Example 1 (unknown):

CREATE FUNCTION test(int, real) RETURNS ...
CREATE FUNCTION test(smallint, double precision) RETURNS ...

Example 2 (unknown):

CREATE FUNCTION test(int) RETURNS int
    AS 'filename', 'test_1arg'
    LANGUAGE C;
CREATE FUNCTION test(int, int) RETURNS int
    AS 'filename', 'test_2arg'
    LANGUAGE C;

---

PostgreSQL: Documentation: 18: Chapter 42. PL/Tcl — Tcl Procedural Language

URL: https://www.postgresql.org/docs/current/pltcl.html

Contents:

• Chapter 42. PL/Tcl — Tcl Procedural Language

PL/Tcl is a loadable procedural language for the PostgreSQL database system that enables the Tcl language to be used to write PostgreSQL functions and procedures.

---

PostgreSQL: Documentation: 18: 35.37. role_table_grants

URL: https://www.postgresql.org/docs/current/infoschema-role-table-grants.html

Contents:

• 35.37. role_table_grants #

The view role_table_grants identifies all privileges granted on tables or views where the grantor or grantee is a currently enabled role. Further information can be found under table_privileges. The only effective difference between this view and table_privileges is that this view omits tables that have been made accessible to the current user by way of a grant to PUBLIC.

Table 35.35. role_table_grants Columns

grantor sql_identifier

Name of the role that granted the privilege

grantee sql_identifier

Name of the role that the privilege was granted to

table_catalog sql_identifier

Name of the database that contains the table (always the current database)

table_schema sql_identifier

Name of the schema that contains the table

table_name sql_identifier

privilege_type character_data

Type of the privilege: SELECT, INSERT, UPDATE, DELETE, TRUNCATE, REFERENCES, or TRIGGER

is_grantable yes_or_no

YES if the privilege is grantable, NO if not

with_hierarchy yes_or_no

In the SQL standard, WITH HIERARCHY OPTION is a separate (sub-)privilege allowing certain operations on table inheritance hierarchies. In PostgreSQL, this is included in the SELECT privilege, so this column shows YES if the privilege is SELECT, else NO.

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PostgreSQL: Documentation: 18: DESCRIBE

URL: https://www.postgresql.org/docs/current/ecpg-sql-describe.html

Contents:

• DESCRIBE
• Synopsis
• Description
• Parameters
• Examples
• Compatibility
• See Also

DESCRIBE — obtain information about a prepared statement or result set

DESCRIBE retrieves metadata information about the result columns contained in a prepared statement, without actually fetching a row.

The name of a prepared statement. This can be an SQL identifier or a host variable.

A descriptor name. It is case sensitive. It can be an SQL identifier or a host variable.

The name of an SQLDA variable.

DESCRIBE is specified in the SQL standard.

Examples:

Example 1 (unknown):

DESCRIBE [ OUTPUT ] prepared_name USING [ SQL ] DESCRIPTOR descriptor_name
DESCRIBE [ OUTPUT ] prepared_name INTO [ SQL ] DESCRIPTOR descriptor_name
DESCRIBE [ OUTPUT ] prepared_name INTO sqlda_name

Example 2 (unknown):

EXEC SQL ALLOCATE DESCRIPTOR mydesc;
EXEC SQL PREPARE stmt1 FROM :sql_stmt;
EXEC SQL DESCRIBE stmt1 INTO SQL DESCRIPTOR mydesc;
EXEC SQL GET DESCRIPTOR mydesc VALUE 1 :charvar = NAME;
EXEC SQL DEALLOCATE DESCRIPTOR mydesc;

---

PostgreSQL: Documentation: 18: 35.53. table_privileges

URL: https://www.postgresql.org/docs/current/infoschema-table-privileges.html

Contents:

• 35.53. table_privileges #

The view table_privileges identifies all privileges granted on tables or views to a currently enabled role or by a currently enabled role. There is one row for each combination of table, grantor, and grantee.

Table 35.51. table_privileges Columns

grantor sql_identifier

Name of the role that granted the privilege

grantee sql_identifier

Name of the role that the privilege was granted to

table_catalog sql_identifier

Name of the database that contains the table (always the current database)

table_schema sql_identifier

Name of the schema that contains the table

table_name sql_identifier

privilege_type character_data

Type of the privilege: SELECT, INSERT, UPDATE, DELETE, TRUNCATE, REFERENCES, or TRIGGER

is_grantable yes_or_no

YES if the privilege is grantable, NO if not

with_hierarchy yes_or_no

In the SQL standard, WITH HIERARCHY OPTION is a separate (sub-)privilege allowing certain operations on table inheritance hierarchies. In PostgreSQL, this is included in the SELECT privilege, so this column shows YES if the privilege is SELECT, else NO.

---

PostgreSQL: Documentation: 18: Part V. Server Programming

URL: https://www.postgresql.org/docs/current/server-programming.html

Contents:

• Part V. Server Programming

This part is about extending the server functionality with user-defined functions, data types, triggers, etc. These are advanced topics which should be approached only after all the other user documentation about PostgreSQL has been understood. Later chapters in this part describe the server-side programming languages available in the PostgreSQL distribution as well as general issues concerning server-side programming. It is essential to read at least the earlier sections of Chapter 36 (covering functions) before diving into the material about server-side programming.

---

PostgreSQL: Documentation: 18: 36.15. Operator Optimization Information

URL: https://www.postgresql.org/docs/current/xoper-optimization.html

Contents:

• 36.15. Operator Optimization Information #
  • 36.15.1. COMMUTATOR #
  • 36.15.2. NEGATOR #
  • 36.15.3. RESTRICT #
  • 36.15.4. JOIN #
  • 36.15.5. HASHES #
  • Note
  • Note
  • 36.15.6. MERGES #
  • Note

A PostgreSQL operator definition can include several optional clauses that tell the system useful things about how the operator behaves. These clauses should be provided whenever appropriate, because they can make for considerable speedups in execution of queries that use the operator. But if you provide them, you must be sure that they are right! Incorrect use of an optimization clause can result in slow queries, subtly wrong output, or other Bad Things. You can always leave out an optimization clause if you are not sure about it; the only consequence is that queries might run slower than they need to.

Additional optimization clauses might be added in future versions of PostgreSQL. The ones described here are all the ones that release 18.0 understands.

It is also possible to attach a planner support function to the function that underlies an operator, providing another way of telling the system about the behavior of the operator. See Section 36.11 for more information.

The COMMUTATOR clause, if provided, names an operator that is the commutator of the operator being defined. We say that operator A is the commutator of operator B if (x A y) equals (y B x) for all possible input values x, y. Notice that B is also the commutator of A. For example, operators < and > for a particular data type are usually each others' commutators, and operator + is usually commutative with itself. But operator - is usually not commutative with anything.

The left operand type of a commutable operator is the same as the right operand type of its commutator, and vice versa. So the name of the commutator operator is all that PostgreSQL needs to be given to look up the commutator, and that's all that needs to be provided in the COMMUTATOR clause.

It's critical to provide commutator information for operators that will be used in indexes and join clauses, because this allows the query optimizer to “flip around” such a clause to the forms needed for different plan types. For example, consider a query with a WHERE clause like tab1.x = tab2.y, where tab1.x and tab2.y are of a user-defined type, and suppose that tab2.y is indexed. The optimizer cannot generate an index scan unless it can determine how to flip the clause around to tab2.y = tab1.x, because the index-scan machinery expects to see the indexed column on the left of the operator it is given. PostgreSQL will not simply assume that this is a valid transformation — the creator of the = operator must specify that it is valid, by marking the operator with commutator information.

The NEGATOR clause, if provided, names an operator that is the negator of the operator being defined. We say that operator A is the negator of operator B if both return Boolean results and (x A y) equals NOT (x B y) for all possible inputs x, y. Notice that B is also the negator of A. For example, < and >= are a negator pair for most data types. An operator can never validly be its own negator.

Unlike commutators, a pair of unary operators could validly be marked as each other's negators; that would mean (A x) equals NOT (B x) for all x.

An operator's negator must have the same left and/or right operand types as the operator to be defined, so just as with COMMUTATOR, only the operator name need be given in the NEGATOR clause.

Providing a negator is very helpful to the query optimizer since it allows expressions like NOT (x = y) to be simplified into x <> y. This comes up more often than you might think, because NOT operations can be inserted as a consequence of other rearrangements.

The RESTRICT clause, if provided, names a restriction selectivity estimation function for the operator. (Note that this is a function name, not an operator name.) RESTRICT clauses only make sense for binary operators that return boolean. The idea behind a restriction selectivity estimator is to guess what fraction of the rows in a table will satisfy a WHERE-clause condition of the form:

for the current operator and a particular constant value. This assists the optimizer by giving it some idea of how many rows will be eliminated by WHERE clauses that have this form. (What happens if the constant is on the left, you might be wondering? Well, that's one of the things that COMMUTATOR is for...)

Writing new restriction selectivity estimation functions is far beyond the scope of this chapter, but fortunately you can usually just use one of the system's standard estimators for many of your own operators. These are the standard restriction estimators:

You can frequently get away with using either eqsel or neqsel for operators that have very high or very low selectivity, even if they aren't really equality or inequality. For example, the approximate-equality geometric operators use eqsel on the assumption that they'll usually only match a small fraction of the entries in a table.

You can use scalarltsel, scalarlesel, scalargtsel and scalargesel for comparisons on data types that have some sensible means of being converted into numeric scalars for range comparisons. If possible, add the data type to those understood by the function convert_to_scalar() in src/backend/utils/adt/selfuncs.c. (Eventually, this function should be replaced by per-data-type functions identified through a column of the pg_type system catalog; but that hasn't happened yet.) If you do not do this, things will still work, but the optimizer's estimates won't be as good as they could be.

Another useful built-in selectivity estimation function is matchingsel, which will work for almost any binary operator, if standard MCV and/or histogram statistics are collected for the input data type(s). Its default estimate is set to twice the default estimate used in eqsel, making it most suitable for comparison operators that are somewhat less strict than equality. (Or you could call the underlying generic_restriction_selectivity function, providing a different default estimate.)

There are additional selectivity estimation functions designed for geometric operators in src/backend/utils/adt/geo_selfuncs.c: areasel, positionsel, and contsel. At this writing these are just stubs, but you might want to use them (or even better, improve them) anyway.

The JOIN clause, if provided, names a join selectivity estimation function for the operator. (Note that this is a function name, not an operator name.) JOIN clauses only make sense for binary operators that return boolean. The idea behind a join selectivity estimator is to guess what fraction of the rows in a pair of tables will satisfy a WHERE-clause condition of the form:

for the current operator. As with the RESTRICT clause, this helps the optimizer very substantially by letting it figure out which of several possible join sequences is likely to take the least work.

As before, this chapter will make no attempt to explain how to write a join selectivity estimator function, but will just suggest that you use one of the standard estimators if one is applicable:

The HASHES clause, if present, tells the system that it is permissible to use the hash join method for a join based on this operator. HASHES only makes sense for a binary operator that returns boolean, and in practice the operator must represent equality for some data type or pair of data types.

The assumption underlying hash join is that the join operator can only return true for pairs of left and right values that hash to the same hash code. If two values get put in different hash buckets, the join will never compare them at all, implicitly assuming that the result of the join operator must be false. So it never makes sense to specify HASHES for operators that do not represent some form of equality. In most cases it is only practical to support hashing for operators that take the same data type on both sides. However, sometimes it is possible to design compatible hash functions for two or more data types; that is, functions that will generate the same hash codes for “equal” values, even though the values have different representations. For example, it's fairly simple to arrange this property when hashing integers of different widths.

To be marked HASHES, the join operator must appear in a hash index operator family. This is not enforced when you create the operator, since of course the referencing operator family couldn't exist yet. But attempts to use the operator in hash joins will fail at run time if no such operator family exists. The system needs the operator family to find the data-type-specific hash function(s) for the operator's input data type(s). Of course, you must also create suitable hash functions before you can create the operator family.

Care should be exercised when preparing a hash function, because there are machine-dependent ways in which it might fail to do the right thing. For example, if your data type is a structure in which there might be uninteresting pad bits, you cannot simply pass the whole structure to hash_any. (Unless you write your other operators and functions to ensure that the unused bits are always zero, which is the recommended strategy.) Another example is that on machines that meet the IEEE floating-point standard, negative zero and positive zero are different values (different bit patterns) but they are defined to compare equal. If a float value might contain negative zero then extra steps are needed to ensure it generates the same hash value as positive zero.

A hash-joinable operator must have a commutator (itself if the two operand data types are the same, or a related equality operator if they are different) that appears in the same operator family. If this is not the case, planner errors might occur when the operator is used. Also, it is a good idea (but not strictly required) for a hash operator family that supports multiple data types to provide equality operators for every combination of the data types; this allows better optimization.

The function underlying a hash-joinable operator must be marked immutable or stable. If it is volatile, the system will never attempt to use the operator for a hash join.

If a hash-joinable operator has an underlying function that is marked strict, the function must also be complete: that is, it should return true or false, never null, for any two nonnull inputs. If this rule is not followed, hash-optimization of IN operations might generate wrong results. (Specifically, IN might return false where the correct answer according to the standard would be null; or it might yield an error complaining that it wasn't prepared for a null result.)

The MERGES clause, if present, tells the system that it is permissible to use the merge-join method for a join based on this operator. MERGES only makes sense for a binary operator that returns boolean, and in practice the operator must represent equality for some data type or pair of data types.

Merge join is based on the idea of sorting the left- and right-hand tables into order and then scanning them in parallel. So, both data types must be capable of being fully ordered, and the join operator must be one that can only succeed for pairs of values that fall at the “same place” in the sort order. In practice this means that the join operator must behave like equality. But it is possible to merge-join two distinct data types so long as they are logically compatible. For example, the smallint-versus-integer equality operator is merge-joinable. We only need sorting operators that will bring both data types into a logically compatible sequence.

To be marked MERGES, the join operator must appear as an equality member of a btree index operator family. This is not enforced when you create the operator, since of course the referencing operator family couldn't exist yet. But the operator will not actually be used for merge joins unless a matching operator family can be found. The MERGES flag thus acts as a hint to the planner that it's worth looking for a matching operator family.

A merge-joinable operator must have a commutator (itself if the two operand data types are the same, or a related equality operator if they are different) that appears in the same operator family. If this is not the case, planner errors might occur when the operator is used. Also, it is a good idea (but not strictly required) for a btree operator family that supports multiple data types to provide equality operators for every combination of the data types; this allows better optimization.

The function underlying a merge-joinable operator must be marked immutable or stable. If it is volatile, the system will never attempt to use the operator for a merge join.

Examples:

Example 1 (unknown):

column OP constant

Example 2 (unknown):

table1.column1 OP table2.column2

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PostgreSQL: Documentation: 18: Chapter 57. Writing a Procedural Language Handler

URL: https://www.postgresql.org/docs/current/plhandler.html

Contents:

• Chapter 57. Writing a Procedural Language Handler

All calls to functions that are written in a language other than the current “version 1” interface for compiled languages (this includes functions in user-defined procedural languages and functions written in SQL) go through a call handler function for the specific language. It is the responsibility of the call handler to execute the function in a meaningful way, such as by interpreting the supplied source text. This chapter outlines how a new procedural language's call handler can be written.

The call handler for a procedural language is a “normal” function that must be written in a compiled language such as C, using the version-1 interface, and registered with PostgreSQL as taking no arguments and returning the type language_handler. This special pseudo-type identifies the function as a call handler and prevents it from being called directly in SQL commands. For more details on C language calling conventions and dynamic loading, see Section 36.10.

The call handler is called in the same way as any other function: It receives a pointer to a FunctionCallInfoBaseData struct containing argument values and information about the called function, and it is expected to return a Datum result (and possibly set the isnull field of the FunctionCallInfoBaseData structure, if it wishes to return an SQL null result). The difference between a call handler and an ordinary callee function is that the flinfo->fn_oid field of the FunctionCallInfoBaseData structure will contain the OID of the actual function to be called, not of the call handler itself. The call handler must use this field to determine which function to execute. Also, the passed argument list has been set up according to the declaration of the target function, not of the call handler.

It's up to the call handler to fetch the entry of the function from the pg_proc system catalog and to analyze the argument and return types of the called function. The AS clause from the CREATE FUNCTION command for the function will be found in the prosrc column of the pg_proc row. This is commonly source text in the procedural language, but in theory it could be something else, such as a path name to a file, or anything else that tells the call handler what to do in detail.

Often, the same function is called many times per SQL statement. A call handler can avoid repeated lookups of information about the called function by using the flinfo->fn_extra field. This will initially be NULL, but can be set by the call handler to point at information about the called function. On subsequent calls, if flinfo->fn_extra is already non-NULL then it can be used and the information lookup step skipped. The call handler must make sure that flinfo->fn_extra is made to point at memory that will live at least until the end of the current query, since an FmgrInfo data structure could be kept that long. One way to do this is to allocate the extra data in the memory context specified by flinfo->fn_mcxt; such data will normally have the same lifespan as the FmgrInfo itself. But the handler could also choose to use a longer-lived memory context so that it can cache function definition information across queries.

When a procedural-language function is invoked as a trigger, no arguments are passed in the usual way, but the FunctionCallInfoBaseData's context field points at a TriggerData structure, rather than being NULL as it is in a plain function call. A language handler should provide mechanisms for procedural-language functions to get at the trigger information.

A template for a procedural-language handler written as a C extension is provided in src/test/modules/plsample. This is a working sample demonstrating one way to create a procedural-language handler, process parameters, and return a value.

Although providing a call handler is sufficient to create a minimal procedural language, there are two other functions that can optionally be provided to make the language more convenient to use. These are a validator and an inline handler. A validator can be provided to allow language-specific checking to be done during CREATE FUNCTION. An inline handler can be provided to allow the language to support anonymous code blocks executed via the DO command.

If a validator is provided by a procedural language, it must be declared as a function taking a single parameter of type oid. The validator's result is ignored, so it is customarily declared to return void. The validator will be called at the end of a CREATE FUNCTION command that has created or updated a function written in the procedural language. The passed-in OID is the OID of the function's pg_proc row. The validator must fetch this row in the usual way, and do whatever checking is appropriate. First, call CheckFunctionValidatorAccess() to diagnose explicit calls to the validator that the user could not achieve through CREATE FUNCTION. Typical checks then include verifying that the function's argument and result types are supported by the language, and that the function's body is syntactically correct in the language. If the validator finds the function to be okay, it should just return. If it finds an error, it should report that via the normal ereport() error reporting mechanism. Throwing an error will force a transaction rollback and thus prevent the incorrect function definition from being committed.

Validator functions should typically honor the check_function_bodies parameter: if it is turned off then any expensive or context-sensitive checking should be skipped. If the language provides for code execution at compilation time, the validator must suppress checks that would induce such execution. In particular, this parameter is turned off by pg_dump so that it can load procedural language functions without worrying about side effects or dependencies of the function bodies on other database objects. (Because of this requirement, the call handler should avoid assuming that the validator has fully checked the function. The point of having a validator is not to let the call handler omit checks, but to notify the user immediately if there are obvious errors in a CREATE FUNCTION command.) While the choice of exactly what to check is mostly left to the discretion of the validator function, note that the core CREATE FUNCTION code only executes SET clauses attached to a function when check_function_bodies is on. Therefore, checks whose results might be affected by GUC parameters definitely should be skipped when check_function_bodies is off, to avoid false failures when restoring a dump.

If an inline handler is provided by a procedural language, it must be declared as a function taking a single parameter of type internal. The inline handler's result is ignored, so it is customarily declared to return void. The inline handler will be called when a DO statement is executed specifying the procedural language. The parameter actually passed is a pointer to an InlineCodeBlock struct, which contains information about the DO statement's parameters, in particular the text of the anonymous code block to be executed. The inline handler should execute this code and return.

It's recommended that you wrap all these function declarations, as well as the CREATE LANGUAGE command itself, into an extension so that a simple CREATE EXTENSION command is sufficient to install the language. See Section 36.17 for information about writing extensions.

The procedural languages included in the standard distribution are good references when trying to write your own language handler. Look into the src/pl subdirectory of the source tree. The CREATE LANGUAGE reference page also has some useful details.

---

PostgreSQL: Documentation: 18: 9.10. Enum Support Functions

URL: https://www.postgresql.org/docs/current/functions-enum.html

Contents:

• 9.10. Enum Support Functions #

For enum types (described in Section 8.7), there are several functions that allow cleaner programming without hard-coding particular values of an enum type. These are listed in Table 9.35. The examples assume an enum type created as:

Table 9.35. Enum Support Functions

enum_first ( anyenum ) → anyenum

Returns the first value of the input enum type.

enum_first(null::rainbow) → red

enum_last ( anyenum ) → anyenum

Returns the last value of the input enum type.

enum_last(null::rainbow) → purple

enum_range ( anyenum ) → anyarray

Returns all values of the input enum type in an ordered array.

enum_range(null::rainbow) → {red,orange,yellow,​green,blue,purple}

enum_range ( anyenum, anyenum ) → anyarray

Returns the range between the two given enum values, as an ordered array. The values must be from the same enum type. If the first parameter is null, the result will start with the first value of the enum type. If the second parameter is null, the result will end with the last value of the enum type.

enum_range('orange'::rainbow, 'green'::rainbow) → {orange,yellow,green}

enum_range(NULL, 'green'::rainbow) → {red,orange,​yellow,green}

enum_range('orange'::rainbow, NULL) → {orange,yellow,green,​blue,purple}

Notice that except for the two-argument form of enum_range, these functions disregard the specific value passed to them; they care only about its declared data type. Either null or a specific value of the type can be passed, with the same result. It is more common to apply these functions to a table column or function argument than to a hardwired type name as used in the examples.

Examples:

Example 1 (unknown):

CREATE TYPE rainbow AS ENUM ('red', 'orange', 'yellow', 'green', 'blue', 'purple');

---

PostgreSQL: Documentation: 18: 22.5. Destroying a Database

URL: https://www.postgresql.org/docs/current/manage-ag-dropdb.html

Contents:

• 22.5. Destroying a Database #

Databases are destroyed with the command DROP DATABASE:

Only the owner of the database, or a superuser, can drop a database. Dropping a database removes all objects that were contained within the database. The destruction of a database cannot be undone.

You cannot execute the DROP DATABASE command while connected to the victim database. You can, however, be connected to any other database, including the template1 database. template1 would be the only option for dropping the last user database of a given cluster.

For convenience, there is also a shell program to drop databases, dropdb:

(Unlike createdb, it is not the default action to drop the database with the current user name.)

Examples:

Example 1 (unknown):

DROP DATABASE name;

Example 2 (unknown):

dropdb dbname

---

PostgreSQL: Documentation: 18: 13.4. Data Consistency Checks at the Application Level

URL: https://www.postgresql.org/docs/current/applevel-consistency.html

Contents:

• 13.4. Data Consistency Checks at the Application Level #
  • 13.4.1. Enforcing Consistency with Serializable Transactions #
  • Warning: Serializable Transactions and Data Replication
  • 13.4.2. Enforcing Consistency with Explicit Blocking Locks #

It is very difficult to enforce business rules regarding data integrity using Read Committed transactions because the view of the data is shifting with each statement, and even a single statement may not restrict itself to the statement's snapshot if a write conflict occurs.

While a Repeatable Read transaction has a stable view of the data throughout its execution, there is a subtle issue with using MVCC snapshots for data consistency checks, involving something known as read/write conflicts. If one transaction writes data and a concurrent transaction attempts to read the same data (whether before or after the write), it cannot see the work of the other transaction. The reader then appears to have executed first regardless of which started first or which committed first. If that is as far as it goes, there is no problem, but if the reader also writes data which is read by a concurrent transaction there is now a transaction which appears to have run before either of the previously mentioned transactions. If the transaction which appears to have executed last actually commits first, it is very easy for a cycle to appear in a graph of the order of execution of the transactions. When such a cycle appears, integrity checks will not work correctly without some help.

As mentioned in Section 13.2.3, Serializable transactions are just Repeatable Read transactions which add nonblocking monitoring for dangerous patterns of read/write conflicts. When a pattern is detected which could cause a cycle in the apparent order of execution, one of the transactions involved is rolled back to break the cycle.

If the Serializable transaction isolation level is used for all writes and for all reads which need a consistent view of the data, no other effort is required to ensure consistency. Software from other environments which is written to use serializable transactions to ensure consistency should “just work” in this regard in PostgreSQL.

When using this technique, it will avoid creating an unnecessary burden for application programmers if the application software goes through a framework which automatically retries transactions which are rolled back with a serialization failure. It may be a good idea to set default_transaction_isolation to serializable. It would also be wise to take some action to ensure that no other transaction isolation level is used, either inadvertently or to subvert integrity checks, through checks of the transaction isolation level in triggers.

See Section 13.2.3 for performance suggestions.

This level of integrity protection using Serializable transactions does not yet extend to hot standby mode (Section 26.4) or logical replicas. Because of that, those using hot standby or logical replication may want to use Repeatable Read and explicit locking on the primary.

When non-serializable writes are possible, to ensure the current validity of a row and protect it against concurrent updates one must use SELECT FOR UPDATE, SELECT FOR SHARE, or an appropriate LOCK TABLE statement. (SELECT FOR UPDATE and SELECT FOR SHARE lock just the returned rows against concurrent updates, while LOCK TABLE locks the whole table.) This should be taken into account when porting applications to PostgreSQL from other environments.

Also of note to those converting from other environments is the fact that SELECT FOR UPDATE does not ensure that a concurrent transaction will not update or delete a selected row. To do that in PostgreSQL you must actually update the row, even if no values need to be changed. SELECT FOR UPDATE temporarily blocks other transactions from acquiring the same lock or executing an UPDATE or DELETE which would affect the locked row, but once the transaction holding this lock commits or rolls back, a blocked transaction will proceed with the conflicting operation unless an actual UPDATE of the row was performed while the lock was held.

Global validity checks require extra thought under non-serializable MVCC. For example, a banking application might wish to check that the sum of all credits in one table equals the sum of debits in another table, when both tables are being actively updated. Comparing the results of two successive SELECT sum(...) commands will not work reliably in Read Committed mode, since the second query will likely include the results of transactions not counted by the first. Doing the two sums in a single repeatable read transaction will give an accurate picture of only the effects of transactions that committed before the repeatable read transaction started — but one might legitimately wonder whether the answer is still relevant by the time it is delivered. If the repeatable read transaction itself applied some changes before trying to make the consistency check, the usefulness of the check becomes even more debatable, since now it includes some but not all post-transaction-start changes. In such cases a careful person might wish to lock all tables needed for the check, in order to get an indisputable picture of current reality. A SHARE mode (or higher) lock guarantees that there are no uncommitted changes in the locked table, other than those of the current transaction.

Note also that if one is relying on explicit locking to prevent concurrent changes, one should either use Read Committed mode, or in Repeatable Read mode be careful to obtain locks before performing queries. A lock obtained by a repeatable read transaction guarantees that no other transactions modifying the table are still running, but if the snapshot seen by the transaction predates obtaining the lock, it might predate some now-committed changes in the table. A repeatable read transaction's snapshot is actually frozen at the start of its first query or data-modification command (SELECT, INSERT, UPDATE, DELETE, or MERGE), so it is possible to obtain locks explicitly before the snapshot is frozen.

---

PostgreSQL: Documentation: 18: 8.17. Range Types

URL: https://www.postgresql.org/docs/current/rangetypes.html

Contents:

• 8.17. Range Types #
  • 8.17.1. Built-in Range and Multirange Types #
  • 8.17.2. Examples #
  • 8.17.3. Inclusive and Exclusive Bounds #
  • 8.17.4. Infinite (Unbounded) Ranges #
  • 8.17.5. Range Input/Output #
  • Note
  • 8.17.6. Constructing Ranges and Multiranges #
  • 8.17.7. Discrete Range Types #
  • 8.17.8. Defining New Range Types #

Range types are data types representing a range of values of some element type (called the range's subtype). For instance, ranges of timestamp might be used to represent the ranges of time that a meeting room is reserved. In this case the data type is tsrange (short for “timestamp range”), and timestamp is the subtype. The subtype must have a total order so that it is well-defined whether element values are within, before, or after a range of values.

Range types are useful because they represent many element values in a single range value, and because concepts such as overlapping ranges can be expressed clearly. The use of time and date ranges for scheduling purposes is the clearest example; but price ranges, measurement ranges from an instrument, and so forth can also be useful.

Every range type has a corresponding multirange type. A multirange is an ordered list of non-contiguous, non-empty, non-null ranges. Most range operators also work on multiranges, and they have a few functions of their own.

PostgreSQL comes with the following built-in range types:

int4range — Range of integer, int4multirange — corresponding Multirange

int8range — Range of bigint, int8multirange — corresponding Multirange

numrange — Range of numeric, nummultirange — corresponding Multirange

tsrange — Range of timestamp without time zone, tsmultirange — corresponding Multirange

tstzrange — Range of timestamp with time zone, tstzmultirange — corresponding Multirange

daterange — Range of date, datemultirange — corresponding Multirange

In addition, you can define your own range types; see CREATE TYPE for more information.

See Table 9.58 and Table 9.60 for complete lists of operators and functions on range types.

Every non-empty range has two bounds, the lower bound and the upper bound. All points between these values are included in the range. An inclusive bound means that the boundary point itself is included in the range as well, while an exclusive bound means that the boundary point is not included in the range.

In the text form of a range, an inclusive lower bound is represented by “[” while an exclusive lower bound is represented by “(”. Likewise, an inclusive upper bound is represented by “]”, while an exclusive upper bound is represented by “)”. (See Section 8.17.5 for more details.)

The functions lower_inc and upper_inc test the inclusivity of the lower and upper bounds of a range value, respectively.

The lower bound of a range can be omitted, meaning that all values less than the upper bound are included in the range, e.g., (,3]. Likewise, if the upper bound of the range is omitted, then all values greater than the lower bound are included in the range. If both lower and upper bounds are omitted, all values of the element type are considered to be in the range. Specifying a missing bound as inclusive is automatically converted to exclusive, e.g., [,] is converted to (,). You can think of these missing values as +/-infinity, but they are special range type values and are considered to be beyond any range element type's +/-infinity values.

Element types that have the notion of “infinity” can use them as explicit bound values. For example, with timestamp ranges, [today,infinity) excludes the special timestamp value infinity, while [today,infinity] include it, as does [today,) and [today,].

The functions lower_inf and upper_inf test for infinite lower and upper bounds of a range, respectively.

The input for a range value must follow one of the following patterns:

The parentheses or brackets indicate whether the lower and upper bounds are exclusive or inclusive, as described previously. Notice that the final pattern is empty, which represents an empty range (a range that contains no points).

The lower-bound may be either a string that is valid input for the subtype, or empty to indicate no lower bound. Likewise, upper-bound may be either a string that is valid input for the subtype, or empty to indicate no upper bound.

Each bound value can be quoted using " (double quote) characters. This is necessary if the bound value contains parentheses, brackets, commas, double quotes, or backslashes, since these characters would otherwise be taken as part of the range syntax. To put a double quote or backslash in a quoted bound value, precede it with a backslash. (Also, a pair of double quotes within a double-quoted bound value is taken to represent a double quote character, analogously to the rules for single quotes in SQL literal strings.) Alternatively, you can avoid quoting and use backslash-escaping to protect all data characters that would otherwise be taken as range syntax. Also, to write a bound value that is an empty string, write "", since writing nothing means an infinite bound.

Whitespace is allowed before and after the range value, but any whitespace between the parentheses or brackets is taken as part of the lower or upper bound value. (Depending on the element type, it might or might not be significant.)

These rules are very similar to those for writing field values in composite-type literals. See Section 8.16.6 for additional commentary.

The input for a multirange is curly brackets ({ and }) containing zero or more valid ranges, separated by commas. Whitespace is permitted around the brackets and commas. This is intended to be reminiscent of array syntax, although multiranges are much simpler: they have just one dimension and there is no need to quote their contents. (The bounds of their ranges may be quoted as above however.)

Each range type has a constructor function with the same name as the range type. Using the constructor function is frequently more convenient than writing a range literal constant, since it avoids the need for extra quoting of the bound values. The constructor function accepts two or three arguments. The two-argument form constructs a range in standard form (lower bound inclusive, upper bound exclusive), while the three-argument form constructs a range with bounds of the form specified by the third argument. The third argument must be one of the strings “()”, “(]”, “[)”, or “[]”. For example:

Each range type also has a multirange constructor with the same name as the multirange type. The constructor function takes zero or more arguments which are all ranges of the appropriate type. For example:

A discrete range is one whose element type has a well-defined “step”, such as integer or date. In these types two elements can be said to be adjacent, when there are no valid values between them. This contrasts with continuous ranges, where it's always (or almost always) possible to identify other element values between two given values. For example, a range over the numeric type is continuous, as is a range over timestamp. (Even though timestamp has limited precision, and so could theoretically be treated as discrete, it's better to consider it continuous since the step size is normally not of interest.)

Another way to think about a discrete range type is that there is a clear idea of a “next” or “previous” value for each element value. Knowing that, it is possible to convert between inclusive and exclusive representations of a range's bounds, by choosing the next or previous element value instead of the one originally given. For example, in an integer range type [4,8] and (3,9) denote the same set of values; but this would not be so for a range over numeric.

A discrete range type should have a canonicalization function that is aware of the desired step size for the element type. The canonicalization function is charged with converting equivalent values of the range type to have identical representations, in particular consistently inclusive or exclusive bounds. If a canonicalization function is not specified, then ranges with different formatting will always be treated as unequal, even though they might represent the same set of values in reality.

The built-in range types int4range, int8range, and daterange all use a canonical form that includes the lower bound and excludes the upper bound; that is, [). User-defined range types can use other conventions, however.

Users can define their own range types. The most common reason to do this is to use ranges over subtypes not provided among the built-in range types. For example, to define a new range type of subtype float8:

Because float8 has no meaningful “step”, we do not define a canonicalization function in this example.

When you define your own range you automatically get a corresponding multirange type.

Defining your own range type also allows you to specify a different subtype B-tree operator class or collation to use, so as to change the sort ordering that determines which values fall into a given range.

If the subtype is considered to have discrete rather than continuous values, the CREATE TYPE command should specify a canonical function. The canonicalization function takes an input range value, and must return an equivalent range value that may have different bounds and formatting. The canonical output for two ranges that represent the same set of values, for example the integer ranges [1, 7] and [1, 8), must be identical. It doesn't matter which representation you choose to be the canonical one, so long as two equivalent values with different formattings are always mapped to the same value with the same formatting. In addition to adjusting the inclusive/exclusive bounds format, a canonicalization function might round off boundary values, in case the desired step size is larger than what the subtype is capable of storing. For instance, a range type over timestamp could be defined to have a step size of an hour, in which case the canonicalization function would need to round off bounds that weren't a multiple of an hour, or perhaps throw an error instead.

In addition, any range type that is meant to be used with GiST or SP-GiST indexes should define a subtype difference, or subtype_diff, function. (The index will still work without subtype_diff, but it is likely to be considerably less efficient than if a difference function is provided.) The subtype difference function takes two input values of the subtype, and returns their difference (i.e., X minus Y) represented as a float8 value. In our example above, the function float8mi that underlies the regular float8 minus operator can be used; but for any other subtype, some type conversion would be necessary. Some creative thought about how to represent differences as numbers might be needed, too. To the greatest extent possible, the subtype_diff function should agree with the sort ordering implied by the selected operator class and collation; that is, its result should be positive whenever its first argument is greater than its second according to the sort ordering.

A less-oversimplified example of a subtype_diff function is:

See CREATE TYPE for more information about creating range types.

GiST and SP-GiST indexes can be created for table columns of range types. GiST indexes can be also created for table columns of multirange types. For instance, to create a GiST index:

A GiST or SP-GiST index on ranges can accelerate queries involving these range operators: =, &&, <@, @>, <<, >>, -|-, &<, and &>. A GiST index on multiranges can accelerate queries involving the same set of multirange operators. A GiST index on ranges and GiST index on multiranges can also accelerate queries involving these cross-type range to multirange and multirange to range operators correspondingly: &&, <@, @>, <<, >>, -|-, &<, and &>. See Table 9.58 for more information.

In addition, B-tree and hash indexes can be created for table columns of range types. For these index types, basically the only useful range operation is equality. There is a B-tree sort ordering defined for range values, with corresponding < and > operators, but the ordering is rather arbitrary and not usually useful in the real world. Range types' B-tree and hash support is primarily meant to allow sorting and hashing internally in queries, rather than creation of actual indexes.

While UNIQUE is a natural constraint for scalar values, it is usually unsuitable for range types. Instead, an exclusion constraint is often more appropriate (see CREATE TABLE ... CONSTRAINT ... EXCLUDE). Exclusion constraints allow the specification of constraints such as “non-overlapping” on a range type. For example:

That constraint will prevent any overlapping values from existing in the table at the same time:

You can use the btree_gist extension to define exclusion constraints on plain scalar data types, which can then be combined with range exclusions for maximum flexibility. For example, after btree_gist is installed, the following constraint will reject overlapping ranges only if the meeting room numbers are equal:

Examples:

Example 1 (unknown):

CREATE TABLE reservation (room int, during tsrange);
INSERT INTO reservation VALUES
    (1108, '[2010-01-01 14:30, 2010-01-01 15:30)');

-- Containment
SELECT int4range(10, 20) @> 3;

-- Overlaps
SELECT numrange(11.1, 22.2) && numrange(20.0, 30.0);

-- Extract the upper bound
SELECT upper(int8range(15, 25));

-- Compute the intersection
SELECT int4range(10, 20) * int4range(15, 25);

-- Is the range empty?
SELECT isempty(numrange(1, 5));

Example 2 (unknown):

(lower-bound,upper-bound)
(lower-bound,upper-bound]
[lower-bound,upper-bound)
[lower-bound,upper-bound]
empty

Example 3 (unknown):

-- includes 3, does not include 7, and does include all points in between
SELECT '[3,7)'::int4range;

-- does not include either 3 or 7, but includes all points in between
SELECT '(3,7)'::int4range;

-- includes only the single point 4
SELECT '[4,4]'::int4range;

-- includes no points (and will be normalized to 'empty')
SELECT '[4,4)'::int4range;

Example 4 (unknown):

SELECT '{}'::int4multirange;
SELECT '{[3,7)}'::int4multirange;
SELECT '{[3,7), [8,9)}'::int4multirange;

---

PostgreSQL: Documentation: 18: 34.15. Informix Compatibility Mode

URL: https://www.postgresql.org/docs/current/ecpg-informix-compat.html

Contents:

• 34.15. Informix Compatibility Mode #
  • Note
  • 34.15.1. Additional Types #
  • 34.15.2. Additional/Missing Embedded SQL Statements #
  • 34.15.3. Informix-compatible SQLDA Descriptor Areas #
  • 34.15.4. Additional Functions #
  • 34.15.5. Additional Constants #

ecpg can be run in a so-called Informix compatibility mode. If this mode is active, it tries to behave as if it were the Informix precompiler for Informix E/SQL. Generally spoken this will allow you to use the dollar sign instead of the EXEC SQL primitive to introduce embedded SQL commands:

There must not be any white space between the $ and a following preprocessor directive, that is, include, define, ifdef, etc. Otherwise, the preprocessor will parse the token as a host variable.

There are two compatibility modes: INFORMIX, INFORMIX_SE

When linking programs that use this compatibility mode, remember to link against libcompat that is shipped with ECPG.

Besides the previously explained syntactic sugar, the Informix compatibility mode ports some functions for input, output and transformation of data as well as embedded SQL statements known from E/SQL to ECPG.

Informix compatibility mode is closely connected to the pgtypeslib library of ECPG. pgtypeslib maps SQL data types to data types within the C host program and most of the additional functions of the Informix compatibility mode allow you to operate on those C host program types. Note however that the extent of the compatibility is limited. It does not try to copy Informix behavior; it allows you to do more or less the same operations and gives you functions that have the same name and the same basic behavior but it is no drop-in replacement if you are using Informix at the moment. Moreover, some of the data types are different. For example, PostgreSQL's datetime and interval types do not know about ranges like for example YEAR TO MINUTE so you won't find support in ECPG for that either.

The Informix-special "string" pseudo-type for storing right-trimmed character string data is now supported in Informix-mode without using typedef. In fact, in Informix-mode, ECPG refuses to process source files that contain typedef sometype string;

This statement closes the current connection. In fact, this is a synonym for ECPG's DISCONNECT CURRENT:

Due to differences in how ECPG works compared to Informix's ESQL/C (namely, which steps are purely grammar transformations and which steps rely on the underlying run-time library) there is no FREE cursor_name statement in ECPG. This is because in ECPG, DECLARE CURSOR doesn't translate to a function call into the run-time library that uses to the cursor name. This means that there's no run-time bookkeeping of SQL cursors in the ECPG run-time library, only in the PostgreSQL server.

FREE statement_name is a synonym for DEALLOCATE PREPARE statement_name.

Informix-compatible mode supports a different structure than the one described in Section 34.7.2. See below:

The global properties are:

The number of fields in the SQLDA descriptor.

Pointer to the per-field properties.

Unused, filled with zero-bytes.

Size of the allocated structure.

Pointer to the next SQLDA structure if the result set contains more than one record.

Unused pointer, contains NULL. Kept for Informix-compatibility.

The per-field properties are below, they are stored in the sqlvar array:

Type of the field. Constants are in sqltypes.h

Length of the field data.

Pointer to the field data. The pointer is of char * type, the data pointed by it is in a binary format. Example:

Pointer to the NULL indicator. If returned by DESCRIBE or FETCH then it's always a valid pointer. If used as input for EXECUTE ... USING sqlda; then NULL-pointer value means that the value for this field is non-NULL. Otherwise a valid pointer and sqlitype has to be properly set. Example:

Name of the field. 0-terminated string.

Reserved in Informix, value of PQfformat for the field.

Type of the NULL indicator data. It's always SQLSMINT when returning data from the server. When the SQLDA is used for a parameterized query, the data is treated according to the set type.

Length of the NULL indicator data.

Extended type of the field, result of PQftype.

It equals to sqldata if sqllen is larger than 32kB.

For more information, see the sqlda.h header and the src/interfaces/ecpg/test/compat_informix/sqlda.pgc regression test.

Add two decimal type values.

The function receives a pointer to the first operand of type decimal (arg1), a pointer to the second operand of type decimal (arg2) and a pointer to a value of type decimal that will contain the sum (sum). On success, the function returns 0. ECPG_INFORMIX_NUM_OVERFLOW is returned in case of overflow and ECPG_INFORMIX_NUM_UNDERFLOW in case of underflow. -1 is returned for other failures and errno is set to the respective errno number of the pgtypeslib.

Compare two variables of type decimal.

The function receives a pointer to the first decimal value (arg1), a pointer to the second decimal value (arg2) and returns an integer value that indicates which is the bigger value.

1, if the value that arg1 points to is bigger than the value that var2 points to

-1, if the value that arg1 points to is smaller than the value that arg2 points to

0, if the value that arg1 points to and the value that arg2 points to are equal

Copy a decimal value.

The function receives a pointer to the decimal value that should be copied as the first argument (src) and a pointer to the target structure of type decimal (target) as the second argument.

Convert a value from its ASCII representation into a decimal type.

The function receives a pointer to string that contains the string representation of the number to be converted (cp) as well as its length len. np is a pointer to the decimal value that saves the result of the operation.

Valid formats are for example: -2, .794, +3.44, 592.49E07 or -32.84e-4.

The function returns 0 on success. If overflow or underflow occurred, ECPG_INFORMIX_NUM_OVERFLOW or ECPG_INFORMIX_NUM_UNDERFLOW is returned. If the ASCII representation could not be parsed, ECPG_INFORMIX_BAD_NUMERIC is returned or ECPG_INFORMIX_BAD_EXPONENT if this problem occurred while parsing the exponent.

Convert a value of type double to a value of type decimal.

The function receives the variable of type double that should be converted as its first argument (dbl). As the second argument (np), the function receives a pointer to the decimal variable that should hold the result of the operation.

The function returns 0 on success and a negative value if the conversion failed.

Convert a value of type int to a value of type decimal.

The function receives the variable of type int that should be converted as its first argument (in). As the second argument (np), the function receives a pointer to the decimal variable that should hold the result of the operation.

The function returns 0 on success and a negative value if the conversion failed.

Convert a value of type long to a value of type decimal.

The function receives the variable of type long that should be converted as its first argument (lng). As the second argument (np), the function receives a pointer to the decimal variable that should hold the result of the operation.

The function returns 0 on success and a negative value if the conversion failed.

Divide two variables of type decimal.

The function receives pointers to the variables that are the first (n1) and the second (n2) operands and calculates n1/n2. result is a pointer to the variable that should hold the result of the operation.

On success, 0 is returned and a negative value if the division fails. If overflow or underflow occurred, the function returns ECPG_INFORMIX_NUM_OVERFLOW or ECPG_INFORMIX_NUM_UNDERFLOW respectively. If an attempt to divide by zero is observed, the function returns ECPG_INFORMIX_DIVIDE_ZERO.

Multiply two decimal values.

The function receives pointers to the variables that are the first (n1) and the second (n2) operands and calculates n1*n2. result is a pointer to the variable that should hold the result of the operation.

On success, 0 is returned and a negative value if the multiplication fails. If overflow or underflow occurred, the function returns ECPG_INFORMIX_NUM_OVERFLOW or ECPG_INFORMIX_NUM_UNDERFLOW respectively.

Subtract one decimal value from another.

The function receives pointers to the variables that are the first (n1) and the second (n2) operands and calculates n1-n2. result is a pointer to the variable that should hold the result of the operation.

On success, 0 is returned and a negative value if the subtraction fails. If overflow or underflow occurred, the function returns ECPG_INFORMIX_NUM_OVERFLOW or ECPG_INFORMIX_NUM_UNDERFLOW respectively.

Convert a variable of type decimal to its ASCII representation in a C char* string.

The function receives a pointer to a variable of type decimal (np) that it converts to its textual representation. cp is the buffer that should hold the result of the operation. The parameter right specifies, how many digits right of the decimal point should be included in the output. The result will be rounded to this number of decimal digits. Setting right to -1 indicates that all available decimal digits should be included in the output. If the length of the output buffer, which is indicated by len is not sufficient to hold the textual representation including the trailing zero byte, only a single * character is stored in the result and -1 is returned.

The function returns either -1 if the buffer cp was too small or ECPG_INFORMIX_OUT_OF_MEMORY if memory was exhausted.

Convert a variable of type decimal to a double.

The function receives a pointer to the decimal value to convert (np) and a pointer to the double variable that should hold the result of the operation (dblp).

On success, 0 is returned and a negative value if the conversion failed.

Convert a variable of type decimal to an integer.

The function receives a pointer to the decimal value to convert (np) and a pointer to the integer variable that should hold the result of the operation (ip).

On success, 0 is returned and a negative value if the conversion failed. If an overflow occurred, ECPG_INFORMIX_NUM_OVERFLOW is returned.

Note that the ECPG implementation differs from the Informix implementation. Informix limits an integer to the range from -32767 to 32767, while the limits in the ECPG implementation depend on the architecture (INT_MIN .. INT_MAX).

Convert a variable of type decimal to a long integer.

The function receives a pointer to the decimal value to convert (np) and a pointer to the long variable that should hold the result of the operation (lngp).

On success, 0 is returned and a negative value if the conversion failed. If an overflow occurred, ECPG_INFORMIX_NUM_OVERFLOW is returned.

Note that the ECPG implementation differs from the Informix implementation. Informix limits a long integer to the range from -2,147,483,647 to 2,147,483,647, while the limits in the ECPG implementation depend on the architecture (-LONG_MAX .. LONG_MAX).

Converts a date to a C char* string.

The function receives two arguments, the first one is the date to convert (d) and the second one is a pointer to the target string. The output format is always yyyy-mm-dd, so you need to allocate at least 11 bytes (including the zero-byte terminator) for the string.

The function returns 0 on success and a negative value in case of error.

Note that ECPG's implementation differs from the Informix implementation. In Informix the format can be influenced by setting environment variables. In ECPG however, you cannot change the output format.

Parse the textual representation of a date.

The function receives the textual representation of the date to convert (str) and a pointer to a variable of type date (d). This function does not allow you to specify a format mask. It uses the default format mask of Informix which is mm/dd/yyyy. Internally, this function is implemented by means of rdefmtdate. Therefore, rstrdate is not faster and if you have the choice you should opt for rdefmtdate which allows you to specify the format mask explicitly.

The function returns the same values as rdefmtdate.

Get the current date.

The function receives a pointer to a date variable (d) that it sets to the current date.

Internally this function uses the PGTYPESdate_today function.

Extract the values for the day, the month and the year from a variable of type date.

The function receives the date d and a pointer to an array of 3 short integer values mdy. The variable name indicates the sequential order: mdy[0] will be set to contain the number of the month, mdy[1] will be set to the value of the day and mdy[2] will contain the year.

The function always returns 0 at the moment.

Internally the function uses the PGTYPESdate_julmdy function.

Use a format mask to convert a character string to a value of type date.

The function receives a pointer to the date value that should hold the result of the operation (d), the format mask to use for parsing the date (fmt) and the C char* string containing the textual representation of the date (str). The textual representation is expected to match the format mask. However you do not need to have a 1:1 mapping of the string to the format mask. The function only analyzes the sequential order and looks for the literals yy or yyyy that indicate the position of the year, mm to indicate the position of the month and dd to indicate the position of the day.

The function returns the following values:

0 - The function terminated successfully.

ECPG_INFORMIX_ENOSHORTDATE - The date does not contain delimiters between day, month and year. In this case the input string must be exactly 6 or 8 bytes long but isn't.

ECPG_INFORMIX_ENOTDMY - The format string did not correctly indicate the sequential order of year, month and day.

ECPG_INFORMIX_BAD_DAY - The input string does not contain a valid day.

ECPG_INFORMIX_BAD_MONTH - The input string does not contain a valid month.

ECPG_INFORMIX_BAD_YEAR - The input string does not contain a valid year.

Internally this function is implemented to use the PGTYPESdate_defmt_asc function. See the reference there for a table of example input.

Convert a variable of type date to its textual representation using a format mask.

The function receives the date to convert (d), the format mask (fmt) and the string that will hold the textual representation of the date (str).

On success, 0 is returned and a negative value if an error occurred.

Internally this function uses the PGTYPESdate_fmt_asc function, see the reference there for examples.

Create a date value from an array of 3 short integers that specify the day, the month and the year of the date.

The function receives the array of the 3 short integers (mdy) and a pointer to a variable of type date that should hold the result of the operation.

Currently the function returns always 0.

Internally the function is implemented to use the function PGTYPESdate_mdyjul.

Return a number representing the day of the week for a date value.

The function receives the date variable d as its only argument and returns an integer that indicates the day of the week for this date.

Internally the function is implemented to use the function PGTYPESdate_dayofweek.

Retrieve the current timestamp.

The function retrieves the current timestamp and saves it into the timestamp variable that ts points to.

Parses a timestamp from its textual representation into a timestamp variable.

The function receives the string to parse (str) and a pointer to the timestamp variable that should hold the result of the operation (ts).

The function returns 0 on success and a negative value in case of error.

Internally this function uses the PGTYPEStimestamp_from_asc function. See the reference there for a table with example inputs.

Parses a timestamp from its textual representation using a format mask into a timestamp variable.

The function receives the string to parse (inbuf), the format mask to use (fmtstr) and a pointer to the timestamp variable that should hold the result of the operation (dtvalue).

This function is implemented by means of the PGTYPEStimestamp_defmt_asc function. See the documentation there for a list of format specifiers that can be used.

The function returns 0 on success and a negative value in case of error.

Subtract one timestamp from another and return a variable of type interval.

The function will subtract the timestamp variable that ts2 points to from the timestamp variable that ts1 points to and will store the result in the interval variable that iv points to.

Upon success, the function returns 0 and a negative value if an error occurred.

Convert a timestamp variable to a C char* string.

The function receives a pointer to the timestamp variable to convert (ts) and the string that should hold the result of the operation (output). It converts ts to its textual representation according to the SQL standard, which is be YYYY-MM-DD HH:MM:SS.

Upon success, the function returns 0 and a negative value if an error occurred.

Convert a timestamp variable to a C char* using a format mask.

The function receives a pointer to the timestamp to convert as its first argument (ts), a pointer to the output buffer (output), the maximal length that has been allocated for the output buffer (str_len) and the format mask to use for the conversion (fmtstr).

Upon success, the function returns 0 and a negative value if an error occurred.

Internally, this function uses the PGTYPEStimestamp_fmt_asc function. See the reference there for information on what format mask specifiers can be used.

Convert an interval variable to a C char* string.

The function receives a pointer to the interval variable to convert (i) and the string that should hold the result of the operation (str). It converts i to its textual representation according to the SQL standard, which is be YYYY-MM-DD HH:MM:SS.

Upon success, the function returns 0 and a negative value if an error occurred.

Convert a long integer value to its textual representation using a format mask.

The function receives the long value lng_val, the format mask fmt and a pointer to the output buffer outbuf. It converts the long value according to the format mask to its textual representation.

The format mask can be composed of the following format specifying characters:

• (asterisk) - if this position would be blank otherwise, fill it with an asterisk.

& (ampersand) - if this position would be blank otherwise, fill it with a zero.

- turn leading zeroes into blanks.

< - left-justify the number in the string.

, (comma) - group numbers of four or more digits into groups of three digits separated by a comma.

. (period) - this character separates the whole-number part of the number from the fractional part.

• (minus) - the minus sign appears if the number is a negative value.

• (plus) - the plus sign appears if the number is a positive value.

( - this replaces the minus sign in front of the negative number. The minus sign will not appear.

) - this character replaces the minus and is printed behind the negative value.

$ - the currency symbol.

Convert a string to upper case.

The function receives a pointer to the string and transforms every lower case character to upper case.

Return the number of characters in a string without counting trailing blanks.

The function expects a fixed-length string as its first argument (str) and its length as its second argument (len). It returns the number of significant characters, that is the length of the string without trailing blanks.

Copy a fixed-length string into a null-terminated string.

The function receives the fixed-length string to copy (src), its length (len) and a pointer to the destination memory (dest). Note that you need to reserve at least len+1 bytes for the string that dest points to. The function copies at most len bytes to the new location (less if the source string has trailing blanks) and adds the null-terminator.

This function exists but is not implemented at the moment!

This function exists but is not implemented at the moment!

This function exists but is not implemented at the moment!

This function exists but is not implemented at the moment!

Set a variable to NULL.

The function receives an integer that indicates the type of the variable and a pointer to the variable itself that is cast to a C char* pointer.

The following types exist:

CCHARTYPE - For a variable of type char or char*

CSHORTTYPE - For a variable of type short int

CINTTYPE - For a variable of type int

CBOOLTYPE - For a variable of type boolean

CFLOATTYPE - For a variable of type float

CLONGTYPE - For a variable of type long

CDOUBLETYPE - For a variable of type double

CDECIMALTYPE - For a variable of type decimal

CDATETYPE - For a variable of type date

CDTIMETYPE - For a variable of type timestamp

Here is an example of a call to this function:

Test if a variable is NULL.

The function receives the type of the variable to test (t) as well a pointer to this variable (ptr). Note that the latter needs to be cast to a char*. See the function rsetnull for a list of possible variable types.

Here is an example of how to use this function:

Note that all constants here describe errors and all of them are defined to represent negative values. In the descriptions of the different constants you can also find the value that the constants represent in the current implementation. However you should not rely on this number. You can however rely on the fact all of them are defined to represent negative values.

Functions return this value if an overflow occurred in a calculation. Internally it is defined as -1200 (the Informix definition).

Functions return this value if an underflow occurred in a calculation. Internally it is defined as -1201 (the Informix definition).

Functions return this value if an attempt to divide by zero is observed. Internally it is defined as -1202 (the Informix definition).

Functions return this value if a bad value for a year was found while parsing a date. Internally it is defined as -1204 (the Informix definition).

Functions return this value if a bad value for a month was found while parsing a date. Internally it is defined as -1205 (the Informix definition).

Functions return this value if a bad value for a day was found while parsing a date. Internally it is defined as -1206 (the Informix definition).

Functions return this value if a parsing routine needs a short date representation but did not get the date string in the right length. Internally it is defined as -1209 (the Informix definition).

Functions return this value if an error occurred during date formatting. Internally it is defined as -1210 (the Informix definition).

Functions return this value if memory was exhausted during their operation. Internally it is defined as -1211 (the Informix definition).

Functions return this value if a parsing routine was supposed to get a format mask (like mmddyy) but not all fields were listed correctly. Internally it is defined as -1212 (the Informix definition).

Functions return this value either if a parsing routine cannot parse the textual representation for a numeric value because it contains errors or if a routine cannot complete a calculation involving numeric variables because at least one of the numeric variables is invalid. Internally it is defined as -1213 (the Informix definition).

Functions return this value if a parsing routine cannot parse an exponent. Internally it is defined as -1216 (the Informix definition).

Functions return this value if a parsing routine cannot parse a date. Internally it is defined as -1218 (the Informix definition).

Functions return this value if a parsing routine is passed extra characters it cannot parse. Internally it is defined as -1264 (the Informix definition).

Examples:

Example 1 (unknown):

$int j = 3;
$CONNECT TO :dbname;
$CREATE TABLE test(i INT PRIMARY KEY, j INT);
$INSERT INTO test(i, j) VALUES (7, :j);
$COMMIT;

Example 2 (unknown):

EXEC SQL BEGIN DECLARE SECTION;
string userid; /* this variable will contain trimmed data */
EXEC SQL END DECLARE SECTION;

EXEC SQL FETCH MYCUR INTO :userid;

Example 3 (unknown):

$CLOSE DATABASE;                /* close the current connection */
EXEC SQL CLOSE DATABASE;

Example 4 (unknown):

struct sqlvar_compat
{
    short   sqltype;
    int     sqllen;
    char   *sqldata;
    short  *sqlind;
    char   *sqlname;
    char   *sqlformat;
    short   sqlitype;
    short   sqlilen;
    char   *sqlidata;
    int     sqlxid;
    char   *sqltypename;
    short   sqltypelen;
    short   sqlownerlen;
    short   sqlsourcetype;
    char   *sqlownername;
    int     sqlsourceid;
    char   *sqlilongdata;
    int     sqlflags;
    void   *sqlreserved;
};

struct sqlda_compat
{
    short  sqld;
    struct sqlvar_compat *sqlvar;
    char   desc_name[19];
    short  desc_occ;
    struct sqlda_compat *desc_next;
    void  *reserved;
};

typedef struct sqlvar_compat    sqlvar_t;
typedef struct sqlda_compat     sqlda_t;

---

PostgreSQL: Documentation: 18: 35.31. foreign_tables

URL: https://www.postgresql.org/docs/current/infoschema-foreign-tables.html

Contents:

• 35.31. foreign_tables #

The view foreign_tables contains all foreign tables defined in the current database. Only those foreign tables are shown that the current user has access to (by way of being the owner or having some privilege).

Table 35.29. foreign_tables Columns

foreign_table_catalog sql_identifier

Name of the database that the foreign table is defined in (always the current database)

foreign_table_schema sql_identifier

Name of the schema that contains the foreign table

foreign_table_name sql_identifier

Name of the foreign table

foreign_server_catalog sql_identifier

Name of the database that the foreign server is defined in (always the current database)

foreign_server_name sql_identifier

Name of the foreign server

---

PostgreSQL: Documentation: 18: 35.9. check_constraints

URL: https://www.postgresql.org/docs/current/infoschema-check-constraints.html

Contents:

• 35.9. check_constraints #

The view check_constraints contains all check constraints, either defined on a table or on a domain, that are owned by a currently enabled role. (The owner of the table or domain is the owner of the constraint.)

The SQL standard considers not-null constraints to be check constraints with a CHECK (column_name IS NOT NULL) expression. So not-null constraints are also included here and don't have a separate view.

Table 35.7. check_constraints Columns

constraint_catalog sql_identifier

Name of the database containing the constraint (always the current database)

constraint_schema sql_identifier

Name of the schema containing the constraint

constraint_name sql_identifier

Name of the constraint

check_clause character_data

The check expression of the check constraint

---

PostgreSQL: Documentation: 18: 5.4. Generated Columns

URL: https://www.postgresql.org/docs/current/ddl-generated-columns.html

Contents:

• 5.4. Generated Columns #

A generated column is a special column that is always computed from other columns. Thus, it is for columns what a view is for tables. There are two kinds of generated columns: stored and virtual. A stored generated column is computed when it is written (inserted or updated) and occupies storage as if it were a normal column. A virtual generated column occupies no storage and is computed when it is read. Thus, a virtual generated column is similar to a view and a stored generated column is similar to a materialized view (except that it is always updated automatically).

To create a generated column, use the GENERATED ALWAYS AS clause in CREATE TABLE, for example:

A generated column is by default of the virtual kind. Use the keywords VIRTUAL or STORED to make the choice explicit. See CREATE TABLE for more details.

A generated column cannot be written to directly. In INSERT or UPDATE commands, a value cannot be specified for a generated column, but the keyword DEFAULT may be specified.

Consider the differences between a column with a default and a generated column. The column default is evaluated once when the row is first inserted if no other value was provided; a generated column is updated whenever the row changes and cannot be overridden. A column default may not refer to other columns of the table; a generation expression would normally do so. A column default can use volatile functions, for example random() or functions referring to the current time; this is not allowed for generated columns.

Several restrictions apply to the definition of generated columns and tables involving generated columns:

The generation expression can only use immutable functions and cannot use subqueries or reference anything other than the current row in any way.

A generation expression cannot reference another generated column.

A generation expression cannot reference a system column, except tableoid.

A virtual generated column cannot have a user-defined type, and the generation expression of a virtual generated column must not reference user-defined functions or types, that is, it can only use built-in functions or types. This applies also indirectly, such as for functions or types that underlie operators or casts. (This restriction does not exist for stored generated columns.)

A generated column cannot have a column default or an identity definition.

A generated column cannot be part of a partition key.

Foreign tables can have generated columns. See CREATE FOREIGN TABLE for details.

For inheritance and partitioning:

If a parent column is a generated column, its child column must also be a generated column of the same kind (stored or virtual); however, the child column can have a different generation expression.

For stored generated columns, the generation expression that is actually applied during insert or update of a row is the one associated with the table that the row is physically in. (This is unlike the behavior for column defaults: for those, the default value associated with the table named in the query applies.) For virtual generated columns, the generation expression of the table named in the query applies when a table is read.

If a parent column is not a generated column, its child column must not be generated either.

For inherited tables, if you write a child column definition without any GENERATED clause in CREATE TABLE ... INHERITS, then its GENERATED clause will automatically be copied from the parent. ALTER TABLE ... INHERIT will insist that parent and child columns already match as to generation status, but it will not require their generation expressions to match.

Similarly for partitioned tables, if you write a child column definition without any GENERATED clause in CREATE TABLE ... PARTITION OF, then its GENERATED clause will automatically be copied from the parent. ALTER TABLE ... ATTACH PARTITION will insist that parent and child columns already match as to generation status, but it will not require their generation expressions to match.

In case of multiple inheritance, if one parent column is a generated column, then all parent columns must be generated columns. If they do not all have the same generation expression, then the desired expression for the child must be specified explicitly.

Additional considerations apply to the use of generated columns.

Generated columns maintain access privileges separately from their underlying base columns. So, it is possible to arrange it so that a particular role can read from a generated column but not from the underlying base columns.

For virtual generated columns, this is only fully secure if the generation expression uses only leakproof functions (see CREATE FUNCTION), but this is not enforced by the system.

Privileges of functions used in generation expressions are checked when the expression is actually executed, on write or read respectively, as if the generation expression had been called directly from the query using the generated column. The user of a generated column must have permissions to call all functions used by the generation expression. Functions in the generation expression are executed with the privileges of the user executing the query or the function owner, depending on whether the functions are defined as SECURITY INVOKER or SECURITY DEFINER.

Generated columns are, conceptually, updated after BEFORE triggers have run. Therefore, changes made to base columns in a BEFORE trigger will be reflected in generated columns. But conversely, it is not allowed to access generated columns in BEFORE triggers.

Generated columns are allowed to be replicated during logical replication according to the CREATE PUBLICATION parameter publish_generated_columns or by including them in the column list of the CREATE PUBLICATION command. This is currently only supported for stored generated columns. See Section 29.6 for details.

Examples:

Example 1 (unknown):

CREATE TABLE people (
    ...,
    height_cm numeric,
    height_in numeric GENERATED ALWAYS AS (height_cm / 2.54)
);

---

PostgreSQL: Documentation: 18: 15.1. How Parallel Query Works

URL: https://www.postgresql.org/docs/current/how-parallel-query-works.html

Contents:

• 15.1. How Parallel Query Works #

When the optimizer determines that parallel query is the fastest execution strategy for a particular query, it will create a query plan that includes a Gather or Gather Merge node. Here is a simple example:

In all cases, the Gather or Gather Merge node will have exactly one child plan, which is the portion of the plan that will be executed in parallel. If the Gather or Gather Merge node is at the very top of the plan tree, then the entire query will execute in parallel. If it is somewhere else in the plan tree, then only the portion of the plan below it will run in parallel. In the example above, the query accesses only one table, so there is only one plan node other than the Gather node itself; since that plan node is a child of the Gather node, it will run in parallel.

Using EXPLAIN, you can see the number of workers chosen by the planner. When the Gather node is reached during query execution, the process that is implementing the user's session will request a number of background worker processes equal to the number of workers chosen by the planner. The number of background workers that the planner will consider using is limited to at most max_parallel_workers_per_gather. The total number of background workers that can exist at any one time is limited by both max_worker_processes and max_parallel_workers. Therefore, it is possible for a parallel query to run with fewer workers than planned, or even with no workers at all. The optimal plan may depend on the number of workers that are available, so this can result in poor query performance. If this occurrence is frequent, consider increasing max_worker_processes and max_parallel_workers so that more workers can be run simultaneously or alternatively reducing max_parallel_workers_per_gather so that the planner requests fewer workers.

Every background worker process that is successfully started for a given parallel query will execute the parallel portion of the plan. The leader will also execute that portion of the plan, but it has an additional responsibility: it must also read all of the tuples generated by the workers. When the parallel portion of the plan generates only a small number of tuples, the leader will often behave very much like an additional worker, speeding up query execution. Conversely, when the parallel portion of the plan generates a large number of tuples, the leader may be almost entirely occupied with reading the tuples generated by the workers and performing any further processing steps that are required by plan nodes above the level of the Gather node or Gather Merge node. In such cases, the leader will do very little of the work of executing the parallel portion of the plan.

When the node at the top of the parallel portion of the plan is Gather Merge rather than Gather, it indicates that each process executing the parallel portion of the plan is producing tuples in sorted order, and that the leader is performing an order-preserving merge. In contrast, Gather reads tuples from the workers in whatever order is convenient, destroying any sort order that may have existed.

Examples:

Example 1 (unknown):

EXPLAIN SELECT * FROM pgbench_accounts WHERE filler LIKE '%x%';
                                     QUERY PLAN
-------------------------------------------------------------------​------------------
 Gather  (cost=1000.00..217018.43 rows=1 width=97)
   Workers Planned: 2
   ->  Parallel Seq Scan on pgbench_accounts  (cost=0.00..216018.33 rows=1 width=97)
         Filter: (filler ~~ '%x%'::text)
(4 rows)

---

PostgreSQL: Documentation: 18: Chapter 68. System Catalog Declarations and Initial Contents

URL: https://www.postgresql.org/docs/current/bki.html

Contents:

• Chapter 68. System Catalog Declarations and Initial Contents

PostgreSQL uses many different system catalogs to keep track of the existence and properties of database objects, such as tables and functions. Physically there is no difference between a system catalog and a plain user table, but the backend C code knows the structure and properties of each catalog, and can manipulate it directly at a low level. Thus, for example, it is inadvisable to attempt to alter the structure of a catalog on-the-fly; that would break assumptions built into the C code about how rows of the catalog are laid out. But the structure of the catalogs can change between major versions.

The structures of the catalogs are declared in specially formatted C header files in the src/include/catalog/ directory of the source tree. For each catalog there is a header file named after the catalog (e.g., pg_class.h for pg_class), which defines the set of columns the catalog has, as well as some other basic properties such as its OID.

Many of the catalogs have initial data that must be loaded into them during the “bootstrap” phase of initdb, to bring the system up to a point where it is capable of executing SQL commands. (For example, pg_class.h must contain an entry for itself, as well as one for each other system catalog and index.) This initial data is kept in editable form in data files that are also stored in the src/include/catalog/ directory. For example, pg_proc.dat describes all the initial rows that must be inserted into the pg_proc catalog.

To create the catalog files and load this initial data into them, a backend running in bootstrap mode reads a BKI (Backend Interface) file containing commands and initial data. The postgres.bki file used in this mode is prepared from the aforementioned header and data files, while building a PostgreSQL distribution, by a Perl script named genbki.pl. Although it's specific to a particular PostgreSQL release, postgres.bki is platform-independent and is installed in the share subdirectory of the installation tree.

genbki.pl also produces a derived header file for each catalog, for example pg_class_d.h for the pg_class catalog. This file contains automatically-generated macro definitions, and may contain other macros, enum declarations, and so on that can be useful for client C code that reads a particular catalog.

Most PostgreSQL developers don't need to be directly concerned with the BKI file, but almost any nontrivial feature addition in the backend will require modifying the catalog header files and/or initial data files. The rest of this chapter gives some information about that, and for completeness describes the BKI file format.

---

PostgreSQL: Documentation: 18: 35.4. administrable_role_​authorizations

URL: https://www.postgresql.org/docs/current/infoschema-administrable-role-authorizations.html

Contents:

• 35.4. administrable_role_​authorizations #

The view administrable_role_authorizations identifies all roles that the current user has the admin option for.

Table 35.2. administrable_role_authorizations Columns

grantee sql_identifier

Name of the role to which this role membership was granted (can be the current user, or a different role in case of nested role memberships)

role_name sql_identifier

is_grantable yes_or_no

---

PostgreSQL: Documentation: 18: Chapter 24. Routine Database Maintenance Tasks

URL: https://www.postgresql.org/docs/current/maintenance.html

Contents:

• Chapter 24. Routine Database Maintenance Tasks

PostgreSQL, like any database software, requires that certain tasks be performed regularly to achieve optimum performance. The tasks discussed here are required, but they are repetitive in nature and can easily be automated using standard tools such as cron scripts or Windows' Task Scheduler. It is the database administrator's responsibility to set up appropriate scripts, and to check that they execute successfully.

One obvious maintenance task is the creation of backup copies of the data on a regular schedule. Without a recent backup, you have no chance of recovery after a catastrophe (disk failure, fire, mistakenly dropping a critical table, etc.). The backup and recovery mechanisms available in PostgreSQL are discussed at length in Chapter 25.

The other main category of maintenance task is periodic “vacuuming” of the database. This activity is discussed in Section 24.1. Closely related to this is updating the statistics that will be used by the query planner, as discussed in Section 24.1.3.

Another task that might need periodic attention is log file management. This is discussed in Section 24.3.

check_postgres is available for monitoring database health and reporting unusual conditions. check_postgres integrates with Nagios and MRTG, but can be run standalone too.

PostgreSQL is low-maintenance compared to some other database management systems. Nonetheless, appropriate attention to these tasks will go far towards ensuring a pleasant and productive experience with the system.

---

PostgreSQL: Documentation: 18: 5.5. Constraints

URL: https://www.postgresql.org/docs/current/ddl-constraints.html

Contents:

• 5.5. Constraints #
  • 5.5.1. Check Constraints #
  • Note
  • Note
  • 5.5.2. Not-Null Constraints #
  • Tip
  • 5.5.3. Unique Constraints #
  • 5.5.4. Primary Keys #
  • 5.5.5. Foreign Keys #
  • 5.5.6. Exclusion Constraints #

Data types are a way to limit the kind of data that can be stored in a table. For many applications, however, the constraint they provide is too coarse. For example, a column containing a product price should probably only accept positive values. But there is no standard data type that accepts only positive numbers. Another issue is that you might want to constrain column data with respect to other columns or rows. For example, in a table containing product information, there should be only one row for each product number.

To that end, SQL allows you to define constraints on columns and tables. Constraints give you as much control over the data in your tables as you wish. If a user attempts to store data in a column that would violate a constraint, an error is raised. This applies even if the value came from the default value definition.

A check constraint is the most generic constraint type. It allows you to specify that the value in a certain column must satisfy a Boolean (truth-value) expression. For instance, to require positive product prices, you could use:

As you see, the constraint definition comes after the data type, just like default value definitions. Default values and constraints can be listed in any order. A check constraint consists of the key word CHECK followed by an expression in parentheses. The check constraint expression should involve the column thus constrained, otherwise the constraint would not make too much sense.

You can also give the constraint a separate name. This clarifies error messages and allows you to refer to the constraint when you need to change it. The syntax is:

So, to specify a named constraint, use the key word CONSTRAINT followed by an identifier followed by the constraint definition. (If you don't specify a constraint name in this way, the system chooses a name for you.)

A check constraint can also refer to several columns. Say you store a regular price and a discounted price, and you want to ensure that the discounted price is lower than the regular price:

The first two constraints should look familiar. The third one uses a new syntax. It is not attached to a particular column, instead it appears as a separate item in the comma-separated column list. Column definitions and these constraint definitions can be listed in mixed order.

We say that the first two constraints are column constraints, whereas the third one is a table constraint because it is written separately from any one column definition. Column constraints can also be written as table constraints, while the reverse is not necessarily possible, since a column constraint is supposed to refer to only the column it is attached to. (PostgreSQL doesn't enforce that rule, but you should follow it if you want your table definitions to work with other database systems.) The above example could also be written as:

It's a matter of taste.

Names can be assigned to table constraints in the same way as column constraints:

It should be noted that a check constraint is satisfied if the check expression evaluates to true or the null value. Since most expressions will evaluate to the null value if any operand is null, they will not prevent null values in the constrained columns. To ensure that a column does not contain null values, the not-null constraint described in the next section can be used.

PostgreSQL does not support CHECK constraints that reference table data other than the new or updated row being checked. While a CHECK constraint that violates this rule may appear to work in simple tests, it cannot guarantee that the database will not reach a state in which the constraint condition is false (due to subsequent changes of the other row(s) involved). This would cause a database dump and restore to fail. The restore could fail even when the complete database state is consistent with the constraint, due to rows not being loaded in an order that will satisfy the constraint. If possible, use UNIQUE, EXCLUDE, or FOREIGN KEY constraints to express cross-row and cross-table restrictions.

If what you desire is a one-time check against other rows at row insertion, rather than a continuously-maintained consistency guarantee, a custom trigger can be used to implement that. (This approach avoids the dump/restore problem because pg_dump does not reinstall triggers until after restoring data, so that the check will not be enforced during a dump/restore.)

PostgreSQL assumes that CHECK constraints' conditions are immutable, that is, they will always give the same result for the same input row. This assumption is what justifies examining CHECK constraints only when rows are inserted or updated, and not at other times. (The warning above about not referencing other table data is really a special case of this restriction.)

An example of a common way to break this assumption is to reference a user-defined function in a CHECK expression, and then change the behavior of that function. PostgreSQL does not disallow that, but it will not notice if there are rows in the table that now violate the CHECK constraint. That would cause a subsequent database dump and restore to fail. The recommended way to handle such a change is to drop the constraint (using ALTER TABLE), adjust the function definition, and re-add the constraint, thereby rechecking it against all table rows.

A not-null constraint simply specifies that a column must not assume the null value. A syntax example:

An explicit constraint name can also be specified, for example:

A not-null constraint is usually written as a column constraint. The syntax for writing it as a table constraint is

But this syntax is not standard and mainly intended for use by pg_dump.

A not-null constraint is functionally equivalent to creating a check constraint CHECK (column_name IS NOT NULL), but in PostgreSQL creating an explicit not-null constraint is more efficient.

Of course, a column can have more than one constraint. Just write the constraints one after another:

The order doesn't matter. It does not necessarily determine in which order the constraints are checked.

However, a column can have at most one explicit not-null constraint.

The NOT NULL constraint has an inverse: the NULL constraint. This does not mean that the column must be null, which would surely be useless. Instead, this simply selects the default behavior that the column might be null. The NULL constraint is not present in the SQL standard and should not be used in portable applications. (It was only added to PostgreSQL to be compatible with some other database systems.) Some users, however, like it because it makes it easy to toggle the constraint in a script file. For example, you could start with:

and then insert the NOT key word where desired.

In most database designs the majority of columns should be marked not null.

Unique constraints ensure that the data contained in a column, or a group of columns, is unique among all the rows in the table. The syntax is:

when written as a column constraint, and:

when written as a table constraint.

To define a unique constraint for a group of columns, write it as a table constraint with the column names separated by commas:

This specifies that the combination of values in the indicated columns is unique across the whole table, though any one of the columns need not be (and ordinarily isn't) unique.

You can assign your own name for a unique constraint, in the usual way:

Adding a unique constraint will automatically create a unique B-tree index on the column or group of columns listed in the constraint. A uniqueness restriction covering only some rows cannot be written as a unique constraint, but it is possible to enforce such a restriction by creating a unique partial index.

In general, a unique constraint is violated if there is more than one row in the table where the values of all of the columns included in the constraint are equal. By default, two null values are not considered equal in this comparison. That means even in the presence of a unique constraint it is possible to store duplicate rows that contain a null value in at least one of the constrained columns. This behavior can be changed by adding the clause NULLS NOT DISTINCT, like

The default behavior can be specified explicitly using NULLS DISTINCT. The default null treatment in unique constraints is implementation-defined according to the SQL standard, and other implementations have a different behavior. So be careful when developing applications that are intended to be portable.

A primary key constraint indicates that a column, or group of columns, can be used as a unique identifier for rows in the table. This requires that the values be both unique and not null. So, the following two table definitions accept the same data:

Primary keys can span more than one column; the syntax is similar to unique constraints:

Adding a primary key will automatically create a unique B-tree index on the column or group of columns listed in the primary key, and will force the column(s) to be marked NOT NULL.

A table can have at most one primary key. (There can be any number of unique constraints, which combined with not-null constraints are functionally almost the same thing, but only one can be identified as the primary key.) Relational database theory dictates that every table must have a primary key. This rule is not enforced by PostgreSQL, but it is usually best to follow it.

Primary keys are useful both for documentation purposes and for client applications. For example, a GUI application that allows modifying row values probably needs to know the primary key of a table to be able to identify rows uniquely. There are also various ways in which the database system makes use of a primary key if one has been declared; for example, the primary key defines the default target column(s) for foreign keys referencing its table.

A foreign key constraint specifies that the values in a column (or a group of columns) must match the values appearing in some row of another table. We say this maintains the referential integrity between two related tables.

Say you have the product table that we have used several times already:

Let's also assume you have a table storing orders of those products. We want to ensure that the orders table only contains orders of products that actually exist. So we define a foreign key constraint in the orders table that references the products table:

Now it is impossible to create orders with non-NULL product_no entries that do not appear in the products table.

We say that in this situation the orders table is the referencing table and the products table is the referenced table. Similarly, there are referencing and referenced columns.

You can also shorten the above command to:

because in absence of a column list the primary key of the referenced table is used as the referenced column(s).

You can assign your own name for a foreign key constraint, in the usual way.

A foreign key can also constrain and reference a group of columns. As usual, it then needs to be written in table constraint form. Here is a contrived syntax example:

Of course, the number and type of the constrained columns need to match the number and type of the referenced columns.

Sometimes it is useful for the “other table” of a foreign key constraint to be the same table; this is called a self-referential foreign key. For example, if you want rows of a table to represent nodes of a tree structure, you could write

A top-level node would have NULL parent_id, while non-NULL parent_id entries would be constrained to reference valid rows of the table.

A table can have more than one foreign key constraint. This is used to implement many-to-many relationships between tables. Say you have tables about products and orders, but now you want to allow one order to contain possibly many products (which the structure above did not allow). You could use this table structure:

Notice that the primary key overlaps with the foreign keys in the last table.

We know that the foreign keys disallow creation of orders that do not relate to any products. But what if a product is removed after an order is created that references it? SQL allows you to handle that as well. Intuitively, we have a few options:

Disallow deleting a referenced product

Delete the orders as well

To illustrate this, let's implement the following policy on the many-to-many relationship example above: when someone wants to remove a product that is still referenced by an order (via order_items), we disallow it. If someone removes an order, the order items are removed as well:

The default ON DELETE action is ON DELETE NO ACTION; this does not need to be specified. This means that the deletion in the referenced table is allowed to proceed. But the foreign-key constraint is still required to be satisfied, so this operation will usually result in an error. But checking of foreign-key constraints can also be deferred to later in the transaction (not covered in this chapter). In that case, the NO ACTION setting would allow other commands to “fix” the situation before the constraint is checked, for example by inserting another suitable row into the referenced table or by deleting the now-dangling rows from the referencing table.

RESTRICT is a stricter setting than NO ACTION. It prevents deletion of a referenced row. RESTRICT does not allow the check to be deferred until later in the transaction.

CASCADE specifies that when a referenced row is deleted, row(s) referencing it should be automatically deleted as well.

There are two other options: SET NULL and SET DEFAULT. These cause the referencing column(s) in the referencing row(s) to be set to nulls or their default values, respectively, when the referenced row is deleted. Note that these do not excuse you from observing any constraints. For example, if an action specifies SET DEFAULT but the default value would not satisfy the foreign key constraint, the operation will fail.

The appropriate choice of ON DELETE action depends on what kinds of objects the related tables represent. When the referencing table represents something that is a component of what is represented by the referenced table and cannot exist independently, then CASCADE could be appropriate. If the two tables represent independent objects, then RESTRICT or NO ACTION is more appropriate; an application that actually wants to delete both objects would then have to be explicit about this and run two delete commands. In the above example, order items are part of an order, and it is convenient if they are deleted automatically if an order is deleted. But products and orders are different things, and so making a deletion of a product automatically cause the deletion of some order items could be considered problematic. The actions SET NULL or SET DEFAULT can be appropriate if a foreign-key relationship represents optional information. For example, if the products table contained a reference to a product manager, and the product manager entry gets deleted, then setting the product's product manager to null or a default might be useful.

The actions SET NULL and SET DEFAULT can take a column list to specify which columns to set. Normally, all columns of the foreign-key constraint are set; setting only a subset is useful in some special cases. Consider the following example:

Without the specification of the column, the foreign key would also set the column tenant_id to null, but that column is still required as part of the primary key.

Analogous to ON DELETE there is also ON UPDATE which is invoked when a referenced column is changed (updated). The possible actions are the same, except that column lists cannot be specified for SET NULL and SET DEFAULT. In this case, CASCADE means that the updated values of the referenced column(s) should be copied into the referencing row(s). There is also a noticeable difference between ON UPDATE NO ACTION (the default) and ON UPDATE RESTRICT. The former will allow the update to proceed and the foreign-key constraint will be checked against the state after the update. The latter will prevent the update to run even if the state after the update would still satisfy the constraint. This prevents updating a referenced row to a value that is distinct but compares as equal (for example, a character string with a different case variant, if a character string type with a case-insensitive collation is used).

Normally, a referencing row need not satisfy the foreign key constraint if any of its referencing columns are null. If MATCH FULL is added to the foreign key declaration, a referencing row escapes satisfying the constraint only if all its referencing columns are null (so a mix of null and non-null values is guaranteed to fail a MATCH FULL constraint). If you don't want referencing rows to be able to avoid satisfying the foreign key constraint, declare the referencing column(s) as NOT NULL.

A foreign key must reference columns that either are a primary key or form a unique constraint, or are columns from a non-partial unique index. This means that the referenced columns always have an index to allow efficient lookups on whether a referencing row has a match. Since a DELETE of a row from the referenced table or an UPDATE of a referenced column will require a scan of the referencing table for rows matching the old value, it is often a good idea to index the referencing columns too. Because this is not always needed, and there are many choices available on how to index, the declaration of a foreign key constraint does not automatically create an index on the referencing columns.

More information about updating and deleting data is in Chapter 6. Also see the description of foreign key constraint syntax in the reference documentation for CREATE TABLE.

Exclusion constraints ensure that if any two rows are compared on the specified columns or expressions using the specified operators, at least one of these operator comparisons will return false or null. The syntax is:

See also CREATE TABLE ... CONSTRAINT ... EXCLUDE for details.

Adding an exclusion constraint will automatically create an index of the type specified in the constraint declaration.

Examples:

Example 1 (unknown):

CREATE TABLE products (
    product_no integer,
    name text,
    price numeric CHECK (price > 0)
);

Example 2 (unknown):

CREATE TABLE products (
    product_no integer,
    name text,
    price numeric CONSTRAINT positive_price CHECK (price > 0)
);

Example 3 (unknown):

CREATE TABLE products (
    product_no integer,
    name text,
    price numeric CHECK (price > 0),
    discounted_price numeric CHECK (discounted_price > 0),
    CHECK (price > discounted_price)
);

Example 4 (unknown):

CREATE TABLE products (
    product_no integer,
    name text,
    price numeric,
    CHECK (price > 0),
    discounted_price numeric,
    CHECK (discounted_price > 0),
    CHECK (price > discounted_price)
);

---

PostgreSQL: Documentation: 18: 32.7. Canceling Queries in Progress

URL: https://www.postgresql.org/docs/current/libpq-cancel.html

Contents:

• 32.7. Canceling Queries in Progress #
  • 32.7.1. Functions for Sending Cancel Requests #
  • 32.7.2. Obsolete Functions for Sending Cancel Requests #

Prepares a connection over which a cancel request can be sent.

PQcancelCreate creates a PGcancelConn object, but it won't instantly start sending a cancel request over this connection. A cancel request can be sent over this connection in a blocking manner using PQcancelBlocking and in a non-blocking manner using PQcancelStart. The return value can be passed to PQcancelStatus to check if the PGcancelConn object was created successfully. The PGcancelConn object is an opaque structure that is not meant to be accessed directly by the application. This PGcancelConn object can be used to cancel the query that's running on the original connection in a thread-safe way.

Many connection parameters of the original client will be reused when setting up the connection for the cancel request. Importantly, if the original connection requires encryption of the connection and/or verification of the target host (using sslmode or gssencmode), then the connection for the cancel request is made with these same requirements. Any connection options that are only used during authentication or after authentication of the client are ignored though, because cancellation requests do not require authentication and the connection is closed right after the cancellation request is submitted.

Note that when PQcancelCreate returns a non-null pointer, you must call PQcancelFinish when you are finished with it, in order to dispose of the structure and any associated memory blocks. This must be done even if the cancel request failed or was abandoned.

Requests that the server abandons processing of the current command in a blocking manner.

The request is made over the given PGcancelConn, which needs to be created with PQcancelCreate. The return value of PQcancelBlocking is 1 if the cancel request was successfully dispatched and 0 if not. If it was unsuccessful, the error message can be retrieved using PQcancelErrorMessage .

Successful dispatch of the cancellation is no guarantee that the request will have any effect, however. If the cancellation is effective, the command being canceled will terminate early and return an error result. If the cancellation fails (say, because the server was already done processing the command), then there will be no visible result at all.

Requests that the server abandons processing of the current command in a non-blocking manner.

The request is made over the given PGcancelConn, which needs to be created with PQcancelCreate. The return value of PQcancelStart is 1 if the cancellation request could be started and 0 if not. If it was unsuccessful, the error message can be retrieved using PQcancelErrorMessage .

If PQcancelStart succeeds, the next stage is to poll libpq so that it can proceed with the cancel connection sequence. Use PQcancelSocket to obtain the descriptor of the socket underlying the database connection. (Caution: do not assume that the socket remains the same across PQcancelPoll calls.) Loop thus: If PQcancelPoll(cancelConn) last returned PGRES_POLLING_READING, wait until the socket is ready to read (as indicated by select(), poll(), or similar system function). Then call PQcancelPoll(cancelConn) again. Conversely, if PQcancelPoll(cancelConn) last returned PGRES_POLLING_WRITING, wait until the socket is ready to write, then call PQcancelPoll(cancelConn) again. On the first iteration, i.e., if you have yet to call PQcancelPoll(cancelConn), behave as if it last returned PGRES_POLLING_WRITING. Continue this loop until PQcancelPoll(cancelConn) returns PGRES_POLLING_FAILED, indicating the connection procedure has failed, or PGRES_POLLING_OK, indicating cancel request was successfully dispatched.

Successful dispatch of the cancellation is no guarantee that the request will have any effect, however. If the cancellation is effective, the command being canceled will terminate early and return an error result. If the cancellation fails (say, because the server was already done processing the command), then there will be no visible result at all.

At any time during connection, the status of the connection can be checked by calling PQcancelStatus. If this call returns CONNECTION_BAD, then the cancel procedure has failed; if the call returns CONNECTION_OK, then cancel request was successfully dispatched. Both of these states are equally detectable from the return value of PQcancelPoll, described above. Other states might also occur during (and only during) an asynchronous connection procedure. These indicate the current stage of the connection procedure and might be useful to provide feedback to the user for example. These statuses are:

Waiting for a call to PQcancelStart or PQcancelBlocking, to actually open the socket. This is the connection state right after calling PQcancelCreate or PQcancelReset. No connection to the server has been initiated yet at this point. To actually start sending the cancel request use PQcancelStart or PQcancelBlocking.

Waiting for connection to be made.

Connection OK; waiting to send.

Waiting for a response from the server.

Negotiating SSL encryption.

Negotiating GSS encryption.

Note that, although these constants will remain (in order to maintain compatibility), an application should never rely upon these occurring in a particular order, or at all, or on the status always being one of these documented values. An application might do something like this:

The connect_timeout connection parameter is ignored when using PQcancelPoll; it is the application's responsibility to decide whether an excessive amount of time has elapsed. Otherwise, PQcancelStart followed by a PQcancelPoll loop is equivalent to PQcancelBlocking.

Returns the status of the cancel connection.

The status can be one of a number of values. However, only three of these are seen outside of an asynchronous cancel procedure: CONNECTION_ALLOCATED, CONNECTION_OK and CONNECTION_BAD. The initial state of a PGcancelConn that's successfully created using PQcancelCreate is CONNECTION_ALLOCATED. A cancel request that was successfully dispatched has the status CONNECTION_OK. A failed cancel attempt is signaled by status CONNECTION_BAD. An OK status will remain so until PQcancelFinish or PQcancelReset is called.

See the entry for PQcancelStart with regards to other status codes that might be returned.

Successful dispatch of the cancellation is no guarantee that the request will have any effect, however. If the cancellation is effective, the command being canceled will terminate early and return an error result. If the cancellation fails (say, because the server was already done processing the command), then there will be no visible result at all.

Obtains the file descriptor number of the cancel connection socket to the server.

A valid descriptor will be greater than or equal to 0; a result of -1 indicates that no server connection is currently open. This might change as a result of calling any of the functions in this section on the PGcancelConn (except for PQcancelErrorMessage and PQcancelSocket itself).

Returns the error message most recently generated by an operation on the cancel connection.

Nearly all libpq functions that take a PGcancelConn will set a message for PQcancelErrorMessage if they fail. Note that by libpq convention, a nonempty PQcancelErrorMessage result can consist of multiple lines, and will include a trailing newline. The caller should not free the result directly. It will be freed when the associated PGcancelConn handle is passed to PQcancelFinish. The result string should not be expected to remain the same across operations on the PGcancelConn structure.

Closes the cancel connection (if it did not finish sending the cancel request yet). Also frees memory used by the PGcancelConn object.

Note that even if the cancel attempt fails (as indicated by PQcancelStatus), the application should call PQcancelFinish to free the memory used by the PGcancelConn object. The PGcancelConn pointer must not be used again after PQcancelFinish has been called.

Resets the PGcancelConn so it can be reused for a new cancel connection.

If the PGcancelConn is currently used to send a cancel request, then this connection is closed. It will then prepare the PGcancelConn object such that it can be used to send a new cancel request.

This can be used to create one PGcancelConn for a PGconn and reuse it multiple times throughout the lifetime of the original PGconn.

These functions represent older methods of sending cancel requests. Although they still work, they are deprecated due to not sending the cancel requests in an encrypted manner, even when the original connection specified sslmode or gssencmode to require encryption. Thus these older methods are heavily discouraged from being used in new code, and it is recommended to change existing code to use the new functions instead.

Creates a data structure containing the information needed to cancel a command using PQcancel.

PQgetCancel creates a PGcancel object given a PGconn connection object. It will return NULL if the given conn is NULL or an invalid connection. The PGcancel object is an opaque structure that is not meant to be accessed directly by the application; it can only be passed to PQcancel or PQfreeCancel.

Frees a data structure created by PQgetCancel.

PQfreeCancel frees a data object previously created by PQgetCancel.

PQcancel is a deprecated and insecure variant of PQcancelBlocking, but one that can be used safely from within a signal handler.

PQcancel only exists because of backwards compatibility reasons. PQcancelBlocking should be used instead. The only benefit that PQcancel has is that it can be safely invoked from a signal handler, if the errbuf is a local variable in the signal handler. However, this is generally not considered a big enough benefit to be worth the security issues that this function has.

The PGcancel object is read-only as far as PQcancel is concerned, so it can also be invoked from a thread that is separate from the one manipulating the PGconn object.

The return value of PQcancel is 1 if the cancel request was successfully dispatched and 0 if not. If not, errbuf is filled with an explanatory error message. errbuf must be a char array of size errbufsize (the recommended size is 256 bytes).

PQrequestCancel is a deprecated and insecure variant of PQcancelBlocking.

PQrequestCancel only exists because of backwards compatibility reasons. PQcancelBlocking should be used instead. There is no benefit to using PQrequestCancel over PQcancelBlocking.

Requests that the server abandon processing of the current command. It operates directly on the PGconn object, and in case of failure stores the error message in the PGconn object (whence it can be retrieved by PQerrorMessage ). Although the functionality is the same, this approach is not safe within multiple-thread programs or signal handlers, since it is possible that overwriting the PGconn's error message will mess up the operation currently in progress on the connection.

Examples:

Example 1 (unknown):

PGcancelConn *PQcancelCreate(PGconn *conn);

Example 2 (unknown):

int PQcancelBlocking(PGcancelConn *cancelConn);

Example 3 (unknown):

int PQcancelStart(PGcancelConn *cancelConn);

PostgresPollingStatusType PQcancelPoll(PGcancelConn *cancelConn);

Example 4 (unknown):

switch(PQcancelStatus(conn))
{
        case CONNECTION_STARTED:
            feedback = "Connecting...";
            break;

        case CONNECTION_MADE:
            feedback = "Connected to server...";
            break;
.
.
.
        default:
            feedback = "Connecting...";
}

---

PostgreSQL: Documentation: 18: 8.14. JSON Types

URL: https://www.postgresql.org/docs/current/datatype-json.html

Contents:

• 8.14. JSON Types #
  • Note
  • 8.14.1. JSON Input and Output Syntax #
  • 8.14.2. Designing JSON Documents #
  • 8.14.3. jsonb Containment and Existence #
  • Tip
  • 8.14.4. jsonb Indexing #
  • 8.14.5. jsonb Subscripting #
  • 8.14.6. Transforms #
  • 8.14.7. jsonpath Type #

JSON data types are for storing JSON (JavaScript Object Notation) data, as specified in RFC 7159. Such data can also be stored as text, but the JSON data types have the advantage of enforcing that each stored value is valid according to the JSON rules. There are also assorted JSON-specific functions and operators available for data stored in these data types; see Section 9.16.

PostgreSQL offers two types for storing JSON data: json and jsonb. To implement efficient query mechanisms for these data types, PostgreSQL also provides the jsonpath data type described in Section 8.14.7.

The json and jsonb data types accept almost identical sets of values as input. The major practical difference is one of efficiency. The json data type stores an exact copy of the input text, which processing functions must reparse on each execution; while jsonb data is stored in a decomposed binary format that makes it slightly slower to input due to added conversion overhead, but significantly faster to process, since no reparsing is needed. jsonb also supports indexing, which can be a significant advantage.

Because the json type stores an exact copy of the input text, it will preserve semantically-insignificant white space between tokens, as well as the order of keys within JSON objects. Also, if a JSON object within the value contains the same key more than once, all the key/value pairs are kept. (The processing functions consider the last value as the operative one.) By contrast, jsonb does not preserve white space, does not preserve the order of object keys, and does not keep duplicate object keys. If duplicate keys are specified in the input, only the last value is kept.

In general, most applications should prefer to store JSON data as jsonb, unless there are quite specialized needs, such as legacy assumptions about ordering of object keys.

RFC 7159 specifies that JSON strings should be encoded in UTF8. It is therefore not possible for the JSON types to conform rigidly to the JSON specification unless the database encoding is UTF8. Attempts to directly include characters that cannot be represented in the database encoding will fail; conversely, characters that can be represented in the database encoding but not in UTF8 will be allowed.

RFC 7159 permits JSON strings to contain Unicode escape sequences denoted by \uXXXX. In the input function for the json type, Unicode escapes are allowed regardless of the database encoding, and are checked only for syntactic correctness (that is, that four hex digits follow \u). However, the input function for jsonb is stricter: it disallows Unicode escapes for characters that cannot be represented in the database encoding. The jsonb type also rejects \u0000 (because that cannot be represented in PostgreSQL's text type), and it insists that any use of Unicode surrogate pairs to designate characters outside the Unicode Basic Multilingual Plane be correct. Valid Unicode escapes are converted to the equivalent single character for storage; this includes folding surrogate pairs into a single character.

Many of the JSON processing functions described in Section 9.16 will convert Unicode escapes to regular characters, and will therefore throw the same types of errors just described even if their input is of type json not jsonb. The fact that the json input function does not make these checks may be considered a historical artifact, although it does allow for simple storage (without processing) of JSON Unicode escapes in a database encoding that does not support the represented characters.

When converting textual JSON input into jsonb, the primitive types described by RFC 7159 are effectively mapped onto native PostgreSQL types, as shown in Table 8.23. Therefore, there are some minor additional constraints on what constitutes valid jsonb data that do not apply to the json type, nor to JSON in the abstract, corresponding to limits on what can be represented by the underlying data type. Notably, jsonb will reject numbers that are outside the range of the PostgreSQL numeric data type, while json will not. Such implementation-defined restrictions are permitted by RFC 7159. However, in practice such problems are far more likely to occur in other implementations, as it is common to represent JSON's number primitive type as IEEE 754 double precision floating point (which RFC 7159 explicitly anticipates and allows for). When using JSON as an interchange format with such systems, the danger of losing numeric precision compared to data originally stored by PostgreSQL should be considered.

Conversely, as noted in the table there are some minor restrictions on the input format of JSON primitive types that do not apply to the corresponding PostgreSQL types.

Table 8.23. JSON Primitive Types and Corresponding PostgreSQL Types

The input/output syntax for the JSON data types is as specified in RFC 7159.

The following are all valid json (or jsonb) expressions:

As previously stated, when a JSON value is input and then printed without any additional processing, json outputs the same text that was input, while jsonb does not preserve semantically-insignificant details such as whitespace. For example, note the differences here:

One semantically-insignificant detail worth noting is that in jsonb, numbers will be printed according to the behavior of the underlying numeric type. In practice this means that numbers entered with E notation will be printed without it, for example:

However, jsonb will preserve trailing fractional zeroes, as seen in this example, even though those are semantically insignificant for purposes such as equality checks.

For the list of built-in functions and operators available for constructing and processing JSON values, see Section 9.16.

Representing data as JSON can be considerably more flexible than the traditional relational data model, which is compelling in environments where requirements are fluid. It is quite possible for both approaches to co-exist and complement each other within the same application. However, even for applications where maximal flexibility is desired, it is still recommended that JSON documents have a somewhat fixed structure. The structure is typically unenforced (though enforcing some business rules declaratively is possible), but having a predictable structure makes it easier to write queries that usefully summarize a set of “documents” (datums) in a table.

JSON data is subject to the same concurrency-control considerations as any other data type when stored in a table. Although storing large documents is practicable, keep in mind that any update acquires a row-level lock on the whole row. Consider limiting JSON documents to a manageable size in order to decrease lock contention among updating transactions. Ideally, JSON documents should each represent an atomic datum that business rules dictate cannot reasonably be further subdivided into smaller datums that could be modified independently.

Testing containment is an important capability of jsonb. There is no parallel set of facilities for the json type. Containment tests whether one jsonb document has contained within it another one. These examples return true except as noted:

The general principle is that the contained object must match the containing object as to structure and data contents, possibly after discarding some non-matching array elements or object key/value pairs from the containing object. But remember that the order of array elements is not significant when doing a containment match, and duplicate array elements are effectively considered only once.

As a special exception to the general principle that the structures must match, an array may contain a primitive value:

jsonb also has an existence operator, which is a variation on the theme of containment: it tests whether a string (given as a text value) appears as an object key or array element at the top level of the jsonb value. These examples return true except as noted:

JSON objects are better suited than arrays for testing containment or existence when there are many keys or elements involved, because unlike arrays they are internally optimized for searching, and do not need to be searched linearly.

Because JSON containment is nested, an appropriate query can skip explicit selection of sub-objects. As an example, suppose that we have a doc column containing objects at the top level, with most objects containing tags fields that contain arrays of sub-objects. This query finds entries in which sub-objects containing both "term":"paris" and "term":"food" appear, while ignoring any such keys outside the tags array:

One could accomplish the same thing with, say,

but that approach is less flexible, and often less efficient as well.

On the other hand, the JSON existence operator is not nested: it will only look for the specified key or array element at top level of the JSON value.

The various containment and existence operators, along with all other JSON operators and functions are documented in Section 9.16.

GIN indexes can be used to efficiently search for keys or key/value pairs occurring within a large number of jsonb documents (datums). Two GIN “operator classes” are provided, offering different performance and flexibility trade-offs.

The default GIN operator class for jsonb supports queries with the key-exists operators ?, ?| and ?&, the containment operator @>, and the jsonpath match operators @? and @@. (For details of the semantics that these operators implement, see Table 9.48.) An example of creating an index with this operator class is:

The non-default GIN operator class jsonb_path_ops does not support the key-exists operators, but it does support @>, @? and @@. An example of creating an index with this operator class is:

Consider the example of a table that stores JSON documents retrieved from a third-party web service, with a documented schema definition. A typical document is:

We store these documents in a table named api, in a jsonb column named jdoc. If a GIN index is created on this column, queries like the following can make use of the index:

However, the index could not be used for queries like the following, because though the operator ? is indexable, it is not applied directly to the indexed column jdoc:

Still, with appropriate use of expression indexes, the above query can use an index. If querying for particular items within the "tags" key is common, defining an index like this may be worthwhile:

Now, the WHERE clause jdoc -> 'tags' ? 'qui' will be recognized as an application of the indexable operator ? to the indexed expression jdoc -> 'tags'. (More information on expression indexes can be found in Section 11.7.)

Another approach to querying is to exploit containment, for example:

A simple GIN index on the jdoc column can support this query. But note that such an index will store copies of every key and value in the jdoc column, whereas the expression index of the previous example stores only data found under the tags key. While the simple-index approach is far more flexible (since it supports queries about any key), targeted expression indexes are likely to be smaller and faster to search than a simple index.

GIN indexes also support the @? and @@ operators, which perform jsonpath matching. Examples are

For these operators, a GIN index extracts clauses of the form accessors_chain == constant out of the jsonpath pattern, and does the index search based on the keys and values mentioned in these clauses. The accessors chain may include .key, [], and [index] accessors. The jsonb_ops operator class also supports . and .** accessors, but the jsonb_path_ops operator class does not.

Although the jsonb_path_ops operator class supports only queries with the @>, @? and @@ operators, it has notable performance advantages over the default operator class jsonb_ops. A jsonb_path_ops index is usually much smaller than a jsonb_ops index over the same data, and the specificity of searches is better, particularly when queries contain keys that appear frequently in the data. Therefore search operations typically perform better than with the default operator class.

The technical difference between a jsonb_ops and a jsonb_path_ops GIN index is that the former creates independent index items for each key and value in the data, while the latter creates index items only for each value in the data. [7] Basically, each jsonb_path_ops index item is a hash of the value and the key(s) leading to it; for example to index {"foo": {"bar": "baz"}}, a single index item would be created incorporating all three of foo, bar, and baz into the hash value. Thus a containment query looking for this structure would result in an extremely specific index search; but there is no way at all to find out whether foo appears as a key. On the other hand, a jsonb_ops index would create three index items representing foo, bar, and baz separately; then to do the containment query, it would look for rows containing all three of these items. While GIN indexes can perform such an AND search fairly efficiently, it will still be less specific and slower than the equivalent jsonb_path_ops search, especially if there are a very large number of rows containing any single one of the three index items.

A disadvantage of the jsonb_path_ops approach is that it produces no index entries for JSON structures not containing any values, such as {"a": {}}. If a search for documents containing such a structure is requested, it will require a full-index scan, which is quite slow. jsonb_path_ops is therefore ill-suited for applications that often perform such searches.

jsonb also supports btree and hash indexes. These are usually useful only if it's important to check equality of complete JSON documents. The btree ordering for jsonb datums is seldom of great interest, but for completeness it is:

with the exception that (for historical reasons) an empty top level array sorts less than null. Objects with equal numbers of pairs are compared in the order:

Note that object keys are compared in their storage order; in particular, since shorter keys are stored before longer keys, this can lead to results that might be unintuitive, such as:

Similarly, arrays with equal numbers of elements are compared in the order:

Primitive JSON values are compared using the same comparison rules as for the underlying PostgreSQL data type. Strings are compared using the default database collation.

The jsonb data type supports array-style subscripting expressions to extract and modify elements. Nested values can be indicated by chaining subscripting expressions, following the same rules as the path argument in the jsonb_set function. If a jsonb value is an array, numeric subscripts start at zero, and negative integers count backwards from the last element of the array. Slice expressions are not supported. The result of a subscripting expression is always of the jsonb data type.

UPDATE statements may use subscripting in the SET clause to modify jsonb values. Subscript paths must be traversable for all affected values insofar as they exist. For instance, the path val['a']['b']['c'] can be traversed all the way to c if every val, val['a'], and val['a']['b'] is an object. If any val['a'] or val['a']['b'] is not defined, it will be created as an empty object and filled as necessary. However, if any val itself or one of the intermediary values is defined as a non-object such as a string, number, or jsonb null, traversal cannot proceed so an error is raised and the transaction aborted.

An example of subscripting syntax:

jsonb assignment via subscripting handles a few edge cases differently from jsonb_set. When a source jsonb value is NULL, assignment via subscripting will proceed as if it was an empty JSON value of the type (object or array) implied by the subscript key:

If an index is specified for an array containing too few elements, NULL elements will be appended until the index is reachable and the value can be set.

A jsonb value will accept assignments to nonexistent subscript paths as long as the last existing element to be traversed is an object or array, as implied by the corresponding subscript (the element indicated by the last subscript in the path is not traversed and may be anything). Nested array and object structures will be created, and in the former case null-padded, as specified by the subscript path until the assigned value can be placed.

Additional extensions are available that implement transforms for the jsonb type for different procedural languages.

The extensions for PL/Perl are called jsonb_plperl and jsonb_plperlu. If you use them, jsonb values are mapped to Perl arrays, hashes, and scalars, as appropriate.

The extension for PL/Python is called jsonb_plpython3u. If you use it, jsonb values are mapped to Python dictionaries, lists, and scalars, as appropriate.

Of these extensions, jsonb_plperl is considered “trusted”, that is, it can be installed by non-superusers who have CREATE privilege on the current database. The rest require superuser privilege to install.

The jsonpath type implements support for the SQL/JSON path language in PostgreSQL to efficiently query JSON data. It provides a binary representation of the parsed SQL/JSON path expression that specifies the items to be retrieved by the path engine from the JSON data for further processing with the SQL/JSON query functions.

The semantics of SQL/JSON path predicates and operators generally follow SQL. At the same time, to provide a natural way of working with JSON data, SQL/JSON path syntax uses some JavaScript conventions:

Dot (.) is used for member access.

Square brackets ([]) are used for array access.

SQL/JSON arrays are 0-relative, unlike regular SQL arrays that start from 1.

Numeric literals in SQL/JSON path expressions follow JavaScript rules, which are different from both SQL and JSON in some minor details. For example, SQL/JSON path allows .1 and 1., which are invalid in JSON. Non-decimal integer literals and underscore separators are supported, for example, 1_000_000, 0x1EEE_FFFF, 0o273, 0b100101. In SQL/JSON path (and in JavaScript, but not in SQL proper), there must not be an underscore separator directly after the radix prefix.

An SQL/JSON path expression is typically written in an SQL query as an SQL character string literal, so it must be enclosed in single quotes, and any single quotes desired within the value must be doubled (see Section 4.1.2.1). Some forms of path expressions require string literals within them. These embedded string literals follow JavaScript/ECMAScript conventions: they must be surrounded by double quotes, and backslash escapes may be used within them to represent otherwise-hard-to-type characters. In particular, the way to write a double quote within an embedded string literal is \", and to write a backslash itself, you must write \. Other special backslash sequences include those recognized in JavaScript strings: \b, \f, \n, \r, \t, \v for various ASCII control characters, \xNN for a character code written with only two hex digits, \uNNNN for a Unicode character identified by its 4-hex-digit code point, and \u{N...} for a Unicode character code point written with 1 to 6 hex digits.

A path expression consists of a sequence of path elements, which can be any of the following:

Path literals of JSON primitive types: Unicode text, numeric, true, false, or null.

Path variables listed in Table 8.24.

Accessor operators listed in Table 8.25.

jsonpath operators and methods listed in Section 9.16.2.3.

Parentheses, which can be used to provide filter expressions or define the order of path evaluation.

For details on using jsonpath expressions with SQL/JSON query functions, see Section 9.16.2.

Table 8.24. jsonpath Variables

Table 8.25. jsonpath Accessors

Member accessor that returns an object member with the specified key. If the key name matches some named variable starting with $ or does not meet the JavaScript rules for an identifier, it must be enclosed in double quotes to make it a string literal.

Wildcard member accessor that returns the values of all members located at the top level of the current object.

Recursive wildcard member accessor that processes all levels of the JSON hierarchy of the current object and returns all the member values, regardless of their nesting level. This is a PostgreSQL extension of the SQL/JSON standard.

.**{start_level to end_level}

Like .**, but selects only the specified levels of the JSON hierarchy. Nesting levels are specified as integers. Level zero corresponds to the current object. To access the lowest nesting level, you can use the last keyword. This is a PostgreSQL extension of the SQL/JSON standard.

Array element accessor. subscript can be given in two forms: index or start_index to end_index. The first form returns a single array element by its index. The second form returns an array slice by the range of indexes, including the elements that correspond to the provided start_index and end_index.

The specified index can be an integer, as well as an expression returning a single numeric value, which is automatically cast to integer. Index zero corresponds to the first array element. You can also use the last keyword to denote the last array element, which is useful for handling arrays of unknown length.

Wildcard array element accessor that returns all array elements.

[7] For this purpose, the term “value” includes array elements, though JSON terminology sometimes considers array elements distinct from values within objects.

Examples:

Example 1 (unknown):

-- Simple scalar/primitive value
-- Primitive values can be numbers, quoted strings, true, false, or null
SELECT '5'::json;

-- Array of zero or more elements (elements need not be of same type)
SELECT '[1, 2, "foo", null]'::json;

-- Object containing pairs of keys and values
-- Note that object keys must always be quoted strings
SELECT '{"bar": "baz", "balance": 7.77, "active": false}'::json;

-- Arrays and objects can be nested arbitrarily
SELECT '{"foo": [true, "bar"], "tags": {"a": 1, "b": null}}'::json;

Example 2 (unknown):

SELECT '{"bar": "baz", "balance": 7.77, "active":false}'::json;
                      json
-------------------------------------------------
 {"bar": "baz", "balance": 7.77, "active":false}
(1 row)

SELECT '{"bar": "baz", "balance": 7.77, "active":false}'::jsonb;
                      jsonb
--------------------------------------------------
 {"bar": "baz", "active": false, "balance": 7.77}
(1 row)

Example 3 (unknown):

SELECT '{"reading": 1.230e-5}'::json, '{"reading": 1.230e-5}'::jsonb;
         json          |          jsonb
-----------------------+-------------------------
 {"reading": 1.230e-5} | {"reading": 0.00001230}
(1 row)

Example 4 (unknown):

-- Simple scalar/primitive values contain only the identical value:
SELECT '"foo"'::jsonb @> '"foo"'::jsonb;

-- The array on the right side is contained within the one on the left:
SELECT '[1, 2, 3]'::jsonb @> '[1, 3]'::jsonb;

-- Order of array elements is not significant, so this is also true:
SELECT '[1, 2, 3]'::jsonb @> '[3, 1]'::jsonb;

-- Duplicate array elements don't matter either:
SELECT '[1, 2, 3]'::jsonb @> '[1, 2, 2]'::jsonb;

-- The object with a single pair on the right side is contained
-- within the object on the left side:
SELECT '{"product": "PostgreSQL", "version": 9.4, "jsonb": true}'::jsonb @> '{"version": 9.4}'::jsonb;

-- The array on the right side is not considered contained within the
-- array on the left, even though a similar array is nested within it:
SELECT '[1, 2, [1, 3]]'::jsonb @> '[1, 3]'::jsonb;  -- yields false

-- But with a layer of nesting, it is contained:
SELECT '[1, 2, [1, 3]]'::jsonb @> '[[1, 3]]'::jsonb;

-- Similarly, containment is not reported here:
SELECT '{"foo": {"bar": "baz"}}'::jsonb @> '{"bar": "baz"}'::jsonb;  -- yields false

-- A top-level key and an empty object is contained:
SELECT '{"foo": {"bar": "baz"}}'::jsonb @> '{"foo": {}}'::jsonb;

---

PostgreSQL: Documentation: 18: 23.3. Character Set Support

URL: https://www.postgresql.org/docs/current/multibyte.html

Contents:

• 23.3. Character Set Support #
  • 23.3.1. Supported Character Sets #
  • 23.3.2. Setting the Character Set #
  • Important
  • 23.3.3. Automatic Character Set Conversion Between Server and Client #
  • 23.3.4. Available Character Set Conversions #
  • 23.3.5. Further Reading #

The character set support in PostgreSQL allows you to store text in a variety of character sets (also called encodings), including single-byte character sets such as the ISO 8859 series and multiple-byte character sets such as EUC (Extended Unix Code), UTF-8, and Mule internal code. All supported character sets can be used transparently by clients, but a few are not supported for use within the server (that is, as a server-side encoding). The default character set is selected while initializing your PostgreSQL database cluster using initdb. It can be overridden when you create a database, so you can have multiple databases each with a different character set.

An important restriction, however, is that each database's character set must be compatible with the database's LC_CTYPE (character classification) and LC_COLLATE (string sort order) locale settings. For C or POSIX locale, any character set is allowed, but for other libc-provided locales there is only one character set that will work correctly. (On Windows, however, UTF-8 encoding can be used with any locale.) If you have ICU support configured, ICU-provided locales can be used with most but not all server-side encodings.

Table 23.3 shows the character sets available for use in PostgreSQL.

Table 23.3. PostgreSQL Character Sets

Not all client APIs support all the listed character sets. For example, the PostgreSQL JDBC driver does not support MULE_INTERNAL, LATIN6, LATIN8, and LATIN10.

The SQL_ASCII setting behaves considerably differently from the other settings. When the server character set is SQL_ASCII, the server interprets byte values 0–127 according to the ASCII standard, while byte values 128–255 are taken as uninterpreted characters. No encoding conversion will be done when the setting is SQL_ASCII. Thus, this setting is not so much a declaration that a specific encoding is in use, as a declaration of ignorance about the encoding. In most cases, if you are working with any non-ASCII data, it is unwise to use the SQL_ASCII setting because PostgreSQL will be unable to help you by converting or validating non-ASCII characters.

initdb defines the default character set (encoding) for a PostgreSQL cluster. For example,

sets the default character set to EUC_JP (Extended Unix Code for Japanese). You can use --encoding instead of -E if you prefer longer option strings. If no -E or --encoding option is given, initdb attempts to determine the appropriate encoding to use based on the specified or default locale.

You can specify a non-default encoding at database creation time, provided that the encoding is compatible with the selected locale:

This will create a database named korean that uses the character set EUC_KR, and locale ko_KR. Another way to accomplish this is to use this SQL command:

Notice that the above commands specify copying the template0 database. When copying any other database, the encoding and locale settings cannot be changed from those of the source database, because that might result in corrupt data. For more information see Section 22.3.

The encoding for a database is stored in the system catalog pg_database. You can see it by using the psql -l option or the \l command.

On most modern operating systems, PostgreSQL can determine which character set is implied by the LC_CTYPE setting, and it will enforce that only the matching database encoding is used. On older systems it is your responsibility to ensure that you use the encoding expected by the locale you have selected. A mistake in this area is likely to lead to strange behavior of locale-dependent operations such as sorting.

PostgreSQL will allow superusers to create databases with SQL_ASCII encoding even when LC_CTYPE is not C or POSIX. As noted above, SQL_ASCII does not enforce that the data stored in the database has any particular encoding, and so this choice poses risks of locale-dependent misbehavior. Using this combination of settings is deprecated and may someday be forbidden altogether.

PostgreSQL supports automatic character set conversion between server and client for many combinations of character sets (Section 23.3.4 shows which ones).

To enable automatic character set conversion, you have to tell PostgreSQL the character set (encoding) you would like to use in the client. There are several ways to accomplish this:

Using the \encoding command in psql. \encoding allows you to change client encoding on the fly. For example, to change the encoding to SJIS, type:

libpq (Section 32.11) has functions to control the client encoding.

Using SET client_encoding TO. Setting the client encoding can be done with this SQL command:

Also you can use the standard SQL syntax SET NAMES for this purpose:

To query the current client encoding:

To return to the default encoding:

Using PGCLIENTENCODING. If the environment variable PGCLIENTENCODING is defined in the client's environment, that client encoding is automatically selected when a connection to the server is made. (This can subsequently be overridden using any of the other methods mentioned above.)

Using the configuration variable client_encoding. If the client_encoding variable is set, that client encoding is automatically selected when a connection to the server is made. (This can subsequently be overridden using any of the other methods mentioned above.)

If the conversion of a particular character is not possible — suppose you chose EUC_JP for the server and LATIN1 for the client, and some Japanese characters are returned that do not have a representation in LATIN1 — an error is reported.

If the client character set is defined as SQL_ASCII, encoding conversion is disabled, regardless of the server's character set. (However, if the server's character set is not SQL_ASCII, the server will still check that incoming data is valid for that encoding; so the net effect is as though the client character set were the same as the server's.) Just as for the server, use of SQL_ASCII is unwise unless you are working with all-ASCII data.

PostgreSQL allows conversion between any two character sets for which a conversion function is listed in the pg_conversion system catalog. PostgreSQL comes with some predefined conversions, as summarized in Table 23.4 and shown in more detail in Table 23.5. You can create a new conversion using the SQL command CREATE CONVERSION. (To be used for automatic client/server conversions, a conversion must be marked as “default” for its character set pair.)

Table 23.4. Built-in Client/Server Character Set Conversions

Table 23.5. All Built-in Character Set Conversions

[a] The conversion names follow a standard naming scheme: The official name of the source encoding with all non-alphanumeric characters replaced by underscores, followed by to, followed by the similarly processed destination encoding name. Therefore, these names sometimes deviate from the customary encoding names shown in Table 23.3.

These are good sources to start learning about various kinds of encoding systems.

Contains detailed explanations of EUC_JP, EUC_CN, EUC_KR, EUC_TW.

The web site of the Unicode Consortium.

UTF-8 (8-bit UCS/Unicode Transformation Format) is defined here.

Examples:

Example 1 (unknown):

initdb -E EUC_JP

Example 2 (unknown):

createdb -E EUC_KR -T template0 --lc-collate=ko_KR.euckr --lc-ctype=ko_KR.euckr korean

Example 3 (unknown):

CREATE DATABASE korean WITH ENCODING 'EUC_KR' LC_COLLATE='ko_KR.euckr' LC_CTYPE='ko_KR.euckr' TEMPLATE=template0;

Example 4 (unknown):

$ psql -l
                                         List of databases
   Name    |  Owner   | Encoding  |  Collation  |    Ctype    |          Access Privileges
-----------+----------+-----------+-------------+-------------+-------------------------------------
 clocaledb | hlinnaka | SQL_ASCII | C           | C           |
 englishdb | hlinnaka | UTF8      | en_GB.UTF8  | en_GB.UTF8  |
 japanese  | hlinnaka | UTF8      | ja_JP.UTF8  | ja_JP.UTF8  |
 korean    | hlinnaka | EUC_KR    | ko_KR.euckr | ko_KR.euckr |
 postgres  | hlinnaka | UTF8      | fi_FI.UTF8  | fi_FI.UTF8  |
 template0 | hlinnaka | UTF8      | fi_FI.UTF8  | fi_FI.UTF8  | {=c/hlinnaka,hlinnaka=CTc/hlinnaka}
 template1 | hlinnaka | UTF8      | fi_FI.UTF8  | fi_FI.UTF8  | {=c/hlinnaka,hlinnaka=CTc/hlinnaka}
(7 rows)

---

PostgreSQL: Documentation: 18: PostgreSQL 18.0 Documentation

URL: https://www.postgresql.org/docs/current/

Contents:

• PostgreSQL 18.0 Documentation
  • The PostgreSQL Global Development Group

Copyright © 1996–2025 The PostgreSQL Global Development Group

---

PostgreSQL: Documentation: 18: 20.16. Authentication Problems

URL: https://www.postgresql.org/docs/current/client-authentication-problems.html

Contents:

• 20.16. Authentication Problems #
  • Tip

Authentication failures and related problems generally manifest themselves through error messages like the following:

This is what you are most likely to get if you succeed in contacting the server, but it does not want to talk to you. As the message suggests, the server refused the connection request because it found no matching entry in its pg_hba.conf configuration file.

Messages like this indicate that you contacted the server, and it is willing to talk to you, but not until you pass the authorization method specified in the pg_hba.conf file. Check the password you are providing, or check your Kerberos or ident software if the complaint mentions one of those authentication types.

The indicated database user name was not found.

The database you are trying to connect to does not exist. Note that if you do not specify a database name, it defaults to the database user name.

The server log might contain more information about an authentication failure than is reported to the client. If you are confused about the reason for a failure, check the server log.

Examples:

Example 1 (unknown):

FATAL:  no pg_hba.conf entry for host "123.123.123.123", user "andym", database "testdb"

Example 2 (unknown):

FATAL:  password authentication failed for user "andym"

Example 3 (unknown):

FATAL:  user "andym" does not exist

Example 4 (unknown):

FATAL:  database "testdb" does not exist

---

PostgreSQL: Documentation: 18: 11.2. Index Types

URL: https://www.postgresql.org/docs/current/indexes-types.html

Contents:

• 11.2. Index Types #
  • 11.2.1. B-Tree #
  • 11.2.2. Hash #
  • 11.2.3. GiST #
  • 11.2.4. SP-GiST #
  • 11.2.5. GIN #
  • 11.2.6. BRIN #

PostgreSQL provides several index types: B-tree, Hash, GiST, SP-GiST, GIN, BRIN, and the extension bloom. Each index type uses a different algorithm that is best suited to different types of indexable clauses. By default, the CREATE INDEX command creates B-tree indexes, which fit the most common situations. The other index types are selected by writing the keyword USING followed by the index type name. For example, to create a Hash index:

B-trees can handle equality and range queries on data that can be sorted into some ordering. In particular, the PostgreSQL query planner will consider using a B-tree index whenever an indexed column is involved in a comparison using one of these operators:

Constructs equivalent to combinations of these operators, such as BETWEEN and IN, can also be implemented with a B-tree index search. Also, an IS NULL or IS NOT NULL condition on an index column can be used with a B-tree index.

The optimizer can also use a B-tree index for queries involving the pattern matching operators LIKE and ~ if the pattern is a constant and is anchored to the beginning of the string — for example, col LIKE 'foo%' or col ~ '^foo', but not col LIKE '%bar'. However, if your database does not use the C locale you will need to create the index with a special operator class to support indexing of pattern-matching queries; see Section 11.10 below. It is also possible to use B-tree indexes for ILIKE and ~*, but only if the pattern starts with non-alphabetic characters, i.e., characters that are not affected by upper/lower case conversion.

B-tree indexes can also be used to retrieve data in sorted order. This is not always faster than a simple scan and sort, but it is often helpful.

Hash indexes store a 32-bit hash code derived from the value of the indexed column. Hence, such indexes can only handle simple equality comparisons. The query planner will consider using a hash index whenever an indexed column is involved in a comparison using the equal operator:

GiST indexes are not a single kind of index, but rather an infrastructure within which many different indexing strategies can be implemented. Accordingly, the particular operators with which a GiST index can be used vary depending on the indexing strategy (the operator class). As an example, the standard distribution of PostgreSQL includes GiST operator classes for several two-dimensional geometric data types, which support indexed queries using these operators:

(See Section 9.11 for the meaning of these operators.) The GiST operator classes included in the standard distribution are documented in Table 65.1. Many other GiST operator classes are available in the contrib collection or as separate projects. For more information see Section 65.2.

GiST indexes are also capable of optimizing “nearest-neighbor” searches, such as

which finds the ten places closest to a given target point. The ability to do this is again dependent on the particular operator class being used. In Table 65.1, operators that can be used in this way are listed in the column “Ordering Operators”.

SP-GiST indexes, like GiST indexes, offer an infrastructure that supports various kinds of searches. SP-GiST permits implementation of a wide range of different non-balanced disk-based data structures, such as quadtrees, k-d trees, and radix trees (tries). As an example, the standard distribution of PostgreSQL includes SP-GiST operator classes for two-dimensional points, which support indexed queries using these operators:

(See Section 9.11 for the meaning of these operators.) The SP-GiST operator classes included in the standard distribution are documented in Table 65.2. For more information see Section 65.3.

Like GiST, SP-GiST supports “nearest-neighbor” searches. For SP-GiST operator classes that support distance ordering, the corresponding operator is listed in the “Ordering Operators” column in Table 65.2.

GIN indexes are “inverted indexes” which are appropriate for data values that contain multiple component values, such as arrays. An inverted index contains a separate entry for each component value, and can efficiently handle queries that test for the presence of specific component values.

Like GiST and SP-GiST, GIN can support many different user-defined indexing strategies, and the particular operators with which a GIN index can be used vary depending on the indexing strategy. As an example, the standard distribution of PostgreSQL includes a GIN operator class for arrays, which supports indexed queries using these operators:

(See Section 9.19 for the meaning of these operators.) The GIN operator classes included in the standard distribution are documented in Table 65.3. Many other GIN operator classes are available in the contrib collection or as separate projects. For more information see Section 65.4.

BRIN indexes (a shorthand for Block Range INdexes) store summaries about the values stored in consecutive physical block ranges of a table. Thus, they are most effective for columns whose values are well-correlated with the physical order of the table rows. Like GiST, SP-GiST and GIN, BRIN can support many different indexing strategies, and the particular operators with which a BRIN index can be used vary depending on the indexing strategy. For data types that have a linear sort order, the indexed data corresponds to the minimum and maximum values of the values in the column for each block range. This supports indexed queries using these operators:

The BRIN operator classes included in the standard distribution are documented in Table 65.4. For more information see Section 65.5.

Examples:

Example 1 (unknown):

CREATE INDEX name ON table USING HASH (column);

Example 2 (unknown):

<   <=   =   >=   >

Example 3 (unknown):

<<   &<   &>   >>   <<|   &<|   |&>   |>>   @>   <@   ~=   &&

Example 4 (unknown):

SELECT * FROM places ORDER BY location <-> point '(101,456)' LIMIT 10;

---

PostgreSQL: Documentation: 18: 30.1. What Is JIT compilation?

URL: https://www.postgresql.org/docs/current/jit-reason.html

Contents:

• 30.1. What Is JIT compilation? #
  • 30.1.1. JIT Accelerated Operations #
  • 30.1.2. Inlining #
  • 30.1.3. Optimization #

Just-in-Time (JIT) compilation is the process of turning some form of interpreted program evaluation into a native program, and doing so at run time. For example, instead of using general-purpose code that can evaluate arbitrary SQL expressions to evaluate a particular SQL predicate like WHERE a.col = 3, it is possible to generate a function that is specific to that expression and can be natively executed by the CPU, yielding a speedup.

PostgreSQL has builtin support to perform JIT compilation using LLVM when PostgreSQL is built with --with-llvm.

See src/backend/jit/README for further details.

Currently PostgreSQL's JIT implementation has support for accelerating expression evaluation and tuple deforming. Several other operations could be accelerated in the future.

Expression evaluation is used to evaluate WHERE clauses, target lists, aggregates and projections. It can be accelerated by generating code specific to each case.

Tuple deforming is the process of transforming an on-disk tuple (see Section 66.6.1) into its in-memory representation. It can be accelerated by creating a function specific to the table layout and the number of columns to be extracted.

PostgreSQL is very extensible and allows new data types, functions, operators and other database objects to be defined; see Chapter 36. In fact the built-in objects are implemented using nearly the same mechanisms. This extensibility implies some overhead, for example due to function calls (see Section 36.3). To reduce that overhead, JIT compilation can inline the bodies of small functions into the expressions using them. That allows a significant percentage of the overhead to be optimized away.

LLVM has support for optimizing generated code. Some of the optimizations are cheap enough to be performed whenever JIT is used, while others are only beneficial for longer-running queries. See https://llvm.org/docs/Passes.html#transform-passes for more details about optimizations.

---

PostgreSQL: Documentation: 18: 28.1. Reliability

URL: https://www.postgresql.org/docs/current/wal-reliability.html

Contents:

• 28.1. Reliability #

Reliability is an important property of any serious database system, and PostgreSQL does everything possible to guarantee reliable operation. One aspect of reliable operation is that all data recorded by a committed transaction should be stored in a nonvolatile area that is safe from power loss, operating system failure, and hardware failure (except failure of the nonvolatile area itself, of course). Successfully writing the data to the computer's permanent storage (disk drive or equivalent) ordinarily meets this requirement. In fact, even if a computer is fatally damaged, if the disk drives survive they can be moved to another computer with similar hardware and all committed transactions will remain intact.

While forcing data to the disk platters periodically might seem like a simple operation, it is not. Because disk drives are dramatically slower than main memory and CPUs, several layers of caching exist between the computer's main memory and the disk platters. First, there is the operating system's buffer cache, which caches frequently requested disk blocks and combines disk writes. Fortunately, all operating systems give applications a way to force writes from the buffer cache to disk, and PostgreSQL uses those features. (See the wal_sync_method parameter to adjust how this is done.)

Next, there might be a cache in the disk drive controller; this is particularly common on RAID controller cards. Some of these caches are write-through, meaning writes are sent to the drive as soon as they arrive. Others are write-back, meaning data is sent to the drive at some later time. Such caches can be a reliability hazard because the memory in the disk controller cache is volatile, and will lose its contents in a power failure. Better controller cards have battery-backup units (BBUs), meaning the card has a battery that maintains power to the cache in case of system power loss. After power is restored the data will be written to the disk drives.

And finally, most disk drives have caches. Some are write-through while some are write-back, and the same concerns about data loss exist for write-back drive caches as for disk controller caches. Consumer-grade IDE and SATA drives are particularly likely to have write-back caches that will not survive a power failure. Many solid-state drives (SSD) also have volatile write-back caches.

These caches can typically be disabled; however, the method for doing this varies by operating system and drive type:

On Linux, IDE and SATA drives can be queried using hdparm -I; write caching is enabled if there is a * next to Write cache. hdparm -W 0 can be used to turn off write caching. SCSI drives can be queried using sdparm. Use sdparm --get=WCE to check whether the write cache is enabled and sdparm --clear=WCE to disable it.

On FreeBSD, IDE drives can be queried using camcontrol identify and write caching turned off using hw.ata.wc=0 in /boot/loader.conf; SCSI drives can be queried using camcontrol identify, and the write cache both queried and changed using sdparm when available.

On Solaris, the disk write cache is controlled by format -e. (The Solaris ZFS file system is safe with disk write-cache enabled because it issues its own disk cache flush commands.)

On Windows, if wal_sync_method is open_datasync (the default), write caching can be disabled by unchecking My Computer\Open\disk drive\Properties\Hardware\Properties\Policies\Enable write caching on the disk. Alternatively, set wal_sync_method to fdatasync (NTFS only) or fsync, which prevent write caching.

On macOS, write caching can be prevented by setting wal_sync_method to fsync_writethrough.

Recent SATA drives (those following ATAPI-6 or later) offer a drive cache flush command (FLUSH CACHE EXT), while SCSI drives have long supported a similar command SYNCHRONIZE CACHE. These commands are not directly accessible to PostgreSQL, but some file systems (e.g., ZFS, ext4) can use them to flush data to the platters on write-back-enabled drives. Unfortunately, such file systems behave suboptimally when combined with battery-backup unit (BBU) disk controllers. In such setups, the synchronize command forces all data from the controller cache to the disks, eliminating much of the benefit of the BBU. You can run the pg_test_fsync program to see if you are affected. If you are affected, the performance benefits of the BBU can be regained by turning off write barriers in the file system or reconfiguring the disk controller, if that is an option. If write barriers are turned off, make sure the battery remains functional; a faulty battery can potentially lead to data loss. Hopefully file system and disk controller designers will eventually address this suboptimal behavior.

When the operating system sends a write request to the storage hardware, there is little it can do to make sure the data has arrived at a truly non-volatile storage area. Rather, it is the administrator's responsibility to make certain that all storage components ensure integrity for both data and file-system metadata. Avoid disk controllers that have non-battery-backed write caches. At the drive level, disable write-back caching if the drive cannot guarantee the data will be written before shutdown. If you use SSDs, be aware that many of these do not honor cache flush commands by default. You can test for reliable I/O subsystem behavior using diskchecker.pl.

Another risk of data loss is posed by the disk platter write operations themselves. Disk platters are divided into sectors, commonly 512 bytes each. Every physical read or write operation processes a whole sector. When a write request arrives at the drive, it might be for some multiple of 512 bytes (PostgreSQL typically writes 8192 bytes, or 16 sectors, at a time), and the process of writing could fail due to power loss at any time, meaning some of the 512-byte sectors were written while others were not. To guard against such failures, PostgreSQL periodically writes full page images to permanent WAL storage before modifying the actual page on disk. By doing this, during crash recovery PostgreSQL can restore partially-written pages from WAL. If you have file-system software that prevents partial page writes (e.g., ZFS), you can turn off this page imaging by turning off the full_page_writes parameter. Battery-Backed Unit (BBU) disk controllers do not prevent partial page writes unless they guarantee that data is written to the BBU as full (8kB) pages.

PostgreSQL also protects against some kinds of data corruption on storage devices that may occur because of hardware errors or media failure over time, such as reading/writing garbage data.

Each individual record in a WAL file is protected by a CRC-32C (32-bit) check that allows us to tell if record contents are correct. The CRC value is set when we write each WAL record and checked during crash recovery, archive recovery and replication.

Data pages are checksummed by default, and full page images recorded in WAL records are always checksum protected.

Internal data structures such as pg_xact, pg_subtrans, pg_multixact, pg_serial, pg_notify, pg_stat, pg_snapshots are not directly checksummed, nor are pages protected by full page writes. However, where such data structures are persistent, WAL records are written that allow recent changes to be accurately rebuilt at crash recovery and those WAL records are protected as discussed above.

Individual state files in pg_twophase are protected by CRC-32C.

Temporary data files used in larger SQL queries for sorts, materializations and intermediate results are not currently checksummed, nor will WAL records be written for changes to those files.

PostgreSQL does not protect against correctable memory errors and it is assumed you will operate using RAM that uses industry standard Error Correcting Codes (ECC) or better protection.

---

PostgreSQL: Documentation: 18: Appendix F. Additional Supplied Modules and Extensions

URL: https://www.postgresql.org/docs/current/contrib.html

Contents:

• Appendix F. Additional Supplied Modules and Extensions

This appendix and the next one contain information on the optional components found in the contrib directory of the PostgreSQL distribution. These include porting tools, analysis utilities, and plug-in features that are not part of the core PostgreSQL system. They are separate mainly because they address a limited audience or are too experimental to be part of the main source tree. This does not preclude their usefulness.

This appendix covers extensions and other server plug-in module libraries found in contrib. Appendix G covers utility programs.

When building from the source distribution, these optional components are not built automatically, unless you build the "world" target (see Step 2). You can build and install all of them by running:

in the contrib directory of a configured source tree; or to build and install just one selected module, do the same in that module's subdirectory. Many of the modules have regression tests, which can be executed by running:

before installation or

once you have a PostgreSQL server running.

If you are using a pre-packaged version of PostgreSQL, these components are typically made available as a separate subpackage, such as postgresql-contrib.

Many components supply new user-defined functions, operators, or types, packaged as extensions. To make use of one of these extensions, after you have installed the code you need to register the new SQL objects in the database system. This is done by executing a CREATE EXTENSION command. In a fresh database, you can simply do

This command registers the new SQL objects in the current database only, so you need to run it in every database in which you want the extension's facilities to be available. Alternatively, run it in database template1 so that the extension will be copied into subsequently-created databases by default.

For all extensions, the CREATE EXTENSION command must be run by a database superuser, unless the extension is considered “trusted”. Trusted extensions can be run by any user who has CREATE privilege on the current database. Extensions that are trusted are identified as such in the sections that follow. Generally, trusted extensions are ones that cannot provide access to outside-the-database functionality.

The following extensions are trusted in a default installation:

Many extensions allow you to install their objects in a schema of your choice. To do that, add SCHEMA schema_name to the CREATE EXTENSION command. By default, the objects will be placed in your current creation target schema, which in turn defaults to public.

Note, however, that some of these components are not “extensions” in this sense, but are loaded into the server in some other way, for instance by way of shared_preload_libraries. See the documentation of each component for details.

Examples:

Example 1 (unknown):

make
make install

Example 2 (unknown):

make installcheck

Example 3 (unknown):

CREATE EXTENSION extension_name;

---

PostgreSQL: Documentation: 18: 8.20. pg_lsn Type

URL: https://www.postgresql.org/docs/current/datatype-pg-lsn.html

Contents:

• 8.20. pg_lsn Type #

The pg_lsn data type can be used to store LSN (Log Sequence Number) data which is a pointer to a location in the WAL. This type is a representation of XLogRecPtr and an internal system type of PostgreSQL.

Internally, an LSN is a 64-bit integer, representing a byte position in the write-ahead log stream. It is printed as two hexadecimal numbers of up to 8 digits each, separated by a slash; for example, 16/B374D848. The pg_lsn type supports the standard comparison operators, like = and >. Two LSNs can be subtracted using the - operator; the result is the number of bytes separating those write-ahead log locations. Also the number of bytes can be added into and subtracted from LSN using the +(pg_lsn,numeric) and -(pg_lsn,numeric) operators, respectively. Note that the calculated LSN should be in the range of pg_lsn type, i.e., between 0/0 and FFFFFFFF/FFFFFFFF.

---

PostgreSQL: Documentation: 18: 19.7. Query Planning

URL: https://www.postgresql.org/docs/current/runtime-config-query.html

Contents:

• 19.7. Query Planning #
  • 19.7.1. Planner Method Configuration #
  • 19.7.2. Planner Cost Constants #
  • Note
  • Tip
  • 19.7.3. Genetic Query Optimizer #
  • 19.7.4. Other Planner Options #

These configuration parameters provide a crude method of influencing the query plans chosen by the query optimizer. If the default plan chosen by the optimizer for a particular query is not optimal, a temporary solution is to use one of these configuration parameters to force the optimizer to choose a different plan. Better ways to improve the quality of the plans chosen by the optimizer include adjusting the planner cost constants (see Section 19.7.2), running ANALYZE manually, increasing the value of the default_statistics_target configuration parameter, and increasing the amount of statistics collected for specific columns using ALTER TABLE SET STATISTICS.

Enables or disables the query planner's use of async-aware append plan types. The default is on.

Enables or disables the query planner's use of bitmap-scan plan types. The default is on.

Enables or disables the query planner's ability to reorder DISTINCT keys to match the input path's pathkeys. The default is on.

Enables or disables the query planner's use of gather merge plan types. The default is on.

Controls if the query planner will produce a plan which will provide GROUP BY keys sorted in the order of keys of a child node of the plan, such as an index scan. When disabled, the query planner will produce a plan with GROUP BY keys only sorted to match the ORDER BY clause, if any. When enabled, the planner will try to produce a more efficient plan. The default value is on.

Enables or disables the query planner's use of hashed aggregation plan types. The default is on.

Enables or disables the query planner's use of hash-join plan types. The default is on.

Enables or disables the query planner's use of incremental sort steps. The default is on.

Enables or disables the query planner's use of index-scan and index-only-scan plan types. The default is on. Also see enable_indexonlyscan.

Enables or disables the query planner's use of index-only-scan plan types (see Section 11.9). The default is on. The enable_indexscan setting must also be enabled to have the query planner consider index-only-scans.

Enables or disables the query planner's use of materialization. It is impossible to suppress materialization entirely, but turning this variable off prevents the planner from inserting materialize nodes except in cases where it is required for correctness. The default is on.

Enables or disables the query planner's use of memoize plans for caching results from parameterized scans inside nested-loop joins. This plan type allows scans to the underlying plans to be skipped when the results for the current parameters are already in the cache. Less commonly looked up results may be evicted from the cache when more space is required for new entries. The default is on.

Enables or disables the query planner's use of merge-join plan types. The default is on.

Enables or disables the query planner's use of nested-loop join plans. It is impossible to suppress nested-loop joins entirely, but turning this variable off discourages the planner from using one if there are other methods available. The default is on.

Enables or disables the query planner's use of parallel-aware append plan types. The default is on.

Enables or disables the query planner's use of hash-join plan types with parallel hash. Has no effect if hash-join plans are not also enabled. The default is on.

Enables or disables the query planner's ability to eliminate a partitioned table's partitions from query plans. This also controls the planner's ability to generate query plans which allow the query executor to remove (ignore) partitions during query execution. The default is on. See Section 5.12.4 for details.

Enables or disables the query planner's use of partitionwise join, which allows a join between partitioned tables to be performed by joining the matching partitions. Partitionwise join currently applies only when the join conditions include all the partition keys, which must be of the same data type and have one-to-one matching sets of child partitions. With this setting enabled, the number of nodes whose memory usage is restricted by work_mem appearing in the final plan can increase linearly according to the number of partitions being scanned. This can result in a large increase in overall memory consumption during the execution of the query. Query planning also becomes significantly more expensive in terms of memory and CPU. The default value is off.

Enables or disables the query planner's use of partitionwise grouping or aggregation, which allows grouping or aggregation on partitioned tables to be performed separately for each partition. If the GROUP BY clause does not include the partition keys, only partial aggregation can be performed on a per-partition basis, and finalization must be performed later. With this setting enabled, the number of nodes whose memory usage is restricted by work_mem appearing in the final plan can increase linearly according to the number of partitions being scanned. This can result in a large increase in overall memory consumption during the execution of the query. Query planning also becomes significantly more expensive in terms of memory and CPU. The default value is off.

Controls if the query planner will produce a plan which will provide rows which are presorted in the order required for the query's ORDER BY / DISTINCT aggregate functions. When disabled, the query planner will produce a plan which will always require the executor to perform a sort before performing aggregation of each aggregate function containing an ORDER BY or DISTINCT clause. When enabled, the planner will try to produce a more efficient plan which provides input to the aggregate functions which is presorted in the order they require for aggregation. The default value is on.

Enables or disables the query planner's optimization which analyses the query tree and replaces self joins with semantically equivalent single scans. Takes into consideration only plain tables. The default is on.

Enables or disables the query planner's use of sequential scan plan types. It is impossible to suppress sequential scans entirely, but turning this variable off discourages the planner from using one if there are other methods available. The default is on.

Enables or disables the query planner's use of explicit sort steps. It is impossible to suppress explicit sorts entirely, but turning this variable off discourages the planner from using one if there are other methods available. The default is on.

Enables or disables the query planner's use of TID scan plan types. The default is on.

The cost variables described in this section are measured on an arbitrary scale. Only their relative values matter, hence scaling them all up or down by the same factor will result in no change in the planner's choices. By default, these cost variables are based on the cost of sequential page fetches; that is, seq_page_cost is conventionally set to 1.0 and the other cost variables are set with reference to that. But you can use a different scale if you prefer, such as actual execution times in milliseconds on a particular machine.

Unfortunately, there is no well-defined method for determining ideal values for the cost variables. They are best treated as averages over the entire mix of queries that a particular installation will receive. This means that changing them on the basis of just a few experiments is very risky.

Sets the planner's estimate of the cost of a disk page fetch that is part of a series of sequential fetches. The default is 1.0. This value can be overridden for tables and indexes in a particular tablespace by setting the tablespace parameter of the same name (see ALTER TABLESPACE).

Sets the planner's estimate of the cost of a non-sequentially-fetched disk page. The default is 4.0. This value can be overridden for tables and indexes in a particular tablespace by setting the tablespace parameter of the same name (see ALTER TABLESPACE).

Reducing this value relative to seq_page_cost will cause the system to prefer index scans; raising it will make index scans look relatively more expensive. You can raise or lower both values together to change the importance of disk I/O costs relative to CPU costs, which are described by the following parameters.

Random access to mechanical disk storage is normally much more expensive than four times sequential access. However, a lower default is used (4.0) because the majority of random accesses to disk, such as indexed reads, are assumed to be in cache. The default value can be thought of as modeling random access as 40 times slower than sequential, while expecting 90% of random reads to be cached.

If you believe a 90% cache rate is an incorrect assumption for your workload, you can increase random_page_cost to better reflect the true cost of random storage reads. Correspondingly, if your data is likely to be completely in cache, such as when the database is smaller than the total server memory, decreasing random_page_cost can be appropriate. Storage that has a low random read cost relative to sequential, e.g., solid-state drives, might also be better modeled with a lower value for random_page_cost, e.g., 1.1.

Although the system will let you set random_page_cost to less than seq_page_cost, it is not physically sensible to do so. However, setting them equal makes sense if the database is entirely cached in RAM, since in that case there is no penalty for touching pages out of sequence. Also, in a heavily-cached database you should lower both values relative to the CPU parameters, since the cost of fetching a page already in RAM is much smaller than it would normally be.

Sets the planner's estimate of the cost of processing each row during a query. The default is 0.01.

Sets the planner's estimate of the cost of processing each index entry during an index scan. The default is 0.005.

Sets the planner's estimate of the cost of processing each operator or function executed during a query. The default is 0.0025.

Sets the planner's estimate of the cost of launching parallel worker processes. The default is 1000.

Sets the planner's estimate of the cost of transferring one tuple from a parallel worker process to another process. The default is 0.1.

Sets the minimum amount of table data that must be scanned in order for a parallel scan to be considered. For a parallel sequential scan, the amount of table data scanned is always equal to the size of the table, but when indexes are used the amount of table data scanned will normally be less. If this value is specified without units, it is taken as blocks, that is BLCKSZ bytes, typically 8kB. The default is 8 megabytes (8MB).

Sets the minimum amount of index data that must be scanned in order for a parallel scan to be considered. Note that a parallel index scan typically won't touch the entire index; it is the number of pages which the planner believes will actually be touched by the scan which is relevant. This parameter is also used to decide whether a particular index can participate in a parallel vacuum. See VACUUM. If this value is specified without units, it is taken as blocks, that is BLCKSZ bytes, typically 8kB. The default is 512 kilobytes (512kB).

Sets the planner's assumption about the effective size of the disk cache that is available to a single query. This is factored into estimates of the cost of using an index; a higher value makes it more likely index scans will be used, a lower value makes it more likely sequential scans will be used. When setting this parameter you should consider both PostgreSQL's shared buffers and the portion of the kernel's disk cache that will be used for PostgreSQL data files, though some data might exist in both places. Also, take into account the expected number of concurrent queries on different tables, since they will have to share the available space. This parameter has no effect on the size of shared memory allocated by PostgreSQL, nor does it reserve kernel disk cache; it is used only for estimation purposes. The system also does not assume data remains in the disk cache between queries. If this value is specified without units, it is taken as blocks, that is BLCKSZ bytes, typically 8kB. The default is 4 gigabytes (4GB). (If BLCKSZ is not 8kB, the default value scales proportionally to it.)

Sets the query cost above which JIT compilation is activated, if enabled (see Chapter 30). Performing JIT costs planning time but can accelerate query execution. Setting this to -1 disables JIT compilation. The default is 100000.

Sets the query cost above which JIT compilation attempts to inline functions and operators. Inlining adds planning time, but can improve execution speed. It is not meaningful to set this to less than jit_above_cost. Setting this to -1 disables inlining. The default is 500000.

Sets the query cost above which JIT compilation applies expensive optimizations. Such optimization adds planning time, but can improve execution speed. It is not meaningful to set this to less than jit_above_cost, and it is unlikely to be beneficial to set it to more than jit_inline_above_cost. Setting this to -1 disables expensive optimizations. The default is 500000.

The genetic query optimizer (GEQO) is an algorithm that does query planning using heuristic searching. This reduces planning time for complex queries (those joining many relations), at the cost of producing plans that are sometimes inferior to those found by the normal exhaustive-search algorithm. For more information see Chapter 61.

Enables or disables genetic query optimization. This is on by default. It is usually best not to turn it off in production; the geqo_threshold variable provides more granular control of GEQO.

Use genetic query optimization to plan queries with at least this many FROM items involved. (Note that a FULL OUTER JOIN construct counts as only one FROM item.) The default is 12. For simpler queries it is usually best to use the regular, exhaustive-search planner, but for queries with many tables the exhaustive search takes too long, often longer than the penalty of executing a suboptimal plan. Thus, a threshold on the size of the query is a convenient way to manage use of GEQO.

Controls the trade-off between planning time and query plan quality in GEQO. This variable must be an integer in the range from 1 to 10. The default value is five. Larger values increase the time spent doing query planning, but also increase the likelihood that an efficient query plan will be chosen.

geqo_effort doesn't actually do anything directly; it is only used to compute the default values for the other variables that influence GEQO behavior (described below). If you prefer, you can set the other parameters by hand instead.

Controls the pool size used by GEQO, that is the number of individuals in the genetic population. It must be at least two, and useful values are typically 100 to 1000. If it is set to zero (the default setting) then a suitable value is chosen based on geqo_effort and the number of tables in the query.

Controls the number of generations used by GEQO, that is the number of iterations of the algorithm. It must be at least one, and useful values are in the same range as the pool size. If it is set to zero (the default setting) then a suitable value is chosen based on geqo_pool_size.

Controls the selection bias used by GEQO. The selection bias is the selective pressure within the population. Values can be from 1.50 to 2.00; the latter is the default.

Controls the initial value of the random number generator used by GEQO to select random paths through the join order search space. The value can range from zero (the default) to one. Varying the value changes the set of join paths explored, and may result in a better or worse best path being found.

Sets the default statistics target for table columns without a column-specific target set via ALTER TABLE SET STATISTICS. Larger values increase the time needed to do ANALYZE, but might improve the quality of the planner's estimates. The default is 100. For more information on the use of statistics by the PostgreSQL query planner, refer to Section 14.2.

Controls the query planner's use of table constraints to optimize queries. The allowed values of constraint_exclusion are on (examine constraints for all tables), off (never examine constraints), and partition (examine constraints only for inheritance child tables and UNION ALL subqueries). partition is the default setting. It is often used with traditional inheritance trees to improve performance.

When this parameter allows it for a particular table, the planner compares query conditions with the table's CHECK constraints, and omits scanning tables for which the conditions contradict the constraints. For example:

With constraint exclusion enabled, this SELECT will not scan child1000 at all, improving performance.

Currently, constraint exclusion is enabled by default only for cases that are often used to implement table partitioning via inheritance trees. Turning it on for all tables imposes extra planning overhead that is quite noticeable on simple queries, and most often will yield no benefit for simple queries. If you have no tables that are partitioned using traditional inheritance, you might prefer to turn it off entirely. (Note that the equivalent feature for partitioned tables is controlled by a separate parameter, enable_partition_pruning.)

Refer to Section 5.12.5 for more information on using constraint exclusion to implement partitioning.

Sets the planner's estimate of the fraction of a cursor's rows that will be retrieved. The default is 0.1. Smaller values of this setting bias the planner towards using “fast start” plans for cursors, which will retrieve the first few rows quickly while perhaps taking a long time to fetch all rows. Larger values put more emphasis on the total estimated time. At the maximum setting of 1.0, cursors are planned exactly like regular queries, considering only the total estimated time and not how soon the first rows might be delivered.

The planner will merge sub-queries into upper queries if the resulting FROM list would have no more than this many items. Smaller values reduce planning time but might yield inferior query plans. The default is eight. For more information see Section 14.3.

Setting this value to geqo_threshold or more may trigger use of the GEQO planner, resulting in non-optimal plans. See Section 19.7.3.

Determines whether JIT compilation may be used by PostgreSQL, if available (see Chapter 30). The default is on.

The planner will rewrite explicit JOIN constructs (except FULL JOINs) into lists of FROM items whenever a list of no more than this many items would result. Smaller values reduce planning time but might yield inferior query plans.

By default, this variable is set the same as from_collapse_limit, which is appropriate for most uses. Setting it to 1 prevents any reordering of explicit JOINs. Thus, the explicit join order specified in the query will be the actual order in which the relations are joined. Because the query planner does not always choose the optimal join order, advanced users can elect to temporarily set this variable to 1, and then specify the join order they desire explicitly. For more information see Section 14.3.

Setting this value to geqo_threshold or more may trigger use of the GEQO planner, resulting in non-optimal plans. See Section 19.7.3.

Prepared statements (either explicitly prepared or implicitly generated, for example by PL/pgSQL) can be executed using custom or generic plans. Custom plans are made afresh for each execution using its specific set of parameter values, while generic plans do not rely on the parameter values and can be re-used across executions. Thus, use of a generic plan saves planning time, but if the ideal plan depends strongly on the parameter values then a generic plan may be inefficient. The choice between these options is normally made automatically, but it can be overridden with plan_cache_mode. The allowed values are auto (the default), force_custom_plan and force_generic_plan. This setting is considered when a cached plan is to be executed, not when it is prepared. For more information see PREPARE.

Sets the planner's estimate of the average size of the working table of a recursive query, as a multiple of the estimated size of the initial non-recursive term of the query. This helps the planner choose the most appropriate method for joining the working table to the query's other tables. The default value is 10.0. A smaller value such as 1.0 can be helpful when the recursion has low “fan-out” from one step to the next, as for example in shortest-path queries. Graph analytics queries may benefit from larger-than-default values.

Examples:

Example 1 (unknown):

CREATE TABLE parent(key integer, ...);
CREATE TABLE child1000(check (key between 1000 and 1999)) INHERITS(parent);
CREATE TABLE child2000(check (key between 2000 and 2999)) INHERITS(parent);
...
SELECT * FROM parent WHERE key = 2400;

---

PostgreSQL: Documentation: 18: 36.14. User-Defined Operators

URL: https://www.postgresql.org/docs/current/xoper.html

Contents:

• 36.14. User-Defined Operators #

Every operator is “syntactic sugar” for a call to an underlying function that does the real work; so you must first create the underlying function before you can create the operator. However, an operator is not merely syntactic sugar, because it carries additional information that helps the query planner optimize queries that use the operator. The next section will be devoted to explaining that additional information.

PostgreSQL supports prefix and infix operators. Operators can be overloaded; that is, the same operator name can be used for different operators that have different numbers and types of operands. When a query is executed, the system determines the operator to call from the number and types of the provided operands.

Here is an example of creating an operator for adding two complex numbers. We assume we've already created the definition of type complex (see Section 36.13). First we need a function that does the work, then we can define the operator:

Now we could execute a query like this:

We've shown how to create a binary operator here. To create a prefix operator, just omit the leftarg. The function clause and the argument clauses are the only required items in CREATE OPERATOR. The commutator clause shown in the example is an optional hint to the query optimizer. Further details about commutator and other optimizer hints appear in the next section.

Examples:

Example 1 (unknown):

CREATE FUNCTION complex_add(complex, complex)
    RETURNS complex
    AS 'filename', 'complex_add'
    LANGUAGE C IMMUTABLE STRICT;

CREATE OPERATOR + (
    leftarg = complex,
    rightarg = complex,
    function = complex_add,
    commutator = +
);

Example 2 (unknown):

SELECT (a + b) AS c FROM test_complex;

        c
-----------------
 (5.2,6.05)
 (133.42,144.95)

---

PostgreSQL: Documentation: 18: 9.1. Logical Operators

URL: https://www.postgresql.org/docs/current/functions-logical.html

Contents:

• 9.1. Logical Operators #

The usual logical operators are available:

SQL uses a three-valued logic system with true, false, and null, which represents “unknown”. Observe the following truth tables:

The operators AND and OR are commutative, that is, you can switch the left and right operands without affecting the result. (However, it is not guaranteed that the left operand is evaluated before the right operand. See Section 4.2.14 for more information about the order of evaluation of subexpressions.)

Examples:

Example 1 (unknown):

boolean AND boolean → boolean
boolean OR boolean → boolean
NOT boolean → boolean

---

PostgreSQL: Documentation: 18: 35.30. foreign_table_options

URL: https://www.postgresql.org/docs/current/infoschema-foreign-table-options.html

Contents:

• 35.30. foreign_table_options #

The view foreign_table_options contains all the options defined for foreign tables in the current database. Only those foreign tables are shown that the current user has access to (by way of being the owner or having some privilege).

Table 35.28. foreign_table_options Columns

foreign_table_catalog sql_identifier

Name of the database that contains the foreign table (always the current database)

foreign_table_schema sql_identifier

Name of the schema that contains the foreign table

foreign_table_name sql_identifier

Name of the foreign table

option_name sql_identifier

option_value character_data

---

PostgreSQL: Documentation: 18: 35.12. column_column_usage

URL: https://www.postgresql.org/docs/current/infoschema-column-column-usage.html

Contents:

• 35.12. column_column_usage #

The view column_column_usage identifies all generated columns that depend on another base column in the same table. Only tables owned by a currently enabled role are included.

Table 35.10. column_column_usage Columns

table_catalog sql_identifier

Name of the database containing the table (always the current database)

table_schema sql_identifier

Name of the schema containing the table

table_name sql_identifier

column_name sql_identifier

Name of the base column that a generated column depends on

dependent_column sql_identifier

Name of the generated column

---

PostgreSQL: Documentation: 18: 15.3. Parallel Plans

URL: https://www.postgresql.org/docs/current/parallel-plans.html

Contents:

• 15.3. Parallel Plans #
  • 15.3.1. Parallel Scans #
  • 15.3.2. Parallel Joins #
  • 15.3.3. Parallel Aggregation #
  • 15.3.4. Parallel Append #
  • 15.3.5. Parallel Plan Tips #

Because each worker executes the parallel portion of the plan to completion, it is not possible to simply take an ordinary query plan and run it using multiple workers. Each worker would produce a full copy of the output result set, so the query would not run any faster than normal but would produce incorrect results. Instead, the parallel portion of the plan must be what is known internally to the query optimizer as a partial plan; that is, it must be constructed so that each process that executes the plan will generate only a subset of the output rows in such a way that each required output row is guaranteed to be generated by exactly one of the cooperating processes. Generally, this means that the scan on the driving table of the query must be a parallel-aware scan.

The following types of parallel-aware table scans are currently supported.

In a parallel sequential scan, the table's blocks will be divided into ranges and shared among the cooperating processes. Each worker process will complete the scanning of its given range of blocks before requesting an additional range of blocks.

In a parallel bitmap heap scan, one process is chosen as the leader. That process performs a scan of one or more indexes and builds a bitmap indicating which table blocks need to be visited. These blocks are then divided among the cooperating processes as in a parallel sequential scan. In other words, the heap scan is performed in parallel, but the underlying index scan is not.

In a parallel index scan or parallel index-only scan, the cooperating processes take turns reading data from the index. Currently, parallel index scans are supported only for btree indexes. Each process will claim a single index block and will scan and return all tuples referenced by that block; other processes can at the same time be returning tuples from a different index block. The results of a parallel btree scan are returned in sorted order within each worker process.

Other scan types, such as scans of non-btree indexes, may support parallel scans in the future.

Just as in a non-parallel plan, the driving table may be joined to one or more other tables using a nested loop, hash join, or merge join. The inner side of the join may be any kind of non-parallel plan that is otherwise supported by the planner provided that it is safe to run within a parallel worker. Depending on the join type, the inner side may also be a parallel plan.

In a nested loop join, the inner side is always non-parallel. Although it is executed in full, this is efficient if the inner side is an index scan, because the outer tuples and thus the loops that look up values in the index are divided over the cooperating processes.

In a merge join, the inner side is always a non-parallel plan and therefore executed in full. This may be inefficient, especially if a sort must be performed, because the work and resulting data are duplicated in every cooperating process.

In a hash join (without the "parallel" prefix), the inner side is executed in full by every cooperating process to build identical copies of the hash table. This may be inefficient if the hash table is large or the plan is expensive. In a parallel hash join, the inner side is a parallel hash that divides the work of building a shared hash table over the cooperating processes.

PostgreSQL supports parallel aggregation by aggregating in two stages. First, each process participating in the parallel portion of the query performs an aggregation step, producing a partial result for each group of which that process is aware. This is reflected in the plan as a Partial Aggregate node. Second, the partial results are transferred to the leader via Gather or Gather Merge. Finally, the leader re-aggregates the results across all workers in order to produce the final result. This is reflected in the plan as a Finalize Aggregate node.

Because the Finalize Aggregate node runs on the leader process, queries that produce a relatively large number of groups in comparison to the number of input rows will appear less favorable to the query planner. For example, in the worst-case scenario the number of groups seen by the Finalize Aggregate node could be as many as the number of input rows that were seen by all worker processes in the Partial Aggregate stage. For such cases, there is clearly going to be no performance benefit to using parallel aggregation. The query planner takes this into account during the planning process and is unlikely to choose parallel aggregate in this scenario.

Parallel aggregation is not supported in all situations. Each aggregate must be safe for parallelism and must have a combine function. If the aggregate has a transition state of type internal, it must have serialization and deserialization functions. See CREATE AGGREGATE for more details. Parallel aggregation is not supported if any aggregate function call contains DISTINCT or ORDER BY clause and is also not supported for ordered set aggregates or when the query involves GROUPING SETS. It can only be used when all joins involved in the query are also part of the parallel portion of the plan.

Whenever PostgreSQL needs to combine rows from multiple sources into a single result set, it uses an Append or MergeAppend plan node. This commonly happens when implementing UNION ALL or when scanning a partitioned table. Such nodes can be used in parallel plans just as they can in any other plan. However, in a parallel plan, the planner may instead use a Parallel Append node.

When an Append node is used in a parallel plan, each process will execute the child plans in the order in which they appear, so that all participating processes cooperate to execute the first child plan until it is complete and then move to the second plan at around the same time. When a Parallel Append is used instead, the executor will instead spread out the participating processes as evenly as possible across its child plans, so that multiple child plans are executed simultaneously. This avoids contention, and also avoids paying the startup cost of a child plan in those processes that never execute it.

Also, unlike a regular Append node, which can only have partial children when used within a parallel plan, a Parallel Append node can have both partial and non-partial child plans. Non-partial children will be scanned by only a single process, since scanning them more than once would produce duplicate results. Plans that involve appending multiple result sets can therefore achieve coarse-grained parallelism even when efficient partial plans are not available. For example, consider a query against a partitioned table that can only be implemented efficiently by using an index that does not support parallel scans. The planner might choose a Parallel Append of regular Index Scan plans; each individual index scan would have to be executed to completion by a single process, but different scans could be performed at the same time by different processes.

enable_parallel_append can be used to disable this feature.

If a query that is expected to do so does not produce a parallel plan, you can try reducing parallel_setup_cost or parallel_tuple_cost. Of course, this plan may turn out to be slower than the serial plan that the planner preferred, but this will not always be the case. If you don't get a parallel plan even with very small values of these settings (e.g., after setting them both to zero), there may be some reason why the query planner is unable to generate a parallel plan for your query. See Section 15.2 and Section 15.4 for information on why this may be the case.

When executing a parallel plan, you can use EXPLAIN (ANALYZE, VERBOSE) to display per-worker statistics for each plan node. This may be useful in determining whether the work is being evenly distributed between all plan nodes and more generally in understanding the performance characteristics of the plan.

---

PostgreSQL: Documentation: 18: 35.62. user_mappings

URL: https://www.postgresql.org/docs/current/infoschema-user-mappings.html

Contents:

• 35.62. user_mappings #

The view user_mappings contains all user mappings defined in the current database. Only those user mappings are shown where the current user has access to the corresponding foreign server (by way of being the owner or having some privilege).

Table 35.60. user_mappings Columns

authorization_identifier sql_identifier

Name of the user being mapped, or PUBLIC if the mapping is public

foreign_server_catalog sql_identifier

Name of the database that the foreign server used by this mapping is defined in (always the current database)

foreign_server_name sql_identifier

Name of the foreign server used by this mapping

---

PostgreSQL: Documentation: 18: 9.5. Binary String Functions and Operators

URL: https://www.postgresql.org/docs/current/functions-binarystring.html

Contents:

• 9.5. Binary String Functions and Operators #

This section describes functions and operators for examining and manipulating binary strings, that is values of type bytea. Many of these are equivalent, in purpose and syntax, to the text-string functions described in the previous section.

SQL defines some string functions that use key words, rather than commas, to separate arguments. Details are in Table 9.11. PostgreSQL also provides versions of these functions that use the regular function invocation syntax (see Table 9.12).

Table 9.11. SQL Binary String Functions and Operators

bytea || bytea → bytea

Concatenates the two binary strings.

'\x123456'::bytea || '\x789a00bcde'::bytea → \x123456789a00bcde

bit_length ( bytea ) → integer

Returns number of bits in the binary string (8 times the octet_length).

bit_length('\x123456'::bytea) → 24

btrim ( bytes bytea, bytesremoved bytea ) → bytea

Removes the longest string containing only bytes appearing in bytesremoved from the start and end of bytes.

btrim('\x1234567890'::bytea, '\x9012'::bytea) → \x345678

ltrim ( bytes bytea, bytesremoved bytea ) → bytea

Removes the longest string containing only bytes appearing in bytesremoved from the start of bytes.

ltrim('\x1234567890'::bytea, '\x9012'::bytea) → \x34567890

octet_length ( bytea ) → integer

Returns number of bytes in the binary string.

octet_length('\x123456'::bytea) → 3

overlay ( bytes bytea PLACING newsubstring bytea FROM start integer [ FOR count integer ] ) → bytea

Replaces the substring of bytes that starts at the start'th byte and extends for count bytes with newsubstring. If count is omitted, it defaults to the length of newsubstring.

overlay('\x1234567890'::bytea placing '\002\003'::bytea from 2 for 3) → \x12020390

position ( substring bytea IN bytes bytea ) → integer

Returns first starting index of the specified substring within bytes, or zero if it's not present.

position('\x5678'::bytea in '\x1234567890'::bytea) → 3

rtrim ( bytes bytea, bytesremoved bytea ) → bytea

Removes the longest string containing only bytes appearing in bytesremoved from the end of bytes.

rtrim('\x1234567890'::bytea, '\x9012'::bytea) → \x12345678

substring ( bytes bytea [ FROM start integer ] [ FOR count integer ] ) → bytea

Extracts the substring of bytes starting at the start'th byte if that is specified, and stopping after count bytes if that is specified. Provide at least one of start and count.

substring('\x1234567890'::bytea from 3 for 2) → \x5678

trim ( [ LEADING | TRAILING | BOTH ] bytesremoved bytea FROM bytes bytea ) → bytea

Removes the longest string containing only bytes appearing in bytesremoved from the start, end, or both ends (BOTH is the default) of bytes.

trim('\x9012'::bytea from '\x1234567890'::bytea) → \x345678

trim ( [ LEADING | TRAILING | BOTH ] [ FROM ] bytes bytea, bytesremoved bytea ) → bytea

This is a non-standard syntax for trim().

trim(both from '\x1234567890'::bytea, '\x9012'::bytea) → \x345678

Additional binary string manipulation functions are available and are listed in Table 9.12. Some of them are used internally to implement the SQL-standard string functions listed in Table 9.11.

Table 9.12. Other Binary String Functions

bit_count ( bytes bytea ) → bigint

Returns the number of bits set in the binary string (also known as “popcount”).

bit_count('\x1234567890'::bytea) → 15

crc32 ( bytea ) → bigint

Computes the CRC-32 value of the binary string.

crc32('abc'::bytea) → 891568578

crc32c ( bytea ) → bigint

Computes the CRC-32C value of the binary string.

crc32c('abc'::bytea) → 910901175

get_bit ( bytes bytea, n bigint ) → integer

Extracts n'th bit from binary string.

get_bit('\x1234567890'::bytea, 30) → 1

get_byte ( bytes bytea, n integer ) → integer

Extracts n'th byte from binary string.

get_byte('\x1234567890'::bytea, 4) → 144

length ( bytea ) → integer

Returns the number of bytes in the binary string.

length('\x1234567890'::bytea) → 5

length ( bytes bytea, encoding name ) → integer

Returns the number of characters in the binary string, assuming that it is text in the given encoding.

length('jose'::bytea, 'UTF8') → 4

Computes the MD5 hash of the binary string, with the result written in hexadecimal.

md5('Th\000omas'::bytea) → 8ab2d3c968​b4958c334c

reverse ( bytea ) → bytea

Reverses the order of the bytes in the binary string.

reverse('\xabcd'::bytea) → \xcdab

set_bit ( bytes bytea, n bigint, newvalue integer ) → bytea

Sets n'th bit in binary string to newvalue.

set_bit('\x1234567890'::bytea, 30, 0) → \x1234563890

set_byte ( bytes bytea, n integer, newvalue integer ) → bytea

Sets n'th byte in binary string to newvalue.

set_byte('\x1234567890'::bytea, 4, 64) → \x1234567840

sha224 ( bytea ) → bytea

Computes the SHA-224 hash of the binary string.

sha224('abc'::bytea) → \x23097d223405d8228642a477bda2​55b32aadbc

sha256 ( bytea ) → bytea

Computes the SHA-256 hash of the binary string.

sha256('abc'::bytea) → \xba7816bf8f01cfea414140de5dae2223​b00361a396

sha384 ( bytea ) → bytea

Computes the SHA-384 hash of the binary string.

sha384('abc'::bytea) → \xcb00753f45a35e8bb5a03d699ac65007​272c32ab0e​8086072ba1

sha512 ( bytea ) → bytea

Computes the SHA-512 hash of the binary string.

sha512('abc'::bytea) → \xddaf35a193617abacc417349ae204131​12e6fa4e89​2192992a27​454d442364

substr ( bytes bytea, start integer [, count integer ] ) → bytea

Extracts the substring of bytes starting at the start'th byte, and extending for count bytes if that is specified. (Same as substring(bytes from start for count).)

substr('\x1234567890'::bytea, 3, 2) → \x5678

Functions get_byte and set_byte number the first byte of a binary string as byte 0. Functions get_bit and set_bit number bits from the right within each byte; for example bit 0 is the least significant bit of the first byte, and bit 15 is the most significant bit of the second byte.

For historical reasons, the function md5 returns a hex-encoded value of type text whereas the SHA-2 functions return type bytea. Use the functions encode and decode to convert between the two. For example write encode(sha256('abc'), 'hex') to get a hex-encoded text representation, or decode(md5('abc'), 'hex') to get a bytea value.

Functions for converting strings between different character sets (encodings), and for representing arbitrary binary data in textual form, are shown in Table 9.13. For these functions, an argument or result of type text is expressed in the database's default encoding, while arguments or results of type bytea are in an encoding named by another argument.

Table 9.13. Text/Binary String Conversion Functions

convert ( bytes bytea, src_encoding name, dest_encoding name ) → bytea

Converts a binary string representing text in encoding src_encoding to a binary string in encoding dest_encoding (see Section 23.3.4 for available conversions).

convert('text_in_utf8', 'UTF8', 'LATIN1') → \x746578745f696e5f75746638

convert_from ( bytes bytea, src_encoding name ) → text

Converts a binary string representing text in encoding src_encoding to text in the database encoding (see Section 23.3.4 for available conversions).

convert_from('text_in_utf8', 'UTF8') → text_in_utf8

convert_to ( string text, dest_encoding name ) → bytea

Converts a text string (in the database encoding) to a binary string encoded in encoding dest_encoding (see Section 23.3.4 for available conversions).

convert_to('some_text', 'UTF8') → \x736f6d655f74657874

encode ( bytes bytea, format text ) → text

Encodes binary data into a textual representation; supported format values are: base64, escape, hex.

encode('123\000\001', 'base64') → MTIzAAE=

decode ( string text, format text ) → bytea

Decodes binary data from a textual representation; supported format values are the same as for encode.

decode('MTIzAAE=', 'base64') → \x3132330001

The encode and decode functions support the following textual formats:

The base64 format is that of RFC 2045 Section 6.8. As per the RFC, encoded lines are broken at 76 characters. However instead of the MIME CRLF end-of-line marker, only a newline is used for end-of-line. The decode function ignores carriage-return, newline, space, and tab characters. Otherwise, an error is raised when decode is supplied invalid base64 data — including when trailing padding is incorrect.

The escape format converts zero bytes and bytes with the high bit set into octal escape sequences (\nnn), and it doubles backslashes. Other byte values are represented literally. The decode function will raise an error if a backslash is not followed by either a second backslash or three octal digits; it accepts other byte values unchanged.

The hex format represents each 4 bits of data as one hexadecimal digit, 0 through f, writing the higher-order digit of each byte first. The encode function outputs the a-f hex digits in lower case. Because the smallest unit of data is 8 bits, there are always an even number of characters returned by encode. The decode function accepts the a-f characters in either upper or lower case. An error is raised when decode is given invalid hex data — including when given an odd number of characters.

In addition, it is possible to cast integral values to and from type bytea. Casting an integer to bytea produces 2, 4, or 8 bytes, depending on the width of the integer type. The result is the two's complement representation of the integer, with the most significant byte first. Some examples:

Casting a bytea to an integer will raise an error if the length of the bytea exceeds the width of the integer type.

See also the aggregate function string_agg in Section 9.21 and the large object functions in Section 33.4.

Examples:

Example 1 (unknown):

1234::smallint::bytea          \x04d2
cast(1234 as bytea)            \x000004d2
cast(-1234 as bytea)           \xfffffb2e
'\x8000'::bytea::smallint      -32768
'\x8000'::bytea::integer       32768

---

PostgreSQL: Documentation: 18: 35.10. collations

URL: https://www.postgresql.org/docs/current/infoschema-collations.html

Contents:

• 35.10. collations #

The view collations contains the collations available in the current database.

Table 35.8. collations Columns

collation_catalog sql_identifier

Name of the database containing the collation (always the current database)

collation_schema sql_identifier

Name of the schema containing the collation

collation_name sql_identifier

Name of the default collation

pad_attribute character_data

Always NO PAD (The alternative PAD SPACE is not supported by PostgreSQL.)

---

PostgreSQL: Documentation: 18: Chapter 10. Type Conversion

URL: https://www.postgresql.org/docs/current/typeconv.html

Contents:

• Chapter 10. Type Conversion

SQL statements can, intentionally or not, require the mixing of different data types in the same expression. PostgreSQL has extensive facilities for evaluating mixed-type expressions.

In many cases a user does not need to understand the details of the type conversion mechanism. However, implicit conversions done by PostgreSQL can affect the results of a query. When necessary, these results can be tailored by using explicit type conversion.

This chapter introduces the PostgreSQL type conversion mechanisms and conventions. Refer to the relevant sections in Chapter 8 and Chapter 9 for more information on specific data types and allowed functions and operators.

---

PostgreSQL: Documentation: 18: 25.1. SQL Dump

URL: https://www.postgresql.org/docs/current/backup-dump.html

Contents:

• 25.1. SQL Dump #
  • 25.1.1. Restoring the Dump #
  • Important
  • 25.1.2. Using pg_dumpall #
  • 25.1.3. Handling Large Databases #

The idea behind this dump method is to generate a file with SQL commands that, when fed back to the server, will recreate the database in the same state as it was at the time of the dump. PostgreSQL provides the utility program pg_dump for this purpose. The basic usage of this command is:

As you see, pg_dump writes its result to the standard output. We will see below how this can be useful. While the above command creates a text file, pg_dump can create files in other formats that allow for parallelism and more fine-grained control of object restoration.

pg_dump is a regular PostgreSQL client application (albeit a particularly clever one). This means that you can perform this backup procedure from any remote host that has access to the database. But remember that pg_dump does not operate with special permissions. In particular, it must have read access to all tables that you want to back up, so in order to back up the entire database you almost always have to run it as a database superuser. (If you do not have sufficient privileges to back up the entire database, you can still back up portions of the database to which you do have access using options such as -n schema or -t table.)

To specify which database server pg_dump should contact, use the command line options -h host and -p port. The default host is the local host or whatever your PGHOST environment variable specifies. Similarly, the default port is indicated by the PGPORT environment variable or, failing that, by the compiled-in default. (Conveniently, the server will normally have the same compiled-in default.)

Like any other PostgreSQL client application, pg_dump will by default connect with the database user name that is equal to the current operating system user name. To override this, either specify the -U option or set the environment variable PGUSER. Remember that pg_dump connections are subject to the normal client authentication mechanisms (which are described in Chapter 20).

An important advantage of pg_dump over the other backup methods described later is that pg_dump's output can generally be re-loaded into newer versions of PostgreSQL, whereas file-level backups and continuous archiving are both extremely server-version-specific. pg_dump is also the only method that will work when transferring a database to a different machine architecture, such as going from a 32-bit to a 64-bit server.

Dumps created by pg_dump are internally consistent, meaning, the dump represents a snapshot of the database at the time pg_dump began running. pg_dump does not block other operations on the database while it is working. (Exceptions are those operations that need to operate with an exclusive lock, such as most forms of ALTER TABLE.)

Text files created by pg_dump are intended to be read by the psql program using its default settings. The general command form to restore a text dump is

where dumpfile is the file output by the pg_dump command. The database dbname will not be created by this command, so you must create it yourself from template0 before executing psql (e.g., with createdb -T template0 dbname). To ensure psql runs with its default settings, use the -X (--no-psqlrc) option. psql supports options similar to pg_dump for specifying the database server to connect to and the user name to use. See the psql reference page for more information.

Non-text file dumps should be restored using the pg_restore utility.

Before restoring an SQL dump, all the users who own objects or were granted permissions on objects in the dumped database must already exist. If they do not, the restore will fail to recreate the objects with the original ownership and/or permissions. (Sometimes this is what you want, but usually it is not.)

By default, the psql script will continue to execute after an SQL error is encountered. You might wish to run psql with the ON_ERROR_STOP variable set to alter that behavior and have psql exit with an exit status of 3 if an SQL error occurs:

Either way, you will only have a partially restored database. Alternatively, you can specify that the whole dump should be restored as a single transaction, so the restore is either fully completed or fully rolled back. This mode can be specified by passing the -1 or --single-transaction command-line options to psql. When using this mode, be aware that even a minor error can rollback a restore that has already run for many hours. However, that might still be preferable to manually cleaning up a complex database after a partially restored dump.

The ability of pg_dump and psql to write to or read from pipes makes it possible to dump a database directly from one server to another, for example:

The dumps produced by pg_dump are relative to template0. This means that any languages, procedures, etc. added via template1 will also be dumped by pg_dump. As a result, when restoring, if you are using a customized template1, you must create the empty database from template0, as in the example above.

After restoring a backup, it is wise to run ANALYZE on each database so the query optimizer has useful statistics; see Section 24.1.3 and Section 24.1.6 for more information. For more advice on how to load large amounts of data into PostgreSQL efficiently, refer to Section 14.4.

pg_dump dumps only a single database at a time, and it does not dump information about roles or tablespaces (because those are cluster-wide rather than per-database). To support convenient dumping of the entire contents of a database cluster, the pg_dumpall program is provided. pg_dumpall backs up each database in a given cluster, and also preserves cluster-wide data such as role and tablespace definitions. The basic usage of this command is:

The resulting dump can be restored with psql:

(Actually, you can specify any existing database name to start from, but if you are loading into an empty cluster then postgres should usually be used.) It is always necessary to have database superuser access when restoring a pg_dumpall dump, as that is required to restore the role and tablespace information. If you use tablespaces, make sure that the tablespace paths in the dump are appropriate for the new installation.

pg_dumpall works by emitting commands to re-create roles, tablespaces, and empty databases, then invoking pg_dump for each database. This means that while each database will be internally consistent, the snapshots of different databases are not synchronized.

Cluster-wide data can be dumped alone using the pg_dumpall --globals-only option. This is necessary to fully backup the cluster if running the pg_dump command on individual databases.

Some operating systems have maximum file size limits that cause problems when creating large pg_dump output files. Fortunately, pg_dump can write to the standard output, so you can use standard Unix tools to work around this potential problem. There are several possible methods:

Use compressed dumps. You can use your favorite compression program, for example gzip:

Use split. The split command allows you to split the output into smaller files that are acceptable in size to the underlying file system. For example, to make 2 gigabyte chunks:

If using GNU split, it is possible to use it and gzip together:

It can be restored using zcat.

Use pg_dump's custom dump format. If PostgreSQL was built on a system with the zlib compression library installed, the custom dump format will compress data as it writes it to the output file. This will produce dump file sizes similar to using gzip, but it has the added advantage that tables can be restored selectively. The following command dumps a database using the custom dump format:

A custom-format dump is not a script for psql, but instead must be restored with pg_restore, for example:

See the pg_dump and pg_restore reference pages for details.

For very large databases, you might need to combine split with one of the other two approaches.

Use pg_dump's parallel dump feature. To speed up the dump of a large database, you can use pg_dump's parallel mode. This will dump multiple tables at the same time. You can control the degree of parallelism with the -j parameter. Parallel dumps are only supported for the "directory" archive format.

You can use pg_restore -j to restore a dump in parallel. This will work for any archive of either the "custom" or the "directory" archive mode, whether or not it has been created with pg_dump -j.

Examples:

Example 1 (unknown):

pg_dump dbname > dumpfile

Example 2 (unknown):

psql -X dbname < dumpfile

Example 3 (unknown):

psql -X --set ON_ERROR_STOP=on dbname < dumpfile

Example 4 (unknown):

pg_dump -h host1 dbname | psql -X -h host2 dbname

---

PostgreSQL: Documentation: 18: 11.9. Index-Only Scans and Covering Indexes

URL: https://www.postgresql.org/docs/current/indexes-index-only-scans.html

Contents:

• 11.9. Index-Only Scans and Covering Indexes #

All indexes in PostgreSQL are secondary indexes, meaning that each index is stored separately from the table's main data area (which is called the table's heap in PostgreSQL terminology). This means that in an ordinary index scan, each row retrieval requires fetching data from both the index and the heap. Furthermore, while the index entries that match a given indexable WHERE condition are usually close together in the index, the table rows they reference might be anywhere in the heap. The heap-access portion of an index scan thus involves a lot of random access into the heap, which can be slow, particularly on traditional rotating media. (As described in Section 11.5, bitmap scans try to alleviate this cost by doing the heap accesses in sorted order, but that only goes so far.)

To solve this performance problem, PostgreSQL supports index-only scans, which can answer queries from an index alone without any heap access. The basic idea is to return values directly out of each index entry instead of consulting the associated heap entry. There are two fundamental restrictions on when this method can be used:

The index type must support index-only scans. B-tree indexes always do. GiST and SP-GiST indexes support index-only scans for some operator classes but not others. Other index types have no support. The underlying requirement is that the index must physically store, or else be able to reconstruct, the original data value for each index entry. As a counterexample, GIN indexes cannot support index-only scans because each index entry typically holds only part of the original data value.

The query must reference only columns stored in the index. For example, given an index on columns x and y of a table that also has a column z, these queries could use index-only scans:

but these queries could not:

(Expression indexes and partial indexes complicate this rule, as discussed below.)

If these two fundamental requirements are met, then all the data values required by the query are available from the index, so an index-only scan is physically possible. But there is an additional requirement for any table scan in PostgreSQL: it must verify that each retrieved row be “visible” to the query's MVCC snapshot, as discussed in Chapter 13. Visibility information is not stored in index entries, only in heap entries; so at first glance it would seem that every row retrieval would require a heap access anyway. And this is indeed the case, if the table row has been modified recently. However, for seldom-changing data there is a way around this problem. PostgreSQL tracks, for each page in a table's heap, whether all rows stored in that page are old enough to be visible to all current and future transactions. This information is stored in a bit in the table's visibility map. An index-only scan, after finding a candidate index entry, checks the visibility map bit for the corresponding heap page. If it's set, the row is known visible and so the data can be returned with no further work. If it's not set, the heap entry must be visited to find out whether it's visible, so no performance advantage is gained over a standard index scan. Even in the successful case, this approach trades visibility map accesses for heap accesses; but since the visibility map is four orders of magnitude smaller than the heap it describes, far less physical I/O is needed to access it. In most situations the visibility map remains cached in memory all the time.

In short, while an index-only scan is possible given the two fundamental requirements, it will be a win only if a significant fraction of the table's heap pages have their all-visible map bits set. But tables in which a large fraction of the rows are unchanging are common enough to make this type of scan very useful in practice.

To make effective use of the index-only scan feature, you might choose to create a covering index, which is an index specifically designed to include the columns needed by a particular type of query that you run frequently. Since queries typically need to retrieve more columns than just the ones they search on, PostgreSQL allows you to create an index in which some columns are just “payload” and are not part of the search key. This is done by adding an INCLUDE clause listing the extra columns. For example, if you commonly run queries like

the traditional approach to speeding up such queries would be to create an index on x only. However, an index defined as

could handle these queries as index-only scans, because y can be obtained from the index without visiting the heap.

Because column y is not part of the index's search key, it does not have to be of a data type that the index can handle; it's merely stored in the index and is not interpreted by the index machinery. Also, if the index is a unique index, that is

the uniqueness condition applies to just column x, not to the combination of x and y. (An INCLUDE clause can also be written in UNIQUE and PRIMARY KEY constraints, providing alternative syntax for setting up an index like this.)

It's wise to be conservative about adding non-key payload columns to an index, especially wide columns. If an index tuple exceeds the maximum size allowed for the index type, data insertion will fail. In any case, non-key columns duplicate data from the index's table and bloat the size of the index, thus potentially slowing searches. And remember that there is little point in including payload columns in an index unless the table changes slowly enough that an index-only scan is likely to not need to access the heap. If the heap tuple must be visited anyway, it costs nothing more to get the column's value from there. Other restrictions are that expressions are not currently supported as included columns, and that only B-tree, GiST and SP-GiST indexes currently support included columns.

Before PostgreSQL had the INCLUDE feature, people sometimes made covering indexes by writing the payload columns as ordinary index columns, that is writing

even though they had no intention of ever using y as part of a WHERE clause. This works fine as long as the extra columns are trailing columns; making them be leading columns is unwise for the reasons explained in Section 11.3. However, this method doesn't support the case where you want the index to enforce uniqueness on the key column(s).

Suffix truncation always removes non-key columns from upper B-Tree levels. As payload columns, they are never used to guide index scans. The truncation process also removes one or more trailing key column(s) when the remaining prefix of key column(s) happens to be sufficient to describe tuples on the lowest B-Tree level. In practice, covering indexes without an INCLUDE clause often avoid storing columns that are effectively payload in the upper levels. However, explicitly defining payload columns as non-key columns reliably keeps the tuples in upper levels small.

In principle, index-only scans can be used with expression indexes. For example, given an index on f(x) where x is a table column, it should be possible to execute

as an index-only scan; and this is very attractive if f() is an expensive-to-compute function. However, PostgreSQL's planner is currently not very smart about such cases. It considers a query to be potentially executable by index-only scan only when all columns needed by the query are available from the index. In this example, x is not needed except in the context f(x), but the planner does not notice that and concludes that an index-only scan is not possible. If an index-only scan seems sufficiently worthwhile, this can be worked around by adding x as an included column, for example

An additional caveat, if the goal is to avoid recalculating f(x), is that the planner won't necessarily match uses of f(x) that aren't in indexable WHERE clauses to the index column. It will usually get this right in simple queries such as shown above, but not in queries that involve joins. These deficiencies may be remedied in future versions of PostgreSQL.

Partial indexes also have interesting interactions with index-only scans. Consider the partial index shown in Example 11.3:

In principle, we could do an index-only scan on this index to satisfy a query like

But there's a problem: the WHERE clause refers to success which is not available as a result column of the index. Nonetheless, an index-only scan is possible because the plan does not need to recheck that part of the WHERE clause at run time: all entries found in the index necessarily have success = true so this need not be explicitly checked in the plan. PostgreSQL versions 9.6 and later will recognize such cases and allow index-only scans to be generated, but older versions will not.

Examples:

Example 1 (unknown):

SELECT x, y FROM tab WHERE x = 'key';
SELECT x FROM tab WHERE x = 'key' AND y < 42;

Example 2 (unknown):

SELECT x, z FROM tab WHERE x = 'key';
SELECT x FROM tab WHERE x = 'key' AND z < 42;

Example 3 (unknown):

SELECT y FROM tab WHERE x = 'key';

Example 4 (unknown):

CREATE INDEX tab_x_y ON tab(x) INCLUDE (y);

---

PostgreSQL: Documentation: 18: Chapter 48. Replication Progress Tracking

URL: https://www.postgresql.org/docs/current/replication-origins.html

Contents:

• Chapter 48. Replication Progress Tracking

Replication origins are intended to make it easier to implement logical replication solutions on top of logical decoding. They provide a solution to two common problems:

How to safely keep track of replication progress

How to change replication behavior based on the origin of a row; for example, to prevent loops in bi-directional replication setups

Replication origins have just two properties, a name and an ID. The name, which is what should be used to refer to the origin across systems, is free-form text. It should be used in a way that makes conflicts between replication origins created by different replication solutions unlikely; e.g., by prefixing the replication solution's name to it. The ID is used only to avoid having to store the long version in situations where space efficiency is important. It should never be shared across systems.

Replication origins can be created using the function pg_replication_origin_create(); dropped using pg_replication_origin_drop(); and seen in the pg_replication_origin system catalog.

One nontrivial part of building a replication solution is to keep track of replay progress in a safe manner. When the applying process, or the whole cluster, dies, it needs to be possible to find out up to where data has successfully been replicated. Naive solutions to this, such as updating a row in a table for every replayed transaction, have problems like run-time overhead and database bloat.

Using the replication origin infrastructure a session can be marked as replaying from a remote node (using the pg_replication_origin_session_setup() function). Additionally the LSN and commit time stamp of every source transaction can be configured on a per transaction basis using pg_replication_origin_xact_setup(). If that's done replication progress will persist in a crash safe manner. Replay progress for all replication origins can be seen in the pg_replication_origin_status view. An individual origin's progress, e.g., when resuming replication, can be acquired using pg_replication_origin_progress() for any origin or pg_replication_origin_session_progress() for the origin configured in the current session.

In replication topologies more complex than replication from exactly one system to one other system, another problem can be that it is hard to avoid replicating replayed rows again. That can lead both to cycles in the replication and inefficiencies. Replication origins provide an optional mechanism to recognize and prevent that. When configured using the functions referenced in the previous paragraph, every change and transaction passed to output plugin callbacks (see Section 47.6) generated by the session is tagged with the replication origin of the generating session. This allows treating them differently in the output plugin, e.g., ignoring all but locally-originating rows. Additionally the filter_by_origin_cb callback can be used to filter the logical decoding change stream based on the source. While less flexible, filtering via that callback is considerably more efficient than doing it in the output plugin.

---

PostgreSQL: Documentation: 18: Chapter 7. Queries

URL: https://www.postgresql.org/docs/current/queries.html

Contents:

• Chapter 7. Queries

The previous chapters explained how to create tables, how to fill them with data, and how to manipulate that data. Now we finally discuss how to retrieve the data from the database.

---

PostgreSQL: Documentation: 18: 8.4. Binary Data Types

URL: https://www.postgresql.org/docs/current/datatype-binary.html

Contents:

• 8.4. Binary Data Types #
  • 8.4.1. bytea Hex Format #
  • 8.4.2. bytea Escape Format #

The bytea data type allows storage of binary strings; see Table 8.6.

Table 8.6. Binary Data Types

A binary string is a sequence of octets (or bytes). Binary strings are distinguished from character strings in two ways. First, binary strings specifically allow storing octets of value zero and other “non-printable” octets (usually, octets outside the decimal range 32 to 126). Character strings disallow zero octets, and also disallow any other octet values and sequences of octet values that are invalid according to the database's selected character set encoding. Second, operations on binary strings process the actual bytes, whereas the processing of character strings depends on locale settings. In short, binary strings are appropriate for storing data that the programmer thinks of as “raw bytes”, whereas character strings are appropriate for storing text.

The bytea type supports two formats for input and output: “hex” format and PostgreSQL's historical “escape” format. Both of these are always accepted on input. The output format depends on the configuration parameter bytea_output; the default is hex. (Note that the hex format was introduced in PostgreSQL 9.0; earlier versions and some tools don't understand it.)

The SQL standard defines a different binary string type, called BLOB or BINARY LARGE OBJECT. The input format is different from bytea, but the provided functions and operators are mostly the same.

The “hex” format encodes binary data as 2 hexadecimal digits per byte, most significant nibble first. The entire string is preceded by the sequence \x (to distinguish it from the escape format). In some contexts, the initial backslash may need to be escaped by doubling it (see Section 4.1.2.1). For input, the hexadecimal digits can be either upper or lower case, and whitespace is permitted between digit pairs (but not within a digit pair nor in the starting \x sequence). The hex format is compatible with a wide range of external applications and protocols, and it tends to be faster to convert than the escape format, so its use is preferred.

The “escape” format is the traditional PostgreSQL format for the bytea type. It takes the approach of representing a binary string as a sequence of ASCII characters, while converting those bytes that cannot be represented as an ASCII character into special escape sequences. If, from the point of view of the application, representing bytes as characters makes sense, then this representation can be convenient. But in practice it is usually confusing because it fuzzes up the distinction between binary strings and character strings, and also the particular escape mechanism that was chosen is somewhat unwieldy. Therefore, this format should probably be avoided for most new applications.

When entering bytea values in escape format, octets of certain values must be escaped, while all octet values can be escaped. In general, to escape an octet, convert it into its three-digit octal value and precede it by a backslash. Backslash itself (octet decimal value 92) can alternatively be represented by double backslashes. Table 8.7 shows the characters that must be escaped, and gives the alternative escape sequences where applicable.

Table 8.7. bytea Literal Escaped Octets

The requirement to escape non-printable octets varies depending on locale settings. In some instances you can get away with leaving them unescaped.

The reason that single quotes must be doubled, as shown in Table 8.7, is that this is true for any string literal in an SQL command. The generic string-literal parser consumes the outermost single quotes and reduces any pair of single quotes to one data character. What the bytea input function sees is just one single quote, which it treats as a plain data character. However, the bytea input function treats backslashes as special, and the other behaviors shown in Table 8.7 are implemented by that function.

In some contexts, backslashes must be doubled compared to what is shown above, because the generic string-literal parser will also reduce pairs of backslashes to one data character; see Section 4.1.2.1.

Bytea octets are output in hex format by default. If you change bytea_output to escape, “non-printable” octets are converted to their equivalent three-digit octal value and preceded by one backslash. Most “printable” octets are output by their standard representation in the client character set, e.g.:

The octet with decimal value 92 (backslash) is doubled in the output. Details are in Table 8.8.

Table 8.8. bytea Output Escaped Octets

Depending on the front end to PostgreSQL you use, you might have additional work to do in terms of escaping and unescaping bytea strings. For example, you might also have to escape line feeds and carriage returns if your interface automatically translates these.

Examples:

Example 1 (unknown):

SET bytea_output = 'hex';

SELECT '\xDEADBEEF'::bytea;
   bytea
------------
 \xdeadbeef

Example 2 (unknown):

SET bytea_output = 'escape';

SELECT 'abc \153\154\155 \052\251\124'::bytea;
     bytea
----------------
 abc klm *\251T

---

PostgreSQL: Documentation: 18: 34.12. Large Objects

URL: https://www.postgresql.org/docs/current/ecpg-lo.html

Contents:

• 34.12. Large Objects #

Large objects are not directly supported by ECPG, but ECPG application can manipulate large objects through the libpq large object functions, obtaining the necessary PGconn object by calling the ECPGget_PGconn() function. (However, use of the ECPGget_PGconn() function and touching PGconn objects directly should be done very carefully and ideally not mixed with other ECPG database access calls.)

For more details about the ECPGget_PGconn(), see Section 34.11. For information about the large object function interface, see Chapter 33.

Large object functions have to be called in a transaction block, so when autocommit is off, BEGIN commands have to be issued explicitly.

Example 34.2 shows an example program that illustrates how to create, write, and read a large object in an ECPG application.

Example 34.2. ECPG Program Accessing Large Objects

Examples:

Example 1 (cpp):

#include <stdio.h>
#include <stdlib.h>
#include <libpq-fe.h>
#include <libpq/libpq-fs.h>

EXEC SQL WHENEVER SQLERROR STOP;

int
main(void)
{
    PGconn     *conn;
    Oid         loid;
    int         fd;
    char        buf[256];
    int         buflen = 256;
    char        buf2[256];
    int         rc;

    memset(buf, 1, buflen);

    EXEC SQL CONNECT TO testdb AS con1;
    EXEC SQL SELECT pg_catalog.set_config('search_path', '', false); EXEC SQL COMMIT;

    conn = ECPGget_PGconn("con1");
    printf("conn = %p\n", conn);

    /* create */
    loid = lo_create(conn, 0);
    if (loid &lt; 0)
        printf("lo_create() failed: %s", PQerrorMessage(conn));

    printf("loid = %d\n", loid);

    /* write test */
    fd = lo_open(conn, loid, INV_READ|INV_WRITE);
    if (fd &lt; 0)
        printf("lo_open() failed: %s", PQerrorMessage(conn));

    printf("fd = %d\n", fd);

    rc = lo_write(conn, fd, buf, buflen);
    if (rc &lt; 0)
        printf("lo_write() failed\n");

    rc = lo_close(conn, fd);
    if (rc &lt; 0)
        printf("lo_close() failed: %s", PQerrorMessage(conn));

    /* read test */
    fd = lo_open(conn, loid, INV_READ);
    if (fd &lt; 0)
        printf("lo_open() failed: %s", PQerrorMessage(conn));

    printf("fd = %d\n", fd);

    rc = lo_read(conn, fd, buf2, buflen);
    if (rc &lt; 0)
        printf("lo_read() failed\n");

    rc = lo_close(conn, fd);
    if (rc &lt; 0)
        printf("lo_close() failed: %s", PQerrorMessage(conn));

    /* check */
    rc = memcmp(buf, buf2, buflen);
    printf("memcmp() = %d\n", rc);

    /* cleanup */
    rc = lo_unlink(conn, loid);
    if (rc &lt; 0)
        printf("lo_unlink() failed: %s", PQerrorMessage(conn));

    EXEC SQL COMMIT;
    EXEC SQL DISCONNECT ALL;
    return 0;
}

---

PostgreSQL: Documentation: 18: 9.2. Comparison Functions and Operators

URL: https://www.postgresql.org/docs/current/functions-comparison.html

Contents:

• 9.2. Comparison Functions and Operators #
  • Note
  • Note
  • Tip

The usual comparison operators are available, as shown in Table 9.1.

Table 9.1. Comparison Operators

<> is the standard SQL notation for “not equal”. != is an alias, which is converted to <> at a very early stage of parsing. Hence, it is not possible to implement != and <> operators that do different things.

These comparison operators are available for all built-in data types that have a natural ordering, including numeric, string, and date/time types. In addition, arrays, composite types, and ranges can be compared if their component data types are comparable.

It is usually possible to compare values of related data types as well; for example integer > bigint will work. Some cases of this sort are implemented directly by “cross-type” comparison operators, but if no such operator is available, the parser will coerce the less-general type to the more-general type and apply the latter's comparison operator.

As shown above, all comparison operators are binary operators that return values of type boolean. Thus, expressions like 1 < 2 < 3 are not valid (because there is no < operator to compare a Boolean value with 3). Use the BETWEEN predicates shown below to perform range tests.

There are also some comparison predicates, as shown in Table 9.2. These behave much like operators, but have special syntax mandated by the SQL standard.

Table 9.2. Comparison Predicates

datatype BETWEEN datatype AND datatype → boolean

Between (inclusive of the range endpoints).

2 BETWEEN 1 AND 3 → t

2 BETWEEN 3 AND 1 → f

datatype NOT BETWEEN datatype AND datatype → boolean

Not between (the negation of BETWEEN).

2 NOT BETWEEN 1 AND 3 → f

datatype BETWEEN SYMMETRIC datatype AND datatype → boolean

Between, after sorting the two endpoint values.

2 BETWEEN SYMMETRIC 3 AND 1 → t

datatype NOT BETWEEN SYMMETRIC datatype AND datatype → boolean

Not between, after sorting the two endpoint values.

2 NOT BETWEEN SYMMETRIC 3 AND 1 → f

datatype IS DISTINCT FROM datatype → boolean

Not equal, treating null as a comparable value.

1 IS DISTINCT FROM NULL → t (rather than NULL)

NULL IS DISTINCT FROM NULL → f (rather than NULL)

datatype IS NOT DISTINCT FROM datatype → boolean

Equal, treating null as a comparable value.

1 IS NOT DISTINCT FROM NULL → f (rather than NULL)

NULL IS NOT DISTINCT FROM NULL → t (rather than NULL)

datatype IS NULL → boolean

Test whether value is null.

datatype IS NOT NULL → boolean

Test whether value is not null.

'null' IS NOT NULL → t

datatype ISNULL → boolean

Test whether value is null (nonstandard syntax).

datatype NOTNULL → boolean

Test whether value is not null (nonstandard syntax).

boolean IS TRUE → boolean

Test whether boolean expression yields true.

NULL::boolean IS TRUE → f (rather than NULL)

boolean IS NOT TRUE → boolean

Test whether boolean expression yields false or unknown.

NULL::boolean IS NOT TRUE → t (rather than NULL)

boolean IS FALSE → boolean

Test whether boolean expression yields false.

NULL::boolean IS FALSE → f (rather than NULL)

boolean IS NOT FALSE → boolean

Test whether boolean expression yields true or unknown.

true IS NOT FALSE → t

NULL::boolean IS NOT FALSE → t (rather than NULL)

boolean IS UNKNOWN → boolean

Test whether boolean expression yields unknown.

NULL::boolean IS UNKNOWN → t (rather than NULL)

boolean IS NOT UNKNOWN → boolean

Test whether boolean expression yields true or false.

true IS NOT UNKNOWN → t

NULL::boolean IS NOT UNKNOWN → f (rather than NULL)

The BETWEEN predicate simplifies range tests:

Notice that BETWEEN treats the endpoint values as included in the range. BETWEEN SYMMETRIC is like BETWEEN except there is no requirement that the argument to the left of AND be less than or equal to the argument on the right. If it is not, those two arguments are automatically swapped, so that a nonempty range is always implied.

The various variants of BETWEEN are implemented in terms of the ordinary comparison operators, and therefore will work for any data type(s) that can be compared.

The use of AND in the BETWEEN syntax creates an ambiguity with the use of AND as a logical operator. To resolve this, only a limited set of expression types are allowed as the second argument of a BETWEEN clause. If you need to write a more complex sub-expression in BETWEEN, write parentheses around the sub-expression.

Ordinary comparison operators yield null (signifying “unknown”), not true or false, when either input is null. For example, 7 = NULL yields null, as does 7 <> NULL. When this behavior is not suitable, use the IS [ NOT ] DISTINCT FROM predicates:

For non-null inputs, IS DISTINCT FROM is the same as the <> operator. However, if both inputs are null it returns false, and if only one input is null it returns true. Similarly, IS NOT DISTINCT FROM is identical to = for non-null inputs, but it returns true when both inputs are null, and false when only one input is null. Thus, these predicates effectively act as though null were a normal data value, rather than “unknown”.

To check whether a value is or is not null, use the predicates:

or the equivalent, but nonstandard, predicates:

Do not write expression = NULL because NULL is not “equal to” NULL. (The null value represents an unknown value, and it is not known whether two unknown values are equal.)

Some applications might expect that expression = NULL returns true if expression evaluates to the null value. It is highly recommended that these applications be modified to comply with the SQL standard. However, if that cannot be done the transform_null_equals configuration variable is available. If it is enabled, PostgreSQL will convert x = NULL clauses to x IS NULL.

If the expression is row-valued, then IS NULL is true when the row expression itself is null or when all the row's fields are null, while IS NOT NULL is true when the row expression itself is non-null and all the row's fields are non-null. Because of this behavior, IS NULL and IS NOT NULL do not always return inverse results for row-valued expressions; in particular, a row-valued expression that contains both null and non-null fields will return false for both tests. For example:

In some cases, it may be preferable to write row IS DISTINCT FROM NULL or row IS NOT DISTINCT FROM NULL, which will simply check whether the overall row value is null without any additional tests on the row fields.

Boolean values can also be tested using the predicates

These will always return true or false, never a null value, even when the operand is null. A null input is treated as the logical value “unknown”. Notice that IS UNKNOWN and IS NOT UNKNOWN are effectively the same as IS NULL and IS NOT NULL, respectively, except that the input expression must be of Boolean type.

Some comparison-related functions are also available, as shown in Table 9.3.

Table 9.3. Comparison Functions

num_nonnulls ( VARIADIC "any" ) → integer

Returns the number of non-null arguments.

num_nonnulls(1, NULL, 2) → 2

num_nulls ( VARIADIC "any" ) → integer

Returns the number of null arguments.

num_nulls(1, NULL, 2) → 1

Examples:

Example 1 (unknown):

a BETWEEN x AND y

Example 2 (unknown):

a >= x AND a <= y

Example 3 (unknown):

a IS DISTINCT FROM b
a IS NOT DISTINCT FROM b

Example 4 (unknown):

expression IS NULL
expression IS NOT NULL

---

PostgreSQL: Documentation: 18: 14.4. Populating a Database

URL: https://www.postgresql.org/docs/current/populate.html

Contents:

• 14.4. Populating a Database #
  • 14.4.1. Disable Autocommit #
  • 14.4.2. Use COPY #
  • 14.4.3. Remove Indexes #
  • 14.4.4. Remove Foreign Key Constraints #
  • 14.4.5. Increase maintenance_work_mem #
  • 14.4.6. Increase max_wal_size #
  • 14.4.7. Disable WAL Archival and Streaming Replication #
  • 14.4.8. Run ANALYZE Afterwards #
  • 14.4.9. Some Notes about pg_dump #

One might need to insert a large amount of data when first populating a database. This section contains some suggestions on how to make this process as efficient as possible.

When using multiple INSERTs, turn off autocommit and just do one commit at the end. (In plain SQL, this means issuing BEGIN at the start and COMMIT at the end. Some client libraries might do this behind your back, in which case you need to make sure the library does it when you want it done.) If you allow each insertion to be committed separately, PostgreSQL is doing a lot of work for each row that is added. An additional benefit of doing all insertions in one transaction is that if the insertion of one row were to fail then the insertion of all rows inserted up to that point would be rolled back, so you won't be stuck with partially loaded data.

Use COPY to load all the rows in one command, instead of using a series of INSERT commands. The COPY command is optimized for loading large numbers of rows; it is less flexible than INSERT, but incurs significantly less overhead for large data loads. Since COPY is a single command, there is no need to disable autocommit if you use this method to populate a table.

If you cannot use COPY, it might help to use PREPARE to create a prepared INSERT statement, and then use EXECUTE as many times as required. This avoids some of the overhead of repeatedly parsing and planning INSERT. Different interfaces provide this facility in different ways; look for “prepared statements” in the interface documentation.

Note that loading a large number of rows using COPY is almost always faster than using INSERT, even if PREPARE is used and multiple insertions are batched into a single transaction.

COPY is fastest when used within the same transaction as an earlier CREATE TABLE or TRUNCATE command. In such cases no WAL needs to be written, because in case of an error, the files containing the newly loaded data will be removed anyway. However, this consideration only applies when wal_level is minimal as all commands must write WAL otherwise.

If you are loading a freshly created table, the fastest method is to create the table, bulk load the table's data using COPY, then create any indexes needed for the table. Creating an index on pre-existing data is quicker than updating it incrementally as each row is loaded.

If you are adding large amounts of data to an existing table, it might be a win to drop the indexes, load the table, and then recreate the indexes. Of course, the database performance for other users might suffer during the time the indexes are missing. One should also think twice before dropping a unique index, since the error checking afforded by the unique constraint will be lost while the index is missing.

Just as with indexes, a foreign key constraint can be checked “in bulk” more efficiently than row-by-row. So it might be useful to drop foreign key constraints, load data, and re-create the constraints. Again, there is a trade-off between data load speed and loss of error checking while the constraint is missing.

What's more, when you load data into a table with existing foreign key constraints, each new row requires an entry in the server's list of pending trigger events (since it is the firing of a trigger that checks the row's foreign key constraint). Loading many millions of rows can cause the trigger event queue to overflow available memory, leading to intolerable swapping or even outright failure of the command. Therefore it may be necessary, not just desirable, to drop and re-apply foreign keys when loading large amounts of data. If temporarily removing the constraint isn't acceptable, the only other recourse may be to split up the load operation into smaller transactions.

Temporarily increasing the maintenance_work_mem configuration variable when loading large amounts of data can lead to improved performance. This will help to speed up CREATE INDEX commands and ALTER TABLE ADD FOREIGN KEY commands. It won't do much for COPY itself, so this advice is only useful when you are using one or both of the above techniques.

Temporarily increasing the max_wal_size configuration variable can also make large data loads faster. This is because loading a large amount of data into PostgreSQL will cause checkpoints to occur more often than the normal checkpoint frequency (specified by the checkpoint_timeout configuration variable). Whenever a checkpoint occurs, all dirty pages must be flushed to disk. By increasing max_wal_size temporarily during bulk data loads, the number of checkpoints that are required can be reduced.

When loading large amounts of data into an installation that uses WAL archiving or streaming replication, it might be faster to take a new base backup after the load has completed than to process a large amount of incremental WAL data. To prevent incremental WAL logging while loading, disable archiving and streaming replication, by setting wal_level to minimal, archive_mode to off, and max_wal_senders to zero. But note that changing these settings requires a server restart, and makes any base backups taken before unavailable for archive recovery and standby server, which may lead to data loss.

Aside from avoiding the time for the archiver or WAL sender to process the WAL data, doing this will actually make certain commands faster, because they do not to write WAL at all if wal_level is minimal and the current subtransaction (or top-level transaction) created or truncated the table or index they change. (They can guarantee crash safety more cheaply by doing an fsync at the end than by writing WAL.)

Whenever you have significantly altered the distribution of data within a table, running ANALYZE is strongly recommended. This includes bulk loading large amounts of data into the table. Running ANALYZE (or VACUUM ANALYZE) ensures that the planner has up-to-date statistics about the table. With no statistics or obsolete statistics, the planner might make poor decisions during query planning, leading to poor performance on any tables with inaccurate or nonexistent statistics. Note that if the autovacuum daemon is enabled, it might run ANALYZE automatically; see Section 24.1.3 and Section 24.1.6 for more information.

Dump scripts generated by pg_dump automatically apply several, but not all, of the above guidelines. To restore a pg_dump dump as quickly as possible, you need to do a few extra things manually. (Note that these points apply while restoring a dump, not while creating it. The same points apply whether loading a text dump with psql or using pg_restore to load from a pg_dump archive file.)

By default, pg_dump uses COPY, and when it is generating a complete schema-and-data dump, it is careful to load data before creating indexes and foreign keys. So in this case several guidelines are handled automatically. What is left for you to do is to:

Set appropriate (i.e., larger than normal) values for maintenance_work_mem and max_wal_size.

If using WAL archiving or streaming replication, consider disabling them during the restore. To do that, set archive_mode to off, wal_level to minimal, and max_wal_senders to zero before loading the dump. Afterwards, set them back to the right values and take a fresh base backup.

Experiment with the parallel dump and restore modes of both pg_dump and pg_restore and find the optimal number of concurrent jobs to use. Dumping and restoring in parallel by means of the -j option should give you a significantly higher performance over the serial mode.

Consider whether the whole dump should be restored as a single transaction. To do that, pass the -1 or --single-transaction command-line option to psql or pg_restore. When using this mode, even the smallest of errors will rollback the entire restore, possibly discarding many hours of processing. Depending on how interrelated the data is, that might seem preferable to manual cleanup, or not. COPY commands will run fastest if you use a single transaction and have WAL archiving turned off.

If multiple CPUs are available in the database server, consider using pg_restore's --jobs option. This allows concurrent data loading and index creation.

Run ANALYZE afterwards.

A data-only dump will still use COPY, but it does not drop or recreate indexes, and it does not normally touch foreign keys. [14] So when loading a data-only dump, it is up to you to drop and recreate indexes and foreign keys if you wish to use those techniques. It's still useful to increase max_wal_size while loading the data, but don't bother increasing maintenance_work_mem; rather, you'd do that while manually recreating indexes and foreign keys afterwards. And don't forget to ANALYZE when you're done; see Section 24.1.3 and Section 24.1.6 for more information.

[14] You can get the effect of disabling foreign keys by using the --disable-triggers option — but realize that that eliminates, rather than just postpones, foreign key validation, and so it is possible to insert bad data if you use it.

---

PostgreSQL: Documentation: 18: 33.4. Server-Side Functions

URL: https://www.postgresql.org/docs/current/lo-funcs.html

Contents:

• 33.4. Server-Side Functions #
  • Caution

Server-side functions tailored for manipulating large objects from SQL are listed in Table 33.1.

Table 33.1. SQL-Oriented Large Object Functions

lo_from_bytea ( loid oid, data bytea ) → oid

Creates a large object and stores data in it. If loid is zero then the system will choose a free OID, otherwise that OID is used (with an error if some large object already has that OID). On success, the large object's OID is returned.

lo_from_bytea(0, '\xffffff00') → 24528

lo_put ( loid oid, offset bigint, data bytea ) → void

Writes data starting at the given offset within the large object; the large object is enlarged if necessary.

lo_put(24528, 1, '\xaa') →

lo_get ( loid oid [, offset bigint, length integer ] ) → bytea

Extracts the large object's contents, or a substring thereof.

lo_get(24528, 0, 3) → \xffaaff

There are additional server-side functions corresponding to each of the client-side functions described earlier; indeed, for the most part the client-side functions are simply interfaces to the equivalent server-side functions. The ones just as convenient to call via SQL commands are lo_creat, lo_create, lo_unlink, lo_import, and lo_export. Here are examples of their use:

The server-side lo_import and lo_export functions behave considerably differently from their client-side analogs. These two functions read and write files in the server's file system, using the permissions of the database's owning user. Therefore, by default their use is restricted to superusers. In contrast, the client-side import and export functions read and write files in the client's file system, using the permissions of the client program. The client-side functions do not require any database privileges, except the privilege to read or write the large object in question.

It is possible to GRANT use of the server-side lo_import and lo_export functions to non-superusers, but careful consideration of the security implications is required. A malicious user of such privileges could easily parlay them into becoming superuser (for example by rewriting server configuration files), or could attack the rest of the server's file system without bothering to obtain database superuser privileges as such. Access to roles having such privilege must therefore be guarded just as carefully as access to superuser roles. Nonetheless, if use of server-side lo_import or lo_export is needed for some routine task, it's safer to use a role with such privileges than one with full superuser privileges, as that helps to reduce the risk of damage from accidental errors.

The functionality of lo_read and lo_write is also available via server-side calls, but the names of the server-side functions differ from the client side interfaces in that they do not contain underscores. You must call these functions as loread and lowrite.

Examples:

Example 1 (unknown):

CREATE TABLE image (
    name            text,
    raster          oid
);

SELECT lo_creat(-1);       -- returns OID of new, empty large object

SELECT lo_create(43213);   -- attempts to create large object with OID 43213

SELECT lo_unlink(173454);  -- deletes large object with OID 173454

INSERT INTO image (name, raster)
    VALUES ('beautiful image', lo_import('/etc/motd'));

INSERT INTO image (name, raster)  -- same as above, but specify OID to use
    VALUES ('beautiful image', lo_import('/etc/motd', 68583));

SELECT lo_export(image.raster, '/tmp/motd') FROM image
    WHERE name = 'beautiful image';

---

PostgreSQL: Documentation: 18: 35.47. sequences

URL: https://www.postgresql.org/docs/current/infoschema-sequences.html

Contents:

• 35.47. sequences #

The view sequences contains all sequences defined in the current database. Only those sequences are shown that the current user has access to (by way of being the owner or having some privilege).

Table 35.45. sequences Columns

sequence_catalog sql_identifier

Name of the database that contains the sequence (always the current database)

sequence_schema sql_identifier

Name of the schema that contains the sequence

sequence_name sql_identifier

data_type character_data

The data type of the sequence.

numeric_precision cardinal_number

This column contains the (declared or implicit) precision of the sequence data type (see above). The precision indicates the number of significant digits. It can be expressed in decimal (base 10) or binary (base 2) terms, as specified in the column numeric_precision_radix.

numeric_precision_radix cardinal_number

This column indicates in which base the values in the columns numeric_precision and numeric_scale are expressed. The value is either 2 or 10.

numeric_scale cardinal_number

This column contains the (declared or implicit) scale of the sequence data type (see above). The scale indicates the number of significant digits to the right of the decimal point. It can be expressed in decimal (base 10) or binary (base 2) terms, as specified in the column numeric_precision_radix.

start_value character_data

The start value of the sequence

minimum_value character_data

The minimum value of the sequence

maximum_value character_data

The maximum value of the sequence

increment character_data

The increment of the sequence

cycle_option yes_or_no

YES if the sequence cycles, else NO

Note that in accordance with the SQL standard, the start, minimum, maximum, and increment values are returned as character strings.

---

PostgreSQL: Documentation: 18: 8.15. Arrays

URL: https://www.postgresql.org/docs/current/arrays.html

Contents:

• 8.15. Arrays #
  • 8.15.1. Declaration of Array Types #
  • 8.15.2. Array Value Input #
  • 8.15.3. Accessing Arrays #
  • 8.15.4. Modifying Arrays #
  • 8.15.5. Searching in Arrays #
  • Tip
  • 8.15.6. Array Input and Output Syntax #
  • Tip

PostgreSQL allows columns of a table to be defined as variable-length multidimensional arrays. Arrays of any built-in or user-defined base type, enum type, composite type, range type, or domain can be created.

To illustrate the use of array types, we create this table:

As shown, an array data type is named by appending square brackets ([]) to the data type name of the array elements. The above command will create a table named sal_emp with a column of type text (name), a one-dimensional array of type integer (pay_by_quarter), which represents the employee's salary by quarter, and a two-dimensional array of text (schedule), which represents the employee's weekly schedule.

The syntax for CREATE TABLE allows the exact size of arrays to be specified, for example:

However, the current implementation ignores any supplied array size limits, i.e., the behavior is the same as for arrays of unspecified length.

The current implementation does not enforce the declared number of dimensions either. Arrays of a particular element type are all considered to be of the same type, regardless of size or number of dimensions. So, declaring the array size or number of dimensions in CREATE TABLE is simply documentation; it does not affect run-time behavior.

An alternative syntax, which conforms to the SQL standard by using the keyword ARRAY, can be used for one-dimensional arrays. pay_by_quarter could have been defined as:

Or, if no array size is to be specified:

As before, however, PostgreSQL does not enforce the size restriction in any case.

To write an array value as a literal constant, enclose the element values within curly braces and separate them by commas. (If you know C, this is not unlike the C syntax for initializing structures.) You can put double quotes around any element value, and must do so if it contains commas or curly braces. (More details appear below.) Thus, the general format of an array constant is the following:

where delim is the delimiter character for the type, as recorded in its pg_type entry. Among the standard data types provided in the PostgreSQL distribution, all use a comma (,), except for type box which uses a semicolon (;). Each val is either a constant of the array element type, or a subarray. An example of an array constant is:

This constant is a two-dimensional, 3-by-3 array consisting of three subarrays of integers.

To set an element of an array constant to NULL, write NULL for the element value. (Any upper- or lower-case variant of NULL will do.) If you want an actual string value “NULL”, you must put double quotes around it.

(These kinds of array constants are actually only a special case of the generic type constants discussed in Section 4.1.2.7. The constant is initially treated as a string and passed to the array input conversion routine. An explicit type specification might be necessary.)

Now we can show some INSERT statements:

The result of the previous two inserts looks like this:

Multidimensional arrays must have matching extents for each dimension. A mismatch causes an error, for example:

The ARRAY constructor syntax can also be used:

Notice that the array elements are ordinary SQL constants or expressions; for instance, string literals are single quoted, instead of double quoted as they would be in an array literal. The ARRAY constructor syntax is discussed in more detail in Section 4.2.12.

Now, we can run some queries on the table. First, we show how to access a single element of an array. This query retrieves the names of the employees whose pay changed in the second quarter:

The array subscript numbers are written within square brackets. By default PostgreSQL uses a one-based numbering convention for arrays, that is, an array of n elements starts with array[1] and ends with array[n].

This query retrieves the third quarter pay of all employees:

We can also access arbitrary rectangular slices of an array, or subarrays. An array slice is denoted by writing lower-bound:upper-bound for one or more array dimensions. For example, this query retrieves the first item on Bill's schedule for the first two days of the week:

If any dimension is written as a slice, i.e., contains a colon, then all dimensions are treated as slices. Any dimension that has only a single number (no colon) is treated as being from 1 to the number specified. For example, [2] is treated as [1:2], as in this example:

To avoid confusion with the non-slice case, it's best to use slice syntax for all dimensions, e.g., [1:2][1:1], not [2][1:1].

It is possible to omit the lower-bound and/or upper-bound of a slice specifier; the missing bound is replaced by the lower or upper limit of the array's subscripts. For example:

An array subscript expression will return null if either the array itself or any of the subscript expressions are null. Also, null is returned if a subscript is outside the array bounds (this case does not raise an error). For example, if schedule currently has the dimensions [1:3][1:2] then referencing schedule[3][3] yields NULL. Similarly, an array reference with the wrong number of subscripts yields a null rather than an error.

An array slice expression likewise yields null if the array itself or any of the subscript expressions are null. However, in other cases such as selecting an array slice that is completely outside the current array bounds, a slice expression yields an empty (zero-dimensional) array instead of null. (This does not match non-slice behavior and is done for historical reasons.) If the requested slice partially overlaps the array bounds, then it is silently reduced to just the overlapping region instead of returning null.

The current dimensions of any array value can be retrieved with the array_dims function:

array_dims produces a text result, which is convenient for people to read but perhaps inconvenient for programs. Dimensions can also be retrieved with array_upper and array_lower, which return the upper and lower bound of a specified array dimension, respectively:

array_length will return the length of a specified array dimension:

cardinality returns the total number of elements in an array across all dimensions. It is effectively the number of rows a call to unnest would yield:

An array value can be replaced completely:

or using the ARRAY expression syntax:

An array can also be updated at a single element:

or updated in a slice:

The slice syntaxes with omitted lower-bound and/or upper-bound can be used too, but only when updating an array value that is not NULL or zero-dimensional (otherwise, there is no existing subscript limit to substitute).

A stored array value can be enlarged by assigning to elements not already present. Any positions between those previously present and the newly assigned elements will be filled with nulls. For example, if array myarray currently has 4 elements, it will have six elements after an update that assigns to myarray[6]; myarray[5] will contain null. Currently, enlargement in this fashion is only allowed for one-dimensional arrays, not multidimensional arrays.

Subscripted assignment allows creation of arrays that do not use one-based subscripts. For example one might assign to myarray[-2:7] to create an array with subscript values from -2 to 7.

New array values can also be constructed using the concatenation operator, ||:

The concatenation operator allows a single element to be pushed onto the beginning or end of a one-dimensional array. It also accepts two N-dimensional arrays, or an N-dimensional and an N+1-dimensional array.

When a single element is pushed onto either the beginning or end of a one-dimensional array, the result is an array with the same lower bound subscript as the array operand. For example:

When two arrays with an equal number of dimensions are concatenated, the result retains the lower bound subscript of the left-hand operand's outer dimension. The result is an array comprising every element of the left-hand operand followed by every element of the right-hand operand. For example:

When an N-dimensional array is pushed onto the beginning or end of an N+1-dimensional array, the result is analogous to the element-array case above. Each N-dimensional sub-array is essentially an element of the N+1-dimensional array's outer dimension. For example:

An array can also be constructed by using the functions array_prepend, array_append, or array_cat. The first two only support one-dimensional arrays, but array_cat supports multidimensional arrays. Some examples:

In simple cases, the concatenation operator discussed above is preferred over direct use of these functions. However, because the concatenation operator is overloaded to serve all three cases, there are situations where use of one of the functions is helpful to avoid ambiguity. For example consider:

In the examples above, the parser sees an integer array on one side of the concatenation operator, and a constant of undetermined type on the other. The heuristic it uses to resolve the constant's type is to assume it's of the same type as the operator's other input — in this case, integer array. So the concatenation operator is presumed to represent array_cat, not array_append. When that's the wrong choice, it could be fixed by casting the constant to the array's element type; but explicit use of array_append might be a preferable solution.

To search for a value in an array, each value must be checked. This can be done manually, if you know the size of the array. For example:

However, this quickly becomes tedious for large arrays, and is not helpful if the size of the array is unknown. An alternative method is described in Section 9.25. The above query could be replaced by:

In addition, you can find rows where the array has all values equal to 10000 with:

Alternatively, the generate_subscripts function can be used. For example:

This function is described in Table 9.70.

You can also search an array using the && operator, which checks whether the left operand overlaps with the right operand. For instance:

This and other array operators are further described in Section 9.19. It can be accelerated by an appropriate index, as described in Section 11.2.

You can also search for specific values in an array using the array_position and array_positions functions. The former returns the subscript of the first occurrence of a value in an array; the latter returns an array with the subscripts of all occurrences of the value in the array. For example:

Arrays are not sets; searching for specific array elements can be a sign of database misdesign. Consider using a separate table with a row for each item that would be an array element. This will be easier to search, and is likely to scale better for a large number of elements.

The external text representation of an array value consists of items that are interpreted according to the I/O conversion rules for the array's element type, plus decoration that indicates the array structure. The decoration consists of curly braces ({ and }) around the array value plus delimiter characters between adjacent items. The delimiter character is usually a comma (,) but can be something else: it is determined by the typdelim setting for the array's element type. Among the standard data types provided in the PostgreSQL distribution, all use a comma, except for type box, which uses a semicolon (;). In a multidimensional array, each dimension (row, plane, cube, etc.) gets its own level of curly braces, and delimiters must be written between adjacent curly-braced entities of the same level.

The array output routine will put double quotes around element values if they are empty strings, contain curly braces, delimiter characters, double quotes, backslashes, or white space, or match the word NULL. Double quotes and backslashes embedded in element values will be backslash-escaped. For numeric data types it is safe to assume that double quotes will never appear, but for textual data types one should be prepared to cope with either the presence or absence of quotes.

By default, the lower bound index value of an array's dimensions is set to one. To represent arrays with other lower bounds, the array subscript ranges can be specified explicitly before writing the array contents. This decoration consists of square brackets ([]) around each array dimension's lower and upper bounds, with a colon (:) delimiter character in between. The array dimension decoration is followed by an equal sign (=). For example:

The array output routine will include explicit dimensions in its result only when there are one or more lower bounds different from one.

If the value written for an element is NULL (in any case variant), the element is taken to be NULL. The presence of any quotes or backslashes disables this and allows the literal string value “NULL” to be entered. Also, for backward compatibility with pre-8.2 versions of PostgreSQL, the array_nulls configuration parameter can be turned off to suppress recognition of NULL as a NULL.

As shown previously, when writing an array value you can use double quotes around any individual array element. You must do so if the element value would otherwise confuse the array-value parser. For example, elements containing curly braces, commas (or the data type's delimiter character), double quotes, backslashes, or leading or trailing whitespace must be double-quoted. Empty strings and strings matching the word NULL must be quoted, too. To put a double quote or backslash in a quoted array element value, precede it with a backslash. Alternatively, you can avoid quotes and use backslash-escaping to protect all data characters that would otherwise be taken as array syntax.

You can add whitespace before a left brace or after a right brace. You can also add whitespace before or after any individual item string. In all of these cases the whitespace will be ignored. However, whitespace within double-quoted elements, or surrounded on both sides by non-whitespace characters of an element, is not ignored.

The ARRAY constructor syntax (see Section 4.2.12) is often easier to work with than the array-literal syntax when writing array values in SQL commands. In ARRAY, individual element values are written the same way they would be written when not members of an array.

Examples:

Example 1 (unknown):

CREATE TABLE sal_emp (
    name            text,
    pay_by_quarter  integer[],
    schedule        text[][]
);

Example 2 (unknown):

CREATE TABLE tictactoe (
    squares   integer[3][3]
);

Example 3 (unknown):

pay_by_quarter  integer ARRAY[4],

Example 4 (unknown):

pay_by_quarter  integer ARRAY,

---

PostgreSQL: Documentation: 18: Appendix N. Color Support

URL: https://www.postgresql.org/docs/current/color.html

Contents:

• Appendix N. Color Support

Most programs in the PostgreSQL package can produce colorized console output. This appendix describes how that is configured.

---

PostgreSQL: Documentation: 18: 4.3. Calling Functions

URL: https://www.postgresql.org/docs/current/sql-syntax-calling-funcs.html

Contents:

• 4.3. Calling Functions #
  • 4.3.1. Using Positional Notation #
  • 4.3.2. Using Named Notation #
  • 4.3.3. Using Mixed Notation #
  • Note

PostgreSQL allows functions that have named parameters to be called using either positional or named notation. Named notation is especially useful for functions that have a large number of parameters, since it makes the associations between parameters and actual arguments more explicit and reliable. In positional notation, a function call is written with its argument values in the same order as they are defined in the function declaration. In named notation, the arguments are matched to the function parameters by name and can be written in any order. For each notation, also consider the effect of function argument types, documented in Section 10.3.

In either notation, parameters that have default values given in the function declaration need not be written in the call at all. But this is particularly useful in named notation, since any combination of parameters can be omitted; while in positional notation parameters can only be omitted from right to left.

PostgreSQL also supports mixed notation, which combines positional and named notation. In this case, positional parameters are written first and named parameters appear after them.

The following examples will illustrate the usage of all three notations, using the following function definition:

Function concat_lower_or_upper has two mandatory parameters, a and b. Additionally there is one optional parameter uppercase which defaults to false. The a and b inputs will be concatenated, and forced to either upper or lower case depending on the uppercase parameter. The remaining details of this function definition are not important here (see Chapter 36 for more information).

Positional notation is the traditional mechanism for passing arguments to functions in PostgreSQL. An example is:

All arguments are specified in order. The result is upper case since uppercase is specified as true. Another example is:

Here, the uppercase parameter is omitted, so it receives its default value of false, resulting in lower case output. In positional notation, arguments can be omitted from right to left so long as they have defaults.

In named notation, each argument's name is specified using => to separate it from the argument expression. For example:

Again, the argument uppercase was omitted so it is set to false implicitly. One advantage of using named notation is that the arguments may be specified in any order, for example:

An older syntax based on ":=" is supported for backward compatibility:

The mixed notation combines positional and named notation. However, as already mentioned, named arguments cannot precede positional arguments. For example:

In the above query, the arguments a and b are specified positionally, while uppercase is specified by name. In this example, that adds little except documentation. With a more complex function having numerous parameters that have default values, named or mixed notation can save a great deal of writing and reduce chances for error.

Named and mixed call notations currently cannot be used when calling an aggregate function (but they do work when an aggregate function is used as a window function).

Examples:

Example 1 (unknown):

CREATE FUNCTION concat_lower_or_upper(a text, b text, uppercase boolean DEFAULT false)
RETURNS text
AS
$$
 SELECT CASE
        WHEN $3 THEN UPPER($1 || ' ' || $2)
        ELSE LOWER($1 || ' ' || $2)
        END;
$$
LANGUAGE SQL IMMUTABLE STRICT;

Example 2 (unknown):

SELECT concat_lower_or_upper('Hello', 'World', true);
 concat_lower_or_upper
-----------------------
 HELLO WORLD
(1 row)

Example 3 (unknown):

SELECT concat_lower_or_upper('Hello', 'World');
 concat_lower_or_upper
-----------------------
 hello world
(1 row)

Example 4 (javascript):

SELECT concat_lower_or_upper(a => 'Hello', b => 'World');
 concat_lower_or_upper
-----------------------
 hello world
(1 row)

---

PostgreSQL: Documentation: 18: 32.9. Asynchronous Notification

URL: https://www.postgresql.org/docs/current/libpq-notify.html

Contents:

• 32.9. Asynchronous Notification #

PostgreSQL offers asynchronous notification via the LISTEN and NOTIFY commands. A client session registers its interest in a particular notification channel with the LISTEN command (and can stop listening with the UNLISTEN command). All sessions listening on a particular channel will be notified asynchronously when a NOTIFY command with that channel name is executed by any session. A “payload” string can be passed to communicate additional data to the listeners.

libpq applications submit LISTEN, UNLISTEN, and NOTIFY commands as ordinary SQL commands. The arrival of NOTIFY messages can subsequently be detected by calling PQnotifies.

The function PQnotifies returns the next notification from a list of unhandled notification messages received from the server. It returns a null pointer if there are no pending notifications. Once a notification is returned from PQnotifies, it is considered handled and will be removed from the list of notifications.

After processing a PGnotify object returned by PQnotifies, be sure to free it with PQfreemem. It is sufficient to free the PGnotify pointer; the relname and extra fields do not represent separate allocations. (The names of these fields are historical; in particular, channel names need not have anything to do with relation names.)

Example 32.2 gives a sample program that illustrates the use of asynchronous notification.

PQnotifies does not actually read data from the server; it just returns messages previously absorbed by another libpq function. In ancient releases of libpq, the only way to ensure timely receipt of NOTIFY messages was to constantly submit commands, even empty ones, and then check PQnotifies after each PQexec. While this still works, it is deprecated as a waste of processing power.

A better way to check for NOTIFY messages when you have no useful commands to execute is to call PQconsumeInput , then check PQnotifies. You can use select() to wait for data to arrive from the server, thereby using no CPU power unless there is something to do. (See PQsocket to obtain the file descriptor number to use with select().) Note that this will work OK whether you submit commands with PQsendQuery/PQgetResult or simply use PQexec. You should, however, remember to check PQnotifies after each PQgetResult or PQexec, to see if any notifications came in during the processing of the command.

Examples:

Example 1 (unknown):

PGnotify *PQnotifies(PGconn *conn);

typedef struct pgNotify
{
    char *relname;              /* notification channel name */
    int  be_pid;                /* process ID of notifying server process */
    char *extra;                /* notification payload string */
} PGnotify;

---

PostgreSQL: Documentation: 18: CREATE TYPE

URL: https://www.postgresql.org/docs/current/sql-createtype.html

Contents:

• CREATE TYPE
• Synopsis
• Description
  • Composite Types
  • Enumerated Types
  • Range Types
  • Base Types
  • Array Types
• Parameters
• Notes

CREATE TYPE — define a new data type

CREATE TYPE registers a new data type for use in the current database. The user who defines a type becomes its owner.

If a schema name is given then the type is created in the specified schema. Otherwise it is created in the current schema. The type name must be distinct from the name of any existing type or domain in the same schema. (Because tables have associated data types, the type name must also be distinct from the name of any existing table in the same schema.)

There are five forms of CREATE TYPE, as shown in the syntax synopsis above. They respectively create a composite type, an enum type, a range type, a base type, or a shell type. The first four of these are discussed in turn below. A shell type is simply a placeholder for a type to be defined later; it is created by issuing CREATE TYPE with no parameters except for the type name. Shell types are needed as forward references when creating range types and base types, as discussed in those sections.

The first form of CREATE TYPE creates a composite type. The composite type is specified by a list of attribute names and data types. An attribute's collation can be specified too, if its data type is collatable. A composite type is essentially the same as the row type of a table, but using CREATE TYPE avoids the need to create an actual table when all that is wanted is to define a type. A stand-alone composite type is useful, for example, as the argument or return type of a function.

To be able to create a composite type, you must have USAGE privilege on all attribute types.

The second form of CREATE TYPE creates an enumerated (enum) type, as described in Section 8.7. Enum types take a list of quoted labels, each of which must be less than NAMEDATALEN bytes long (64 bytes in a standard PostgreSQL build). (It is possible to create an enumerated type with zero labels, but such a type cannot be used to hold values before at least one label is added using ALTER TYPE.)

The third form of CREATE TYPE creates a new range type, as described in Section 8.17.

The range type's subtype can be any type with an associated b-tree operator class (to determine the ordering of values for the range type). Normally the subtype's default b-tree operator class is used to determine ordering; to use a non-default operator class, specify its name with subtype_opclass. If the subtype is collatable, and you want to use a non-default collation in the range's ordering, specify the desired collation with the collation option.

The optional canonical function must take one argument of the range type being defined, and return a value of the same type. This is used to convert range values to a canonical form, when applicable. See Section 8.17.8 for more information. Creating a canonical function is a bit tricky, since it must be defined before the range type can be declared. To do this, you must first create a shell type, which is a placeholder type that has no properties except a name and an owner. This is done by issuing the command CREATE TYPE name, with no additional parameters. Then the function can be declared using the shell type as argument and result, and finally the range type can be declared using the same name. This automatically replaces the shell type entry with a valid range type.

The optional subtype_diff function must take two values of the subtype type as argument, and return a double precision value representing the difference between the two given values. While this is optional, providing it allows much greater efficiency of GiST indexes on columns of the range type. See Section 8.17.8 for more information.

The optional multirange_type_name parameter specifies the name of the corresponding multirange type. If not specified, this name is chosen automatically as follows. If the range type name contains the substring range, then the multirange type name is formed by replacement of the range substring with multirange in the range type name. Otherwise, the multirange type name is formed by appending a _multirange suffix to the range type name.

The fourth form of CREATE TYPE creates a new base type (scalar type). To create a new base type, you must be a superuser. (This restriction is made because an erroneous type definition could confuse or even crash the server.)

The parameters can appear in any order, not only that illustrated above, and most are optional. You must register two or more functions (using CREATE FUNCTION) before defining the type. The support functions input_function and output_function are required, while the functions receive_function, send_function, type_modifier_input_function, type_modifier_output_function, analyze_function, and subscript_function are optional. Generally these functions have to be coded in C or another low-level language.

The input_function converts the type's external textual representation to the internal representation used by the operators and functions defined for the type. output_function performs the reverse transformation. The input function can be declared as taking one argument of type cstring, or as taking three arguments of types cstring, oid, integer. The first argument is the input text as a C string, the second argument is the type's own OID (except for array types, which instead receive their element type's OID), and the third is the typmod of the destination column, if known (-1 will be passed if not). The input function must return a value of the data type itself. Usually, an input function should be declared STRICT; if it is not, it will be called with a NULL first parameter when reading a NULL input value. The function must still return NULL in this case, unless it raises an error. (This case is mainly meant to support domain input functions, which might need to reject NULL inputs.) The output function must be declared as taking one argument of the new data type. The output function must return type cstring. Output functions are not invoked for NULL values.

The optional receive_function converts the type's external binary representation to the internal representation. If this function is not supplied, the type cannot participate in binary input. The binary representation should be chosen to be cheap to convert to internal form, while being reasonably portable. (For example, the standard integer data types use network byte order as the external binary representation, while the internal representation is in the machine's native byte order.) The receive function should perform adequate checking to ensure that the value is valid. The receive function can be declared as taking one argument of type internal, or as taking three arguments of types internal, oid, integer. The first argument is a pointer to a StringInfo buffer holding the received byte string; the optional arguments are the same as for the text input function. The receive function must return a value of the data type itself. Usually, a receive function should be declared STRICT; if it is not, it will be called with a NULL first parameter when reading a NULL input value. The function must still return NULL in this case, unless it raises an error. (This case is mainly meant to support domain receive functions, which might need to reject NULL inputs.) Similarly, the optional send_function converts from the internal representation to the external binary representation. If this function is not supplied, the type cannot participate in binary output. The send function must be declared as taking one argument of the new data type. The send function must return type bytea. Send functions are not invoked for NULL values.

You should at this point be wondering how the input and output functions can be declared to have results or arguments of the new type, when they have to be created before the new type can be created. The answer is that the type should first be defined as a shell type, which is a placeholder type that has no properties except a name and an owner. This is done by issuing the command CREATE TYPE name, with no additional parameters. Then the C I/O functions can be defined referencing the shell type. Finally, CREATE TYPE with a full definition replaces the shell entry with a complete, valid type definition, after which the new type can be used normally.

The optional type_modifier_input_function and type_modifier_output_function are needed if the type supports modifiers, that is optional constraints attached to a type declaration, such as char(5) or numeric(30,2). PostgreSQL allows user-defined types to take one or more simple constants or identifiers as modifiers. However, this information must be capable of being packed into a single non-negative integer value for storage in the system catalogs. The type_modifier_input_function is passed the declared modifier(s) in the form of a cstring array. It must check the values for validity (throwing an error if they are wrong), and if they are correct, return a single non-negative integer value that will be stored as the column “typmod”. Type modifiers will be rejected if the type does not have a type_modifier_input_function. The type_modifier_output_function converts the internal integer typmod value back to the correct form for user display. It must return a cstring value that is the exact string to append to the type name; for example numeric's function might return (30,2). It is allowed to omit the type_modifier_output_function, in which case the default display format is just the stored typmod integer value enclosed in parentheses.

The optional analyze_function performs type-specific statistics collection for columns of the data type. By default, ANALYZE will attempt to gather statistics using the type's “equals” and “less-than” operators, if there is a default b-tree operator class for the type. For non-scalar types this behavior is likely to be unsuitable, so it can be overridden by specifying a custom analysis function. The analysis function must be declared to take a single argument of type internal, and return a boolean result. The detailed API for analysis functions appears in src/include/commands/vacuum.h.

The optional subscript_function allows the data type to be subscripted in SQL commands. Specifying this function does not cause the type to be considered a “true” array type; for example, it will not be a candidate for the result type of ARRAY[] constructs. But if subscripting a value of the type is a natural notation for extracting data from it, then a subscript_function can be written to define what that means. The subscript function must be declared to take a single argument of type internal, and return an internal result, which is a pointer to a struct of methods (functions) that implement subscripting. The detailed API for subscript functions appears in src/include/nodes/subscripting.h. It may also be useful to read the array implementation in src/backend/utils/adt/arraysubs.c, or the simpler code in contrib/hstore/hstore_subs.c. Additional information appears in Array Types below.

While the details of the new type's internal representation are only known to the I/O functions and other functions you create to work with the type, there are several properties of the internal representation that must be declared to PostgreSQL. Foremost of these is internallength. Base data types can be fixed-length, in which case internallength is a positive integer, or variable-length, indicated by setting internallength to VARIABLE. (Internally, this is represented by setting typlen to -1.) The internal representation of all variable-length types must start with a 4-byte integer giving the total length of this value of the type. (Note that the length field is often encoded, as described in Section 66.2; it's unwise to access it directly.)

The optional flag PASSEDBYVALUE indicates that values of this data type are passed by value, rather than by reference. Types passed by value must be fixed-length, and their internal representation cannot be larger than the size of the Datum type (4 bytes on some machines, 8 bytes on others).

The alignment parameter specifies the storage alignment required for the data type. The allowed values equate to alignment on 1, 2, 4, or 8 byte boundaries. Note that variable-length types must have an alignment of at least 4, since they necessarily contain an int4 as their first component.

The storage parameter allows selection of storage strategies for variable-length data types. (Only plain is allowed for fixed-length types.) plain specifies that data of the type will always be stored in-line and not compressed. extended specifies that the system will first try to compress a long data value, and will move the value out of the main table row if it's still too long. external allows the value to be moved out of the main table, but the system will not try to compress it. main allows compression, but discourages moving the value out of the main table. (Data items with this storage strategy might still be moved out of the main table if there is no other way to make a row fit, but they will be kept in the main table preferentially over extended and external items.)

All storage values other than plain imply that the functions of the data type can handle values that have been toasted, as described in Section 66.2 and Section 36.13.1. The specific other value given merely determines the default TOAST storage strategy for columns of a toastable data type; users can pick other strategies for individual columns using ALTER TABLE SET STORAGE.

The like_type parameter provides an alternative method for specifying the basic representation properties of a data type: copy them from some existing type. The values of internallength, passedbyvalue, alignment, and storage are copied from the named type. (It is possible, though usually undesirable, to override some of these values by specifying them along with the LIKE clause.) Specifying representation this way is especially useful when the low-level implementation of the new type “piggybacks” on an existing type in some fashion.

The category and preferred parameters can be used to help control which implicit cast will be applied in ambiguous situations. Each data type belongs to a category named by a single ASCII character, and each type is either “preferred” or not within its category. The parser will prefer casting to preferred types (but only from other types within the same category) when this rule is helpful in resolving overloaded functions or operators. For more details see Chapter 10. For types that have no implicit casts to or from any other types, it is sufficient to leave these settings at the defaults. However, for a group of related types that have implicit casts, it is often helpful to mark them all as belonging to a category and select one or two of the “most general” types as being preferred within the category. The category parameter is especially useful when adding a user-defined type to an existing built-in category, such as the numeric or string types. However, it is also possible to create new entirely-user-defined type categories. Select any ASCII character other than an upper-case letter to name such a category.

A default value can be specified, in case a user wants columns of the data type to default to something other than the null value. Specify the default with the DEFAULT key word. (Such a default can be overridden by an explicit DEFAULT clause attached to a particular column.)

To indicate that a type is a fixed-length array type, specify the type of the array elements using the ELEMENT key word. For example, to define an array of 4-byte integers (int4), specify ELEMENT = int4. For more details, see Array Types below.

To indicate the delimiter to be used between values in the external representation of arrays of this type, delimiter can be set to a specific character. The default delimiter is the comma (,). Note that the delimiter is associated with the array element type, not the array type itself.

If the optional Boolean parameter collatable is true, column definitions and expressions of the type may carry collation information through use of the COLLATE clause. It is up to the implementations of the functions operating on the type to actually make use of the collation information; this does not happen automatically merely by marking the type collatable.

Whenever a user-defined type is created, PostgreSQL automatically creates an associated array type, whose name consists of the element type's name prepended with an underscore, and truncated if necessary to keep it less than NAMEDATALEN bytes long. (If the name so generated collides with an existing type name, the process is repeated until a non-colliding name is found.) This implicitly-created array type is variable length and uses the built-in input and output functions array_in and array_out. Furthermore, this type is what the system uses for constructs such as ARRAY[] over the user-defined type. The array type tracks any changes in its element type's owner or schema, and is dropped if the element type is.

You might reasonably ask why there is an ELEMENT option, if the system makes the correct array type automatically. The main case where it's useful to use ELEMENT is when you are making a fixed-length type that happens to be internally an array of a number of identical things, and you want to allow these things to be accessed directly by subscripting, in addition to whatever operations you plan to provide for the type as a whole. For example, type point is represented as just two floating-point numbers, which can be accessed using point[0] and point[1]. Note that this facility only works for fixed-length types whose internal form is exactly a sequence of identical fixed-length fields. For historical reasons (i.e., this is clearly wrong but it's far too late to change it), subscripting of fixed-length array types starts from zero, rather than from one as for variable-length arrays.

Specifying the SUBSCRIPT option allows a data type to be subscripted, even though the system does not otherwise regard it as an array type. The behavior just described for fixed-length arrays is actually implemented by the SUBSCRIPT handler function raw_array_subscript_handler, which is used automatically if you specify ELEMENT for a fixed-length type without also writing SUBSCRIPT.

When specifying a custom SUBSCRIPT function, it is not necessary to specify ELEMENT unless the SUBSCRIPT handler function needs to consult typelem to find out what to return. Be aware that specifying ELEMENT causes the system to assume that the new type contains, or is somehow physically dependent on, the element type; thus for example changing properties of the element type won't be allowed if there are any columns of the dependent type.

The name (optionally schema-qualified) of a type to be created.

The name of an attribute (column) for the composite type.

The name of an existing data type to become a column of the composite type.

The name of an existing collation to be associated with a column of a composite type, or with a range type.

A string literal representing the textual label associated with one value of an enum type.

The name of the element type that the range type will represent ranges of.

The name of a b-tree operator class for the subtype.

The name of the canonicalization function for the range type.

The name of a difference function for the subtype.

The name of the corresponding multirange type.

The name of a function that converts data from the type's external textual form to its internal form.

The name of a function that converts data from the type's internal form to its external textual form.

The name of a function that converts data from the type's external binary form to its internal form.

The name of a function that converts data from the type's internal form to its external binary form.

The name of a function that converts an array of modifier(s) for the type into internal form.

The name of a function that converts the internal form of the type's modifier(s) to external textual form.

The name of a function that performs statistical analysis for the data type.

The name of a function that defines what subscripting a value of the data type does.

A numeric constant that specifies the length in bytes of the new type's internal representation. The default assumption is that it is variable-length.

The storage alignment requirement of the data type. If specified, it must be char, int2, int4, or double; the default is int4.

The storage strategy for the data type. If specified, must be plain, external, extended, or main; the default is plain.

The name of an existing data type that the new type will have the same representation as. The values of internallength, passedbyvalue, alignment, and storage are copied from that type, unless overridden by explicit specification elsewhere in this CREATE TYPE command.

The category code (a single ASCII character) for this type. The default is 'U' for “user-defined type”. Other standard category codes can be found in Table 52.65. You may also choose other ASCII characters in order to create custom categories.

True if this type is a preferred type within its type category, else false. The default is false. Be very careful about creating a new preferred type within an existing type category, as this could cause surprising changes in behavior.

The default value for the data type. If this is omitted, the default is null.

The type being created is an array; this specifies the type of the array elements.

The delimiter character to be used between values in arrays made of this type.

True if this type's operations can use collation information. The default is false.

Because there are no restrictions on use of a data type once it's been created, creating a base type or range type is tantamount to granting public execute permission on the functions mentioned in the type definition. This is usually not an issue for the sorts of functions that are useful in a type definition. But you might want to think twice before designing a type in a way that would require “secret” information to be used while converting it to or from external form.

Before PostgreSQL version 8.3, the name of a generated array type was always exactly the element type's name with one underscore character (_) prepended. (Type names were therefore restricted in length to one fewer character than other names.) While this is still usually the case, the array type name may vary from this in case of maximum-length names or collisions with user type names that begin with underscore. Writing code that depends on this convention is therefore deprecated. Instead, use pg_type.typarray to locate the array type associated with a given type.

It may be advisable to avoid using type and table names that begin with underscore. While the server will change generated array type names to avoid collisions with user-given names, there is still risk of confusion, particularly with old client software that may assume that type names beginning with underscores always represent arrays.

Before PostgreSQL version 8.2, the shell-type creation syntax CREATE TYPE name did not exist. The way to create a new base type was to create its input function first. In this approach, PostgreSQL will first see the name of the new data type as the return type of the input function. The shell type is implicitly created in this situation, and then it can be referenced in the definitions of the remaining I/O functions. This approach still works, but is deprecated and might be disallowed in some future release. Also, to avoid accidentally cluttering the catalogs with shell types as a result of simple typos in function definitions, a shell type will only be made this way when the input function is written in C.

In PostgreSQL version 16 and later, it is desirable for base types' input functions to return “soft” errors using the new errsave()/ereturn() mechanism, rather than throwing ereport() exceptions as in previous versions. See src/backend/utils/fmgr/README for more information.

This example creates a composite type and uses it in a function definition:

This example creates an enumerated type and uses it in a table definition:

This example creates a range type:

This example creates the base data type box and then uses the type in a table definition:

If the internal structure of box were an array of four float4 elements, we might instead use:

which would allow a box value's component numbers to be accessed by subscripting. Otherwise the type behaves the same as before.

This example creates a large object type and uses it in a table definition:

More examples, including suitable input and output functions, are in Section 36.13.

The first form of the CREATE TYPE command, which creates a composite type, conforms to the SQL standard. The other forms are PostgreSQL extensions. The CREATE TYPE statement in the SQL standard also defines other forms that are not implemented in PostgreSQL.

The ability to create a composite type with zero attributes is a PostgreSQL-specific deviation from the standard (analogous to the same case in CREATE TABLE).

Examples:

Example 1 (unknown):

CREATE TYPE name AS
    ( [ attribute_name data_type [ COLLATE collation ] [, ... ] ] )

CREATE TYPE name AS ENUM
    ( [ 'label' [, ... ] ] )

CREATE TYPE name AS RANGE (
    SUBTYPE = subtype
    [ , SUBTYPE_OPCLASS = subtype_operator_class ]
    [ , COLLATION = collation ]
    [ , CANONICAL = canonical_function ]
    [ , SUBTYPE_DIFF = subtype_diff_function ]
    [ , MULTIRANGE_TYPE_NAME = multirange_type_name ]
)

CREATE TYPE name (
    INPUT = input_function,
    OUTPUT = output_function
    [ , RECEIVE = receive_function ]
    [ , SEND = send_function ]
    [ , TYPMOD_IN = type_modifier_input_function ]
    [ , TYPMOD_OUT = type_modifier_output_function ]
    [ , ANALYZE = analyze_function ]
    [ , SUBSCRIPT = subscript_function ]
    [ , INTERNALLENGTH = { internallength | VARIABLE } ]
    [ , PASSEDBYVALUE ]
    [ , ALIGNMENT = alignment ]
    [ , STORAGE = storage ]
    [ , LIKE = like_type ]
    [ , CATEGORY = category ]
    [ , PREFERRED = preferred ]
    [ , DEFAULT = default ]
    [ , ELEMENT = element ]
    [ , DELIMITER = delimiter ]
    [ , COLLATABLE = collatable ]
)

CREATE TYPE name

Example 2 (unknown):

CREATE TYPE compfoo AS (f1 int, f2 text);

CREATE FUNCTION getfoo() RETURNS SETOF compfoo AS $$
    SELECT fooid, fooname FROM foo
$$ LANGUAGE SQL;

Example 3 (unknown):

CREATE TYPE bug_status AS ENUM ('new', 'open', 'closed');

CREATE TABLE bug (
    id serial,
    description text,
    status bug_status
);

Example 4 (unknown):

CREATE TYPE float8_range AS RANGE (subtype = float8, subtype_diff = float8mi);

---

PostgreSQL: Documentation: 18: 36.1. How Extensibility Works

URL: https://www.postgresql.org/docs/current/extend-how.html

Contents:

• 36.1. How Extensibility Works #

PostgreSQL is extensible because its operation is catalog-driven. If you are familiar with standard relational database systems, you know that they store information about databases, tables, columns, etc., in what are commonly known as system catalogs. (Some systems call this the data dictionary.) The catalogs appear to the user as tables like any other, but the DBMS stores its internal bookkeeping in them. One key difference between PostgreSQL and standard relational database systems is that PostgreSQL stores much more information in its catalogs: not only information about tables and columns, but also information about data types, functions, access methods, and so on. These tables can be modified by the user, and since PostgreSQL bases its operation on these tables, this means that PostgreSQL can be extended by users. By comparison, conventional database systems can only be extended by changing hardcoded procedures in the source code or by loading modules specially written by the DBMS vendor.

The PostgreSQL server can moreover incorporate user-written code into itself through dynamic loading. That is, the user can specify an object code file (e.g., a shared library) that implements a new type or function, and PostgreSQL will load it as required. Code written in SQL is even more trivial to add to the server. This ability to modify its operation “on the fly” makes PostgreSQL uniquely suited for rapid prototyping of new applications and storage structures.

---

PostgreSQL: Documentation: 18: 34.8. Error Handling

URL: https://www.postgresql.org/docs/current/ecpg-errors.html

Contents:

• 34.8. Error Handling #
  • 34.8.1. Setting Callbacks #
  • 34.8.2. sqlca #
  • 34.8.3. SQLSTATE vs. SQLCODE #

This section describes how you can handle exceptional conditions and warnings in an embedded SQL program. There are two nonexclusive facilities for this.

One simple method to catch errors and warnings is to set a specific action to be executed whenever a particular condition occurs. In general:

condition can be one of the following:

The specified action is called whenever an error occurs during the execution of an SQL statement.

The specified action is called whenever a warning occurs during the execution of an SQL statement.

The specified action is called whenever an SQL statement retrieves or affects zero rows. (This condition is not an error, but you might be interested in handling it specially.)

action can be one of the following:

This effectively means that the condition is ignored. This is the default.

Jump to the specified label (using a C goto statement).

Print a message to standard error. This is useful for simple programs or during prototyping. The details of the message cannot be configured.

Call exit(1), which will terminate the program.

Execute the C statement break. This should only be used in loops or switch statements.

Execute the C statement continue. This should only be used in loops statements. if executed, will cause the flow of control to return to the top of the loop.

Call the specified C functions with the specified arguments. (This use is different from the meaning of CALL and DO in the normal PostgreSQL grammar.)

The SQL standard only provides for the actions CONTINUE and GOTO (and GO TO).

Here is an example that you might want to use in a simple program. It prints a simple message when a warning occurs and aborts the program when an error happens:

The statement EXEC SQL WHENEVER is a directive of the SQL preprocessor, not a C statement. The error or warning actions that it sets apply to all embedded SQL statements that appear below the point where the handler is set, unless a different action was set for the same condition between the first EXEC SQL WHENEVER and the SQL statement causing the condition, regardless of the flow of control in the C program. So neither of the two following C program excerpts will have the desired effect:

For more powerful error handling, the embedded SQL interface provides a global variable with the name sqlca (SQL communication area) that has the following structure:

(In a multithreaded program, every thread automatically gets its own copy of sqlca. This works similarly to the handling of the standard C global variable errno.)

sqlca covers both warnings and errors. If multiple warnings or errors occur during the execution of a statement, then sqlca will only contain information about the last one.

If no error occurred in the last SQL statement, sqlca.sqlcode will be 0 and sqlca.sqlstate will be "00000". If a warning or error occurred, then sqlca.sqlcode will be negative and sqlca.sqlstate will be different from "00000". A positive sqlca.sqlcode indicates a harmless condition, such as that the last query returned zero rows. sqlcode and sqlstate are two different error code schemes; details appear below.

If the last SQL statement was successful, then sqlca.sqlerrd[1] contains the OID of the processed row, if applicable, and sqlca.sqlerrd[2] contains the number of processed or returned rows, if applicable to the command.

In case of an error or warning, sqlca.sqlerrm.sqlerrmc will contain a string that describes the error. The field sqlca.sqlerrm.sqlerrml contains the length of the error message that is stored in sqlca.sqlerrm.sqlerrmc (the result of strlen(), not really interesting for a C programmer). Note that some messages are too long to fit in the fixed-size sqlerrmc array; they will be truncated.

In case of a warning, sqlca.sqlwarn[2] is set to W. (In all other cases, it is set to something different from W.) If sqlca.sqlwarn[1] is set to W, then a value was truncated when it was stored in a host variable. sqlca.sqlwarn[0] is set to W if any of the other elements are set to indicate a warning.

The fields sqlcaid, sqlabc, sqlerrp, and the remaining elements of sqlerrd and sqlwarn currently contain no useful information.

The structure sqlca is not defined in the SQL standard, but is implemented in several other SQL database systems. The definitions are similar at the core, but if you want to write portable applications, then you should investigate the different implementations carefully.

Here is one example that combines the use of WHENEVER and sqlca, printing out the contents of sqlca when an error occurs. This is perhaps useful for debugging or prototyping applications, before installing a more “user-friendly” error handler.

The result could look as follows (here an error due to a misspelled table name):

The fields sqlca.sqlstate and sqlca.sqlcode are two different schemes that provide error codes. Both are derived from the SQL standard, but SQLCODE has been marked deprecated in the SQL-92 edition of the standard and has been dropped in later editions. Therefore, new applications are strongly encouraged to use SQLSTATE.

SQLSTATE is a five-character array. The five characters contain digits or upper-case letters that represent codes of various error and warning conditions. SQLSTATE has a hierarchical scheme: the first two characters indicate the general class of the condition, the last three characters indicate a subclass of the general condition. A successful state is indicated by the code 00000. The SQLSTATE codes are for the most part defined in the SQL standard. The PostgreSQL server natively supports SQLSTATE error codes; therefore a high degree of consistency can be achieved by using this error code scheme throughout all applications. For further information see Appendix A.

SQLCODE, the deprecated error code scheme, is a simple integer. A value of 0 indicates success, a positive value indicates success with additional information, a negative value indicates an error. The SQL standard only defines the positive value +100, which indicates that the last command returned or affected zero rows, and no specific negative values. Therefore, this scheme can only achieve poor portability and does not have a hierarchical code assignment. Historically, the embedded SQL processor for PostgreSQL has assigned some specific SQLCODE values for its use, which are listed below with their numeric value and their symbolic name. Remember that these are not portable to other SQL implementations. To simplify the porting of applications to the SQLSTATE scheme, the corresponding SQLSTATE is also listed. There is, however, no one-to-one or one-to-many mapping between the two schemes (indeed it is many-to-many), so you should consult the global SQLSTATE listing in Appendix A in each case.

These are the assigned SQLCODE values:

Indicates no error. (SQLSTATE 00000)

This is a harmless condition indicating that the last command retrieved or processed zero rows, or that you are at the end of the cursor. (SQLSTATE 02000)

When processing a cursor in a loop, you could use this code as a way to detect when to abort the loop, like this:

But WHENEVER NOT FOUND DO BREAK effectively does this internally, so there is usually no advantage in writing this out explicitly.

Indicates that your virtual memory is exhausted. The numeric value is defined as -ENOMEM. (SQLSTATE YE001)

Indicates the preprocessor has generated something that the library does not know about. Perhaps you are running incompatible versions of the preprocessor and the library. (SQLSTATE YE002)

This means that the command specified more host variables than the command expected. (SQLSTATE 07001 or 07002)

This means that the command specified fewer host variables than the command expected. (SQLSTATE 07001 or 07002)

This means a query has returned multiple rows but the statement was only prepared to store one result row (for example, because the specified variables are not arrays). (SQLSTATE 21000)

The host variable is of type int and the datum in the database is of a different type and contains a value that cannot be interpreted as an int. The library uses strtol() for this conversion. (SQLSTATE 42804)

The host variable is of type unsigned int and the datum in the database is of a different type and contains a value that cannot be interpreted as an unsigned int. The library uses strtoul() for this conversion. (SQLSTATE 42804)

The host variable is of type float and the datum in the database is of another type and contains a value that cannot be interpreted as a float. The library uses strtod() for this conversion. (SQLSTATE 42804)

The host variable is of type numeric and the datum in the database is of another type and contains a value that cannot be interpreted as a numeric value. (SQLSTATE 42804)

The host variable is of type interval and the datum in the database is of another type and contains a value that cannot be interpreted as an interval value. (SQLSTATE 42804)

The host variable is of type date and the datum in the database is of another type and contains a value that cannot be interpreted as a date value. (SQLSTATE 42804)

The host variable is of type timestamp and the datum in the database is of another type and contains a value that cannot be interpreted as a timestamp value. (SQLSTATE 42804)

This means the host variable is of type bool and the datum in the database is neither 't' nor 'f'. (SQLSTATE 42804)

The statement sent to the PostgreSQL server was empty. (This cannot normally happen in an embedded SQL program, so it might point to an internal error.) (SQLSTATE YE002)

A null value was returned and no null indicator variable was supplied. (SQLSTATE 22002)

An ordinary variable was used in a place that requires an array. (SQLSTATE 42804)

The database returned an ordinary variable in a place that requires array value. (SQLSTATE 42804)

The value could not be inserted into the array. (SQLSTATE 42804)

The program tried to access a connection that does not exist. (SQLSTATE 08003)

The program tried to access a connection that does exist but is not open. (This is an internal error.) (SQLSTATE YE002)

The statement you are trying to use has not been prepared. (SQLSTATE 26000)

Duplicate key error, violation of unique constraint (Informix compatibility mode). (SQLSTATE 23505)

The descriptor specified was not found. The statement you are trying to use has not been prepared. (SQLSTATE 33000)

The descriptor index specified was out of range. (SQLSTATE 07009)

An invalid descriptor item was requested. (This is an internal error.) (SQLSTATE YE002)

During the execution of a dynamic statement, the database returned a numeric value and the host variable was not numeric. (SQLSTATE 07006)

During the execution of a dynamic statement, the database returned a non-numeric value and the host variable was numeric. (SQLSTATE 07006)

A result of the subquery is not single row (Informix compatibility mode). (SQLSTATE 21000)

Some error caused by the PostgreSQL server. The message contains the error message from the PostgreSQL server.

The PostgreSQL server signaled that we cannot start, commit, or rollback the transaction. (SQLSTATE 08007)

The connection attempt to the database did not succeed. (SQLSTATE 08001)

Duplicate key error, violation of unique constraint. (SQLSTATE 23505)

A result for the subquery is not single row. (SQLSTATE 21000)

An invalid cursor name was specified. (SQLSTATE 34000)

Transaction is in progress. (SQLSTATE 25001)

There is no active (in-progress) transaction. (SQLSTATE 25P01)

An existing cursor name was specified. (SQLSTATE 42P03)

Examples:

Example 1 (unknown):

EXEC SQL WHENEVER condition action;

Example 2 (unknown):

EXEC SQL WHENEVER SQLWARNING SQLPRINT;
EXEC SQL WHENEVER SQLERROR STOP;

Example 3 (cpp):

/*
 * WRONG
 */
int main(int argc, char *argv[])
{
    ...
    if (verbose) {
        EXEC SQL WHENEVER SQLWARNING SQLPRINT;
    }
    ...
    EXEC SQL SELECT ...;
    ...
}

Example 4 (cpp):

/*
 * WRONG
 */
int main(int argc, char *argv[])
{
    ...
    set_error_handler();
    ...
    EXEC SQL SELECT ...;
    ...
}

static void set_error_handler(void)
{
    EXEC SQL WHENEVER SQLERROR STOP;
}

---

PostgreSQL: Documentation: 18: 13.7. Locking and Indexes

URL: https://www.postgresql.org/docs/current/locking-indexes.html

Contents:

• 13.7. Locking and Indexes #

Though PostgreSQL provides nonblocking read/write access to table data, nonblocking read/write access is not currently offered for every index access method implemented in PostgreSQL. The various index types are handled as follows:

Short-term share/exclusive page-level locks are used for read/write access. Locks are released immediately after each index row is fetched or inserted. These index types provide the highest concurrency without deadlock conditions.

Share/exclusive hash-bucket-level locks are used for read/write access. Locks are released after the whole bucket is processed. Bucket-level locks provide better concurrency than index-level ones, but deadlock is possible since the locks are held longer than one index operation.

Short-term share/exclusive page-level locks are used for read/write access. Locks are released immediately after each index row is fetched or inserted. But note that insertion of a GIN-indexed value usually produces several index key insertions per row, so GIN might do substantial work for a single value's insertion.

Currently, B-tree indexes offer the best performance for concurrent applications; since they also have more features than hash indexes, they are the recommended index type for concurrent applications that need to index scalar data. When dealing with non-scalar data, B-trees are not useful, and GiST, SP-GiST or GIN indexes should be used instead.

---

PostgreSQL: Documentation: 18: 5.11. Inheritance

URL: https://www.postgresql.org/docs/current/ddl-inherit.html

Contents:

• 5.11. Inheritance #
  • 5.11.1. Caveats #

PostgreSQL implements table inheritance, which can be a useful tool for database designers. (SQL:1999 and later define a type inheritance feature, which differs in many respects from the features described here.)

Let's start with an example: suppose we are trying to build a data model for cities. Each state has many cities, but only one capital. We want to be able to quickly retrieve the capital city for any particular state. This can be done by creating two tables, one for state capitals and one for cities that are not capitals. However, what happens when we want to ask for data about a city, regardless of whether it is a capital or not? The inheritance feature can help to resolve this problem. We define the capitals table so that it inherits from cities:

In this case, the capitals table inherits all the columns of its parent table, cities. State capitals also have an extra column, state, that shows their state.

In PostgreSQL, a table can inherit from zero or more other tables, and a query can reference either all rows of a table or all rows of a table plus all of its descendant tables. The latter behavior is the default. For example, the following query finds the names of all cities, including state capitals, that are located at an elevation over 500 feet:

Given the sample data from the PostgreSQL tutorial (see Section 2.1), this returns:

On the other hand, the following query finds all the cities that are not state capitals and are situated at an elevation over 500 feet:

Here the ONLY keyword indicates that the query should apply only to cities, and not any tables below cities in the inheritance hierarchy. Many of the commands that we have already discussed — SELECT, UPDATE and DELETE — support the ONLY keyword.

You can also write the table name with a trailing * to explicitly specify that descendant tables are included:

Writing * is not necessary, since this behavior is always the default. However, this syntax is still supported for compatibility with older releases where the default could be changed.

In some cases you might wish to know which table a particular row originated from. There is a system column called tableoid in each table which can tell you the originating table:

(If you try to reproduce this example, you will probably get different numeric OIDs.) By doing a join with pg_class you can see the actual table names:

Another way to get the same effect is to use the regclass alias type, which will print the table OID symbolically:

Inheritance does not automatically propagate data from INSERT or COPY commands to other tables in the inheritance hierarchy. In our example, the following INSERT statement will fail:

We might hope that the data would somehow be routed to the capitals table, but this does not happen: INSERT always inserts into exactly the table specified. In some cases it is possible to redirect the insertion using a rule (see Chapter 39). However that does not help for the above case because the cities table does not contain the column state, and so the command will be rejected before the rule can be applied.

All check constraints and not-null constraints on a parent table are automatically inherited by its children, unless explicitly specified otherwise with NO INHERIT clauses. Other types of constraints (unique, primary key, and foreign key constraints) are not inherited.

A table can inherit from more than one parent table, in which case it has the union of the columns defined by the parent tables. Any columns declared in the child table's definition are added to these. If the same column name appears in multiple parent tables, or in both a parent table and the child's definition, then these columns are “merged” so that there is only one such column in the child table. To be merged, columns must have the same data types, else an error is raised. Inheritable check constraints and not-null constraints are merged in a similar fashion. Thus, for example, a merged column will be marked not-null if any one of the column definitions it came from is marked not-null. Check constraints are merged if they have the same name, and the merge will fail if their conditions are different.

Table inheritance is typically established when the child table is created, using the INHERITS clause of the CREATE TABLE statement. Alternatively, a table which is already defined in a compatible way can have a new parent relationship added, using the INHERIT variant of ALTER TABLE. To do this the new child table must already include columns with the same names and types as the columns of the parent. It must also include check constraints with the same names and check expressions as those of the parent. Similarly an inheritance link can be removed from a child using the NO INHERIT variant of ALTER TABLE. Dynamically adding and removing inheritance links like this can be useful when the inheritance relationship is being used for table partitioning (see Section 5.12).

One convenient way to create a compatible table that will later be made a new child is to use the LIKE clause in CREATE TABLE. This creates a new table with the same columns as the source table. If there are any CHECK constraints defined on the source table, the INCLUDING CONSTRAINTS option to LIKE should be specified, as the new child must have constraints matching the parent to be considered compatible.

A parent table cannot be dropped while any of its children remain. Neither can columns or check constraints of child tables be dropped or altered if they are inherited from any parent tables. If you wish to remove a table and all of its descendants, one easy way is to drop the parent table with the CASCADE option (see Section 5.15).

ALTER TABLE will propagate any changes in column data definitions and check constraints down the inheritance hierarchy. Again, dropping columns that are depended on by other tables is only possible when using the CASCADE option. ALTER TABLE follows the same rules for duplicate column merging and rejection that apply during CREATE TABLE.

Inherited queries perform access permission checks on the parent table only. Thus, for example, granting UPDATE permission on the cities table implies permission to update rows in the capitals table as well, when they are accessed through cities. This preserves the appearance that the data is (also) in the parent table. But the capitals table could not be updated directly without an additional grant. In a similar way, the parent table's row security policies (see Section 5.9) are applied to rows coming from child tables during an inherited query. A child table's policies, if any, are applied only when it is the table explicitly named in the query; and in that case, any policies attached to its parent(s) are ignored.

Foreign tables (see Section 5.13) can also be part of inheritance hierarchies, either as parent or child tables, just as regular tables can be. If a foreign table is part of an inheritance hierarchy then any operations not supported by the foreign table are not supported on the whole hierarchy either.

Note that not all SQL commands are able to work on inheritance hierarchies. Commands that are used for data querying, data modification, or schema modification (e.g., SELECT, UPDATE, DELETE, most variants of ALTER TABLE, but not INSERT or ALTER TABLE ... RENAME) typically default to including child tables and support the ONLY notation to exclude them. The majority of commands that do database maintenance and tuning (e.g., REINDEX) only work on individual, physical tables and do not support recursing over inheritance hierarchies. However, both VACUUM and ANALYZE commands default to including child tables and the ONLY notation is supported to allow them to be excluded. The respective behavior of each individual command is documented in its reference page (SQL Commands).

A serious limitation of the inheritance feature is that indexes (including unique constraints) and foreign key constraints only apply to single tables, not to their inheritance children. This is true on both the referencing and referenced sides of a foreign key constraint. Thus, in the terms of the above example:

If we declared cities.name to be UNIQUE or a PRIMARY KEY, this would not stop the capitals table from having rows with names duplicating rows in cities. And those duplicate rows would by default show up in queries from cities. In fact, by default capitals would have no unique constraint at all, and so could contain multiple rows with the same name. You could add a unique constraint to capitals, but this would not prevent duplication compared to cities.

Similarly, if we were to specify that cities.name REFERENCES some other table, this constraint would not automatically propagate to capitals. In this case you could work around it by manually adding the same REFERENCES constraint to capitals.

Specifying that another table's column REFERENCES cities(name) would allow the other table to contain city names, but not capital names. There is no good workaround for this case.

Some functionality not implemented for inheritance hierarchies is implemented for declarative partitioning. Considerable care is needed in deciding whether partitioning with legacy inheritance is useful for your application.

Examples:

Example 1 (unknown):

CREATE TABLE cities (
    name            text,
    population      float,
    elevation       int     -- in feet
);

CREATE TABLE capitals (
    state           char(2)
) INHERITS (cities);

Example 2 (unknown):

SELECT name, elevation
    FROM cities
    WHERE elevation > 500;

Example 3 (unknown):

name    | elevation
-----------+-----------
 Las Vegas |      2174
 Mariposa  |      1953
 Madison   |       845

Example 4 (unknown):

SELECT name, elevation
    FROM ONLY cities
    WHERE elevation > 500;

   name    | elevation
-----------+-----------
 Las Vegas |      2174
 Mariposa  |      1953

---

PostgreSQL: Documentation: 18: 36.9. Internal Functions

URL: https://www.postgresql.org/docs/current/xfunc-internal.html

Contents:

• 36.9. Internal Functions #
  • Note

Internal functions are functions written in C that have been statically linked into the PostgreSQL server. The “body” of the function definition specifies the C-language name of the function, which need not be the same as the name being declared for SQL use. (For reasons of backward compatibility, an empty body is accepted as meaning that the C-language function name is the same as the SQL name.)

Normally, all internal functions present in the server are declared during the initialization of the database cluster (see Section 18.2), but a user could use CREATE FUNCTION to create additional alias names for an internal function. Internal functions are declared in CREATE FUNCTION with language name internal. For instance, to create an alias for the sqrt function:

(Most internal functions expect to be declared “strict”.)

Not all “predefined” functions are “internal” in the above sense. Some predefined functions are written in SQL.

Examples:

Example 1 (unknown):

CREATE FUNCTION square_root(double precision) RETURNS double precision
    AS 'dsqrt'
    LANGUAGE internal
    STRICT;

---

PostgreSQL: Documentation: 18: 18.1. The PostgreSQL User Account

URL: https://www.postgresql.org/docs/current/postgres-user.html

Contents:

• 18.1. The PostgreSQL User Account #

As with any server daemon that is accessible to the outside world, it is advisable to run PostgreSQL under a separate user account. This user account should only own the data that is managed by the server, and should not be shared with other daemons. (For example, using the user nobody is a bad idea.) In particular, it is advisable that this user account not own the PostgreSQL executable files, to ensure that a compromised server process could not modify those executables.

Pre-packaged versions of PostgreSQL will typically create a suitable user account automatically during package installation.

To add a Unix user account to your system, look for a command useradd or adduser. The user name postgres is often used, and is assumed throughout this book, but you can use another name if you like.

---

PostgreSQL: Documentation: 18: 24.1. Routine Vacuuming

URL: https://www.postgresql.org/docs/current/routine-vacuuming.html

Contents:

• 24.1. Routine Vacuuming #
  • 24.1.1. Vacuuming Basics #
  • 24.1.2. Recovering Disk Space #
  • Tip
  • Tip
  • 24.1.3. Updating Planner Statistics #
  • Tip
  • Tip
  • Tip
  • 24.1.4. Updating the Visibility Map #

PostgreSQL databases require periodic maintenance known as vacuuming. For many installations, it is sufficient to let vacuuming be performed by the autovacuum daemon, which is described in Section 24.1.6. You might need to adjust the autovacuuming parameters described there to obtain best results for your situation. Some database administrators will want to supplement or replace the daemon's activities with manually-managed VACUUM commands, which typically are executed according to a schedule by cron or Task Scheduler scripts. To set up manually-managed vacuuming properly, it is essential to understand the issues discussed in the next few subsections. Administrators who rely on autovacuuming may still wish to skim this material to help them understand and adjust autovacuuming.

PostgreSQL's VACUUM command has to process each table on a regular basis for several reasons:

Each of these reasons dictates performing VACUUM operations of varying frequency and scope, as explained in the following subsections.

There are two variants of VACUUM: standard VACUUM and VACUUM FULL. VACUUM FULL can reclaim more disk space but runs much more slowly. Also, the standard form of VACUUM can run in parallel with production database operations. (Commands such as SELECT, INSERT, UPDATE, and DELETE will continue to function normally, though you will not be able to modify the definition of a table with commands such as ALTER TABLE while it is being vacuumed.) VACUUM FULL requires an ACCESS EXCLUSIVE lock on the table it is working on, and therefore cannot be done in parallel with other use of the table. Generally, therefore, administrators should strive to use standard VACUUM and avoid VACUUM FULL.

VACUUM creates a substantial amount of I/O traffic, which can cause poor performance for other active sessions. There are configuration parameters that can be adjusted to reduce the performance impact of background vacuuming — see Section 19.10.2.

In PostgreSQL, an UPDATE or DELETE of a row does not immediately remove the old version of the row. This approach is necessary to gain the benefits of multiversion concurrency control (MVCC, see Chapter 13): the row version must not be deleted while it is still potentially visible to other transactions. But eventually, an outdated or deleted row version is no longer of interest to any transaction. The space it occupies must then be reclaimed for reuse by new rows, to avoid unbounded growth of disk space requirements. This is done by running VACUUM.

The standard form of VACUUM removes dead row versions in tables and indexes and marks the space available for future reuse. However, it will not return the space to the operating system, except in the special case where one or more pages at the end of a table become entirely free and an exclusive table lock can be easily obtained. In contrast, VACUUM FULL actively compacts tables by writing a complete new version of the table file with no dead space. This minimizes the size of the table, but can take a long time. It also requires extra disk space for the new copy of the table, until the operation completes.

The usual goal of routine vacuuming is to do standard VACUUMs often enough to avoid needing VACUUM FULL. The autovacuum daemon attempts to work this way, and in fact will never issue VACUUM FULL. In this approach, the idea is not to keep tables at their minimum size, but to maintain steady-state usage of disk space: each table occupies space equivalent to its minimum size plus however much space gets used up between vacuum runs. Although VACUUM FULL can be used to shrink a table back to its minimum size and return the disk space to the operating system, there is not much point in this if the table will just grow again in the future. Thus, moderately-frequent standard VACUUM runs are a better approach than infrequent VACUUM FULL runs for maintaining heavily-updated tables.

Some administrators prefer to schedule vacuuming themselves, for example doing all the work at night when load is low. The difficulty with doing vacuuming according to a fixed schedule is that if a table has an unexpected spike in update activity, it may get bloated to the point that VACUUM FULL is really necessary to reclaim space. Using the autovacuum daemon alleviates this problem, since the daemon schedules vacuuming dynamically in response to update activity. It is unwise to disable the daemon completely unless you have an extremely predictable workload. One possible compromise is to set the daemon's parameters so that it will only react to unusually heavy update activity, thus keeping things from getting out of hand, while scheduled VACUUMs are expected to do the bulk of the work when the load is typical.

For those not using autovacuum, a typical approach is to schedule a database-wide VACUUM once a day during a low-usage period, supplemented by more frequent vacuuming of heavily-updated tables as necessary. (Some installations with extremely high update rates vacuum their busiest tables as often as once every few minutes.) If you have multiple databases in a cluster, don't forget to VACUUM each one; the program vacuumdb might be helpful.

Plain VACUUM may not be satisfactory when a table contains large numbers of dead row versions as a result of massive update or delete activity. If you have such a table and you need to reclaim the excess disk space it occupies, you will need to use VACUUM FULL, or alternatively CLUSTER or one of the table-rewriting variants of ALTER TABLE. These commands rewrite an entire new copy of the table and build new indexes for it. All these options require an ACCESS EXCLUSIVE lock. Note that they also temporarily use extra disk space approximately equal to the size of the table, since the old copies of the table and indexes can't be released until the new ones are complete.

If you have a table whose entire contents are deleted on a periodic basis, consider doing it with TRUNCATE rather than using DELETE followed by VACUUM. TRUNCATE removes the entire content of the table immediately, without requiring a subsequent VACUUM or VACUUM FULL to reclaim the now-unused disk space. The disadvantage is that strict MVCC semantics are violated.

The PostgreSQL query planner relies on statistical information about the contents of tables in order to generate good plans for queries. These statistics are gathered by the ANALYZE command, which can be invoked by itself or as an optional step in VACUUM. It is important to have reasonably accurate statistics, otherwise poor choices of plans might degrade database performance.

The autovacuum daemon, if enabled, will automatically issue ANALYZE commands whenever the content of a table has changed sufficiently. However, administrators might prefer to rely on manually-scheduled ANALYZE operations, particularly if it is known that update activity on a table will not affect the statistics of “interesting” columns. The daemon schedules ANALYZE strictly as a function of the number of rows inserted or updated; it has no knowledge of whether that will lead to meaningful statistical changes.

Tuples changed in partitions and inheritance children do not trigger analyze on the parent table. If the parent table is empty or rarely changed, it may never be processed by autovacuum, and the statistics for the inheritance tree as a whole won't be collected. It is necessary to run ANALYZE on the parent table manually in order to keep the statistics up to date.

As with vacuuming for space recovery, frequent updates of statistics are more useful for heavily-updated tables than for seldom-updated ones. But even for a heavily-updated table, there might be no need for statistics updates if the statistical distribution of the data is not changing much. A simple rule of thumb is to think about how much the minimum and maximum values of the columns in the table change. For example, a timestamp column that contains the time of row update will have a constantly-increasing maximum value as rows are added and updated; such a column will probably need more frequent statistics updates than, say, a column containing URLs for pages accessed on a website. The URL column might receive changes just as often, but the statistical distribution of its values probably changes relatively slowly.

It is possible to run ANALYZE on specific tables and even just specific columns of a table, so the flexibility exists to update some statistics more frequently than others if your application requires it. In practice, however, it is usually best to just analyze the entire database, because it is a fast operation. ANALYZE uses a statistically random sampling of the rows of a table rather than reading every single row.

Although per-column tweaking of ANALYZE frequency might not be very productive, you might find it worthwhile to do per-column adjustment of the level of detail of the statistics collected by ANALYZE. Columns that are heavily used in WHERE clauses and have highly irregular data distributions might require a finer-grain data histogram than other columns. See ALTER TABLE SET STATISTICS, or change the database-wide default using the default_statistics_target configuration parameter.

Also, by default there is limited information available about the selectivity of functions. However, if you create a statistics object or an expression index that uses a function call, useful statistics will be gathered about the function, which can greatly improve query plans that use the expression index.

The autovacuum daemon does not issue ANALYZE commands for foreign tables, since it has no means of determining how often that might be useful. If your queries require statistics on foreign tables for proper planning, it's a good idea to run manually-managed ANALYZE commands on those tables on a suitable schedule.

The autovacuum daemon does not issue ANALYZE commands for partitioned tables. Inheritance parents will only be analyzed if the parent itself is changed - changes to child tables do not trigger autoanalyze on the parent table. If your queries require statistics on parent tables for proper planning, it is necessary to periodically run a manual ANALYZE on those tables to keep the statistics up to date.

Vacuum maintains a visibility map for each table to keep track of which pages contain only tuples that are known to be visible to all active transactions (and all future transactions, until the page is again modified). This has two purposes. First, vacuum itself can skip such pages on the next run, since there is nothing to clean up.

Second, it allows PostgreSQL to answer some queries using only the index, without reference to the underlying table. Since PostgreSQL indexes don't contain tuple visibility information, a normal index scan fetches the heap tuple for each matching index entry, to check whether it should be seen by the current transaction. An index-only scan, on the other hand, checks the visibility map first. If it's known that all tuples on the page are visible, the heap fetch can be skipped. This is most useful on large data sets where the visibility map can prevent disk accesses. The visibility map is vastly smaller than the heap, so it can easily be cached even when the heap is very large.

PostgreSQL's MVCC transaction semantics depend on being able to compare transaction ID (XID) numbers: a row version with an insertion XID greater than the current transaction's XID is “in the future” and should not be visible to the current transaction. But since transaction IDs have limited size (32 bits) a cluster that runs for a long time (more than 4 billion transactions) would suffer transaction ID wraparound: the XID counter wraps around to zero, and all of a sudden transactions that were in the past appear to be in the future — which means their output become invisible. In short, catastrophic data loss. (Actually the data is still there, but that's cold comfort if you cannot get at it.) To avoid this, it is necessary to vacuum every table in every database at least once every two billion transactions.

The reason that periodic vacuuming solves the problem is that VACUUM will mark rows as frozen, indicating that they were inserted by a transaction that committed sufficiently far in the past that the effects of the inserting transaction are certain to be visible to all current and future transactions. Normal XIDs are compared using modulo-232 arithmetic. This means that for every normal XID, there are two billion XIDs that are “older” and two billion that are “newer”; another way to say it is that the normal XID space is circular with no endpoint. Therefore, once a row version has been created with a particular normal XID, the row version will appear to be “in the past” for the next two billion transactions, no matter which normal XID we are talking about. If the row version still exists after more than two billion transactions, it will suddenly appear to be in the future. To prevent this, PostgreSQL reserves a special XID, FrozenTransactionId, which does not follow the normal XID comparison rules and is always considered older than every normal XID. Frozen row versions are treated as if the inserting XID were FrozenTransactionId, so that they will appear to be “in the past” to all normal transactions regardless of wraparound issues, and so such row versions will be valid until deleted, no matter how long that is.

In PostgreSQL versions before 9.4, freezing was implemented by actually replacing a row's insertion XID with FrozenTransactionId, which was visible in the row's xmin system column. Newer versions just set a flag bit, preserving the row's original xmin for possible forensic use. However, rows with xmin equal to FrozenTransactionId (2) may still be found in databases pg_upgrade'd from pre-9.4 versions.

Also, system catalogs may contain rows with xmin equal to BootstrapTransactionId (1), indicating that they were inserted during the first phase of initdb. Like FrozenTransactionId, this special XID is treated as older than every normal XID.

vacuum_freeze_min_age controls how old an XID value has to be before rows bearing that XID will be frozen. Increasing this setting may avoid unnecessary work if the rows that would otherwise be frozen will soon be modified again, but decreasing this setting increases the number of transactions that can elapse before the table must be vacuumed again.

VACUUM uses the visibility map to determine which pages of a table must be scanned. Normally, it will skip pages that don't have any dead row versions even if those pages might still have row versions with old XID values. Therefore, normal VACUUMs won't always freeze every old row version in the table. When that happens, VACUUM will eventually need to perform an aggressive vacuum, which will freeze all eligible unfrozen XID and MXID values, including those from all-visible but not all-frozen pages.

If a table is building up a backlog of all-visible but not all-frozen pages, a normal vacuum may choose to scan skippable pages in an effort to freeze them. Doing so decreases the number of pages the next aggressive vacuum must scan. These are referred to as eagerly scanned pages. Eager scanning can be tuned to attempt to freeze more all-visible pages by increasing vacuum_max_eager_freeze_failure_rate. Even if eager scanning has kept the number of all-visible but not all-frozen pages to a minimum, most tables still require periodic aggressive vacuuming. However, any pages successfully eager frozen may be skipped during an aggressive vacuum, so eager freezing may minimize the overhead of aggressive vacuums.

vacuum_freeze_table_age controls when a table is aggressively vacuumed. All all-visible but not all-frozen pages are scanned if the number of transactions that have passed since the last such scan is greater than vacuum_freeze_table_age minus vacuum_freeze_min_age. Setting vacuum_freeze_table_age to 0 forces VACUUM to always use its aggressive strategy.

The maximum time that a table can go unvacuumed is two billion transactions minus the vacuum_freeze_min_age value at the time of the last aggressive vacuum. If it were to go unvacuumed for longer than that, data loss could result. To ensure that this does not happen, autovacuum is invoked on any table that might contain unfrozen rows with XIDs older than the age specified by the configuration parameter autovacuum_freeze_max_age. (This will happen even if autovacuum is disabled.)

This implies that if a table is not otherwise vacuumed, autovacuum will be invoked on it approximately once every autovacuum_freeze_max_age minus vacuum_freeze_min_age transactions. For tables that are regularly vacuumed for space reclamation purposes, this is of little importance. However, for static tables (including tables that receive inserts, but no updates or deletes), there is no need to vacuum for space reclamation, so it can be useful to try to maximize the interval between forced autovacuums on very large static tables. Obviously one can do this either by increasing autovacuum_freeze_max_age or decreasing vacuum_freeze_min_age.

The effective maximum for vacuum_freeze_table_age is 0.95 * autovacuum_freeze_max_age; a setting higher than that will be capped to the maximum. A value higher than autovacuum_freeze_max_age wouldn't make sense because an anti-wraparound autovacuum would be triggered at that point anyway, and the 0.95 multiplier leaves some breathing room to run a manual VACUUM before that happens. As a rule of thumb, vacuum_freeze_table_age should be set to a value somewhat below autovacuum_freeze_max_age, leaving enough gap so that a regularly scheduled VACUUM or an autovacuum triggered by normal delete and update activity is run in that window. Setting it too close could lead to anti-wraparound autovacuums, even though the table was recently vacuumed to reclaim space, whereas lower values lead to more frequent aggressive vacuuming.

The sole disadvantage of increasing autovacuum_freeze_max_age (and vacuum_freeze_table_age along with it) is that the pg_xact and pg_commit_ts subdirectories of the database cluster will take more space, because it must store the commit status and (if track_commit_timestamp is enabled) timestamp of all transactions back to the autovacuum_freeze_max_age horizon. The commit status uses two bits per transaction, so if autovacuum_freeze_max_age is set to its maximum allowed value of two billion, pg_xact can be expected to grow to about half a gigabyte and pg_commit_ts to about 20GB. If this is trivial compared to your total database size, setting autovacuum_freeze_max_age to its maximum allowed value is recommended. Otherwise, set it depending on what you are willing to allow for pg_xact and pg_commit_ts storage. (The default, 200 million transactions, translates to about 50MB of pg_xact storage and about 2GB of pg_commit_ts storage.)

One disadvantage of decreasing vacuum_freeze_min_age is that it might cause VACUUM to do useless work: freezing a row version is a waste of time if the row is modified soon thereafter (causing it to acquire a new XID). So the setting should be large enough that rows are not frozen until they are unlikely to change any more.

To track the age of the oldest unfrozen XIDs in a database, VACUUM stores XID statistics in the system tables pg_class and pg_database. In particular, the relfrozenxid column of a table's pg_class row contains the oldest remaining unfrozen XID at the end of the most recent VACUUM that successfully advanced relfrozenxid (typically the most recent aggressive VACUUM). Similarly, the datfrozenxid column of a database's pg_database row is a lower bound on the unfrozen XIDs appearing in that database — it is just the minimum of the per-table relfrozenxid values within the database. A convenient way to examine this information is to execute queries such as:

The age column measures the number of transactions from the cutoff XID to the current transaction's XID.

When the VACUUM command's VERBOSE parameter is specified, VACUUM prints various statistics about the table. This includes information about how relfrozenxid and relminmxid advanced, and the number of newly frozen pages. The same details appear in the server log when autovacuum logging (controlled by log_autovacuum_min_duration) reports on a VACUUM operation executed by autovacuum.

While VACUUM scans mostly pages that have been modified since the last vacuum, it may also eagerly scan some all-visible but not all-frozen pages in an attempt to freeze them, but the relfrozenxid will only be advanced when every page of the table that might contain unfrozen XIDs is scanned. This happens when relfrozenxid is more than vacuum_freeze_table_age transactions old, when VACUUM's FREEZE option is used, or when all pages that are not already all-frozen happen to require vacuuming to remove dead row versions. When VACUUM scans every page in the table that is not already all-frozen, it should set age(relfrozenxid) to a value just a little more than the vacuum_freeze_min_age setting that was used (more by the number of transactions started since the VACUUM started). VACUUM will set relfrozenxid to the oldest XID that remains in the table, so it's possible that the final value will be much more recent than strictly required. If no relfrozenxid-advancing VACUUM is issued on the table until autovacuum_freeze_max_age is reached, an autovacuum will soon be forced for the table.

If for some reason autovacuum fails to clear old XIDs from a table, the system will begin to emit warning messages like this when the database's oldest XIDs reach forty million transactions from the wraparound point:

(A manual VACUUM should fix the problem, as suggested by the hint; but note that the VACUUM should be performed by a superuser, else it will fail to process system catalogs, which prevent it from being able to advance the database's datfrozenxid.) If these warnings are ignored, the system will refuse to assign new XIDs once there are fewer than three million transactions left until wraparound:

In this condition any transactions already in progress can continue, but only read-only transactions can be started. Operations that modify database records or truncate relations will fail. The VACUUM command can still be run normally. Note that, contrary to what was sometimes recommended in earlier releases, it is not necessary or desirable to stop the postmaster or enter single user-mode in order to restore normal operation. Instead, follow these steps:

In earlier versions, it was sometimes necessary to stop the postmaster and VACUUM the database in a single-user mode. In typical scenarios, this is no longer necessary, and should be avoided whenever possible, since it involves taking the system down. It is also riskier, since it disables transaction ID wraparound safeguards that are designed to prevent data loss. The only reason to use single-user mode in this scenario is if you wish to TRUNCATE or DROP unneeded tables to avoid needing to VACUUM them. The three-million-transaction safety margin exists to let the administrator do this. See the postgres reference page for details about using single-user mode.

Multixact IDs are used to support row locking by multiple transactions. Since there is only limited space in a tuple header to store lock information, that information is encoded as a “multiple transaction ID”, or multixact ID for short, whenever there is more than one transaction concurrently locking a row. Information about which transaction IDs are included in any particular multixact ID is stored separately in the pg_multixact subdirectory, and only the multixact ID appears in the xmax field in the tuple header. Like transaction IDs, multixact IDs are implemented as a 32-bit counter and corresponding storage, all of which requires careful aging management, storage cleanup, and wraparound handling. There is a separate storage area which holds the list of members in each multixact, which also uses a 32-bit counter and which must also be managed. The system function pg_get_multixact_members() described in Table 9.84 can be used to examine the transaction IDs associated with a multixact ID.

Whenever VACUUM scans any part of a table, it will replace any multixact ID it encounters which is older than vacuum_multixact_freeze_min_age by a different value, which can be the zero value, a single transaction ID, or a newer multixact ID. For each table, pg_class.relminmxid stores the oldest possible multixact ID still appearing in any tuple of that table. If this value is older than vacuum_multixact_freeze_table_age, an aggressive vacuum is forced. As discussed in the previous section, an aggressive vacuum means that only those pages which are known to be all-frozen will be skipped. mxid_age() can be used on pg_class.relminmxid to find its age.

Aggressive VACUUMs, regardless of what causes them, are guaranteed to be able to advance the table's relminmxid. Eventually, as all tables in all databases are scanned and their oldest multixact values are advanced, on-disk storage for older multixacts can be removed.

As a safety device, an aggressive vacuum scan will occur for any table whose multixact-age is greater than autovacuum_multixact_freeze_max_age. Also, if the storage occupied by multixacts members exceeds about 10GB, aggressive vacuum scans will occur more often for all tables, starting with those that have the oldest multixact-age. Both of these kinds of aggressive scans will occur even if autovacuum is nominally disabled. The members storage area can grow up to about 20GB before reaching wraparound.

Similar to the XID case, if autovacuum fails to clear old MXIDs from a table, the system will begin to emit warning messages when the database's oldest MXIDs reach forty million transactions from the wraparound point. And, just as in the XID case, if these warnings are ignored, the system will refuse to generate new MXIDs once there are fewer than three million left until wraparound.

Normal operation when MXIDs are exhausted can be restored in much the same way as when XIDs are exhausted. Follow the same steps in the previous section, but with the following differences:

PostgreSQL has an optional but highly recommended feature called autovacuum, whose purpose is to automate the execution of VACUUM and ANALYZE commands. When enabled, autovacuum checks for tables that have had a large number of inserted, updated or deleted tuples. These checks use the statistics collection facility; therefore, autovacuum cannot be used unless track_counts is set to true. In the default configuration, autovacuuming is enabled and the related configuration parameters are appropriately set.

The “autovacuum daemon” actually consists of multiple processes. There is a persistent daemon process, called the autovacuum launcher, which is in charge of starting autovacuum worker processes for all databases. The launcher will distribute the work across time, attempting to start one worker within each database every autovacuum_naptime seconds. (Therefore, if the installation has N databases, a new worker will be launched every autovacuum_naptime/N seconds.) A maximum of autovacuum_max_workers worker processes are allowed to run at the same time. If there are more than autovacuum_max_workers databases to be processed, the next database will be processed as soon as the first worker finishes. Each worker process will check each table within its database and execute VACUUM and/or ANALYZE as needed. log_autovacuum_min_duration can be set to monitor autovacuum workers' activity.

If several large tables all become eligible for vacuuming in a short amount of time, all autovacuum workers might become occupied with vacuuming those tables for a long period. This would result in other tables and databases not being vacuumed until a worker becomes available. There is no limit on how many workers might be in a single database, but workers do try to avoid repeating work that has already been done by other workers. Note that the number of running workers does not count towards max_connections or superuser_reserved_connections limits.

Tables whose relfrozenxid value is more than autovacuum_freeze_max_age transactions old are always vacuumed (this also applies to those tables whose freeze max age has been modified via storage parameters; see below). Otherwise, if the number of tuples obsoleted since the last VACUUM exceeds the “vacuum threshold”, the table is vacuumed. The vacuum threshold is defined as:

where the vacuum max threshold is autovacuum_vacuum_max_threshold, the vacuum base threshold is autovacuum_vacuum_threshold, the vacuum scale factor is autovacuum_vacuum_scale_factor, and the number of tuples is pg_class.reltuples.

The table is also vacuumed if the number of tuples inserted since the last vacuum has exceeded the defined insert threshold, which is defined as:

where the vacuum insert base threshold is autovacuum_vacuum_insert_threshold, and vacuum insert scale factor is autovacuum_vacuum_insert_scale_factor. Such vacuums may allow portions of the table to be marked as all visible and also allow tuples to be frozen, which can reduce the work required in subsequent vacuums. For tables which receive INSERT operations but no or almost no UPDATE/DELETE operations, it may be beneficial to lower the table's autovacuum_freeze_min_age as this may allow tuples to be frozen by earlier vacuums. The number of obsolete tuples and the number of inserted tuples are obtained from the cumulative statistics system; it is an eventually-consistent count updated by each UPDATE, DELETE and INSERT operation. If the relfrozenxid value of the table is more than vacuum_freeze_table_age transactions old, an aggressive vacuum is performed to freeze old tuples and advance relfrozenxid.

For analyze, a similar condition is used: the threshold, defined as:

is compared to the total number of tuples inserted, updated, or deleted since the last ANALYZE.

Partitioned tables do not directly store tuples and consequently are not processed by autovacuum. (Autovacuum does process table partitions just like other tables.) Unfortunately, this means that autovacuum does not run ANALYZE on partitioned tables, and this can cause suboptimal plans for queries that reference partitioned table statistics. You can work around this problem by manually running ANALYZE on partitioned tables when they are first populated, and again whenever the distribution of data in their partitions changes significantly.

Temporary tables cannot be accessed by autovacuum. Therefore, appropriate vacuum and analyze operations should be performed via session SQL commands.

The default thresholds and scale factors are taken from postgresql.conf, but it is possible to override them (and many other autovacuum control parameters) on a per-table basis; see Storage Parameters for more information. If a setting has been changed via a table's storage parameters, that value is used when processing that table; otherwise the global settings are used. See Section 19.10.1 for more details on the global settings.

When multiple workers are running, the autovacuum cost delay parameters (see Section 19.10.2) are “balanced” among all the running workers, so that the total I/O impact on the system is the same regardless of the number of workers actually running. However, any workers processing tables whose per-table autovacuum_vacuum_cost_delay or autovacuum_vacuum_cost_limit storage parameters have been set are not considered in the balancing algorithm.

Autovacuum workers generally don't block other commands. If a process attempts to acquire a lock that conflicts with the SHARE UPDATE EXCLUSIVE lock held by autovacuum, lock acquisition will interrupt the autovacuum. For conflicting lock modes, see Table 13.2. However, if the autovacuum is running to prevent transaction ID wraparound (i.e., the autovacuum query name in the pg_stat_activity view ends with (to prevent wraparound)), the autovacuum is not automatically interrupted.

Regularly running commands that acquire locks conflicting with a SHARE UPDATE EXCLUSIVE lock (e.g., ANALYZE) can effectively prevent autovacuums from ever completing.

Examples:

Example 1 (unknown):

SELECT c.oid::regclass as table_name,
       greatest(age(c.relfrozenxid),age(t.relfrozenxid)) as age
FROM pg_class c
LEFT JOIN pg_class t ON c.reltoastrelid = t.oid
WHERE c.relkind IN ('r', 'm');

SELECT datname, age(datfrozenxid) FROM pg_database;

Example 2 (unknown):

WARNING:  database "mydb" must be vacuumed within 39985967 transactions
HINT:  To avoid XID assignment failures, execute a database-wide VACUUM in that database.

Example 3 (unknown):

ERROR:  database is not accepting commands that assign new XIDs to avoid wraparound data loss in database "mydb"
HINT:  Execute a database-wide VACUUM in that database.

Example 4 (unknown):

vacuum threshold = Minimum(vacuum max threshold, vacuum base threshold + vacuum scale factor * number of tuples)

---

PostgreSQL: Documentation: 18: 35.46. schemata

URL: https://www.postgresql.org/docs/current/infoschema-schemata.html

Contents:

• 35.46. schemata #

The view schemata contains all schemas in the current database that the current user has access to (by way of being the owner or having some privilege).

Table 35.44. schemata Columns

catalog_name sql_identifier

Name of the database that the schema is contained in (always the current database)

schema_name sql_identifier

schema_owner sql_identifier

Name of the owner of the schema

default_character_set_catalog sql_identifier

Applies to a feature not available in PostgreSQL

default_character_set_schema sql_identifier

Applies to a feature not available in PostgreSQL

default_character_set_name sql_identifier

Applies to a feature not available in PostgreSQL

sql_path character_data

Applies to a feature not available in PostgreSQL

---

PostgreSQL: Documentation: 18: 32.19. SSL Support

URL: https://www.postgresql.org/docs/current/libpq-ssl.html

Contents:

• 32.19. SSL Support #
  • 32.19.1. Client Verification of Server Certificates #
  • Note
  • Note
  • 32.19.2. Client Certificates #
  • 32.19.3. Protection Provided in Different Modes #
  • 32.19.4. SSL Client File Usage #
  • 32.19.5. SSL Library Initialization #

PostgreSQL has native support for using SSL connections to encrypt client/server communications using TLS protocols for increased security. See Section 18.9 for details about the server-side SSL functionality.

libpq reads the system-wide OpenSSL configuration file. By default, this file is named openssl.cnf and is located in the directory reported by openssl version -d. This default can be overridden by setting environment variable OPENSSL_CONF to the name of the desired configuration file.

By default, PostgreSQL will not perform any verification of the server certificate. This means that it is possible to spoof the server identity (for example by modifying a DNS record or by taking over the server IP address) without the client knowing. In order to prevent spoofing, the client must be able to verify the server's identity via a chain of trust. A chain of trust is established by placing a root (self-signed) certificate authority (CA) certificate on one computer and a leaf certificate signed by the root certificate on another computer. It is also possible to use an “intermediate” certificate which is signed by the root certificate and signs leaf certificates.

To allow the client to verify the identity of the server, place a root certificate on the client and a leaf certificate signed by the root certificate on the server. To allow the server to verify the identity of the client, place a root certificate on the server and a leaf certificate signed by the root certificate on the client. One or more intermediate certificates (usually stored with the leaf certificate) can also be used to link the leaf certificate to the root certificate.

Once a chain of trust has been established, there are two ways for the client to validate the leaf certificate sent by the server. If the parameter sslmode is set to verify-ca, libpq will verify that the server is trustworthy by checking the certificate chain up to the root certificate stored on the client. If sslmode is set to verify-full, libpq will also verify that the server host name matches the name stored in the server certificate. The SSL connection will fail if the server certificate cannot be verified. verify-full is recommended in most security-sensitive environments.

In verify-full mode, the host name is matched against the certificate's Subject Alternative Name attribute(s) (SAN), or against the Common Name attribute if no SAN of type dNSName is present. If the certificate's name attribute starts with an asterisk (*), the asterisk will be treated as a wildcard, which will match all characters except a dot (.). This means the certificate will not match subdomains. If the connection is made using an IP address instead of a host name, the IP address will be matched (without doing any DNS lookups) against SANs of type iPAddress or dNSName. If no iPAddress SAN is present and no matching dNSName SAN is present, the host IP address is matched against the Common Name attribute.

For backward compatibility with earlier versions of PostgreSQL, the host IP address is verified in a manner different from RFC 6125. The host IP address is always matched against dNSName SANs as well as iPAddress SANs, and can be matched against the Common Name attribute if no relevant SANs exist.

To allow server certificate verification, one or more root certificates must be placed in the file ~/.postgresql/root.crt in the user's home directory. (On Microsoft Windows the file is named %APPDATA%\postgresql\root.crt.) Intermediate certificates should also be added to the file if they are needed to link the certificate chain sent by the server to the root certificates stored on the client.

Certificate Revocation List (CRL) entries are also checked if the file ~/.postgresql/root.crl exists (%APPDATA%\postgresql\root.crl on Microsoft Windows).

The location of the root certificate file and the CRL can be changed by setting the connection parameters sslrootcert and sslcrl or the environment variables PGSSLROOTCERT and PGSSLCRL. sslcrldir or the environment variable PGSSLCRLDIR can also be used to specify a directory containing CRL files.

For backwards compatibility with earlier versions of PostgreSQL, if a root CA file exists, the behavior of sslmode=require will be the same as that of verify-ca, meaning the server certificate is validated against the CA. Relying on this behavior is discouraged, and applications that need certificate validation should always use verify-ca or verify-full.

If the server attempts to verify the identity of the client by requesting the client's leaf certificate, libpq will send the certificate(s) stored in file ~/.postgresql/postgresql.crt in the user's home directory. The certificates must chain to the root certificate trusted by the server. A matching private key file ~/.postgresql/postgresql.key must also be present. On Microsoft Windows these files are named %APPDATA%\postgresql\postgresql.crt and %APPDATA%\postgresql\postgresql.key. The location of the certificate and key files can be overridden by the connection parameters sslcert and sslkey, or by the environment variables PGSSLCERT and PGSSLKEY.

On Unix systems, the permissions on the private key file must disallow any access to world or group; achieve this by a command such as chmod 0600 ~/.postgresql/postgresql.key. Alternatively, the file can be owned by root and have group read access (that is, 0640 permissions). That setup is intended for installations where certificate and key files are managed by the operating system. The user of libpq should then be made a member of the group that has access to those certificate and key files. (On Microsoft Windows, there is no file permissions check, since the %APPDATA%\postgresql directory is presumed secure.)

The first certificate in postgresql.crt must be the client's certificate because it must match the client's private key. “Intermediate” certificates can be optionally appended to the file — doing so avoids requiring storage of intermediate certificates on the server (ssl_ca_file).

The certificate and key may be in PEM or ASN.1 DER format.

The key may be stored in cleartext or encrypted with a passphrase using any algorithm supported by OpenSSL, like AES-128. If the key is stored encrypted, then the passphrase may be provided in the sslpassword connection option. If an encrypted key is supplied and the sslpassword option is absent or blank, a password will be prompted for interactively by OpenSSL with a Enter PEM pass phrase: prompt if a TTY is available. Applications can override the client certificate prompt and the handling of the sslpassword parameter by supplying their own key password callback; see PQsetSSLKeyPassHook_OpenSSL.

For instructions on creating certificates, see Section 18.9.5.

The different values for the sslmode parameter provide different levels of protection. SSL can provide protection against three types of attacks:

If a third party can examine the network traffic between the client and the server, it can read both connection information (including the user name and password) and the data that is passed. SSL uses encryption to prevent this.

If a third party can modify the data while passing between the client and server, it can pretend to be the server and therefore see and modify data even if it is encrypted. The third party can then forward the connection information and data to the original server, making it impossible to detect this attack. Common vectors to do this include DNS poisoning and address hijacking, whereby the client is directed to a different server than intended. There are also several other attack methods that can accomplish this. SSL uses certificate verification to prevent this, by authenticating the server to the client.

If a third party can pretend to be an authorized client, it can simply access data it should not have access to. Typically this can happen through insecure password management. SSL uses client certificates to prevent this, by making sure that only holders of valid certificates can access the server.

For a connection to be known SSL-secured, SSL usage must be configured on both the client and the server before the connection is made. If it is only configured on the server, the client may end up sending sensitive information (e.g., passwords) before it knows that the server requires high security. In libpq, secure connections can be ensured by setting the sslmode parameter to verify-full or verify-ca, and providing the system with a root certificate to verify against. This is analogous to using an https URL for encrypted web browsing.

Once the server has been authenticated, the client can pass sensitive data. This means that up until this point, the client does not need to know if certificates will be used for authentication, making it safe to specify that only in the server configuration.

All SSL options carry overhead in the form of encryption and key-exchange, so there is a trade-off that has to be made between performance and security. Table 32.1 illustrates the risks the different sslmode values protect against, and what statement they make about security and overhead.

Table 32.1. SSL Mode Descriptions

The difference between verify-ca and verify-full depends on the policy of the root CA. If a public CA is used, verify-ca allows connections to a server that somebody else may have registered with the CA. In this case, verify-full should always be used. If a local CA is used, or even a self-signed certificate, using verify-ca often provides enough protection.

The default value for sslmode is prefer. As is shown in the table, this makes no sense from a security point of view, and it only promises performance overhead if possible. It is only provided as the default for backward compatibility, and is not recommended in secure deployments.

Table 32.2 summarizes the files that are relevant to the SSL setup on the client.

Table 32.2. Libpq/Client SSL File Usage

Applications which need to be compatible with older versions of PostgreSQL, using OpenSSL version 1.0.2 or older, need to initialize the SSL library before using it. Applications which initialize libssl and/or libcrypto libraries should call PQinitOpenSSL to tell libpq that the libssl and/or libcrypto libraries have been initialized by your application, so that libpq will not also initialize those libraries. However, this is unnecessary when using OpenSSL version 1.1.0 or later, as duplicate initializations are no longer problematic.

Refer to the documentation for the version of PostgreSQL that you are targeting for details on their use.

Allows applications to select which security libraries to initialize.

This function is deprecated and only present for backwards compatibility, it does nothing.

Allows applications to select which security libraries to initialize.

This function is equivalent to PQinitOpenSSL(do_ssl, do_ssl). This function is deprecated and only present for backwards compatibility, it does nothing.

PQinitSSL and PQinitOpenSSL are maintained for backwards compatibility, but are no longer required since PostgreSQL 18. PQinitSSL has been present since PostgreSQL 8.0, while PQinitOpenSSL was added in PostgreSQL 8.4, so PQinitSSL might be preferable for applications that need to work with older versions of libpq.

Examples:

Example 1 (unknown):

void PQinitOpenSSL(int do_ssl, int do_crypto);

Example 2 (unknown):

void PQinitSSL(int do_ssl);

---

PostgreSQL: Documentation: 18: Chapter 13. Concurrency Control

URL: https://www.postgresql.org/docs/current/mvcc.html

Contents:

• Chapter 13. Concurrency Control

This chapter describes the behavior of the PostgreSQL database system when two or more sessions try to access the same data at the same time. The goals in that situation are to allow efficient access for all sessions while maintaining strict data integrity. Every developer of database applications should be familiar with the topics covered in this chapter.

---

PostgreSQL: Documentation: 18: CONNECT

URL: https://www.postgresql.org/docs/current/ecpg-sql-connect.html

Contents:

• CONNECT
• Synopsis
• Description
• Parameters
• Examples
• Compatibility
• See Also

CONNECT — establish a database connection

The CONNECT command establishes a connection between the client and the PostgreSQL server.

connection_target specifies the target server of the connection on one of several forms.

Connect over Unix-domain sockets

containing a value in one of the above forms

host variable of type char[] or VARCHAR[] containing a value in one of the above forms

An optional identifier for the connection, so that it can be referred to in other commands. This can be an SQL identifier or a host variable.

The user name for the database connection.

This parameter can also specify user name and password, using one the forms user_name/password, user_name IDENTIFIED BY password, or user_name USING password.

User name and password can be SQL identifiers, string constants, or host variables.

Use all default connection parameters, as defined by libpq.

Here a several variants for specifying connection parameters:

Here is an example program that illustrates the use of host variables to specify connection parameters:

CONNECT is specified in the SQL standard, but the format of the connection parameters is implementation-specific.

Examples:

Example 1 (unknown):

CONNECT TO connection_target [ AS connection_name ] [ USER connection_user ]
CONNECT TO DEFAULT
CONNECT connection_user
DATABASE connection_target

Example 2 (unknown):

EXEC SQL CONNECT TO "connectdb" AS main;
EXEC SQL CONNECT TO "connectdb" AS second;
EXEC SQL CONNECT TO "unix:postgresql://200.46.204.71/connectdb" AS main USER connectuser;
EXEC SQL CONNECT TO "unix:postgresql://localhost/connectdb" AS main USER connectuser;
EXEC SQL CONNECT TO 'connectdb' AS main;
EXEC SQL CONNECT TO 'unix:postgresql://localhost/connectdb' AS main USER :user;
EXEC SQL CONNECT TO :db AS :id;
EXEC SQL CONNECT TO :db USER connectuser USING :pw;
EXEC SQL CONNECT TO @localhost AS main USER connectdb;
EXEC SQL CONNECT TO REGRESSDB1 as main;
EXEC SQL CONNECT TO AS main USER connectdb;
EXEC SQL CONNECT TO connectdb AS :id;
EXEC SQL CONNECT TO connectdb AS main USER connectuser/connectdb;
EXEC SQL CONNECT TO connectdb AS main;
EXEC SQL CONNECT TO connectdb@localhost AS main;
EXEC SQL CONNECT TO tcp:postgresql://localhost/ USER connectdb;
EXEC SQL CONNECT TO tcp:postgresql://localhost/connectdb USER connectuser IDENTIFIED BY connectpw;
EXEC SQL CONNECT TO tcp:postgresql://localhost:20/connectdb USER connectuser IDENTIFIED BY connectpw;
EXEC SQL CONNECT TO unix:postgresql://localhost/ AS main USER connectdb;
EXEC SQL CONNECT TO unix:postgresql://localhost/connectdb AS main USER connectuser;
EXEC SQL CONNECT TO unix:postgresql://localhost/connectdb USER connectuser IDENTIFIED BY "connectpw";
EXEC SQL CONNECT TO unix:postgresql://localhost/connectdb USER connectuser USING "connectpw";
EXEC SQL CONNECT TO unix:postgresql://localhost/connectdb?connect_timeout=14 USER connectuser;

Example 3 (unknown):

int
main(void)
{
EXEC SQL BEGIN DECLARE SECTION;
    char *dbname     = "testdb";    /* database name */
    char *user       = "testuser";  /* connection user name */
    char *connection = "tcp:postgresql://localhost:5432/testdb";
                                    /* connection string */
    char ver[256];                  /* buffer to store the version string */
EXEC SQL END DECLARE SECTION;

    ECPGdebug(1, stderr);

    EXEC SQL CONNECT TO :dbname USER :user;
    EXEC SQL SELECT pg_catalog.set_config('search_path', '', false); EXEC SQL COMMIT;
    EXEC SQL SELECT version() INTO :ver;
    EXEC SQL DISCONNECT;

    printf("version: %s\n", ver);

    EXEC SQL CONNECT TO :connection USER :user;
    EXEC SQL SELECT pg_catalog.set_config('search_path', '', false); EXEC SQL COMMIT;
    EXEC SQL SELECT version() INTO :ver;
    EXEC SQL DISCONNECT;

    printf("version: %s\n", ver);

    return 0;
}

---

PostgreSQL: Documentation: 18: 36.18. Extension Building Infrastructure

URL: https://www.postgresql.org/docs/current/extend-pgxs.html

Contents:

• 36.18. Extension Building Infrastructure #
  • Tip

If you are thinking about distributing your PostgreSQL extension modules, setting up a portable build system for them can be fairly difficult. Therefore the PostgreSQL installation provides a build infrastructure for extensions, called PGXS, so that simple extension modules can be built simply against an already installed server. PGXS is mainly intended for extensions that include C code, although it can be used for pure-SQL extensions too. Note that PGXS is not intended to be a universal build system framework that can be used to build any software interfacing to PostgreSQL; it simply automates common build rules for simple server extension modules. For more complicated packages, you might need to write your own build system.

To use the PGXS infrastructure for your extension, you must write a simple makefile. In the makefile, you need to set some variables and include the global PGXS makefile. Here is an example that builds an extension module named isbn_issn, consisting of a shared library containing some C code, an extension control file, an SQL script, an include file (only needed if other modules might need to access the extension functions without going via SQL), and a documentation text file:

The last three lines should always be the same. Earlier in the file, you assign variables or add custom make rules.

Set one of these three variables to specify what is built:

list of shared-library objects to be built from source files with same stem (do not include library suffixes in this list)

a shared library to build from multiple source files (list object files in OBJS)

an executable program to build (list object files in OBJS)

The following variables can also be set:

extension name(s); for each name you must provide an extension.control file, which will be installed into prefix/share/extension

subdirectory of prefix/share into which DATA and DOCS files should be installed (if not set, default is extension if EXTENSION is set, or contrib if not)

random files to install into prefix/share/$MODULEDIR

random files to install into prefix/share/$MODULEDIR, which need to be built first

random files to install under prefix/share/tsearch_data

random files to install under prefix/doc/$MODULEDIR

Files to (optionally build and) install under prefix/include/server/$MODULEDIR/$MODULE_big.

Unlike DATA_built, files in HEADERS_built are not removed by the clean target; if you want them removed, also add them to EXTRA_CLEAN or add your own rules to do it.

Files to install (after building if specified) under prefix/include/server/$MODULEDIR/$MODULE, where $MODULE must be a module name used in MODULES or MODULE_big.

Unlike DATA_built, files in HEADERSbuilt$MODULE are not removed by the clean target; if you want them removed, also add them to EXTRA_CLEAN or add your own rules to do it.

It is legal to use both variables for the same module, or any combination, unless you have two module names in the MODULES list that differ only by the presence of a prefix built_, which would cause ambiguity. In that (hopefully unlikely) case, you should use only the HEADERSbuilt$MODULE variables.

script files (not binaries) to install into prefix/bin

script files (not binaries) to install into prefix/bin, which need to be built first

list of regression test cases (without suffix), see below

additional switches to pass to pg_regress

list of isolation test cases, see below for more details

additional switches to pass to pg_isolation_regress

switch defining if TAP tests need to be run, see below

don't define an install target, useful for test modules that don't need their build products to be installed

don't define an installcheck target, useful e.g., if tests require special configuration, or don't use pg_regress

extra files to remove in make clean

will be prepended to CPPFLAGS

will be appended to CFLAGS

will be appended to CXXFLAGS

will be prepended to LDFLAGS

will be added to PROGRAM link line

will be added to MODULE_big link line

path to pg_config program for the PostgreSQL installation to build against (typically just pg_config to use the first one in your PATH)

Put this makefile as Makefile in the directory which holds your extension. Then you can do make to compile, and then make install to install your module. By default, the extension is compiled and installed for the PostgreSQL installation that corresponds to the first pg_config program found in your PATH. You can use a different installation by setting PG_CONFIG to point to its pg_config program, either within the makefile or on the make command line.

You can select a separate directory prefix in which to install your extension's files, by setting the make variable prefix when executing make install like so:

This will install the extension control and SQL files into /usr/local/postgresql/share and the shared modules into /usr/local/postgresql/lib. If the prefix does not include the strings postgres or pgsql, such as

then postgresql will be appended to the directory names, installing the control and SQL files into /usr/local/extras/share/postgresql/extension and the shared modules into /usr/local/extras/lib/postgresql. Either way, you'll need to set extension_control_path and dynamic_library_path to enable the PostgreSQL server to find the files:

You can also run make in a directory outside the source tree of your extension, if you want to keep the build directory separate. This procedure is also called a VPATH build. Here's how:

Alternatively, you can set up a directory for a VPATH build in a similar way to how it is done for the core code. One way to do this is using the core script config/prep_buildtree. Once this has been done you can build by setting the make variable VPATH like this:

This procedure can work with a greater variety of directory layouts.

The scripts listed in the REGRESS variable are used for regression testing of your module, which can be invoked by make installcheck after doing make install. For this to work you must have a running PostgreSQL server. The script files listed in REGRESS must appear in a subdirectory named sql/ in your extension's directory. These files must have extension .sql, which must not be included in the REGRESS list in the makefile. For each test there should also be a file containing the expected output in a subdirectory named expected/, with the same stem and extension .out. make installcheck executes each test script with psql, and compares the resulting output to the matching expected file. Any differences will be written to the file regression.diffs in diff -c format. Note that trying to run a test that is missing its expected file will be reported as “trouble”, so make sure you have all expected files.

The scripts listed in the ISOLATION variable are used for tests stressing behavior of concurrent session with your module, which can be invoked by make installcheck after doing make install. For this to work you must have a running PostgreSQL server. The script files listed in ISOLATION must appear in a subdirectory named specs/ in your extension's directory. These files must have extension .spec, which must not be included in the ISOLATION list in the makefile. For each test there should also be a file containing the expected output in a subdirectory named expected/, with the same stem and extension .out. make installcheck executes each test script, and compares the resulting output to the matching expected file. Any differences will be written to the file output_iso/regression.diffs in diff -c format. Note that trying to run a test that is missing its expected file will be reported as “trouble”, so make sure you have all expected files.

TAP_TESTS enables the use of TAP tests. Data from each run is present in a subdirectory named tmp_check/. See also Section 31.4 for more details.

The easiest way to create the expected files is to create empty files, then do a test run (which will of course report differences). Inspect the actual result files found in the results/ directory (for tests in REGRESS), or output_iso/results/ directory (for tests in ISOLATION), then copy them to expected/ if they match what you expect from the test.

Examples:

Example 1 (unknown):

MODULES = isbn_issn
EXTENSION = isbn_issn
DATA = isbn_issn--1.0.sql
DOCS = README.isbn_issn
HEADERS_isbn_issn = isbn_issn.h

PG_CONFIG = pg_config
PGXS := $(shell $(PG_CONFIG) --pgxs)
include $(PGXS)

Example 2 (unknown):

make install prefix=/usr/local/postgresql

Example 3 (unknown):

make install prefix=/usr/local/extras

Example 4 (unknown):

extension_control_path = '/usr/local/extras/share/postgresql:$system'
dynamic_library_path = '/usr/local/extras/lib/postgresql:$libdir'

---

PostgreSQL: Documentation: 18: 5.7. Modifying Tables

URL: https://www.postgresql.org/docs/current/ddl-alter.html

Contents:

• 5.7. Modifying Tables #
  • 5.7.1. Adding a Column #
  • Tip
  • 5.7.2. Removing a Column #
  • 5.7.3. Adding a Constraint #
  • 5.7.4. Removing a Constraint #
  • 5.7.5. Changing a Column's Default Value #
  • 5.7.6. Changing a Column's Data Type #
  • 5.7.7. Renaming a Column #
  • 5.7.8. Renaming a Table #

When you create a table and you realize that you made a mistake, or the requirements of the application change, you can drop the table and create it again. But this is not a convenient option if the table is already filled with data, or if the table is referenced by other database objects (for instance a foreign key constraint). Therefore PostgreSQL provides a family of commands to make modifications to existing tables. Note that this is conceptually distinct from altering the data contained in the table: here we are interested in altering the definition, or structure, of the table.

Change default values

Change column data types

All these actions are performed using the ALTER TABLE command, whose reference page contains details beyond those given here.

To add a column, use a command like:

The new column is initially filled with whatever default value is given (null if you don't specify a DEFAULT clause).

Adding a column with a constant default value does not require each row of the table to be updated when the ALTER TABLE statement is executed. Instead, the default value will be returned the next time the row is accessed, and applied when the table is rewritten, making the ALTER TABLE very fast even on large tables.

If the default value is volatile (e.g., clock_timestamp()) each row will need to be updated with the value calculated at the time ALTER TABLE is executed. To avoid a potentially lengthy update operation, particularly if you intend to fill the column with mostly nondefault values anyway, it may be preferable to add the column with no default, insert the correct values using UPDATE, and then add any desired default as described below.

You can also define constraints on the column at the same time, using the usual syntax:

In fact all the options that can be applied to a column description in CREATE TABLE can be used here. Keep in mind however that the default value must satisfy the given constraints, or the ADD will fail. Alternatively, you can add constraints later (see below) after you've filled in the new column correctly.

To remove a column, use a command like:

Whatever data was in the column disappears. Table constraints involving the column are dropped, too. However, if the column is referenced by a foreign key constraint of another table, PostgreSQL will not silently drop that constraint. You can authorize dropping everything that depends on the column by adding CASCADE:

See Section 5.15 for a description of the general mechanism behind this.

To add a constraint, the table constraint syntax is used. For example:

To add a not-null constraint, which is normally not written as a table constraint, this special syntax is available:

This command silently does nothing if the column already has a not-null constraint.

The constraint will be checked immediately, so the table data must satisfy the constraint before it can be added.

To remove a constraint you need to know its name. If you gave it a name then that's easy. Otherwise the system assigned a generated name, which you need to find out. The psql command \d tablename can be helpful here; other interfaces might also provide a way to inspect table details. Then the command is:

As with dropping a column, you need to add CASCADE if you want to drop a constraint that something else depends on. An example is that a foreign key constraint depends on a unique or primary key constraint on the referenced column(s).

Simplified syntax is available to drop a not-null constraint:

This mirrors the SET NOT NULL syntax for adding a not-null constraint. This command will silently do nothing if the column does not have a not-null constraint. (Recall that a column can have at most one not-null constraint, so it is never ambiguous which constraint this command acts on.)

To set a new default for a column, use a command like:

Note that this doesn't affect any existing rows in the table, it just changes the default for future INSERT commands.

To remove any default value, use:

This is effectively the same as setting the default to null. As a consequence, it is not an error to drop a default where one hadn't been defined, because the default is implicitly the null value.

To convert a column to a different data type, use a command like:

This will succeed only if each existing entry in the column can be converted to the new type by an implicit cast. If a more complex conversion is needed, you can add a USING clause that specifies how to compute the new values from the old.

PostgreSQL will attempt to convert the column's default value (if any) to the new type, as well as any constraints that involve the column. But these conversions might fail, or might produce surprising results. It's often best to drop any constraints on the column before altering its type, and then add back suitably modified constraints afterwards.

Examples:

Example 1 (unknown):

ALTER TABLE products ADD COLUMN description text;

Example 2 (unknown):

ALTER TABLE products ADD COLUMN description text CHECK (description <> '');

Example 3 (unknown):

ALTER TABLE products DROP COLUMN description;

Example 4 (unknown):

ALTER TABLE products DROP COLUMN description CASCADE;

---

PostgreSQL: Documentation: 18: 11.3. Multicolumn Indexes

URL: https://www.postgresql.org/docs/current/indexes-multicolumn.html

Contents:

• 11.3. Multicolumn Indexes #

An index can be defined on more than one column of a table. For example, if you have a table of this form:

(say, you keep your /dev directory in a database...) and you frequently issue queries like:

then it might be appropriate to define an index on the columns major and minor together, e.g.:

Currently, only the B-tree, GiST, GIN, and BRIN index types support multiple-key-column indexes. Whether there can be multiple key columns is independent of whether INCLUDE columns can be added to the index. Indexes can have up to 32 columns, including INCLUDE columns. (This limit can be altered when building PostgreSQL; see the file pg_config_manual.h.)

A multicolumn B-tree index can be used with query conditions that involve any subset of the index's columns, but the index is most efficient when there are constraints on the leading (leftmost) columns. The exact rule is that equality constraints on leading columns, plus any inequality constraints on the first column that does not have an equality constraint, will always be used to limit the portion of the index that is scanned. Constraints on columns to the right of these columns are checked in the index, so they'll always save visits to the table proper, but they do not necessarily reduce the portion of the index that has to be scanned. If a B-tree index scan can apply the skip scan optimization effectively, it will apply every column constraint when navigating through the index via repeated index searches. This can reduce the portion of the index that has to be read, even though one or more columns (prior to the least significant index column from the query predicate) lacks a conventional equality constraint. Skip scan works by generating a dynamic equality constraint internally, that matches every possible value in an index column (though only given a column that lacks an equality constraint that comes from the query predicate, and only when the generated constraint can be used in conjunction with a later column constraint from the query predicate).

For example, given an index on (x, y), and a query condition WHERE y = 7700, a B-tree index scan might be able to apply the skip scan optimization. This generally happens when the query planner expects that repeated WHERE x = N AND y = 7700 searches for every possible value of N (or for every x value that is actually stored in the index) is the fastest possible approach, given the available indexes on the table. This approach is generally only taken when there are so few distinct x values that the planner expects the scan to skip over most of the index (because most of its leaf pages cannot possibly contain relevant tuples). If there are many distinct x values, then the entire index will have to be scanned, so in most cases the planner will prefer a sequential table scan over using the index.

The skip scan optimization can also be applied selectively, during B-tree scans that have at least some useful constraints from the query predicate. For example, given an index on (a, b, c) and a query condition WHERE a = 5 AND b >= 42 AND c < 77, the index might have to be scanned from the first entry with a = 5 and b = 42 up through the last entry with a = 5. Index entries with c >= 77 will never need to be filtered at the table level, but it may or may not be profitable to skip over them within the index. When skipping takes place, the scan starts a new index search to reposition itself from the end of the current a = 5 and b = N grouping (i.e. from the position in the index where the first tuple a = 5 AND b = N AND c >= 77 appears), to the start of the next such grouping (i.e. the position in the index where the first tuple a = 5 AND b = N + 1 appears).

A multicolumn GiST index can be used with query conditions that involve any subset of the index's columns. Conditions on additional columns restrict the entries returned by the index, but the condition on the first column is the most important one for determining how much of the index needs to be scanned. A GiST index will be relatively ineffective if its first column has only a few distinct values, even if there are many distinct values in additional columns.

A multicolumn GIN index can be used with query conditions that involve any subset of the index's columns. Unlike B-tree or GiST, index search effectiveness is the same regardless of which index column(s) the query conditions use.

A multicolumn BRIN index can be used with query conditions that involve any subset of the index's columns. Like GIN and unlike B-tree or GiST, index search effectiveness is the same regardless of which index column(s) the query conditions use. The only reason to have multiple BRIN indexes instead of one multicolumn BRIN index on a single table is to have a different pages_per_range storage parameter.

Of course, each column must be used with operators appropriate to the index type; clauses that involve other operators will not be considered.

Multicolumn indexes should be used sparingly. In most situations, an index on a single column is sufficient and saves space and time. Indexes with more than three columns are unlikely to be helpful unless the usage of the table is extremely stylized. See also Section 11.5 and Section 11.9 for some discussion of the merits of different index configurations.

Examples:

Example 1 (unknown):

CREATE TABLE test2 (
  major int,
  minor int,
  name varchar
);

Example 2 (unknown):

SELECT name FROM test2 WHERE major = constant AND minor = constant;

Example 3 (unknown):

CREATE INDEX test2_mm_idx ON test2 (major, minor);

---

PostgreSQL: Documentation: 18: 5.14. Other Database Objects

URL: https://www.postgresql.org/docs/current/ddl-others.html

Contents:

• 5.14. Other Database Objects #

Tables are the central objects in a relational database structure, because they hold your data. But they are not the only objects that exist in a database. Many other kinds of objects can be created to make the use and management of the data more efficient or convenient. They are not discussed in this chapter, but we give you a list here so that you are aware of what is possible:

Functions, procedures, and operators

Data types and domains

Triggers and rewrite rules

Detailed information on these topics appears in Part V.

---

PostgreSQL: Documentation: 18: 9.17. Sequence Manipulation Functions

URL: https://www.postgresql.org/docs/current/functions-sequence.html

Contents:

• 9.17. Sequence Manipulation Functions #
  • Caution

This section describes functions for operating on sequence objects, also called sequence generators or just sequences. Sequence objects are special single-row tables created with CREATE SEQUENCE. Sequence objects are commonly used to generate unique identifiers for rows of a table. The sequence functions, listed in Table 9.55, provide simple, multiuser-safe methods for obtaining successive sequence values from sequence objects.

Table 9.55. Sequence Functions

nextval ( regclass ) → bigint

Advances the sequence object to its next value and returns that value. This is done atomically: even if multiple sessions execute nextval concurrently, each will safely receive a distinct sequence value. If the sequence object has been created with default parameters, successive nextval calls will return successive values beginning with 1. Other behaviors can be obtained by using appropriate parameters in the CREATE SEQUENCE command.

This function requires USAGE or UPDATE privilege on the sequence.

setval ( regclass, bigint [, boolean ] ) → bigint

Sets the sequence object's current value, and optionally its is_called flag. The two-parameter form sets the sequence's last_value field to the specified value and sets its is_called field to true, meaning that the next nextval will advance the sequence before returning a value. The value that will be reported by currval is also set to the specified value. In the three-parameter form, is_called can be set to either true or false. true has the same effect as the two-parameter form. If it is set to false, the next nextval will return exactly the specified value, and sequence advancement commences with the following nextval. Furthermore, the value reported by currval is not changed in this case. For example,

The result returned by setval is just the value of its second argument.

This function requires UPDATE privilege on the sequence.

currval ( regclass ) → bigint

Returns the value most recently obtained by nextval for this sequence in the current session. (An error is reported if nextval has never been called for this sequence in this session.) Because this is returning a session-local value, it gives a predictable answer whether or not other sessions have executed nextval since the current session did.

This function requires USAGE or SELECT privilege on the sequence.

Returns the value most recently returned by nextval in the current session. This function is identical to currval, except that instead of taking the sequence name as an argument it refers to whichever sequence nextval was most recently applied to in the current session. It is an error to call lastval if nextval has not yet been called in the current session.

This function requires USAGE or SELECT privilege on the last used sequence.

To avoid blocking concurrent transactions that obtain numbers from the same sequence, the value obtained by nextval is not reclaimed for re-use if the calling transaction later aborts. This means that transaction aborts or database crashes can result in gaps in the sequence of assigned values. That can happen without a transaction abort, too. For example an INSERT with an ON CONFLICT clause will compute the to-be-inserted tuple, including doing any required nextval calls, before detecting any conflict that would cause it to follow the ON CONFLICT rule instead. Thus, PostgreSQL sequence objects cannot be used to obtain “gapless” sequences.

Likewise, sequence state changes made by setval are immediately visible to other transactions, and are not undone if the calling transaction rolls back.

If the database cluster crashes before committing a transaction containing a nextval or setval call, the sequence state change might not have made its way to persistent storage, so that it is uncertain whether the sequence will have its original or updated state after the cluster restarts. This is harmless for usage of the sequence within the database, since other effects of uncommitted transactions will not be visible either. However, if you wish to use a sequence value for persistent outside-the-database purposes, make sure that the nextval call has been committed before doing so.

The sequence to be operated on by a sequence function is specified by a regclass argument, which is simply the OID of the sequence in the pg_class system catalog. You do not have to look up the OID by hand, however, since the regclass data type's input converter will do the work for you. See Section 8.19 for details.

Examples:

Example 1 (unknown):

SELECT setval('myseq', 42);           Next nextval will return 43
SELECT setval('myseq', 42, true);     Same as above
SELECT setval('myseq', 42, false);    Next nextval will return 42

---

PostgreSQL: Documentation: 18: 9.25. Row and Array Comparisons

URL: https://www.postgresql.org/docs/current/functions-comparisons.html

Contents:

• 9.25. Row and Array Comparisons #
  • 9.25.1. IN #
  • 9.25.2. NOT IN #
  • Tip
  • 9.25.3. ANY/SOME (array) #
  • 9.25.4. ALL (array) #
  • 9.25.5. Row Constructor Comparison #
  • 9.25.6. Composite Type Comparison #

This section describes several specialized constructs for making multiple comparisons between groups of values. These forms are syntactically related to the subquery forms of the previous section, but do not involve subqueries. The forms involving array subexpressions are PostgreSQL extensions; the rest are SQL-compliant. All of the expression forms documented in this section return Boolean (true/false) results.

The right-hand side is a parenthesized list of expressions. The result is “true” if the left-hand expression's result is equal to any of the right-hand expressions. This is a shorthand notation for

Note that if the left-hand expression yields null, or if there are no equal right-hand values and at least one right-hand expression yields null, the result of the IN construct will be null, not false. This is in accordance with SQL's normal rules for Boolean combinations of null values.

The right-hand side is a parenthesized list of expressions. The result is “true” if the left-hand expression's result is unequal to all of the right-hand expressions. This is a shorthand notation for

Note that if the left-hand expression yields null, or if there are no equal right-hand values and at least one right-hand expression yields null, the result of the NOT IN construct will be null, not true as one might naively expect. This is in accordance with SQL's normal rules for Boolean combinations of null values.

x NOT IN y is equivalent to NOT (x IN y) in all cases. However, null values are much more likely to trip up the novice when working with NOT IN than when working with IN. It is best to express your condition positively if possible.

The right-hand side is a parenthesized expression, which must yield an array value. The left-hand expression is evaluated and compared to each element of the array using the given operator, which must yield a Boolean result. The result of ANY is “true” if any true result is obtained. The result is “false” if no true result is found (including the case where the array has zero elements).

If the array expression yields a null array, the result of ANY will be null. If the left-hand expression yields null, the result of ANY is ordinarily null (though a non-strict comparison operator could possibly yield a different result). Also, if the right-hand array contains any null elements and no true comparison result is obtained, the result of ANY will be null, not false (again, assuming a strict comparison operator). This is in accordance with SQL's normal rules for Boolean combinations of null values.

SOME is a synonym for ANY.

The right-hand side is a parenthesized expression, which must yield an array value. The left-hand expression is evaluated and compared to each element of the array using the given operator, which must yield a Boolean result. The result of ALL is “true” if all comparisons yield true (including the case where the array has zero elements). The result is “false” if any false result is found.

If the array expression yields a null array, the result of ALL will be null. If the left-hand expression yields null, the result of ALL is ordinarily null (though a non-strict comparison operator could possibly yield a different result). Also, if the right-hand array contains any null elements and no false comparison result is obtained, the result of ALL will be null, not true (again, assuming a strict comparison operator). This is in accordance with SQL's normal rules for Boolean combinations of null values.

Each side is a row constructor, as described in Section 4.2.13. The two row constructors must have the same number of fields. The given operator is applied to each pair of corresponding fields. (Since the fields could be of different types, this means that a different specific operator could be selected for each pair.) All the selected operators must be members of some B-tree operator class, or be the negator of an = member of a B-tree operator class, meaning that row constructor comparison is only possible when the operator is =, <>, <, <=, >, or >=, or has semantics similar to one of these.

The = and <> cases work slightly differently from the others. Two rows are considered equal if all their corresponding members are non-null and equal; the rows are unequal if any corresponding members are non-null and unequal; otherwise the result of the row comparison is unknown (null).

For the <, <=, > and >= cases, the row elements are compared left-to-right, stopping as soon as an unequal or null pair of elements is found. If either of this pair of elements is null, the result of the row comparison is unknown (null); otherwise comparison of this pair of elements determines the result. For example, ROW(1,2,NULL) < ROW(1,3,0) yields true, not null, because the third pair of elements are not considered.

This construct is similar to a <> row comparison, but it does not yield null for null inputs. Instead, any null value is considered unequal to (distinct from) any non-null value, and any two nulls are considered equal (not distinct). Thus the result will either be true or false, never null.

This construct is similar to a = row comparison, but it does not yield null for null inputs. Instead, any null value is considered unequal to (distinct from) any non-null value, and any two nulls are considered equal (not distinct). Thus the result will always be either true or false, never null.

The SQL specification requires row-wise comparison to return NULL if the result depends on comparing two NULL values or a NULL and a non-NULL. PostgreSQL does this only when comparing the results of two row constructors (as in Section 9.25.5) or comparing a row constructor to the output of a subquery (as in Section 9.24). In other contexts where two composite-type values are compared, two NULL field values are considered equal, and a NULL is considered larger than a non-NULL. This is necessary in order to have consistent sorting and indexing behavior for composite types.

Each side is evaluated and they are compared row-wise. Composite type comparisons are allowed when the operator is =, <>, <, <=, > or >=, or has semantics similar to one of these. (To be specific, an operator can be a row comparison operator if it is a member of a B-tree operator class, or is the negator of the = member of a B-tree operator class.) The default behavior of the above operators is the same as for IS [ NOT ] DISTINCT FROM for row constructors (see Section 9.25.5).

To support matching of rows which include elements without a default B-tree operator class, the following operators are defined for composite type comparison: *=, *<>, *<, *<=, *>, and *>=. These operators compare the internal binary representation of the two rows. Two rows might have a different binary representation even though comparisons of the two rows with the equality operator is true. The ordering of rows under these comparison operators is deterministic but not otherwise meaningful. These operators are used internally for materialized views and might be useful for other specialized purposes such as replication and B-Tree deduplication (see Section 65.1.4.3). They are not intended to be generally useful for writing queries, though.

Examples:

Example 1 (unknown):

expression IN (value [, ...])

Example 2 (unknown):

expression = value1
OR
expression = value2
OR
...

Example 3 (unknown):

expression NOT IN (value [, ...])

Example 4 (unknown):

expression <> value1
AND
expression <> value2
AND
...

---

PostgreSQL: Documentation: 18: 28.4. Asynchronous Commit

URL: https://www.postgresql.org/docs/current/wal-async-commit.html

Contents:

• 28.4. Asynchronous Commit #
  • Caution

Asynchronous commit is an option that allows transactions to complete more quickly, at the cost that the most recent transactions may be lost if the database should crash. In many applications this is an acceptable trade-off.

As described in the previous section, transaction commit is normally synchronous: the server waits for the transaction's WAL records to be flushed to permanent storage before returning a success indication to the client. The client is therefore guaranteed that a transaction reported to be committed will be preserved, even in the event of a server crash immediately after. However, for short transactions this delay is a major component of the total transaction time. Selecting asynchronous commit mode means that the server returns success as soon as the transaction is logically completed, before the WAL records it generated have actually made their way to disk. This can provide a significant boost in throughput for small transactions.

Asynchronous commit introduces the risk of data loss. There is a short time window between the report of transaction completion to the client and the time that the transaction is truly committed (that is, it is guaranteed not to be lost if the server crashes). Thus asynchronous commit should not be used if the client will take external actions relying on the assumption that the transaction will be remembered. As an example, a bank would certainly not use asynchronous commit for a transaction recording an ATM's dispensing of cash. But in many scenarios, such as event logging, there is no need for a strong guarantee of this kind.

The risk that is taken by using asynchronous commit is of data loss, not data corruption. If the database should crash, it will recover by replaying WAL up to the last record that was flushed. The database will therefore be restored to a self-consistent state, but any transactions that were not yet flushed to disk will not be reflected in that state. The net effect is therefore loss of the last few transactions. Because the transactions are replayed in commit order, no inconsistency can be introduced — for example, if transaction B made changes relying on the effects of a previous transaction A, it is not possible for A's effects to be lost while B's effects are preserved.

The user can select the commit mode of each transaction, so that it is possible to have both synchronous and asynchronous commit transactions running concurrently. This allows flexible trade-offs between performance and certainty of transaction durability. The commit mode is controlled by the user-settable parameter synchronous_commit, which can be changed in any of the ways that a configuration parameter can be set. The mode used for any one transaction depends on the value of synchronous_commit when transaction commit begins.

Certain utility commands, for instance DROP TABLE, are forced to commit synchronously regardless of the setting of synchronous_commit. This is to ensure consistency between the server's file system and the logical state of the database. The commands supporting two-phase commit, such as PREPARE TRANSACTION, are also always synchronous.

If the database crashes during the risk window between an asynchronous commit and the writing of the transaction's WAL records, then changes made during that transaction will be lost. The duration of the risk window is limited because a background process (the “WAL writer”) flushes unwritten WAL records to disk every wal_writer_delay milliseconds. The actual maximum duration of the risk window is three times wal_writer_delay because the WAL writer is designed to favor writing whole pages at a time during busy periods.

An immediate-mode shutdown is equivalent to a server crash, and will therefore cause loss of any unflushed asynchronous commits.

Asynchronous commit provides behavior different from setting fsync = off. fsync is a server-wide setting that will alter the behavior of all transactions. It disables all logic within PostgreSQL that attempts to synchronize writes to different portions of the database, and therefore a system crash (that is, a hardware or operating system crash, not a failure of PostgreSQL itself) could result in arbitrarily bad corruption of the database state. In many scenarios, asynchronous commit provides most of the performance improvement that could be obtained by turning off fsync, but without the risk of data corruption.

commit_delay also sounds very similar to asynchronous commit, but it is actually a synchronous commit method (in fact, commit_delay is ignored during an asynchronous commit). commit_delay causes a delay just before a transaction flushes WAL to disk, in the hope that a single flush executed by one such transaction can also serve other transactions committing at about the same time. The setting can be thought of as a way of increasing the time window in which transactions can join a group about to participate in a single flush, to amortize the cost of the flush among multiple transactions.

---

PostgreSQL: Documentation: 18: Appendix B. Date/Time Support

URL: https://www.postgresql.org/docs/current/datetime-appendix.html

Contents:

• Appendix B. Date/Time Support

PostgreSQL uses an internal heuristic parser for all date/time input support. Dates and times are input as strings, and are broken up into distinct fields with a preliminary determination of what kind of information can be in the field. Each field is interpreted and either assigned a numeric value, ignored, or rejected. The parser contains internal lookup tables for all textual fields, including months, days of the week, and time zones.

This appendix includes information on the content of these lookup tables and describes the steps used by the parser to decode dates and times.

---

PostgreSQL: Documentation: 18: Chapter 21. Database Roles

URL: https://www.postgresql.org/docs/current/user-manag.html

Contents:

• Chapter 21. Database Roles

PostgreSQL manages database access permissions using the concept of roles. A role can be thought of as either a database user, or a group of database users, depending on how the role is set up. Roles can own database objects (for example, tables and functions) and can assign privileges on those objects to other roles to control who has access to which objects. Furthermore, it is possible to grant membership in a role to another role, thus allowing the member role to use privileges assigned to another role.

The concept of roles subsumes the concepts of “users” and “groups”. In PostgreSQL versions before 8.1, users and groups were distinct kinds of entities, but now there are only roles. Any role can act as a user, a group, or both.

This chapter describes how to create and manage roles. More information about the effects of role privileges on various database objects can be found in Section 5.8.

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PostgreSQL: Documentation: 18: 35.66. views

URL: https://www.postgresql.org/docs/current/infoschema-views.html

Contents:

• 35.66. views #

The view views contains all views defined in the current database. Only those views are shown that the current user has access to (by way of being the owner or having some privilege).

Table 35.64. views Columns

table_catalog sql_identifier

Name of the database that contains the view (always the current database)

table_schema sql_identifier

Name of the schema that contains the view

table_name sql_identifier

view_definition character_data

Query expression defining the view (null if the view is not owned by a currently enabled role)

check_option character_data

CASCADED or LOCAL if the view has a CHECK OPTION defined on it, NONE if not

is_updatable yes_or_no

YES if the view is updatable (allows UPDATE and DELETE), NO if not

is_insertable_into yes_or_no

YES if the view is insertable into (allows INSERT), NO if not

is_trigger_updatable yes_or_no

YES if the view has an INSTEAD OF UPDATE trigger defined on it, NO if not

is_trigger_deletable yes_or_no

YES if the view has an INSTEAD OF DELETE trigger defined on it, NO if not

is_trigger_insertable_into yes_or_no

YES if the view has an INSTEAD OF INSERT trigger defined on it, NO if not

---

PostgreSQL: Documentation: 18: 35.57. triggers

URL: https://www.postgresql.org/docs/current/infoschema-triggers.html

Contents:

• 35.57. triggers #
  • Note

The view triggers contains all triggers defined in the current database on tables and views that the current user owns or has some privilege other than SELECT on.

Table 35.55. triggers Columns

trigger_catalog sql_identifier

Name of the database that contains the trigger (always the current database)

trigger_schema sql_identifier

Name of the schema that contains the trigger

trigger_name sql_identifier

event_manipulation character_data

Event that fires the trigger (INSERT, UPDATE, or DELETE)

event_object_catalog sql_identifier

Name of the database that contains the table that the trigger is defined on (always the current database)

event_object_schema sql_identifier

Name of the schema that contains the table that the trigger is defined on

event_object_table sql_identifier

Name of the table that the trigger is defined on

action_order cardinal_number

Firing order among triggers on the same table having the same event_manipulation, action_timing, and action_orientation. In PostgreSQL, triggers are fired in name order, so this column reflects that.

action_condition character_data

WHEN condition of the trigger, null if none (also null if the table is not owned by a currently enabled role)

action_statement character_data

Statement that is executed by the trigger (currently always EXECUTE FUNCTION function(...))

action_orientation character_data

Identifies whether the trigger fires once for each processed row or once for each statement (ROW or STATEMENT)

action_timing character_data

Time at which the trigger fires (BEFORE, AFTER, or INSTEAD OF)

action_reference_old_table sql_identifier

Name of the “old” transition table, or null if none

action_reference_new_table sql_identifier

Name of the “new” transition table, or null if none

action_reference_old_row sql_identifier

Applies to a feature not available in PostgreSQL

action_reference_new_row sql_identifier

Applies to a feature not available in PostgreSQL

Applies to a feature not available in PostgreSQL

Triggers in PostgreSQL have two incompatibilities with the SQL standard that affect the representation in the information schema. First, trigger names are local to each table in PostgreSQL, rather than being independent schema objects. Therefore there can be duplicate trigger names defined in one schema, so long as they belong to different tables. (trigger_catalog and trigger_schema are really the values pertaining to the table that the trigger is defined on.) Second, triggers can be defined to fire on multiple events in PostgreSQL (e.g., ON INSERT OR UPDATE), whereas the SQL standard only allows one. If a trigger is defined to fire on multiple events, it is represented as multiple rows in the information schema, one for each type of event. As a consequence of these two issues, the primary key of the view triggers is really (trigger_catalog, trigger_schema, event_object_table, trigger_name, event_manipulation) instead of (trigger_catalog, trigger_schema, trigger_name), which is what the SQL standard specifies. Nonetheless, if you define your triggers in a manner that conforms with the SQL standard (trigger names unique in the schema and only one event type per trigger), this will not affect you.

Prior to PostgreSQL 9.1, this view's columns action_timing, action_reference_old_table, action_reference_new_table, action_reference_old_row, and action_reference_new_row were named condition_timing, condition_reference_old_table, condition_reference_new_table, condition_reference_old_row, and condition_reference_new_row respectively. That was how they were named in the SQL:1999 standard. The new naming conforms to SQL:2003 and later.

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PostgreSQL: Documentation: 18: 8.19. Object Identifier Types

URL: https://www.postgresql.org/docs/current/datatype-oid.html

Contents:

• 8.19. Object Identifier Types #
  • Note

Object identifiers (OIDs) are used internally by PostgreSQL as primary keys for various system tables. Type oid represents an object identifier. There are also several alias types for oid, each named regsomething. Table 8.26 shows an overview.

The oid type is currently implemented as an unsigned four-byte integer. Therefore, it is not large enough to provide database-wide uniqueness in large databases, or even in large individual tables.

The oid type itself has few operations beyond comparison. It can be cast to integer, however, and then manipulated using the standard integer operators. (Beware of possible signed-versus-unsigned confusion if you do this.)

The OID alias types have no operations of their own except for specialized input and output routines. These routines are able to accept and display symbolic names for system objects, rather than the raw numeric value that type oid would use. The alias types allow simplified lookup of OID values for objects. For example, to examine the pg_attribute rows related to a table mytable, one could write:

While that doesn't look all that bad by itself, it's still oversimplified. A far more complicated sub-select would be needed to select the right OID if there are multiple tables named mytable in different schemas. The regclass input converter handles the table lookup according to the schema path setting, and so it does the “right thing” automatically. Similarly, casting a table's OID to regclass is handy for symbolic display of a numeric OID.

Table 8.26. Object Identifier Types

All of the OID alias types for objects that are grouped by namespace accept schema-qualified names, and will display schema-qualified names on output if the object would not be found in the current search path without being qualified. For example, myschema.mytable is acceptable input for regclass (if there is such a table). That value might be output as myschema.mytable, or just mytable, depending on the current search path. The regproc and regoper alias types will only accept input names that are unique (not overloaded), so they are of limited use; for most uses regprocedure or regoperator are more appropriate. For regoperator, unary operators are identified by writing NONE for the unused operand.

The input functions for these types allow whitespace between tokens, and will fold upper-case letters to lower case, except within double quotes; this is done to make the syntax rules similar to the way object names are written in SQL. Conversely, the output functions will use double quotes if needed to make the output be a valid SQL identifier. For example, the OID of a function named Foo (with upper case F) taking two integer arguments could be entered as ' "Foo" ( int, integer ) '::regprocedure. The output would look like "Foo"(integer,integer). Both the function name and the argument type names could be schema-qualified, too.

Many built-in PostgreSQL functions accept the OID of a table, or another kind of database object, and for convenience are declared as taking regclass (or the appropriate OID alias type). This means you do not have to look up the object's OID by hand, but can just enter its name as a string literal. For example, the nextval(regclass) function takes a sequence relation's OID, so you could call it like this:

When you write the argument of such a function as an unadorned literal string, it becomes a constant of type regclass (or the appropriate type). Since this is really just an OID, it will track the originally identified object despite later renaming, schema reassignment, etc. This “early binding” behavior is usually desirable for object references in column defaults and views. But sometimes you might want “late binding” where the object reference is resolved at run time. To get late-binding behavior, force the constant to be stored as a text constant instead of regclass:

The to_regclass() function and its siblings can also be used to perform run-time lookups. See Table 9.76.

Another practical example of use of regclass is to look up the OID of a table listed in the information_schema views, which don't supply such OIDs directly. One might for example wish to call the pg_relation_size() function, which requires the table OID. Taking the above rules into account, the correct way to do that is

The quote_ident() function will take care of double-quoting the identifiers where needed. The seemingly easier

is not recommended, because it will fail for tables that are outside your search path or have names that require quoting.

An additional property of most of the OID alias types is the creation of dependencies. If a constant of one of these types appears in a stored expression (such as a column default expression or view), it creates a dependency on the referenced object. For example, if a column has a default expression nextval('my_seq'::regclass), PostgreSQL understands that the default expression depends on the sequence my_seq, so the system will not let the sequence be dropped without first removing the default expression. The alternative of nextval('my_seq'::text) does not create a dependency. (regrole is an exception to this property. Constants of this type are not allowed in stored expressions.)

Another identifier type used by the system is xid, or transaction (abbreviated xact) identifier. This is the data type of the system columns xmin and xmax. Transaction identifiers are 32-bit quantities. In some contexts, a 64-bit variant xid8 is used. Unlike xid values, xid8 values increase strictly monotonically and cannot be reused in the lifetime of a database cluster. See Section 67.1 for more details.

A third identifier type used by the system is cid, or command identifier. This is the data type of the system columns cmin and cmax. Command identifiers are also 32-bit quantities.

A final identifier type used by the system is tid, or tuple identifier (row identifier). This is the data type of the system column ctid. A tuple ID is a pair (block number, tuple index within block) that identifies the physical location of the row within its table.

(The system columns are further explained in Section 5.6.)

Examples:

Example 1 (unknown):

SELECT * FROM pg_attribute WHERE attrelid = 'mytable'::regclass;

Example 2 (unknown):

SELECT * FROM pg_attribute
  WHERE attrelid = (SELECT oid FROM pg_class WHERE relname = 'mytable');

Example 3 (unknown):

nextval('foo')              operates on sequence foo
nextval('FOO')              same as above
nextval('"Foo"')            operates on sequence Foo
nextval('myschema.foo')     operates on myschema.foo
nextval('"myschema".foo')   same as above
nextval('foo')              searches search path for foo

Example 4 (unknown):

nextval('foo'::text)      foo is looked up at runtime

---

PostgreSQL: Documentation: 18: 8.5. Date/Time Types

URL: https://www.postgresql.org/docs/current/datatype-datetime.html

Contents:

• 8.5. Date/Time Types #
  • Note
  • 8.5.1. Date/Time Input #
  • 8.5.1.1. Dates #
  • 8.5.1.2. Times #
  • 8.5.1.3. Time Stamps #
  • 8.5.1.4. Special Values #
  • Caution
  • 8.5.2. Date/Time Output #
  • Note

PostgreSQL supports the full set of SQL date and time types, shown in Table 8.9. The operations available on these data types are described in Section 9.9. Dates are counted according to the Gregorian calendar, even in years before that calendar was introduced (see Section B.6 for more information).

Table 8.9. Date/Time Types

The SQL standard requires that writing just timestamp be equivalent to timestamp without time zone, and PostgreSQL honors that behavior. timestamptz is accepted as an abbreviation for timestamp with time zone; this is a PostgreSQL extension.

time, timestamp, and interval accept an optional precision value p which specifies the number of fractional digits retained in the seconds field. By default, there is no explicit bound on precision. The allowed range of p is from 0 to 6.

The interval type has an additional option, which is to restrict the set of stored fields by writing one of these phrases:

Note that if both fields and p are specified, the fields must include SECOND, since the precision applies only to the seconds.

The type time with time zone is defined by the SQL standard, but the definition exhibits properties which lead to questionable usefulness. In most cases, a combination of date, time, timestamp without time zone, and timestamp with time zone should provide a complete range of date/time functionality required by any application.

Date and time input is accepted in almost any reasonable format, including ISO 8601, SQL-compatible, traditional POSTGRES, and others. For some formats, ordering of day, month, and year in date input is ambiguous and there is support for specifying the expected ordering of these fields. Set the DateStyle parameter to MDY to select month-day-year interpretation, DMY to select day-month-year interpretation, or YMD to select year-month-day interpretation.

PostgreSQL is more flexible in handling date/time input than the SQL standard requires. See Appendix B for the exact parsing rules of date/time input and for the recognized text fields including months, days of the week, and time zones.

Remember that any date or time literal input needs to be enclosed in single quotes, like text strings. Refer to Section 4.1.2.7 for more information. SQL requires the following syntax

where p is an optional precision specification giving the number of fractional digits in the seconds field. Precision can be specified for time, timestamp, and interval types, and can range from 0 to 6. If no precision is specified in a constant specification, it defaults to the precision of the literal value (but not more than 6 digits).

Table 8.10 shows some possible inputs for the date type.

Table 8.10. Date Input

The time-of-day types are time [ (p) ] without time zone and time [ (p) ] with time zone. time alone is equivalent to time without time zone.

Valid input for these types consists of a time of day followed by an optional time zone. (See Table 8.11 and Table 8.12.) If a time zone is specified in the input for time without time zone, it is silently ignored. You can also specify a date but it will be ignored, except when you use a time zone name that involves a daylight-savings rule, such as America/New_York. In this case specifying the date is required in order to determine whether standard or daylight-savings time applies. The appropriate time zone offset is recorded in the time with time zone value and is output as stored; it is not adjusted to the active time zone.

Table 8.11. Time Input

Table 8.12. Time Zone Input

Refer to Section 8.5.3 for more information on how to specify time zones.

Valid input for the time stamp types consists of the concatenation of a date and a time, followed by an optional time zone, followed by an optional AD or BC. (Alternatively, AD/BC can appear before the time zone, but this is not the preferred ordering.) Thus:

are valid values, which follow the ISO 8601 standard. In addition, the common format:

The SQL standard differentiates timestamp without time zone and timestamp with time zone literals by the presence of a “+” or “-” symbol and time zone offset after the time. Hence, according to the standard,

is a timestamp without time zone, while

is a timestamp with time zone. PostgreSQL never examines the content of a literal string before determining its type, and therefore will treat both of the above as timestamp without time zone. To ensure that a literal is treated as timestamp with time zone, give it the correct explicit type:

In a value that has been determined to be timestamp without time zone, PostgreSQL will silently ignore any time zone indication. That is, the resulting value is derived from the date/time fields in the input string, and is not adjusted for time zone.

For timestamp with time zone values, an input string that includes an explicit time zone will be converted to UTC (Universal Coordinated Time) using the appropriate offset for that time zone. If no time zone is stated in the input string, then it is assumed to be in the time zone indicated by the system's TimeZone parameter, and is converted to UTC using the offset for the timezone zone. In either case, the value is stored internally as UTC, and the originally stated or assumed time zone is not retained.

When a timestamp with time zone value is output, it is always converted from UTC to the current timezone zone, and displayed as local time in that zone. To see the time in another time zone, either change timezone or use the AT TIME ZONE construct (see Section 9.9.4).

Conversions between timestamp without time zone and timestamp with time zone normally assume that the timestamp without time zone value should be taken or given as timezone local time. A different time zone can be specified for the conversion using AT TIME ZONE.

PostgreSQL supports several special date/time input values for convenience, as shown in Table 8.13. The values infinity and -infinity are specially represented inside the system and will be displayed unchanged; but the others are simply notational shorthands that will be converted to ordinary date/time values when read. (In particular, now and related strings are converted to a specific time value as soon as they are read.) All of these values need to be enclosed in single quotes when used as constants in SQL commands.

Table 8.13. Special Date/Time Inputs

The following SQL-compatible functions can also be used to obtain the current time value for the corresponding data type: CURRENT_DATE, CURRENT_TIME, CURRENT_TIMESTAMP, LOCALTIME, LOCALTIMESTAMP. (See Section 9.9.5.) Note that these are SQL functions and are not recognized in data input strings.

While the input strings now, today, tomorrow, and yesterday are fine to use in interactive SQL commands, they can have surprising behavior when the command is saved to be executed later, for example in prepared statements, views, and function definitions. The string can be converted to a specific time value that continues to be used long after it becomes stale. Use one of the SQL functions instead in such contexts. For example, CURRENT_DATE + 1 is safer than 'tomorrow'::date.

The output format of the date/time types can be set to one of the four styles ISO 8601, SQL (Ingres), traditional POSTGRES (Unix date format), or German. The default is the ISO format. (The SQL standard requires the use of the ISO 8601 format. The name of the “SQL” output format is a historical accident.) Table 8.14 shows examples of each output style. The output of the date and time types is generally only the date or time part in accordance with the given examples. However, the POSTGRES style outputs date-only values in ISO format.

Table 8.14. Date/Time Output Styles

ISO 8601 specifies the use of uppercase letter T to separate the date and time. PostgreSQL accepts that format on input, but on output it uses a space rather than T, as shown above. This is for readability and for consistency with RFC 3339 as well as some other database systems.

In the SQL and POSTGRES styles, day appears before month if DMY field ordering has been specified, otherwise month appears before day. (See Section 8.5.1 for how this setting also affects interpretation of input values.) Table 8.15 shows examples.

Table 8.15. Date Order Conventions

In the ISO style, the time zone is always shown as a signed numeric offset from UTC, with positive sign used for zones east of Greenwich. The offset will be shown as hh (hours only) if it is an integral number of hours, else as hh:mm if it is an integral number of minutes, else as hh:mm:ss. (The third case is not possible with any modern time zone standard, but it can appear when working with timestamps that predate the adoption of standardized time zones.) In the other date styles, the time zone is shown as an alphabetic abbreviation if one is in common use in the current zone. Otherwise it appears as a signed numeric offset in ISO 8601 basic format (hh or hhmm). The alphabetic abbreviations shown in these styles are taken from the IANA time zone database entry currently selected by the TimeZone run-time parameter; they are not affected by the timezone_abbreviations setting.

The date/time style can be selected by the user using the SET datestyle command, the DateStyle parameter in the postgresql.conf configuration file, or the PGDATESTYLE environment variable on the server or client.

The formatting function to_char (see Section 9.8) is also available as a more flexible way to format date/time output.

Time zones, and time-zone conventions, are influenced by political decisions, not just earth geometry. Time zones around the world became somewhat standardized during the 1900s, but continue to be prone to arbitrary changes, particularly with respect to daylight-savings rules. PostgreSQL uses the widely-used IANA (Olson) time zone database for information about historical time zone rules. For times in the future, the assumption is that the latest known rules for a given time zone will continue to be observed indefinitely far into the future.

PostgreSQL endeavors to be compatible with the SQL standard definitions for typical usage. However, the SQL standard has an odd mix of date and time types and capabilities. Two obvious problems are:

Although the date type cannot have an associated time zone, the time type can. Time zones in the real world have little meaning unless associated with a date as well as a time, since the offset can vary through the year with daylight-saving time boundaries.

The default time zone is specified as a constant numeric offset from UTC. It is therefore impossible to adapt to daylight-saving time when doing date/time arithmetic across DST boundaries.

To address these difficulties, we recommend using date/time types that contain both date and time when using time zones. We do not recommend using the type time with time zone (though it is supported by PostgreSQL for legacy applications and for compliance with the SQL standard). PostgreSQL assumes your local time zone for any type containing only date or time.

All timezone-aware dates and times are stored internally in UTC. They are converted to local time in the zone specified by the TimeZone configuration parameter before being displayed to the client.

PostgreSQL allows you to specify time zones in three different forms:

A full time zone name, for example America/New_York. The recognized time zone names are listed in the pg_timezone_names view (see Section 53.34). PostgreSQL uses the widely-used IANA time zone data for this purpose, so the same time zone names are also recognized by other software.

A time zone abbreviation, for example PST. Such a specification merely defines a particular offset from UTC, in contrast to full time zone names which can imply a set of daylight savings transition rules as well. The recognized abbreviations are listed in the pg_timezone_abbrevs view (see Section 53.33). You cannot set the configuration parameters TimeZone or log_timezone to a time zone abbreviation, but you can use abbreviations in date/time input values and with the AT TIME ZONE operator.

In addition to the timezone names and abbreviations, PostgreSQL will accept POSIX-style time zone specifications, as described in Section B.5. This option is not normally preferable to using a named time zone, but it may be necessary if no suitable IANA time zone entry is available.

In short, this is the difference between abbreviations and full names: abbreviations represent a specific offset from UTC, whereas many of the full names imply a local daylight-savings time rule, and so have two possible UTC offsets. As an example, 2014-06-04 12:00 America/New_York represents noon local time in New York, which for this particular date was Eastern Daylight Time (UTC-4). So 2014-06-04 12:00 EDT specifies that same time instant. But 2014-06-04 12:00 EST specifies noon Eastern Standard Time (UTC-5), regardless of whether daylight savings was nominally in effect on that date.

The sign in POSIX-style time zone specifications has the opposite meaning of the sign in ISO-8601 datetime values. For example, the POSIX time zone for 2014-06-04 12:00+04 would be UTC-4.

To complicate matters, some jurisdictions have used the same timezone abbreviation to mean different UTC offsets at different times; for example, in Moscow MSK has meant UTC+3 in some years and UTC+4 in others. PostgreSQL interprets such abbreviations according to whatever they meant (or had most recently meant) on the specified date; but, as with the EST example above, this is not necessarily the same as local civil time on that date.

In all cases, timezone names and abbreviations are recognized case-insensitively. (This is a change from PostgreSQL versions prior to 8.2, which were case-sensitive in some contexts but not others.)

Neither timezone names nor abbreviations are hard-wired into the server; they are obtained from configuration files stored under .../share/timezone/ and .../share/timezonesets/ of the installation directory (see Section B.4).

The TimeZone configuration parameter can be set in the file postgresql.conf, or in any of the other standard ways described in Chapter 19. There are also some special ways to set it:

The SQL command SET TIME ZONE sets the time zone for the session. This is an alternative spelling of SET TIMEZONE TO with a more SQL-spec-compatible syntax.

The PGTZ environment variable is used by libpq clients to send a SET TIME ZONE command to the server upon connection.

interval values can be written using the following verbose syntax:

where quantity is a number (possibly signed); unit is microsecond, millisecond, second, minute, hour, day, week, month, year, decade, century, millennium, or abbreviations or plurals of these units; direction can be ago or empty. The at sign (@) is optional noise. The amounts of the different units are implicitly added with appropriate sign accounting. ago negates all the fields. This syntax is also used for interval output, if IntervalStyle is set to postgres_verbose.

Quantities of days, hours, minutes, and seconds can be specified without explicit unit markings. For example, '1 12:59:10' is read the same as '1 day 12 hours 59 min 10 sec'. Also, a combination of years and months can be specified with a dash; for example '200-10' is read the same as '200 years 10 months'. (These shorter forms are in fact the only ones allowed by the SQL standard, and are used for output when IntervalStyle is set to sql_standard.)

Interval values can also be written as ISO 8601 time intervals, using either the “format with designators” of the standard's section 4.4.3.2 or the “alternative format” of section 4.4.3.3. The format with designators looks like this:

The string must start with a P, and may include a T that introduces the time-of-day units. The available unit abbreviations are given in Table 8.16. Units may be omitted, and may be specified in any order, but units smaller than a day must appear after T. In particular, the meaning of M depends on whether it is before or after T.

Table 8.16. ISO 8601 Interval Unit Abbreviations

In the alternative format:

the string must begin with P, and a T separates the date and time parts of the interval. The values are given as numbers similar to ISO 8601 dates.

When writing an interval constant with a fields specification, or when assigning a string to an interval column that was defined with a fields specification, the interpretation of unmarked quantities depends on the fields. For example INTERVAL '1' YEAR is read as 1 year, whereas INTERVAL '1' means 1 second. Also, field values “to the right” of the least significant field allowed by the fields specification are silently discarded. For example, writing INTERVAL '1 day 2:03:04' HOUR TO MINUTE results in dropping the seconds field, but not the day field.

According to the SQL standard all fields of an interval value must have the same sign, so a leading negative sign applies to all fields; for example the negative sign in the interval literal '-1 2:03:04' applies to both the days and hour/minute/second parts. PostgreSQL allows the fields to have different signs, and traditionally treats each field in the textual representation as independently signed, so that the hour/minute/second part is considered positive in this example. If IntervalStyle is set to sql_standard then a leading sign is considered to apply to all fields (but only if no additional signs appear). Otherwise the traditional PostgreSQL interpretation is used. To avoid ambiguity, it's recommended to attach an explicit sign to each field if any field is negative.

Internally, interval values are stored as three integral fields: months, days, and microseconds. These fields are kept separate because the number of days in a month varies, while a day can have 23 or 25 hours if a daylight savings time transition is involved. An interval input string that uses other units is normalized into this format, and then reconstructed in a standardized way for output, for example:

Here weeks, which are understood as “7 days”, have been kept separate, while the smaller and larger time units were combined and normalized.

Input field values can have fractional parts, for example '1.5 weeks' or '01:02:03.45'. However, because interval internally stores only integral fields, fractional values must be converted into smaller units. Fractional parts of units greater than months are rounded to be an integer number of months, e.g. '1.5 years' becomes '1 year 6 mons'. Fractional parts of weeks and days are computed to be an integer number of days and microseconds, assuming 30 days per month and 24 hours per day, e.g., '1.75 months' becomes 1 mon 22 days 12:00:00. Only seconds will ever be shown as fractional on output.

Table 8.17 shows some examples of valid interval input.

Table 8.17. Interval Input

As previously explained, PostgreSQL stores interval values as months, days, and microseconds. For output, the months field is converted to years and months by dividing by 12. The days field is shown as-is. The microseconds field is converted to hours, minutes, seconds, and fractional seconds. Thus months, minutes, and seconds will never be shown as exceeding the ranges 0–11, 0–59, and 0–59 respectively, while the displayed years, days, and hours fields can be quite large. (The justify_days and justify_hours functions can be used if it is desirable to transpose large days or hours values into the next higher field.)

The output format of the interval type can be set to one of the four styles sql_standard, postgres, postgres_verbose, or iso_8601, using the command SET intervalstyle. The default is the postgres format. Table 8.18 shows examples of each output style.

The sql_standard style produces output that conforms to the SQL standard's specification for interval literal strings, if the interval value meets the standard's restrictions (either year-month only or day-time only, with no mixing of positive and negative components). Otherwise the output looks like a standard year-month literal string followed by a day-time literal string, with explicit signs added to disambiguate mixed-sign intervals.

The output of the postgres style matches the output of PostgreSQL releases prior to 8.4 when the DateStyle parameter was set to ISO.

The output of the postgres_verbose style matches the output of PostgreSQL releases prior to 8.4 when the DateStyle parameter was set to non-ISO output.

The output of the iso_8601 style matches the “format with designators” described in section 4.4.3.2 of the ISO 8601 standard.

Table 8.18. Interval Output Style Examples

Examples:

Example 1 (unknown):

YEAR
MONTH
DAY
HOUR
MINUTE
SECOND
YEAR TO MONTH
DAY TO HOUR
DAY TO MINUTE
DAY TO SECOND
HOUR TO MINUTE
HOUR TO SECOND
MINUTE TO SECOND

Example 2 (unknown):

type [ (p) ] 'value'

Example 3 (unknown):

1999-01-08 04:05:06

Example 4 (unknown):

1999-01-08 04:05:06 -8:00

---

PostgreSQL: Documentation: 18: 14.1. Using EXPLAIN

URL: https://www.postgresql.org/docs/current/using-explain.html

Contents:

• 14.1. Using EXPLAIN #
  • 14.1.1. EXPLAIN Basics #
  • 14.1.2. EXPLAIN ANALYZE #
  • 14.1.3. Caveats #

PostgreSQL devises a query plan for each query it receives. Choosing the right plan to match the query structure and the properties of the data is absolutely critical for good performance, so the system includes a complex planner that tries to choose good plans. You can use the EXPLAIN command to see what query plan the planner creates for any query. Plan-reading is an art that requires some experience to master, but this section attempts to cover the basics.

Examples in this section are drawn from the regression test database after doing a VACUUM ANALYZE, using v18 development sources. You should be able to get similar results if you try the examples yourself, but your estimated costs and row counts might vary slightly because ANALYZE's statistics are random samples rather than exact, and because costs are inherently somewhat platform-dependent.

The examples use EXPLAIN's default “text” output format, which is compact and convenient for humans to read. If you want to feed EXPLAIN's output to a program for further analysis, you should use one of its machine-readable output formats (XML, JSON, or YAML) instead.

The structure of a query plan is a tree of plan nodes. Nodes at the bottom level of the tree are scan nodes: they return raw rows from a table. There are different types of scan nodes for different table access methods: sequential scans, index scans, and bitmap index scans. There are also non-table row sources, such as VALUES clauses and set-returning functions in FROM, which have their own scan node types. If the query requires joining, aggregation, sorting, or other operations on the raw rows, then there will be additional nodes above the scan nodes to perform these operations. Again, there is usually more than one possible way to do these operations, so different node types can appear here too. The output of EXPLAIN has one line for each node in the plan tree, showing the basic node type plus the cost estimates that the planner made for the execution of that plan node. Additional lines might appear, indented from the node's summary line, to show additional properties of the node. The very first line (the summary line for the topmost node) has the estimated total execution cost for the plan; it is this number that the planner seeks to minimize.

Here is a trivial example, just to show what the output looks like:

Since this query has no WHERE clause, it must scan all the rows of the table, so the planner has chosen to use a simple sequential scan plan. The numbers that are quoted in parentheses are (left to right):

Estimated start-up cost. This is the time expended before the output phase can begin, e.g., time to do the sorting in a sort node.

Estimated total cost. This is stated on the assumption that the plan node is run to completion, i.e., all available rows are retrieved. In practice a node's parent node might stop short of reading all available rows (see the LIMIT example below).

Estimated number of rows output by this plan node. Again, the node is assumed to be run to completion.

Estimated average width of rows output by this plan node (in bytes).

The costs are measured in arbitrary units determined by the planner's cost parameters (see Section 19.7.2). Traditional practice is to measure the costs in units of disk page fetches; that is, seq_page_cost is conventionally set to 1.0 and the other cost parameters are set relative to that. The examples in this section are run with the default cost parameters.

It's important to understand that the cost of an upper-level node includes the cost of all its child nodes. It's also important to realize that the cost only reflects things that the planner cares about. In particular, the cost does not consider the time spent to convert output values to text form or to transmit them to the client, which could be important factors in the real elapsed time; but the planner ignores those costs because it cannot change them by altering the plan. (Every correct plan will output the same row set, we trust.)

The rows value is a little tricky because it is not the number of rows processed or scanned by the plan node, but rather the number emitted by the node. This is often less than the number scanned, as a result of filtering by any WHERE-clause conditions that are being applied at the node. Ideally the top-level rows estimate will approximate the number of rows actually returned, updated, or deleted by the query.

Returning to our example:

These numbers are derived very straightforwardly. If you do:

you will find that tenk1 has 345 disk pages and 10000 rows. The estimated cost is computed as (disk pages read * seq_page_cost) + (rows scanned * cpu_tuple_cost). By default, seq_page_cost is 1.0 and cpu_tuple_cost is 0.01, so the estimated cost is (345 * 1.0) + (10000 * 0.01) = 445.

Now let's modify the query to add a WHERE condition:

Notice that the EXPLAIN output shows the WHERE clause being applied as a “filter” condition attached to the Seq Scan plan node. This means that the plan node checks the condition for each row it scans, and outputs only the ones that pass the condition. The estimate of output rows has been reduced because of the WHERE clause. However, the scan will still have to visit all 10000 rows, so the cost hasn't decreased; in fact it has gone up a bit (by 10000 * cpu_operator_cost, to be exact) to reflect the extra CPU time spent checking the WHERE condition.

The actual number of rows this query would select is 7000, but the rows estimate is only approximate. If you try to duplicate this experiment, you may well get a slightly different estimate; moreover, it can change after each ANALYZE command, because the statistics produced by ANALYZE are taken from a randomized sample of the table.

Now, let's make the condition more restrictive:

Here the planner has decided to use a two-step plan: the child plan node visits an index to find the locations of rows matching the index condition, and then the upper plan node actually fetches those rows from the table itself. Fetching rows separately is much more expensive than reading them sequentially, but because not all the pages of the table have to be visited, this is still cheaper than a sequential scan. (The reason for using two plan levels is that the upper plan node sorts the row locations identified by the index into physical order before reading them, to minimize the cost of separate fetches. The “bitmap” mentioned in the node names is the mechanism that does the sorting.)

Now let's add another condition to the WHERE clause:

The added condition stringu1 = 'xxx' reduces the output row count estimate, but not the cost because we still have to visit the same set of rows. That's because the stringu1 clause cannot be applied as an index condition, since this index is only on the unique1 column. Instead it is applied as a filter on the rows retrieved using the index. Thus the cost has actually gone up slightly to reflect this extra checking.

In some cases the planner will prefer a “simple” index scan plan:

In this type of plan the table rows are fetched in index order, which makes them even more expensive to read, but there are so few that the extra cost of sorting the row locations is not worth it. You'll most often see this plan type for queries that fetch just a single row. It's also often used for queries that have an ORDER BY condition that matches the index order, because then no extra sorting step is needed to satisfy the ORDER BY. In this example, adding ORDER BY unique1 would use the same plan because the index already implicitly provides the requested ordering.

The planner may implement an ORDER BY clause in several ways. The above example shows that such an ordering clause may be implemented implicitly. The planner may also add an explicit Sort step:

If a part of the plan guarantees an ordering on a prefix of the required sort keys, then the planner may instead decide to use an Incremental Sort step:

Compared to regular sorts, sorting incrementally allows returning tuples before the entire result set has been sorted, which particularly enables optimizations with LIMIT queries. It may also reduce memory usage and the likelihood of spilling sorts to disk, but it comes at the cost of the increased overhead of splitting the result set into multiple sorting batches.

If there are separate indexes on several of the columns referenced in WHERE, the planner might choose to use an AND or OR combination of the indexes:

But this requires visiting both indexes, so it's not necessarily a win compared to using just one index and treating the other condition as a filter. If you vary the ranges involved you'll see the plan change accordingly.

Here is an example showing the effects of LIMIT:

This is the same query as above, but we added a LIMIT so that not all the rows need be retrieved, and the planner changed its mind about what to do. Notice that the total cost and row count of the Index Scan node are shown as if it were run to completion. However, the Limit node is expected to stop after retrieving only a fifth of those rows, so its total cost is only a fifth as much, and that's the actual estimated cost of the query. This plan is preferred over adding a Limit node to the previous plan because the Limit could not avoid paying the startup cost of the bitmap scan, so the total cost would be something over 25 units with that approach.

Let's try joining two tables, using the columns we have been discussing:

In this plan, we have a nested-loop join node with two table scans as inputs, or children. The indentation of the node summary lines reflects the plan tree structure. The join's first, or “outer”, child is a bitmap scan similar to those we saw before. Its cost and row count are the same as we'd get from SELECT ... WHERE unique1 < 10 because we are applying the WHERE clause unique1 < 10 at that node. The t1.unique2 = t2.unique2 clause is not relevant yet, so it doesn't affect the row count of the outer scan. The nested-loop join node will run its second, or “inner” child once for each row obtained from the outer child. Column values from the current outer row can be plugged into the inner scan; here, the t1.unique2 value from the outer row is available, so we get a plan and costs similar to what we saw above for a simple SELECT ... WHERE t2.unique2 = constant case. (The estimated cost is actually a bit lower than what was seen above, as a result of caching that's expected to occur during the repeated index scans on t2.) The costs of the loop node are then set on the basis of the cost of the outer scan, plus one repetition of the inner scan for each outer row (10 * 7.90, here), plus a little CPU time for join processing.

In this example the join's output row count is the same as the product of the two scans' row counts, but that's not true in all cases because there can be additional WHERE clauses that mention both tables and so can only be applied at the join point, not to either input scan. Here's an example:

The condition t1.hundred < t2.hundred can't be tested in the tenk2_unique2 index, so it's applied at the join node. This reduces the estimated output row count of the join node, but does not change either input scan.

Notice that here the planner has chosen to “materialize” the inner relation of the join, by putting a Materialize plan node atop it. This means that the t2 index scan will be done just once, even though the nested-loop join node needs to read that data ten times, once for each row from the outer relation. The Materialize node saves the data in memory as it's read, and then returns the data from memory on each subsequent pass.

When dealing with outer joins, you might see join plan nodes with both “Join Filter” and plain “Filter” conditions attached. Join Filter conditions come from the outer join's ON clause, so a row that fails the Join Filter condition could still get emitted as a null-extended row. But a plain Filter condition is applied after the outer-join rules and so acts to remove rows unconditionally. In an inner join there is no semantic difference between these types of filters.

If we change the query's selectivity a bit, we might get a very different join plan:

Here, the planner has chosen to use a hash join, in which rows of one table are entered into an in-memory hash table, after which the other table is scanned and the hash table is probed for matches to each row. Again note how the indentation reflects the plan structure: the bitmap scan on tenk1 is the input to the Hash node, which constructs the hash table. That's then returned to the Hash Join node, which reads rows from its outer child plan and searches the hash table for each one.

Another possible type of join is a merge join, illustrated here:

Merge join requires its input data to be sorted on the join keys. In this example each input is sorted by using an index scan to visit the rows in the correct order; but a sequential scan and sort could also be used. (Sequential-scan-and-sort frequently beats an index scan for sorting many rows, because of the nonsequential disk access required by the index scan.)

One way to look at variant plans is to force the planner to disregard whatever strategy it thought was the cheapest, using the enable/disable flags described in Section 19.7.1. (This is a crude tool, but useful. See also Section 14.3.) For example, if we're unconvinced that merge join is the best join type for the previous example, we could try

which shows that the planner thinks that hash join would be nearly 50% more expensive than merge join for this case. Of course, the next question is whether it's right about that. We can investigate that using EXPLAIN ANALYZE, as discussed below.

When using the enable/disable flags to disable plan node types, many of the flags only discourage the use of the corresponding plan node and don't outright disallow the planner's ability to use the plan node type. This is by design so that the planner still maintains the ability to form a plan for a given query. When the resulting plan contains a disabled node, the EXPLAIN output will indicate this fact.

Because the unit table has no indexes, there is no other means to read the table data, so the sequential scan is the only option available to the query planner.

Some query plans involve subplans, which arise from sub-SELECTs in the original query. Such queries can sometimes be transformed into ordinary join plans, but when they cannot be, we get plans like:

This rather artificial example serves to illustrate a couple of points: values from the outer plan level can be passed down into a subplan (here, t.four is passed down) and the results of the sub-select are available to the outer plan. Those result values are shown by EXPLAIN with notations like (subplan_name).colN, which refers to the N'th output column of the sub-SELECT.

In the example above, the ALL operator runs the subplan again for each row of the outer query (which accounts for the high estimated cost). Some queries can use a hashed subplan to avoid that:

Here, the subplan is run a single time and its output is loaded into an in-memory hash table, which is then probed by the outer ANY operator. This requires that the sub-SELECT not reference any variables of the outer query, and that the ANY's comparison operator be amenable to hashing.

If, in addition to not referencing any variables of the outer query, the sub-SELECT cannot return more than one row, it may instead be implemented as an initplan:

An initplan is run only once per execution of the outer plan, and its results are saved for re-use in later rows of the outer plan. So in this example random() is evaluated only once and all the values of t1.ten are compared to the same randomly-chosen integer. That's quite different from what would happen without the sub-SELECT construct.

It is possible to check the accuracy of the planner's estimates by using EXPLAIN's ANALYZE option. With this option, EXPLAIN actually executes the query, and then displays the true row counts and true run time accumulated within each plan node, along with the same estimates that a plain EXPLAIN shows. For example, we might get a result like this:

Note that the “actual time” values are in milliseconds of real time, whereas the cost estimates are expressed in arbitrary units; so they are unlikely to match up. The thing that's usually most important to look for is whether the estimated row counts are reasonably close to reality. In this example the estimates were all dead-on, but that's quite unusual in practice.

In some query plans, it is possible for a subplan node to be executed more than once. For example, the inner index scan will be executed once per outer row in the above nested-loop plan. In such cases, the loops value reports the total number of executions of the node, and the actual time and rows values shown are averages per-execution. This is done to make the numbers comparable with the way that the cost estimates are shown. Multiply by the loops value to get the total time actually spent in the node. In the above example, we spent a total of 0.030 milliseconds executing the index scans on tenk2.

In some cases EXPLAIN ANALYZE shows additional execution statistics beyond the plan node execution times and row counts. For example, Sort and Hash nodes provide extra information:

The Sort node shows the sort method used (in particular, whether the sort was in-memory or on-disk) and the amount of memory or disk space needed. The Hash node shows the number of hash buckets and batches as well as the peak amount of memory used for the hash table. (If the number of batches exceeds one, there will also be disk space usage involved, but that is not shown.)

Index Scan nodes (as well as Bitmap Index Scan and Index-Only Scan nodes) show an “Index Searches” line that reports the total number of searches across all node executions/loops:

Here we see a Bitmap Index Scan node that needed 4 separate index searches. The scan had to search the index from the tenk1_thous_tenthous index root page once per integer value from the predicate's IN construct. However, the number of index searches often won't have such a simple correspondence to the query predicate:

This variant of our IN query performed only 1 index search. It spent less time traversing the index (compared to the original query) because its IN construct uses values matching index tuples stored next to each other, on the same tenk1_thous_tenthous index leaf page.

The “Index Searches” line is also useful with B-tree index scans that apply the skip scan optimization to more efficiently traverse through an index:

Here we see an Index-Only Scan node using tenk1_four_unique1_idx, a multi-column index on the tenk1 table's four and unique1 columns. The scan performs 3 searches that each read a single index leaf page: “four = 1 AND unique1 = 42”, “four = 2 AND unique1 = 42”, and “four = 3 AND unique1 = 42”. This index is generally a good target for skip scan, since, as discussed in Section 11.3, its leading column (the four column) contains only 4 distinct values, while its second/final column (the unique1 column) contains many distinct values.

Another type of extra information is the number of rows removed by a filter condition:

These counts can be particularly valuable for filter conditions applied at join nodes. The “Rows Removed” line only appears when at least one scanned row, or potential join pair in the case of a join node, is rejected by the filter condition.

A case similar to filter conditions occurs with “lossy” index scans. For example, consider this search for polygons containing a specific point:

The planner thinks (quite correctly) that this sample table is too small to bother with an index scan, so we have a plain sequential scan in which all the rows got rejected by the filter condition. But if we force an index scan to be used, we see:

Here we can see that the index returned one candidate row, which was then rejected by a recheck of the index condition. This happens because a GiST index is “lossy” for polygon containment tests: it actually returns the rows with polygons that overlap the target, and then we have to do the exact containment test on those rows.

EXPLAIN has a BUFFERS option which provides additional detail about I/O operations performed during the planning and execution of the given query. The buffer numbers displayed show the count of the non-distinct buffers hit, read, dirtied, and written for the given node and all of its child nodes. The ANALYZE option implicitly enables the BUFFERS option. If this is undesired, BUFFERS may be explicitly disabled:

Keep in mind that because EXPLAIN ANALYZE actually runs the query, any side-effects will happen as usual, even though whatever results the query might output are discarded in favor of printing the EXPLAIN data. If you want to analyze a data-modifying query without changing your tables, you can roll the command back afterwards, for example:

As seen in this example, when the query is an INSERT, UPDATE, DELETE, or MERGE command, the actual work of applying the table changes is done by a top-level Insert, Update, Delete, or Merge plan node. The plan nodes underneath this node perform the work of locating the old rows and/or computing the new data. So above, we see the same sort of bitmap table scan we've seen already, and its output is fed to an Update node that stores the updated rows. It's worth noting that although the data-modifying node can take a considerable amount of run time (here, it's consuming the lion's share of the time), the planner does not currently add anything to the cost estimates to account for that work. That's because the work to be done is the same for every correct query plan, so it doesn't affect planning decisions.

When an UPDATE, DELETE, or MERGE command affects a partitioned table or inheritance hierarchy, the output might look like this:

In this example the Update node needs to consider three child tables, but not the originally-mentioned partitioned table (since that never stores any data). So there are three input scanning subplans, one per table. For clarity, the Update node is annotated to show the specific target tables that will be updated, in the same order as the corresponding subplans.

The Planning time shown by EXPLAIN ANALYZE is the time it took to generate the query plan from the parsed query and optimize it. It does not include parsing or rewriting.

The Execution time shown by EXPLAIN ANALYZE includes executor start-up and shut-down time, as well as the time to run any triggers that are fired, but it does not include parsing, rewriting, or planning time. Time spent executing BEFORE triggers, if any, is included in the time for the related Insert, Update, or Delete node; but time spent executing AFTER triggers is not counted there because AFTER triggers are fired after completion of the whole plan. The total time spent in each trigger (either BEFORE or AFTER) is also shown separately. Note that deferred constraint triggers will not be executed until end of transaction and are thus not considered at all by EXPLAIN ANALYZE.

The time shown for the top-level node does not include any time needed to convert the query's output data into displayable form or to send it to the client. While EXPLAIN ANALYZE will never send the data to the client, it can be told to convert the query's output data to displayable form and measure the time needed for that, by specifying the SERIALIZE option. That time will be shown separately, and it's also included in the total Execution time.

There are two significant ways in which run times measured by EXPLAIN ANALYZE can deviate from normal execution of the same query. First, since no output rows are delivered to the client, network transmission costs are not included. I/O conversion costs are not included either unless SERIALIZE is specified. Second, the measurement overhead added by EXPLAIN ANALYZE can be significant, especially on machines with slow gettimeofday() operating-system calls. You can use the pg_test_timing tool to measure the overhead of timing on your system.

EXPLAIN results should not be extrapolated to situations much different from the one you are actually testing; for example, results on a toy-sized table cannot be assumed to apply to large tables. The planner's cost estimates are not linear and so it might choose a different plan for a larger or smaller table. An extreme example is that on a table that only occupies one disk page, you'll nearly always get a sequential scan plan whether indexes are available or not. The planner realizes that it's going to take one disk page read to process the table in any case, so there's no value in expending additional page reads to look at an index. (We saw this happening in the polygon_tbl example above.)

There are cases in which the actual and estimated values won't match up well, but nothing is really wrong. One such case occurs when plan node execution is stopped short by a LIMIT or similar effect. For example, in the LIMIT query we used before,

the estimated cost and row count for the Index Scan node are shown as though it were run to completion. But in reality the Limit node stopped requesting rows after it got two, so the actual row count is only 2 and the run time is less than the cost estimate would suggest. This is not an estimation error, only a discrepancy in the way the estimates and true values are displayed.

Merge joins also have measurement artifacts that can confuse the unwary. A merge join will stop reading one input if it's exhausted the other input and the next key value in the one input is greater than the last key value of the other input; in such a case there can be no more matches and so no need to scan the rest of the first input. This results in not reading all of one child, with results like those mentioned for LIMIT. Also, if the outer (first) child contains rows with duplicate key values, the inner (second) child is backed up and rescanned for the portion of its rows matching that key value. EXPLAIN ANALYZE counts these repeated emissions of the same inner rows as if they were real additional rows. When there are many outer duplicates, the reported actual row count for the inner child plan node can be significantly larger than the number of rows that are actually in the inner relation.

BitmapAnd and BitmapOr nodes always report their actual row counts as zero, due to implementation limitations.

Normally, EXPLAIN will display every plan node created by the planner. However, there are cases where the executor can determine that certain nodes need not be executed because they cannot produce any rows, based on parameter values that were not available at planning time. (Currently this can only happen for child nodes of an Append or MergeAppend node that is scanning a partitioned table.) When this happens, those plan nodes are omitted from the EXPLAIN output and a Subplans Removed: N annotation appears instead.

Examples:

Example 1 (unknown):

EXPLAIN SELECT * FROM tenk1;

                         QUERY PLAN
-------------------------------------------------------------
 Seq Scan on tenk1  (cost=0.00..445.00 rows=10000 width=244)

Example 2 (unknown):

EXPLAIN SELECT * FROM tenk1;

                         QUERY PLAN
-------------------------------------------------------------
 Seq Scan on tenk1  (cost=0.00..445.00 rows=10000 width=244)

Example 3 (unknown):

SELECT relpages, reltuples FROM pg_class WHERE relname = 'tenk1';

Example 4 (unknown):

EXPLAIN SELECT * FROM tenk1 WHERE unique1 < 7000;

                         QUERY PLAN
------------------------------------------------------------
 Seq Scan on tenk1  (cost=0.00..470.00 rows=7000 width=244)
   Filter: (unique1 < 7000)

---

PostgreSQL: Documentation: 18: 32.21. Behavior in Threaded Programs

URL: https://www.postgresql.org/docs/current/libpq-threading.html

Contents:

• 32.21. Behavior in Threaded Programs #

As of version 17, libpq is always reentrant and thread-safe. However, one restriction is that no two threads attempt to manipulate the same PGconn object at the same time. In particular, you cannot issue concurrent commands from different threads through the same connection object. (If you need to run concurrent commands, use multiple connections.)

PGresult objects are normally read-only after creation, and so can be passed around freely between threads. However, if you use any of the PGresult-modifying functions described in Section 32.12 or Section 32.14, it's up to you to avoid concurrent operations on the same PGresult, too.

In earlier versions, libpq could be compiled with or without thread support, depending on compiler options. This function allows the querying of libpq's thread-safe status:

Returns the thread safety status of the libpq library.

Returns 1 if the libpq is thread-safe and 0 if it is not. Always returns 1 on version 17 and above.

The deprecated functions PQrequestCancel and PQoidStatus are not thread-safe and should not be used in multithread programs. PQrequestCancel can be replaced by PQcancelBlocking. PQoidStatus can be replaced by PQoidValue.

If you are using Kerberos inside your application (in addition to inside libpq), you will need to do locking around Kerberos calls because Kerberos functions are not thread-safe. See function PQregisterThreadLock in the libpq source code for a way to do cooperative locking between libpq and your application.

Similarly, if you are using Curl inside your application, and you do not already initialize libcurl globally before starting new threads, you will need to cooperatively lock (again via PQregisterThreadLock) around any code that may initialize libcurl. This restriction is lifted for more recent versions of Curl that are built to support thread-safe initialization; those builds can be identified by the advertisement of a threadsafe feature in their version metadata.

Examples:

Example 1 (unknown):

int PQisthreadsafe();

---

PostgreSQL: Documentation: 18: Chapter 52. System Catalogs

URL: https://www.postgresql.org/docs/current/catalogs.html

Contents:

• Chapter 52. System Catalogs

The system catalogs are the place where a relational database management system stores schema metadata, such as information about tables and columns, and internal bookkeeping information. PostgreSQL's system catalogs are regular tables. You can drop and recreate the tables, add columns, insert and update values, and severely mess up your system that way. Normally, one should not change the system catalogs by hand, there are normally SQL commands to do that. (For example, CREATE DATABASE inserts a row into the pg_database catalog — and actually creates the database on disk.) There are some exceptions for particularly esoteric operations, but many of those have been made available as SQL commands over time, and so the need for direct manipulation of the system catalogs is ever decreasing.

---

PostgreSQL: Documentation: 18: SET DESCRIPTOR

URL: https://www.postgresql.org/docs/current/ecpg-sql-set-descriptor.html

Contents:

• SET DESCRIPTOR
• Synopsis
• Description
• Parameters
• Examples
• Compatibility
• See Also

SET DESCRIPTOR — set information in an SQL descriptor area

SET DESCRIPTOR populates an SQL descriptor area with values. The descriptor area is then typically used to bind parameters in a prepared query execution.

This command has two forms: The first form applies to the descriptor “header”, which is independent of a particular datum. The second form assigns values to particular datums, identified by number.

A token identifying which header information item to set. Only COUNT, to set the number of descriptor items, is currently supported.

The number of the descriptor item to set. The count starts at 1.

A token identifying which item of information to set in the descriptor. See Section 34.7.1 for a list of supported items.

A value to store into the descriptor item. This can be an SQL constant or a host variable.

SET DESCRIPTOR is specified in the SQL standard.

Examples:

Example 1 (unknown):

SET DESCRIPTOR descriptor_name descriptor_header_item = value [, ... ]
SET DESCRIPTOR descriptor_name VALUE number descriptor_item = value [, ...]

Example 2 (unknown):

EXEC SQL SET DESCRIPTOR indesc COUNT = 1;
EXEC SQL SET DESCRIPTOR indesc VALUE 1 DATA = 2;
EXEC SQL SET DESCRIPTOR indesc VALUE 1 DATA = :val1;
EXEC SQL SET DESCRIPTOR indesc VALUE 2 INDICATOR = :val1, DATA = 'some string';
EXEC SQL SET DESCRIPTOR indesc VALUE 2 INDICATOR = :val2null, DATA = :val2;

---

PostgreSQL: Documentation: 18: Appendix E. Release Notes

URL: https://www.postgresql.org/docs/current/release.html

Contents:

• Appendix E. Release Notes

The release notes contain the significant changes in each PostgreSQL release, with major features and migration issues listed at the top. The release notes do not contain changes that affect only a few users or changes that are internal and therefore not user-visible. For example, the optimizer is improved in almost every release, but the improvements are usually observed by users as simply faster queries.

A complete list of changes for each release can be obtained by viewing the Git logs for each release. The pgsql-committers email list records all source code changes as well. There is also a web interface that shows changes to specific files.

The name appearing next to each item represents the major developer for that item. Of course all changes involve community discussion and patch review, so each item is truly a community effort.

Section markers (§) in the release notes link to gitweb pages which show the primary git commit messages and source tree changes responsible for the release note item. There might be additional git commits which are not shown.

---

PostgreSQL: Documentation: 18: 34.16. Oracle Compatibility Mode

URL: https://www.postgresql.org/docs/current/ecpg-oracle-compat.html

Contents:

• 34.16. Oracle Compatibility Mode #

ecpg can be run in a so-called Oracle compatibility mode. If this mode is active, it tries to behave as if it were Oracle Pro*C.

Specifically, this mode changes ecpg in three ways:

Pad character arrays receiving character string types with trailing spaces to the specified length

Zero byte terminate these character arrays, and set the indicator variable if truncation occurs

Set the null indicator to -1 when character arrays receive empty character string types

---

PostgreSQL: Documentation: 18: 35.58. udt_privileges

URL: https://www.postgresql.org/docs/current/infoschema-udt-privileges.html

Contents:

• 35.58. udt_privileges #

The view udt_privileges identifies USAGE privileges granted on user-defined types to a currently enabled role or by a currently enabled role. There is one row for each combination of type, grantor, and grantee. This view shows only composite types (see under Section 35.60 for why); see Section 35.59 for domain privileges.

Table 35.56. udt_privileges Columns

grantor sql_identifier

Name of the role that granted the privilege

grantee sql_identifier

Name of the role that the privilege was granted to

udt_catalog sql_identifier

Name of the database containing the type (always the current database)

udt_schema sql_identifier

Name of the schema containing the type

udt_name sql_identifier

privilege_type character_data

is_grantable yes_or_no

YES if the privilege is grantable, NO if not

---

PostgreSQL: Documentation: 18: 35.56. triggered_update_columns

URL: https://www.postgresql.org/docs/current/infoschema-triggered-update-columns.html

Contents:

• 35.56. triggered_update_columns #

For triggers in the current database that specify a column list (like UPDATE OF column1, column2), the view triggered_update_columns identifies these columns. Triggers that do not specify a column list are not included in this view. Only those columns are shown that the current user owns or has some privilege other than SELECT on.

Table 35.54. triggered_update_columns Columns

trigger_catalog sql_identifier

Name of the database that contains the trigger (always the current database)

trigger_schema sql_identifier

Name of the schema that contains the trigger

trigger_name sql_identifier

event_object_catalog sql_identifier

Name of the database that contains the table that the trigger is defined on (always the current database)

event_object_schema sql_identifier

Name of the schema that contains the table that the trigger is defined on

event_object_table sql_identifier

Name of the table that the trigger is defined on

event_object_column sql_identifier

Name of the column that the trigger is defined on

---

PostgreSQL: Documentation: 18: 19.2. File Locations

URL: https://www.postgresql.org/docs/current/runtime-config-file-locations.html

Contents:

• 19.2. File Locations #

In addition to the postgresql.conf file already mentioned, PostgreSQL uses two other manually-edited configuration files, which control client authentication (their use is discussed in Chapter 20). By default, all three configuration files are stored in the database cluster's data directory. The parameters described in this section allow the configuration files to be placed elsewhere. (Doing so can ease administration. In particular it is often easier to ensure that the configuration files are properly backed-up when they are kept separate.)

Specifies the directory to use for data storage. This parameter can only be set at server start.

Specifies the main server configuration file (customarily called postgresql.conf). This parameter can only be set on the postgres command line.

Specifies the configuration file for host-based authentication (customarily called pg_hba.conf). This parameter can only be set at server start.

Specifies the configuration file for user name mapping (customarily called pg_ident.conf). This parameter can only be set at server start. See also Section 20.2.

Specifies the name of an additional process-ID (PID) file that the server should create for use by server administration programs. This parameter can only be set at server start.

In a default installation, none of the above parameters are set explicitly. Instead, the data directory is specified by the -D command-line option or the PGDATA environment variable, and the configuration files are all found within the data directory.

If you wish to keep the configuration files elsewhere than the data directory, the postgres -D command-line option or PGDATA environment variable must point to the directory containing the configuration files, and the data_directory parameter must be set in postgresql.conf (or on the command line) to show where the data directory is actually located. Notice that data_directory overrides -D and PGDATA for the location of the data directory, but not for the location of the configuration files.

If you wish, you can specify the configuration file names and locations individually using the parameters config_file, hba_file and/or ident_file. config_file can only be specified on the postgres command line, but the others can be set within the main configuration file. If all three parameters plus data_directory are explicitly set, then it is not necessary to specify -D or PGDATA.

When setting any of these parameters, a relative path will be interpreted with respect to the directory in which postgres is started.

---

PostgreSQL: Documentation: 18: 13.6. Caveats

URL: https://www.postgresql.org/docs/current/mvcc-caveats.html

Contents:

• 13.6. Caveats #

Some DDL commands, currently only TRUNCATE and the table-rewriting forms of ALTER TABLE, are not MVCC-safe. This means that after the truncation or rewrite commits, the table will appear empty to concurrent transactions, if they are using a snapshot taken before the DDL command committed. This will only be an issue for a transaction that did not access the table in question before the DDL command started — any transaction that has done so would hold at least an ACCESS SHARE table lock, which would block the DDL command until that transaction completes. So these commands will not cause any apparent inconsistency in the table contents for successive queries on the target table, but they could cause visible inconsistency between the contents of the target table and other tables in the database.

Support for the Serializable transaction isolation level has not yet been added to hot standby replication targets (described in Section 26.4). The strictest isolation level currently supported in hot standby mode is Repeatable Read. While performing all permanent database writes within Serializable transactions on the primary will ensure that all standbys will eventually reach a consistent state, a Repeatable Read transaction run on the standby can sometimes see a transient state that is inconsistent with any serial execution of the transactions on the primary.

Internal access to the system catalogs is not done using the isolation level of the current transaction. This means that newly created database objects such as tables are visible to concurrent Repeatable Read and Serializable transactions, even though the rows they contain are not. In contrast, queries that explicitly examine the system catalogs don't see rows representing concurrently created database objects, in the higher isolation levels.

---

PostgreSQL: Documentation: 18: 35.15. column_privileges

URL: https://www.postgresql.org/docs/current/infoschema-column-privileges.html

Contents:

• 35.15. column_privileges #

The view column_privileges identifies all privileges granted on columns to a currently enabled role or by a currently enabled role. There is one row for each combination of column, grantor, and grantee.

If a privilege has been granted on an entire table, it will show up in this view as a grant for each column, but only for the privilege types where column granularity is possible: SELECT, INSERT, UPDATE, REFERENCES.

Table 35.13. column_privileges Columns

grantor sql_identifier

Name of the role that granted the privilege

grantee sql_identifier

Name of the role that the privilege was granted to

table_catalog sql_identifier

Name of the database that contains the table that contains the column (always the current database)

table_schema sql_identifier

Name of the schema that contains the table that contains the column

table_name sql_identifier

Name of the table that contains the column

column_name sql_identifier

privilege_type character_data

Type of the privilege: SELECT, INSERT, UPDATE, or REFERENCES

is_grantable yes_or_no

YES if the privilege is grantable, NO if not

---

PostgreSQL: Documentation: 18: 20.6. GSSAPI Authentication

URL: https://www.postgresql.org/docs/current/gssapi-auth.html

Contents:

• 20.6. GSSAPI Authentication #

GSSAPI is an industry-standard protocol for secure authentication defined in RFC 2743. PostgreSQL supports GSSAPI for authentication, communications encryption, or both. GSSAPI provides automatic authentication (single sign-on) for systems that support it. The authentication itself is secure. If GSSAPI encryption or SSL encryption is used, the data sent along the database connection will be encrypted; otherwise, it will not.

GSSAPI support has to be enabled when PostgreSQL is built; see Chapter 17 for more information.

When GSSAPI uses Kerberos, it uses a standard service principal (authentication identity) name in the format servicename/hostname@realm. The principal name used by a particular installation is not encoded in the PostgreSQL server in any way; rather it is specified in the keytab file that the server reads to determine its identity. If multiple principals are listed in the keytab file, the server will accept any one of them. The server's realm name is the preferred realm specified in the Kerberos configuration file(s) accessible to the server.

When connecting, the client must know the principal name of the server it intends to connect to. The servicename part of the principal is ordinarily postgres, but another value can be selected via libpq's krbsrvname connection parameter. The hostname part is the fully qualified host name that libpq is told to connect to. The realm name is the preferred realm specified in the Kerberos configuration file(s) accessible to the client.

The client will also have a principal name for its own identity (and it must have a valid ticket for this principal). To use GSSAPI for authentication, the client principal must be associated with a PostgreSQL database user name. The pg_ident.conf configuration file can be used to map principals to user names; for example, pgusername@realm could be mapped to just pgusername. Alternatively, you can use the full username@realm principal as the role name in PostgreSQL without any mapping.

PostgreSQL also supports mapping client principals to user names by just stripping the realm from the principal. This method is supported for backwards compatibility and is strongly discouraged as it is then impossible to distinguish different users with the same user name but coming from different realms. To enable this, set include_realm to 0. For simple single-realm installations, doing that combined with setting the krb_realm parameter (which checks that the principal's realm matches exactly what is in the krb_realm parameter) is still secure; but this is a less capable approach compared to specifying an explicit mapping in pg_ident.conf.

The location of the server's keytab file is specified by the krb_server_keyfile configuration parameter. For security reasons, it is recommended to use a separate keytab just for the PostgreSQL server rather than allowing the server to read the system keytab file. Make sure that your server keytab file is readable (and preferably only readable, not writable) by the PostgreSQL server account. (See also Section 18.1.)

The keytab file is generated using the Kerberos software; see the Kerberos documentation for details. The following example shows doing this using the kadmin tool of MIT Kerberos:

The following authentication options are supported for the GSSAPI authentication method:

If set to 0, the realm name from the authenticated user principal is stripped off before being passed through the user name mapping (Section 20.2). This is discouraged and is primarily available for backwards compatibility, as it is not secure in multi-realm environments unless krb_realm is also used. It is recommended to leave include_realm set to the default (1) and to provide an explicit mapping in pg_ident.conf to convert principal names to PostgreSQL user names.

Allows mapping from client principals to database user names. See Section 20.2 for details. For a GSSAPI/Kerberos principal, such as username@EXAMPLE.COM (or, less commonly, username/hostbased@EXAMPLE.COM), the user name used for mapping is username@EXAMPLE.COM (or username/hostbased@EXAMPLE.COM, respectively), unless include_realm has been set to 0, in which case username (or username/hostbased) is what is seen as the system user name when mapping.

Sets the realm to match user principal names against. If this parameter is set, only users of that realm will be accepted. If it is not set, users of any realm can connect, subject to whatever user name mapping is done.

In addition to these settings, which can be different for different pg_hba.conf entries, there is the server-wide krb_caseins_users configuration parameter. If that is set to true, client principals are matched to user map entries case-insensitively. krb_realm, if set, is also matched case-insensitively.

Examples:

Example 1 (unknown):

kadmin% addprinc -randkey postgres/server.my.domain.org
kadmin% ktadd -k krb5.keytab postgres/server.my.domain.org

---

PostgreSQL: Documentation: 18: 30.2. When to JIT?

URL: https://www.postgresql.org/docs/current/jit-decision.html

Contents:

• 30.2. When to JIT? #
  • Note

JIT compilation is beneficial primarily for long-running CPU-bound queries. Frequently these will be analytical queries. For short queries the added overhead of performing JIT compilation will often be higher than the time it can save.

To determine whether JIT compilation should be used, the total estimated cost of a query (see Chapter 69 and Section 19.7.2) is used. The estimated cost of the query will be compared with the setting of jit_above_cost. If the cost is higher, JIT compilation will be performed. Two further decisions are then needed. Firstly, if the estimated cost is more than the setting of jit_inline_above_cost, short functions and operators used in the query will be inlined. Secondly, if the estimated cost is more than the setting of jit_optimize_above_cost, expensive optimizations are applied to improve the generated code. Each of these options increases the JIT compilation overhead, but can reduce query execution time considerably.

These cost-based decisions will be made at plan time, not execution time. This means that when prepared statements are in use, and a generic plan is used (see PREPARE), the values of the configuration parameters in effect at prepare time control the decisions, not the settings at execution time.

If jit is set to off, or if no JIT implementation is available (for example because the server was compiled without --with-llvm), JIT will not be performed, even if it would be beneficial based on the above criteria. Setting jit to off has effects at both plan and execution time.

EXPLAIN can be used to see whether JIT is used or not. As an example, here is a query that is not using JIT:

Given the cost of the plan, it is entirely reasonable that no JIT was used; the cost of JIT would have been bigger than the potential savings. Adjusting the cost limits will lead to JIT use:

As visible here, JIT was used, but inlining and expensive optimization were not. If jit_inline_above_cost or jit_optimize_above_cost were also lowered, that would change.

Examples:

Example 1 (unknown):

=# EXPLAIN ANALYZE SELECT SUM(relpages) FROM pg_class;
                                                 QUERY PLAN
-------------------------------------------------------------------​------------------------------------------
 Aggregate  (cost=16.27..16.29 rows=1 width=8) (actual time=0.303..0.303 rows=1.00 loops=1)
   Buffers: shared hit=14
   ->  Seq Scan on pg_class  (cost=0.00..15.42 rows=342 width=4) (actual time=0.017..0.111 rows=356.00 loops=1)
         Buffers: shared hit=14
 Planning Time: 0.116 ms
 Execution Time: 0.365 ms

Example 2 (unknown):

=# SET jit_above_cost = 10;
SET
=# EXPLAIN ANALYZE SELECT SUM(relpages) FROM pg_class;
                                                 QUERY PLAN
-------------------------------------------------------------------​------------------------------------------
 Aggregate  (cost=16.27..16.29 rows=1 width=8) (actual time=6.049..6.049 rows=1.00 loops=1)
   Buffers: shared hit=14
   ->  Seq Scan on pg_class  (cost=0.00..15.42 rows=342 width=4) (actual time=0.019..0.052 rows=356.00 loops=1)
         Buffers: shared hit=14
 Planning Time: 0.133 ms
 JIT:
   Functions: 3
   Options: Inlining false, Optimization false, Expressions true, Deforming true
   Timing: Generation 1.259 ms (Deform 0.000 ms), Inlining 0.000 ms, Optimization 0.797 ms, Emission 5.048 ms, Total 7.104 ms
 Execution Time: 7.416 ms

---

PostgreSQL: Documentation: 18: 35.13. column_domain_usage

URL: https://www.postgresql.org/docs/current/infoschema-column-domain-usage.html

Contents:

• 35.13. column_domain_usage #

The view column_domain_usage identifies all columns (of a table or a view) that make use of some domain defined in the current database and owned by a currently enabled role.

Table 35.11. column_domain_usage Columns

domain_catalog sql_identifier

Name of the database containing the domain (always the current database)

domain_schema sql_identifier

Name of the schema containing the domain

domain_name sql_identifier

table_catalog sql_identifier

Name of the database containing the table (always the current database)

table_schema sql_identifier

Name of the schema containing the table

table_name sql_identifier

column_name sql_identifier

---

PostgreSQL: Documentation: 18: Chapter 50. OAuth Validator Modules

URL: https://www.postgresql.org/docs/current/oauth-validators.html

Contents:

• Chapter 50. OAuth Validator Modules
  • Warning

PostgreSQL provides infrastructure for creating custom modules to perform server-side validation of OAuth bearer tokens. Because OAuth implementations vary so wildly, and bearer token validation is heavily dependent on the issuing party, the server cannot check the token itself; validator modules provide the integration layer between the server and the OAuth provider in use.

OAuth validator modules must at least consist of an initialization function (see Section 50.2) and the required callback for performing validation (see Section 50.3.2).

Since a misbehaving validator might let unauthorized users into the database, correct implementation is crucial for server safety. See Section 50.1 for design considerations.

---

PostgreSQL: Documentation: 18: Chapter 43. PL/Perl — Perl Procedural Language

URL: https://www.postgresql.org/docs/current/plperl.html

Contents:

• Chapter 43. PL/Perl — Perl Procedural Language
  • Tip
  • Note

PL/Perl is a loadable procedural language that enables you to write PostgreSQL functions and procedures in the Perl programming language.

The main advantage to using PL/Perl is that this allows use, within stored functions and procedures, of the manyfold “string munging” operators and functions available for Perl. Parsing complex strings might be easier using Perl than it is with the string functions and control structures provided in PL/pgSQL.

To install PL/Perl in a particular database, use CREATE EXTENSION plperl.

If a language is installed into template1, all subsequently created databases will have the language installed automatically.

Users of source packages must specially enable the build of PL/Perl during the installation process. (Refer to Chapter 17 for more information.) Users of binary packages might find PL/Perl in a separate subpackage.

---

PostgreSQL: Documentation: 18: 18.12. Registering Event Log on Windows

URL: https://www.postgresql.org/docs/current/event-log-registration.html

Contents:

• 18.12. Registering Event Log on Windows #
  • Note

To register a Windows event log library with the operating system, issue this command:

This creates registry entries used by the event viewer, under the default event source named PostgreSQL.

To specify a different event source name (see event_source), use the /n and /i options:

To unregister the event log library from the operating system, issue this command:

To enable event logging in the database server, modify log_destination to include eventlog in postgresql.conf.

Examples:

Example 1 (unknown):

regsvr32 pgsql_library_directory/pgevent.dll

Example 2 (unknown):

regsvr32 /n /i:event_source_name pgsql_library_directory/pgevent.dll

Example 3 (unknown):

regsvr32 /u [/i:event_source_name] pgsql_library_directory/pgevent.dll

---

PostgreSQL: Documentation: 18: 10.5. UNION, CASE, and Related Constructs

URL: https://www.postgresql.org/docs/current/typeconv-union-case.html

Contents:

• 10.5. UNION, CASE, and Related Constructs #

SQL UNION constructs must match up possibly dissimilar types to become a single result set. The resolution algorithm is applied separately to each output column of a union query. The INTERSECT and EXCEPT constructs resolve dissimilar types in the same way as UNION. Some other constructs, including CASE, ARRAY, VALUES, and the GREATEST and LEAST functions, use the identical algorithm to match up their component expressions and select a result data type.

Type Resolution for UNION, CASE, and Related Constructs

If all inputs are of the same type, and it is not unknown, resolve as that type.

If any input is of a domain type, treat it as being of the domain's base type for all subsequent steps. [12]

If all inputs are of type unknown, resolve as type text (the preferred type of the string category). Otherwise, unknown inputs are ignored for the purposes of the remaining rules.

If the non-unknown inputs are not all of the same type category, fail.

Select the first non-unknown input type as the candidate type, then consider each other non-unknown input type, left to right. [13] If the candidate type can be implicitly converted to the other type, but not vice-versa, select the other type as the new candidate type. Then continue considering the remaining inputs. If, at any stage of this process, a preferred type is selected, stop considering additional inputs.

Convert all inputs to the final candidate type. Fail if there is not an implicit conversion from a given input type to the candidate type.

Some examples follow.

Example 10.10. Type Resolution with Underspecified Types in a Union

Here, the unknown-type literal 'b' will be resolved to type text.

Example 10.11. Type Resolution in a Simple Union

The literal 1.2 is of type numeric, and the integer value 1 can be cast implicitly to numeric, so that type is used.

Example 10.12. Type Resolution in a Transposed Union

Here, since type real cannot be implicitly cast to integer, but integer can be implicitly cast to real, the union result type is resolved as real.

Example 10.13. Type Resolution in a Nested Union

This failure occurs because PostgreSQL treats multiple UNIONs as a nest of pairwise operations; that is, this input is the same as

The inner UNION is resolved as emitting type text, according to the rules given above. Then the outer UNION has inputs of types text and integer, leading to the observed error. The problem can be fixed by ensuring that the leftmost UNION has at least one input of the desired result type.

INTERSECT and EXCEPT operations are likewise resolved pairwise. However, the other constructs described in this section consider all of their inputs in one resolution step.

[12] Somewhat like the treatment of domain inputs for operators and functions, this behavior allows a domain type to be preserved through a UNION or similar construct, so long as the user is careful to ensure that all inputs are implicitly or explicitly of that exact type. Otherwise the domain's base type will be used.

[13] For historical reasons, CASE treats its ELSE clause (if any) as the “first” input, with the THEN clauses(s) considered after that. In all other cases, “left to right” means the order in which the expressions appear in the query text.

Examples:

Example 1 (unknown):

SELECT text 'a' AS "text" UNION SELECT 'b';

 text
------
 a
 b
(2 rows)

Example 2 (unknown):

SELECT 1.2 AS "numeric" UNION SELECT 1;

 numeric
---------
       1
     1.2
(2 rows)

Example 3 (unknown):

SELECT 1 AS "real" UNION SELECT CAST('2.2' AS REAL);

 real
------
    1
  2.2
(2 rows)

Example 4 (unknown):

SELECT NULL UNION SELECT NULL UNION SELECT 1;

ERROR:  UNION types text and integer cannot be matched

---

PostgreSQL: Documentation: 18: 9.18. Conditional Expressions

URL: https://www.postgresql.org/docs/current/functions-conditional.html

Contents:

• 9.18. Conditional Expressions #
  • Tip
  • Note
  • 9.18.1. CASE #
  • Note
  • 9.18.2. COALESCE #
  • 9.18.3. NULLIF #
  • 9.18.4. GREATEST and LEAST #

This section describes the SQL-compliant conditional expressions available in PostgreSQL.

If your needs go beyond the capabilities of these conditional expressions, you might want to consider writing a server-side function in a more expressive programming language.

Although COALESCE, GREATEST, and LEAST are syntactically similar to functions, they are not ordinary functions, and thus cannot be used with explicit VARIADIC array arguments.

The SQL CASE expression is a generic conditional expression, similar to if/else statements in other programming languages:

CASE clauses can be used wherever an expression is valid. Each condition is an expression that returns a boolean result. If the condition's result is true, the value of the CASE expression is the result that follows the condition, and the remainder of the CASE expression is not processed. If the condition's result is not true, any subsequent WHEN clauses are examined in the same manner. If no WHEN condition yields true, the value of the CASE expression is the result of the ELSE clause. If the ELSE clause is omitted and no condition is true, the result is null.

The data types of all the result expressions must be convertible to a single output type. See Section 10.5 for more details.

There is a “simple” form of CASE expression that is a variant of the general form above:

The first expression is computed, then compared to each of the value expressions in the WHEN clauses until one is found that is equal to it. If no match is found, the result of the ELSE clause (or a null value) is returned. This is similar to the switch statement in C.

The example above can be written using the simple CASE syntax:

A CASE expression does not evaluate any subexpressions that are not needed to determine the result. For example, this is a possible way of avoiding a division-by-zero failure:

As described in Section 4.2.14, there are various situations in which subexpressions of an expression are evaluated at different times, so that the principle that “CASE evaluates only necessary subexpressions” is not ironclad. For example a constant 1/0 subexpression will usually result in a division-by-zero failure at planning time, even if it's within a CASE arm that would never be entered at run time.

The COALESCE function returns the first of its arguments that is not null. Null is returned only if all arguments are null. It is often used to substitute a default value for null values when data is retrieved for display, for example:

This returns description if it is not null, otherwise short_description if it is not null, otherwise (none).

The arguments must all be convertible to a common data type, which will be the type of the result (see Section 10.5 for details).

Like a CASE expression, COALESCE only evaluates the arguments that are needed to determine the result; that is, arguments to the right of the first non-null argument are not evaluated. This SQL-standard function provides capabilities similar to NVL and IFNULL, which are used in some other database systems.

The NULLIF function returns a null value if value1 equals value2; otherwise it returns value1. This can be used to perform the inverse operation of the COALESCE example given above:

In this example, if value is (none), null is returned, otherwise the value of value is returned.

The two arguments must be of comparable types. To be specific, they are compared exactly as if you had written value1 = value2, so there must be a suitable = operator available.

The result has the same type as the first argument — but there is a subtlety. What is actually returned is the first argument of the implied = operator, and in some cases that will have been promoted to match the second argument's type. For example, NULLIF(1, 2.2) yields numeric, because there is no integer = numeric operator, only numeric = numeric.

The GREATEST and LEAST functions select the largest or smallest value from a list of any number of expressions. The expressions must all be convertible to a common data type, which will be the type of the result (see Section 10.5 for details).

NULL values in the argument list are ignored. The result will be NULL only if all the expressions evaluate to NULL. (This is a deviation from the SQL standard. According to the standard, the return value is NULL if any argument is NULL. Some other databases behave this way.)

Examples:

Example 1 (unknown):

CASE WHEN condition THEN result
     [WHEN ...]
     [ELSE result]
END

Example 2 (unknown):

SELECT * FROM test;

 a
---
 1
 2
 3


SELECT a,
       CASE WHEN a=1 THEN 'one'
            WHEN a=2 THEN 'two'
            ELSE 'other'
       END
    FROM test;

 a | case
---+-------
 1 | one
 2 | two
 3 | other

Example 3 (unknown):

CASE expression
    WHEN value THEN result
    [WHEN ...]
    [ELSE result]
END

Example 4 (unknown):

SELECT a,
       CASE a WHEN 1 THEN 'one'
              WHEN 2 THEN 'two'
              ELSE 'other'
       END
    FROM test;

 a | case
---+-------
 1 | one
 2 | two
 3 | other

---

PostgreSQL: Documentation: 18: 9.20. Range/Multirange Functions and Operators

URL: https://www.postgresql.org/docs/current/functions-range.html

Contents:

• 9.20. Range/Multirange Functions and Operators #

See Section 8.17 for an overview of range types.

Table 9.58 shows the specialized operators available for range types. Table 9.59 shows the specialized operators available for multirange types. In addition to those, the usual comparison operators shown in Table 9.1 are available for range and multirange types. The comparison operators order first by the range lower bounds, and only if those are equal do they compare the upper bounds. The multirange operators compare each range until one is unequal. This does not usually result in a useful overall ordering, but the operators are provided to allow unique indexes to be constructed on ranges.

Table 9.58. Range Operators

anyrange @> anyrange → boolean

Does the first range contain the second?

int4range(2,4) @> int4range(2,3) → t

anyrange @> anyelement → boolean

Does the range contain the element?

'[2011-01-01,2011-03-01)'::tsrange @> '2011-01-10'::timestamp → t

anyrange <@ anyrange → boolean

Is the first range contained by the second?

int4range(2,4) <@ int4range(1,7) → t

anyelement <@ anyrange → boolean

Is the element contained in the range?

42 <@ int4range(1,7) → f

anyrange && anyrange → boolean

Do the ranges overlap, that is, have any elements in common?

int8range(3,7) && int8range(4,12) → t

anyrange << anyrange → boolean

Is the first range strictly left of the second?

int8range(1,10) << int8range(100,110) → t

anyrange >> anyrange → boolean

Is the first range strictly right of the second?

int8range(50,60) >> int8range(20,30) → t

anyrange &< anyrange → boolean

Does the first range not extend to the right of the second?

int8range(1,20) &< int8range(18,20) → t

anyrange &> anyrange → boolean

Does the first range not extend to the left of the second?

int8range(7,20) &> int8range(5,10) → t

anyrange -|- anyrange → boolean

Are the ranges adjacent?

numrange(1.1,2.2) -|- numrange(2.2,3.3) → t

anyrange + anyrange → anyrange

Computes the union of the ranges. The ranges must overlap or be adjacent, so that the union is a single range (but see range_merge()).

numrange(5,15) + numrange(10,20) → [5,20)

anyrange * anyrange → anyrange

Computes the intersection of the ranges.

int8range(5,15) * int8range(10,20) → [10,15)

anyrange - anyrange → anyrange

Computes the difference of the ranges. The second range must not be contained in the first in such a way that the difference would not be a single range.

int8range(5,15) - int8range(10,20) → [5,10)

Table 9.59. Multirange Operators

anymultirange @> anymultirange → boolean

Does the first multirange contain the second?

'{[2,4)}'::int4multirange @> '{[2,3)}'::int4multirange → t

anymultirange @> anyrange → boolean

Does the multirange contain the range?

'{[2,4)}'::int4multirange @> int4range(2,3) → t

anymultirange @> anyelement → boolean

Does the multirange contain the element?

'{[2011-01-01,2011-03-01)}'::tsmultirange @> '2011-01-10'::timestamp → t

anyrange @> anymultirange → boolean

Does the range contain the multirange?

'[2,4)'::int4range @> '{[2,3)}'::int4multirange → t

anymultirange <@ anymultirange → boolean

Is the first multirange contained by the second?

'{[2,4)}'::int4multirange <@ '{[1,7)}'::int4multirange → t

anymultirange <@ anyrange → boolean

Is the multirange contained by the range?

'{[2,4)}'::int4multirange <@ int4range(1,7) → t

anyrange <@ anymultirange → boolean

Is the range contained by the multirange?

int4range(2,4) <@ '{[1,7)}'::int4multirange → t

anyelement <@ anymultirange → boolean

Is the element contained by the multirange?

4 <@ '{[1,7)}'::int4multirange → t

anymultirange && anymultirange → boolean

Do the multiranges overlap, that is, have any elements in common?

'{[3,7)}'::int8multirange && '{[4,12)}'::int8multirange → t

anymultirange && anyrange → boolean

Does the multirange overlap the range?

'{[3,7)}'::int8multirange && int8range(4,12) → t

anyrange && anymultirange → boolean

Does the range overlap the multirange?

int8range(3,7) && '{[4,12)}'::int8multirange → t

anymultirange << anymultirange → boolean

Is the first multirange strictly left of the second?

'{[1,10)}'::int8multirange << '{[100,110)}'::int8multirange → t

anymultirange << anyrange → boolean

Is the multirange strictly left of the range?

'{[1,10)}'::int8multirange << int8range(100,110) → t

anyrange << anymultirange → boolean

Is the range strictly left of the multirange?

int8range(1,10) << '{[100,110)}'::int8multirange → t

anymultirange >> anymultirange → boolean

Is the first multirange strictly right of the second?

'{[50,60)}'::int8multirange >> '{[20,30)}'::int8multirange → t

anymultirange >> anyrange → boolean

Is the multirange strictly right of the range?

'{[50,60)}'::int8multirange >> int8range(20,30) → t

anyrange >> anymultirange → boolean

Is the range strictly right of the multirange?

int8range(50,60) >> '{[20,30)}'::int8multirange → t

anymultirange &< anymultirange → boolean

Does the first multirange not extend to the right of the second?

'{[1,20)}'::int8multirange &< '{[18,20)}'::int8multirange → t

anymultirange &< anyrange → boolean

Does the multirange not extend to the right of the range?

'{[1,20)}'::int8multirange &< int8range(18,20) → t

anyrange &< anymultirange → boolean

Does the range not extend to the right of the multirange?

int8range(1,20) &< '{[18,20)}'::int8multirange → t

anymultirange &> anymultirange → boolean

Does the first multirange not extend to the left of the second?

'{[7,20)}'::int8multirange &> '{[5,10)}'::int8multirange → t

anymultirange &> anyrange → boolean

Does the multirange not extend to the left of the range?

'{[7,20)}'::int8multirange &> int8range(5,10) → t

anyrange &> anymultirange → boolean

Does the range not extend to the left of the multirange?

int8range(7,20) &> '{[5,10)}'::int8multirange → t

anymultirange -|- anymultirange → boolean

Are the multiranges adjacent?

'{[1.1,2.2)}'::nummultirange -|- '{[2.2,3.3)}'::nummultirange → t

anymultirange -|- anyrange → boolean

Is the multirange adjacent to the range?

'{[1.1,2.2)}'::nummultirange -|- numrange(2.2,3.3) → t

anyrange -|- anymultirange → boolean

Is the range adjacent to the multirange?

numrange(1.1,2.2) -|- '{[2.2,3.3)}'::nummultirange → t

anymultirange + anymultirange → anymultirange

Computes the union of the multiranges. The multiranges need not overlap or be adjacent.

'{[5,10)}'::nummultirange + '{[15,20)}'::nummultirange → {[5,10), [15,20)}

anymultirange * anymultirange → anymultirange

Computes the intersection of the multiranges.

'{[5,15)}'::int8multirange * '{[10,20)}'::int8multirange → {[10,15)}

anymultirange - anymultirange → anymultirange

Computes the difference of the multiranges.

'{[5,20)}'::int8multirange - '{[10,15)}'::int8multirange → {[5,10), [15,20)}

The left-of/right-of/adjacent operators always return false when an empty range or multirange is involved; that is, an empty range is not considered to be either before or after any other range.

Elsewhere empty ranges and multiranges are treated as the additive identity: anything unioned with an empty value is itself. Anything minus an empty value is itself. An empty multirange has exactly the same points as an empty range. Every range contains the empty range. Every multirange contains as many empty ranges as you like.

The range union and difference operators will fail if the resulting range would need to contain two disjoint sub-ranges, as such a range cannot be represented. There are separate operators for union and difference that take multirange parameters and return a multirange, and they do not fail even if their arguments are disjoint. So if you need a union or difference operation for ranges that may be disjoint, you can avoid errors by first casting your ranges to multiranges.

Table 9.60 shows the functions available for use with range types. Table 9.61 shows the functions available for use with multirange types.

Table 9.60. Range Functions

lower ( anyrange ) → anyelement

Extracts the lower bound of the range (NULL if the range is empty or has no lower bound).

lower(numrange(1.1,2.2)) → 1.1

upper ( anyrange ) → anyelement

Extracts the upper bound of the range (NULL if the range is empty or has no upper bound).

upper(numrange(1.1,2.2)) → 2.2

isempty ( anyrange ) → boolean

isempty(numrange(1.1,2.2)) → f

lower_inc ( anyrange ) → boolean

Is the range's lower bound inclusive?

lower_inc(numrange(1.1,2.2)) → t

upper_inc ( anyrange ) → boolean

Is the range's upper bound inclusive?

upper_inc(numrange(1.1,2.2)) → f

lower_inf ( anyrange ) → boolean

Does the range have no lower bound? (A lower bound of -Infinity returns false.)

lower_inf('(,)'::daterange) → t

upper_inf ( anyrange ) → boolean

Does the range have no upper bound? (An upper bound of Infinity returns false.)

upper_inf('(,)'::daterange) → t

range_merge ( anyrange, anyrange ) → anyrange

Computes the smallest range that includes both of the given ranges.

range_merge('[1,2)'::int4range, '[3,4)'::int4range) → [1,4)

Table 9.61. Multirange Functions

lower ( anymultirange ) → anyelement

Extracts the lower bound of the multirange (NULL if the multirange is empty or has no lower bound).

lower('{[1.1,2.2)}'::nummultirange) → 1.1

upper ( anymultirange ) → anyelement

Extracts the upper bound of the multirange (NULL if the multirange is empty or has no upper bound).

upper('{[1.1,2.2)}'::nummultirange) → 2.2

isempty ( anymultirange ) → boolean

Is the multirange empty?

isempty('{[1.1,2.2)}'::nummultirange) → f

lower_inc ( anymultirange ) → boolean

Is the multirange's lower bound inclusive?

lower_inc('{[1.1,2.2)}'::nummultirange) → t

upper_inc ( anymultirange ) → boolean

Is the multirange's upper bound inclusive?

upper_inc('{[1.1,2.2)}'::nummultirange) → f

lower_inf ( anymultirange ) → boolean

Does the multirange have no lower bound? (A lower bound of -Infinity returns false.)

lower_inf('{(,)}'::datemultirange) → t

upper_inf ( anymultirange ) → boolean

Does the multirange have no upper bound? (An upper bound of Infinity returns false.)

upper_inf('{(,)}'::datemultirange) → t

range_merge ( anymultirange ) → anyrange

Computes the smallest range that includes the entire multirange.

range_merge('{[1,2), [3,4)}'::int4multirange) → [1,4)

multirange ( anyrange ) → anymultirange

Returns a multirange containing just the given range.

multirange('[1,2)'::int4range) → {[1,2)}

unnest ( anymultirange ) → setof anyrange

Expands a multirange into a set of ranges in ascending order.

unnest('{[1,2), [3,4)}'::int4multirange) →

The lower_inc, upper_inc, lower_inf, and upper_inf functions all return false for an empty range or multirange.

Examples:

Example 1 (unknown):

[1,2)
 [3,4)

---

PostgreSQL: Documentation: 18: 9.28. System Administration Functions

URL: https://www.postgresql.org/docs/current/functions-admin.html

Contents:

• 9.28. System Administration Functions #
  • 9.28.1. Configuration Settings Functions #
  • 9.28.2. Server Signaling Functions #
  • 9.28.3. Backup Control Functions #
  • 9.28.4. Recovery Control Functions #
  • 9.28.5. Snapshot Synchronization Functions #
  • 9.28.6. Replication Management Functions #
  • Caution
  • 9.28.7. Database Object Management Functions #
  • Warning

The functions described in this section are used to control and monitor a PostgreSQL installation.

Table 9.95 shows the functions available to query and alter run-time configuration parameters.

Table 9.95. Configuration Settings Functions

current_setting ( setting_name text [, missing_ok boolean ] ) → text

Returns the current value of the setting setting_name. If there is no such setting, current_setting throws an error unless missing_ok is supplied and is true (in which case NULL is returned). This function corresponds to the SQL command SHOW.

current_setting('datestyle') → ISO, MDY

set_config ( setting_name text, new_value text, is_local boolean ) → text

Sets the parameter setting_name to new_value, and returns that value. If is_local is true, the new value will only apply during the current transaction. If you want the new value to apply for the rest of the current session, use false instead. This function corresponds to the SQL command SET.

set_config accepts the NULL value for new_value, but as settings cannot be null, it is interpreted as a request to reset the setting to its default value.

set_config('log_statement_stats', 'off', false) → off

The functions shown in Table 9.96 send control signals to other server processes. Use of these functions is restricted to superusers by default but access may be granted to others using GRANT, with noted exceptions.

Each of these functions returns true if the signal was successfully sent and false if sending the signal failed.

Table 9.96. Server Signaling Functions

pg_cancel_backend ( pid integer ) → boolean

Cancels the current query of the session whose backend process has the specified process ID. This is also allowed if the calling role is a member of the role whose backend is being canceled or the calling role has privileges of pg_signal_backend, however only superusers can cancel superuser backends. As an exception, roles with privileges of pg_signal_autovacuum_worker are permitted to cancel autovacuum worker processes, which are otherwise considered superuser backends.

pg_log_backend_memory_contexts ( pid integer ) → boolean

Requests to log the memory contexts of the backend with the specified process ID. This function can send the request to backends and auxiliary processes except logger. These memory contexts will be logged at LOG message level. They will appear in the server log based on the log configuration set (see Section 19.8 for more information), but will not be sent to the client regardless of client_min_messages.

pg_reload_conf () → boolean

Causes all processes of the PostgreSQL server to reload their configuration files. (This is initiated by sending a SIGHUP signal to the postmaster process, which in turn sends SIGHUP to each of its children.) You can use the pg_file_settings, pg_hba_file_rules and pg_ident_file_mappings views to check the configuration files for possible errors, before reloading.

pg_rotate_logfile () → boolean

Signals the log-file manager to switch to a new output file immediately. This works only when the built-in log collector is running, since otherwise there is no log-file manager subprocess.

pg_terminate_backend ( pid integer, timeout bigint DEFAULT 0 ) → boolean

Terminates the session whose backend process has the specified process ID. This is also allowed if the calling role is a member of the role whose backend is being terminated or the calling role has privileges of pg_signal_backend, however only superusers can terminate superuser backends. As an exception, roles with privileges of pg_signal_autovacuum_worker are permitted to terminate autovacuum worker processes, which are otherwise considered superuser backends.

If timeout is not specified or zero, this function returns true whether the process actually terminates or not, indicating only that the sending of the signal was successful. If the timeout is specified (in milliseconds) and greater than zero, the function waits until the process is actually terminated or until the given time has passed. If the process is terminated, the function returns true. On timeout, a warning is emitted and false is returned.

pg_cancel_backend and pg_terminate_backend send signals (SIGINT or SIGTERM respectively) to backend processes identified by process ID. The process ID of an active backend can be found from the pid column of the pg_stat_activity view, or by listing the postgres processes on the server (using ps on Unix or the Task Manager on Windows). The role of an active backend can be found from the usename column of the pg_stat_activity view.

pg_log_backend_memory_contexts can be used to log the memory contexts of a backend process. For example:

One message for each memory context will be logged. For example:

If there are more than 100 child contexts under the same parent, the first 100 child contexts are logged, along with a summary of the remaining contexts. Note that frequent calls to this function could incur significant overhead, because it may generate a large number of log messages.

The functions shown in Table 9.97 assist in making on-line backups. These functions cannot be executed during recovery (except pg_backup_start, pg_backup_stop, and pg_wal_lsn_diff).

For details about proper usage of these functions, see Section 25.3.

Table 9.97. Backup Control Functions

pg_create_restore_point ( name text ) → pg_lsn

Creates a named marker record in the write-ahead log that can later be used as a recovery target, and returns the corresponding write-ahead log location. The given name can then be used with recovery_target_name to specify the point up to which recovery will proceed. Avoid creating multiple restore points with the same name, since recovery will stop at the first one whose name matches the recovery target.

This function is restricted to superusers by default, but other users can be granted EXECUTE to run the function.

pg_current_wal_flush_lsn () → pg_lsn

Returns the current write-ahead log flush location (see notes below).

pg_current_wal_insert_lsn () → pg_lsn

Returns the current write-ahead log insert location (see notes below).

pg_current_wal_lsn () → pg_lsn

Returns the current write-ahead log write location (see notes below).

pg_backup_start ( label text [, fast boolean ] ) → pg_lsn

Prepares the server to begin an on-line backup. The only required parameter is an arbitrary user-defined label for the backup. (Typically this would be the name under which the backup dump file will be stored.) If the optional second parameter is given as true, it specifies executing pg_backup_start as quickly as possible. This forces an immediate checkpoint which will cause a spike in I/O operations, slowing any concurrently executing queries.

This function is restricted to superusers by default, but other users can be granted EXECUTE to run the function.

pg_backup_stop ( [wait_for_archive boolean ] ) → record ( lsn pg_lsn, labelfile text, spcmapfile text )

Finishes performing an on-line backup. The desired contents of the backup label file and the tablespace map file are returned as part of the result of the function and must be written to files in the backup area. These files must not be written to the live data directory (doing so will cause PostgreSQL to fail to restart in the event of a crash).

There is an optional parameter of type boolean. If false, the function will return immediately after the backup is completed, without waiting for WAL to be archived. This behavior is only useful with backup software that independently monitors WAL archiving. Otherwise, WAL required to make the backup consistent might be missing and make the backup useless. By default or when this parameter is true, pg_backup_stop will wait for WAL to be archived when archiving is enabled. (On a standby, this means that it will wait only when archive_mode = always. If write activity on the primary is low, it may be useful to run pg_switch_wal on the primary in order to trigger an immediate segment switch.)

When executed on a primary, this function also creates a backup history file in the write-ahead log archive area. The history file includes the label given to pg_backup_start, the starting and ending write-ahead log locations for the backup, and the starting and ending times of the backup. After recording the ending location, the current write-ahead log insertion point is automatically advanced to the next write-ahead log file, so that the ending write-ahead log file can be archived immediately to complete the backup.

The result of the function is a single record. The lsn column holds the backup's ending write-ahead log location (which again can be ignored). The second column returns the contents of the backup label file, and the third column returns the contents of the tablespace map file. These must be stored as part of the backup and are required as part of the restore process.

This function is restricted to superusers by default, but other users can be granted EXECUTE to run the function.

pg_switch_wal () → pg_lsn

Forces the server to switch to a new write-ahead log file, which allows the current file to be archived (assuming you are using continuous archiving). The result is the ending write-ahead log location plus 1 within the just-completed write-ahead log file. If there has been no write-ahead log activity since the last write-ahead log switch, pg_switch_wal does nothing and returns the start location of the write-ahead log file currently in use.

This function is restricted to superusers by default, but other users can be granted EXECUTE to run the function.

pg_walfile_name ( lsn pg_lsn ) → text

Converts a write-ahead log location to the name of the WAL file holding that location.

pg_walfile_name_offset ( lsn pg_lsn ) → record ( file_name text, file_offset integer )

Converts a write-ahead log location to a WAL file name and byte offset within that file.

pg_split_walfile_name ( file_name text ) → record ( segment_number numeric, timeline_id bigint )

Extracts the sequence number and timeline ID from a WAL file name.

pg_wal_lsn_diff ( lsn1 pg_lsn, lsn2 pg_lsn ) → numeric

Calculates the difference in bytes (lsn1 - lsn2) between two write-ahead log locations. This can be used with pg_stat_replication or some of the functions shown in Table 9.97 to get the replication lag.

pg_current_wal_lsn displays the current write-ahead log write location in the same format used by the above functions. Similarly, pg_current_wal_insert_lsn displays the current write-ahead log insertion location and pg_current_wal_flush_lsn displays the current write-ahead log flush location. The insertion location is the “logical” end of the write-ahead log at any instant, while the write location is the end of what has actually been written out from the server's internal buffers, and the flush location is the last location known to be written to durable storage. The write location is the end of what can be examined from outside the server, and is usually what you want if you are interested in archiving partially-complete write-ahead log files. The insertion and flush locations are made available primarily for server debugging purposes. These are all read-only operations and do not require superuser permissions.

You can use pg_walfile_name_offset to extract the corresponding write-ahead log file name and byte offset from a pg_lsn value. For example:

Similarly, pg_walfile_name extracts just the write-ahead log file name.

pg_split_walfile_name is useful to compute a LSN from a file offset and WAL file name, for example:

The functions shown in Table 9.98 provide information about the current status of a standby server. These functions may be executed both during recovery and in normal running.

Table 9.98. Recovery Information Functions

pg_is_in_recovery () → boolean

Returns true if recovery is still in progress.

pg_last_wal_receive_lsn () → pg_lsn

Returns the last write-ahead log location that has been received and synced to disk by streaming replication. While streaming replication is in progress this will increase monotonically. If recovery has completed then this will remain static at the location of the last WAL record received and synced to disk during recovery. If streaming replication is disabled, or if it has not yet started, the function returns NULL.

pg_last_wal_replay_lsn () → pg_lsn

Returns the last write-ahead log location that has been replayed during recovery. If recovery is still in progress this will increase monotonically. If recovery has completed then this will remain static at the location of the last WAL record applied during recovery. When the server has been started normally without recovery, the function returns NULL.

pg_last_xact_replay_timestamp () → timestamp with time zone

Returns the time stamp of the last transaction replayed during recovery. This is the time at which the commit or abort WAL record for that transaction was generated on the primary. If no transactions have been replayed during recovery, the function returns NULL. Otherwise, if recovery is still in progress this will increase monotonically. If recovery has completed then this will remain static at the time of the last transaction applied during recovery. When the server has been started normally without recovery, the function returns NULL.

pg_get_wal_resource_managers () → setof record ( rm_id integer, rm_name text, rm_builtin boolean )

Returns the currently-loaded WAL resource managers in the system. The column rm_builtin indicates whether it's a built-in resource manager, or a custom resource manager loaded by an extension.

The functions shown in Table 9.99 control the progress of recovery. These functions may be executed only during recovery.

Table 9.99. Recovery Control Functions

pg_is_wal_replay_paused () → boolean

Returns true if recovery pause is requested.

pg_get_wal_replay_pause_state () → text

Returns recovery pause state. The return values are not paused if pause is not requested, pause requested if pause is requested but recovery is not yet paused, and paused if the recovery is actually paused.

pg_promote ( wait boolean DEFAULT true, wait_seconds integer DEFAULT 60 ) → boolean

Promotes a standby server to primary status. With wait set to true (the default), the function waits until promotion is completed or wait_seconds seconds have passed, and returns true if promotion is successful and false otherwise. If wait is set to false, the function returns true immediately after sending a SIGUSR1 signal to the postmaster to trigger promotion.

This function is restricted to superusers by default, but other users can be granted EXECUTE to run the function.

pg_wal_replay_pause () → void

Request to pause recovery. A request doesn't mean that recovery stops right away. If you want a guarantee that recovery is actually paused, you need to check for the recovery pause state returned by pg_get_wal_replay_pause_state(). Note that pg_is_wal_replay_paused() returns whether a request is made. While recovery is paused, no further database changes are applied. If hot standby is active, all new queries will see the same consistent snapshot of the database, and no further query conflicts will be generated until recovery is resumed.

This function is restricted to superusers by default, but other users can be granted EXECUTE to run the function.

pg_wal_replay_resume () → void

Restarts recovery if it was paused.

This function is restricted to superusers by default, but other users can be granted EXECUTE to run the function.

pg_wal_replay_pause and pg_wal_replay_resume cannot be executed while a promotion is ongoing. If a promotion is triggered while recovery is paused, the paused state ends and promotion continues.

If streaming replication is disabled, the paused state may continue indefinitely without a problem. If streaming replication is in progress then WAL records will continue to be received, which will eventually fill available disk space, depending upon the duration of the pause, the rate of WAL generation and available disk space.

PostgreSQL allows database sessions to synchronize their snapshots. A snapshot determines which data is visible to the transaction that is using the snapshot. Synchronized snapshots are necessary when two or more sessions need to see identical content in the database. If two sessions just start their transactions independently, there is always a possibility that some third transaction commits between the executions of the two START TRANSACTION commands, so that one session sees the effects of that transaction and the other does not.

To solve this problem, PostgreSQL allows a transaction to export the snapshot it is using. As long as the exporting transaction remains open, other transactions can import its snapshot, and thereby be guaranteed that they see exactly the same view of the database that the first transaction sees. But note that any database changes made by any one of these transactions remain invisible to the other transactions, as is usual for changes made by uncommitted transactions. So the transactions are synchronized with respect to pre-existing data, but act normally for changes they make themselves.

Snapshots are exported with the pg_export_snapshot function, shown in Table 9.100, and imported with the SET TRANSACTION command.

Table 9.100. Snapshot Synchronization Functions

pg_export_snapshot () → text

Saves the transaction's current snapshot and returns a text string identifying the snapshot. This string must be passed (outside the database) to clients that want to import the snapshot. The snapshot is available for import only until the end of the transaction that exported it.

A transaction can export more than one snapshot, if needed. Note that doing so is only useful in READ COMMITTED transactions, since in REPEATABLE READ and higher isolation levels, transactions use the same snapshot throughout their lifetime. Once a transaction has exported any snapshots, it cannot be prepared with PREPARE TRANSACTION.

pg_log_standby_snapshot () → pg_lsn

Take a snapshot of running transactions and write it to WAL, without having to wait for bgwriter or checkpointer to log one. This is useful for logical decoding on standby, as logical slot creation has to wait until such a record is replayed on the standby.

The functions shown in Table 9.101 are for controlling and interacting with replication features. See Section 26.2.5, Section 26.2.6, and Chapter 48 for information about the underlying features. Use of functions for replication origin is only allowed to the superuser by default, but may be allowed to other users by using the GRANT command. Use of functions for replication slots is restricted to superusers and users having REPLICATION privilege.

Many of these functions have equivalent commands in the replication protocol; see Section 54.4.

The functions described in Section 9.28.3, Section 9.28.4, and Section 9.28.5 are also relevant for replication.

Table 9.101. Replication Management Functions

pg_create_physical_replication_slot ( slot_name name [, immediately_reserve boolean, temporary boolean ] ) → record ( slot_name name, lsn pg_lsn )

Creates a new physical replication slot named slot_name. The optional second parameter, when true, specifies that the LSN for this replication slot be reserved immediately; otherwise the LSN is reserved on first connection from a streaming replication client. Streaming changes from a physical slot is only possible with the streaming-replication protocol — see Section 54.4. The optional third parameter, temporary, when set to true, specifies that the slot should not be permanently stored to disk and is only meant for use by the current session. Temporary slots are also released upon any error. This function corresponds to the replication protocol command CREATE_REPLICATION_SLOT ... PHYSICAL.

pg_drop_replication_slot ( slot_name name ) → void

Drops the physical or logical replication slot named slot_name. Same as replication protocol command DROP_REPLICATION_SLOT.

pg_create_logical_replication_slot ( slot_name name, plugin name [, temporary boolean, twophase boolean, failover boolean ] ) → record ( slot_name name, lsn pg_lsn )

Creates a new logical (decoding) replication slot named slot_name using the output plugin plugin. The optional third parameter, temporary, when set to true, specifies that the slot should not be permanently stored to disk and is only meant for use by the current session. Temporary slots are also released upon any error. The optional fourth parameter, twophase, when set to true, specifies that the decoding of prepared transactions is enabled for this slot. The optional fifth parameter, failover, when set to true, specifies that this slot is enabled to be synced to the standbys so that logical replication can be resumed after failover. A call to this function has the same effect as the replication protocol command CREATE_REPLICATION_SLOT ... LOGICAL.

pg_copy_physical_replication_slot ( src_slot_name name, dst_slot_name name [, temporary boolean ] ) → record ( slot_name name, lsn pg_lsn )

Copies an existing physical replication slot named src_slot_name to a physical replication slot named dst_slot_name. The copied physical slot starts to reserve WAL from the same LSN as the source slot. temporary is optional. If temporary is omitted, the same value as the source slot is used. Copy of an invalidated slot is not allowed.

pg_copy_logical_replication_slot ( src_slot_name name, dst_slot_name name [, temporary boolean [, plugin name ]] ) → record ( slot_name name, lsn pg_lsn )

Copies an existing logical replication slot named src_slot_name to a logical replication slot named dst_slot_name, optionally changing the output plugin and persistence. The copied logical slot starts from the same LSN as the source logical slot. Both temporary and plugin are optional; if they are omitted, the values of the source slot are used. The failover option of the source logical slot is not copied and is set to false by default. This is to avoid the risk of being unable to continue logical replication after failover to standby where the slot is being synchronized. Copy of an invalidated slot is not allowed.

pg_logical_slot_get_changes ( slot_name name, upto_lsn pg_lsn, upto_nchanges integer, VARIADIC options text[] ) → setof record ( lsn pg_lsn, xid xid, data text )

Returns changes in the slot slot_name, starting from the point from which changes have been consumed last. If upto_lsn and upto_nchanges are NULL, logical decoding will continue until end of WAL. If upto_lsn is non-NULL, decoding will include only those transactions which commit prior to the specified LSN. If upto_nchanges is non-NULL, decoding will stop when the number of rows produced by decoding exceeds the specified value. Note, however, that the actual number of rows returned may be larger, since this limit is only checked after adding the rows produced when decoding each new transaction commit. If the specified slot is a logical failover slot then the function will not return until all physical slots specified in synchronized_standby_slots have confirmed WAL receipt.

pg_logical_slot_peek_changes ( slot_name name, upto_lsn pg_lsn, upto_nchanges integer, VARIADIC options text[] ) → setof record ( lsn pg_lsn, xid xid, data text )

Behaves just like the pg_logical_slot_get_changes() function, except that changes are not consumed; that is, they will be returned again on future calls.

pg_logical_slot_get_binary_changes ( slot_name name, upto_lsn pg_lsn, upto_nchanges integer, VARIADIC options text[] ) → setof record ( lsn pg_lsn, xid xid, data bytea )

Behaves just like the pg_logical_slot_get_changes() function, except that changes are returned as bytea.

pg_logical_slot_peek_binary_changes ( slot_name name, upto_lsn pg_lsn, upto_nchanges integer, VARIADIC options text[] ) → setof record ( lsn pg_lsn, xid xid, data bytea )

Behaves just like the pg_logical_slot_peek_changes() function, except that changes are returned as bytea.

pg_replication_slot_advance ( slot_name name, upto_lsn pg_lsn ) → record ( slot_name name, end_lsn pg_lsn )

Advances the current confirmed position of a replication slot named slot_name. The slot will not be moved backwards, and it will not be moved beyond the current insert location. Returns the name of the slot and the actual position that it was advanced to. The updated slot position information is written out at the next checkpoint if any advancing is done. So in the event of a crash, the slot may return to an earlier position. If the specified slot is a logical failover slot then the function will not return until all physical slots specified in synchronized_standby_slots have confirmed WAL receipt.

pg_replication_origin_create ( node_name text ) → oid

Creates a replication origin with the given external name, and returns the internal ID assigned to it. The name must be no longer than 512 bytes.

pg_replication_origin_drop ( node_name text ) → void

Deletes a previously-created replication origin, including any associated replay progress.

pg_replication_origin_oid ( node_name text ) → oid

Looks up a replication origin by name and returns the internal ID. If no such replication origin is found, NULL is returned.

pg_replication_origin_session_setup ( node_name text ) → void

Marks the current session as replaying from the given origin, allowing replay progress to be tracked. Can only be used if no origin is currently selected. Use pg_replication_origin_session_reset to undo.

pg_replication_origin_session_reset () → void

Cancels the effects of pg_replication_origin_session_setup().

pg_replication_origin_session_is_setup () → boolean

Returns true if a replication origin has been selected in the current session.

pg_replication_origin_session_progress ( flush boolean ) → pg_lsn

Returns the replay location for the replication origin selected in the current session. The parameter flush determines whether the corresponding local transaction will be guaranteed to have been flushed to disk or not.

pg_replication_origin_xact_setup ( origin_lsn pg_lsn, origin_timestamp timestamp with time zone ) → void

Marks the current transaction as replaying a transaction that has committed at the given LSN and timestamp. Can only be called when a replication origin has been selected using pg_replication_origin_session_setup.

pg_replication_origin_xact_reset () → void

Cancels the effects of pg_replication_origin_xact_setup().

pg_replication_origin_advance ( node_name text, lsn pg_lsn ) → void

Sets replication progress for the given node to the given location. This is primarily useful for setting up the initial location, or setting a new location after configuration changes and similar. Be aware that careless use of this function can lead to inconsistently replicated data.

pg_replication_origin_progress ( node_name text, flush boolean ) → pg_lsn

Returns the replay location for the given replication origin. The parameter flush determines whether the corresponding local transaction will be guaranteed to have been flushed to disk or not.

pg_logical_emit_message ( transactional boolean, prefix text, content text [, flush boolean DEFAULT false] ) → pg_lsn

pg_logical_emit_message ( transactional boolean, prefix text, content bytea [, flush boolean DEFAULT false] ) → pg_lsn

Emits a logical decoding message. This can be used to pass generic messages to logical decoding plugins through WAL. The transactional parameter specifies if the message should be part of the current transaction, or if it should be written immediately and decoded as soon as the logical decoder reads the record. The prefix parameter is a textual prefix that can be used by logical decoding plugins to easily recognize messages that are interesting for them. The content parameter is the content of the message, given either in text or binary form. The flush parameter (default set to false) controls if the message is immediately flushed to WAL or not. flush has no effect with transactional, as the message's WAL record is flushed along with its transaction.

pg_sync_replication_slots () → void

Synchronize the logical failover replication slots from the primary server to the standby server. This function can only be executed on the standby server. Temporary synced slots, if any, cannot be used for logical decoding and must be dropped after promotion. See Section 47.2.3 for details. Note that this function is primarily intended for testing and debugging purposes and should be used with caution. Additionally, this function cannot be executed if sync_replication_slots is enabled and the slotsync worker is already running to perform the synchronization of slots.

If, after executing the function, hot_standby_feedback is disabled on the standby or the physical slot configured in primary_slot_name is removed, then it is possible that the necessary rows of the synchronized slot will be removed by the VACUUM process on the primary server, resulting in the synchronized slot becoming invalidated.

The functions shown in Table 9.102 calculate the disk space usage of database objects, or assist in presentation or understanding of usage results. bigint results are measured in bytes. If an OID that does not represent an existing object is passed to one of these functions, NULL is returned.

Table 9.102. Database Object Size Functions

pg_column_size ( "any" ) → integer

Shows the number of bytes used to store any individual data value. If applied directly to a table column value, this reflects any compression that was done.

pg_column_compression ( "any" ) → text

Shows the compression algorithm that was used to compress an individual variable-length value. Returns NULL if the value is not compressed.

pg_column_toast_chunk_id ( "any" ) → oid

Shows the chunk_id of an on-disk TOASTed value. Returns NULL if the value is un-TOASTed or not on-disk. See Section 66.2 for more information about TOAST.

pg_database_size ( name ) → bigint

pg_database_size ( oid ) → bigint

Computes the total disk space used by the database with the specified name or OID. To use this function, you must have CONNECT privilege on the specified database (which is granted by default) or have privileges of the pg_read_all_stats role.

pg_indexes_size ( regclass ) → bigint

Computes the total disk space used by indexes attached to the specified table.

pg_relation_size ( relation regclass [, fork text ] ) → bigint

Computes the disk space used by one “fork” of the specified relation. (Note that for most purposes it is more convenient to use the higher-level functions pg_total_relation_size or pg_table_size, which sum the sizes of all forks.) With one argument, this returns the size of the main data fork of the relation. The second argument can be provided to specify which fork to examine:

main returns the size of the main data fork of the relation.

fsm returns the size of the Free Space Map (see Section 66.3) associated with the relation.

vm returns the size of the Visibility Map (see Section 66.4) associated with the relation.

init returns the size of the initialization fork, if any, associated with the relation.

pg_size_bytes ( text ) → bigint

Converts a size in human-readable format (as returned by pg_size_pretty) into bytes. Valid units are bytes, B, kB, MB, GB, TB, and PB.

pg_size_pretty ( bigint ) → text

pg_size_pretty ( numeric ) → text

Converts a size in bytes into a more easily human-readable format with size units (bytes, kB, MB, GB, TB, or PB as appropriate). Note that the units are powers of 2 rather than powers of 10, so 1kB is 1024 bytes, 1MB is 10242 = 1048576 bytes, and so on.

pg_table_size ( regclass ) → bigint

Computes the disk space used by the specified table, excluding indexes (but including its TOAST table if any, free space map, and visibility map).

pg_tablespace_size ( name ) → bigint

pg_tablespace_size ( oid ) → bigint

Computes the total disk space used in the tablespace with the specified name or OID. To use this function, you must have CREATE privilege on the specified tablespace or have privileges of the pg_read_all_stats role, unless it is the default tablespace for the current database.

pg_total_relation_size ( regclass ) → bigint

Computes the total disk space used by the specified table, including all indexes and TOAST data. The result is equivalent to pg_table_size + pg_indexes_size.

The functions above that operate on tables or indexes accept a regclass argument, which is simply the OID of the table or index in the pg_class system catalog. You do not have to look up the OID by hand, however, since the regclass data type's input converter will do the work for you. See Section 8.19 for details.

The functions shown in Table 9.103 assist in identifying the specific disk files associated with database objects.

Table 9.103. Database Object Location Functions

pg_relation_filenode ( relation regclass ) → oid

Returns the “filenode” number currently assigned to the specified relation. The filenode is the base component of the file name(s) used for the relation (see Section 66.1 for more information). For most relations the result is the same as pg_class.relfilenode, but for certain system catalogs relfilenode is zero and this function must be used to get the correct value. The function returns NULL if passed a relation that does not have storage, such as a view.

pg_relation_filepath ( relation regclass ) → text

Returns the entire file path name (relative to the database cluster's data directory, PGDATA) of the relation.

pg_filenode_relation ( tablespace oid, filenode oid ) → regclass

Returns a relation's OID given the tablespace OID and filenode it is stored under. This is essentially the inverse mapping of pg_relation_filepath. For a relation in the database's default tablespace, the tablespace can be specified as zero. Returns NULL if no relation in the current database is associated with the given values, or if dealing with a temporary relation.

Table 9.104 lists functions used to manage collations.

Table 9.104. Collation Management Functions

pg_collation_actual_version ( oid ) → text

Returns the actual version of the collation object as it is currently installed in the operating system. If this is different from the value in pg_collation.collversion, then objects depending on the collation might need to be rebuilt. See also ALTER COLLATION.

pg_database_collation_actual_version ( oid ) → text

Returns the actual version of the database's collation as it is currently installed in the operating system. If this is different from the value in pg_database.datcollversion, then objects depending on the collation might need to be rebuilt. See also ALTER DATABASE.

pg_import_system_collations ( schema regnamespace ) → integer

Adds collations to the system catalog pg_collation based on all the locales it finds in the operating system. This is what initdb uses; see Section 23.2.2 for more details. If additional locales are installed into the operating system later on, this function can be run again to add collations for the new locales. Locales that match existing entries in pg_collation will be skipped. (But collation objects based on locales that are no longer present in the operating system are not removed by this function.) The schema parameter would typically be pg_catalog, but that is not a requirement; the collations could be installed into some other schema as well. The function returns the number of new collation objects it created. Use of this function is restricted to superusers.

Table 9.105 lists functions used to manipulate statistics. These functions cannot be executed during recovery.

Changes made by these statistics manipulation functions are likely to be overwritten by autovacuum (or manual VACUUM or ANALYZE) and should be considered temporary.

Table 9.105. Database Object Statistics Manipulation Functions

pg_restore_relation_stats ( VARIADIC kwargs "any" ) → boolean

Updates table-level statistics. Ordinarily, these statistics are collected automatically or updated as a part of VACUUM or ANALYZE, so it's not necessary to call this function. However, it is useful after a restore to enable the optimizer to choose better plans if ANALYZE has not been run yet.

The tracked statistics may change from version to version, so arguments are passed as pairs of argname and argvalue in the form:

For example, to set the relpages and reltuples values for the table mytable:

The arguments schemaname and relname are required, and specify the table. Other arguments are the names and values of statistics corresponding to certain columns in pg_class. The currently-supported relation statistics are relpages with a value of type integer, reltuples with a value of type real, relallvisible with a value of type integer, and relallfrozen with a value of type integer.

Additionally, this function accepts argument name version of type integer, which specifies the server version from which the statistics originated. This is anticipated to be helpful in porting statistics from older versions of PostgreSQL.

Minor errors are reported as a WARNING and ignored, and remaining statistics will still be restored. If all specified statistics are successfully restored, returns true, otherwise false.

The caller must have the MAINTAIN privilege on the table or be the owner of the database.

pg_clear_relation_stats ( schemaname text, relname text ) → void

Clears table-level statistics for the given relation, as though the table was newly created.

The caller must have the MAINTAIN privilege on the table or be the owner of the database.

pg_restore_attribute_stats ( VARIADIC kwargs "any" ) → boolean

Creates or updates column-level statistics. Ordinarily, these statistics are collected automatically or updated as a part of VACUUM or ANALYZE, so it's not necessary to call this function. However, it is useful after a restore to enable the optimizer to choose better plans if ANALYZE has not been run yet.

The tracked statistics may change from version to version, so arguments are passed as pairs of argname and argvalue in the form:

For example, to set the avg_width and null_frac values for the attribute col1 of the table mytable:

The required arguments are schemaname and relname with a value of type text which specify the table; either attname with a value of type text or attnum with a value of type smallint, which specifies the column; and inherited, which specifies whether the statistics include values from child tables. Other arguments are the names and values of statistics corresponding to columns in pg_stats.

Additionally, this function accepts argument name version of type integer, which specifies the server version from which the statistics originated. This is anticipated to be helpful in porting statistics from older versions of PostgreSQL.

Minor errors are reported as a WARNING and ignored, and remaining statistics will still be restored. If all specified statistics are successfully restored, returns true, otherwise false.

The caller must have the MAINTAIN privilege on the table or be the owner of the database.

pg_clear_attribute_stats ( schemaname text, relname text, attname text, inherited boolean ) → void

Clears column-level statistics for the given relation and attribute, as though the table was newly created.

The caller must have the MAINTAIN privilege on the table or be the owner of the database.

Table 9.106 lists functions that provide information about the structure of partitioned tables.

Table 9.106. Partitioning Information Functions

pg_partition_tree ( regclass ) → setof record ( relid regclass, parentrelid regclass, isleaf boolean, level integer )

Lists the tables or indexes in the partition tree of the given partitioned table or partitioned index, with one row for each partition. Information provided includes the OID of the partition, the OID of its immediate parent, a boolean value telling if the partition is a leaf, and an integer telling its level in the hierarchy. The level value is 0 for the input table or index, 1 for its immediate child partitions, 2 for their partitions, and so on. Returns no rows if the relation does not exist or is not a partition or partitioned table.

pg_partition_ancestors ( regclass ) → setof regclass

Lists the ancestor relations of the given partition, including the relation itself. Returns no rows if the relation does not exist or is not a partition or partitioned table.

pg_partition_root ( regclass ) → regclass

Returns the top-most parent of the partition tree to which the given relation belongs. Returns NULL if the relation does not exist or is not a partition or partitioned table.

For example, to check the total size of the data contained in a partitioned table measurement, one could use the following query:

Table 9.107 shows the functions available for index maintenance tasks. (Note that these maintenance tasks are normally done automatically by autovacuum; use of these functions is only required in special cases.) These functions cannot be executed during recovery. Use of these functions is restricted to superusers and the owner of the given index.

Table 9.107. Index Maintenance Functions

brin_summarize_new_values ( index regclass ) → integer

Scans the specified BRIN index to find page ranges in the base table that are not currently summarized by the index; for any such range it creates a new summary index tuple by scanning those table pages. Returns the number of new page range summaries that were inserted into the index.

brin_summarize_range ( index regclass, blockNumber bigint ) → integer

Summarizes the page range covering the given block, if not already summarized. This is like brin_summarize_new_values except that it only processes the page range that covers the given table block number.

brin_desummarize_range ( index regclass, blockNumber bigint ) → void

Removes the BRIN index tuple that summarizes the page range covering the given table block, if there is one.

gin_clean_pending_list ( index regclass ) → bigint

Cleans up the “pending” list of the specified GIN index by moving entries in it, in bulk, to the main GIN data structure. Returns the number of pages removed from the pending list. If the argument is a GIN index built with the fastupdate option disabled, no cleanup happens and the result is zero, because the index doesn't have a pending list. See Section 65.4.4.1 and Section 65.4.5 for details about the pending list and fastupdate option.

The functions shown in Table 9.108 provide native access to files on the machine hosting the server. Only files within the database cluster directory and the log_directory can be accessed, unless the user is a superuser or is granted the role pg_read_server_files. Use a relative path for files in the cluster directory, and a path matching the log_directory configuration setting for log files.

Note that granting users the EXECUTE privilege on pg_read_file(), or related functions, allows them the ability to read any file on the server that the database server process can read; these functions bypass all in-database privilege checks. This means that, for example, a user with such access is able to read the contents of the pg_authid table where authentication information is stored, as well as read any table data in the database. Therefore, granting access to these functions should be carefully considered.

When granting privilege on these functions, note that the table entries showing optional parameters are mostly implemented as several physical functions with different parameter lists. Privilege must be granted separately on each such function, if it is to be used. psql's \df command can be useful to check what the actual function signatures are.

Some of these functions take an optional missing_ok parameter, which specifies the behavior when the file or directory does not exist. If true, the function returns NULL or an empty result set, as appropriate. If false, an error is raised. (Failure conditions other than “file not found” are reported as errors in any case.) The default is false.

Table 9.108. Generic File Access Functions

pg_ls_dir ( dirname text [, missing_ok boolean, include_dot_dirs boolean ] ) → setof text

Returns the names of all files (and directories and other special files) in the specified directory. The include_dot_dirs parameter indicates whether “.” and “..” are to be included in the result set; the default is to exclude them. Including them can be useful when missing_ok is true, to distinguish an empty directory from a non-existent directory.

This function is restricted to superusers by default, but other users can be granted EXECUTE to run the function.

pg_ls_logdir () → setof record ( name text, size bigint, modification timestamp with time zone )

Returns the name, size, and last modification time (mtime) of each ordinary file in the server's log directory. Filenames beginning with a dot, directories, and other special files are excluded.

This function is restricted to superusers and roles with privileges of the pg_monitor role by default, but other users can be granted EXECUTE to run the function.

pg_ls_waldir () → setof record ( name text, size bigint, modification timestamp with time zone )

Returns the name, size, and last modification time (mtime) of each ordinary file in the server's write-ahead log (WAL) directory. Filenames beginning with a dot, directories, and other special files are excluded.

This function is restricted to superusers and roles with privileges of the pg_monitor role by default, but other users can be granted EXECUTE to run the function.

pg_ls_logicalmapdir () → setof record ( name text, size bigint, modification timestamp with time zone )

Returns the name, size, and last modification time (mtime) of each ordinary file in the server's pg_logical/mappings directory. Filenames beginning with a dot, directories, and other special files are excluded.

This function is restricted to superusers and members of the pg_monitor role by default, but other users can be granted EXECUTE to run the function.

pg_ls_logicalsnapdir () → setof record ( name text, size bigint, modification timestamp with time zone )

Returns the name, size, and last modification time (mtime) of each ordinary file in the server's pg_logical/snapshots directory. Filenames beginning with a dot, directories, and other special files are excluded.

This function is restricted to superusers and members of the pg_monitor role by default, but other users can be granted EXECUTE to run the function.

pg_ls_replslotdir ( slot_name text ) → setof record ( name text, size bigint, modification timestamp with time zone )

Returns the name, size, and last modification time (mtime) of each ordinary file in the server's pg_replslot/slot_name directory, where slot_name is the name of the replication slot provided as input of the function. Filenames beginning with a dot, directories, and other special files are excluded.

This function is restricted to superusers and members of the pg_monitor role by default, but other users can be granted EXECUTE to run the function.

pg_ls_summariesdir () → setof record ( name text, size bigint, modification timestamp with time zone )

Returns the name, size, and last modification time (mtime) of each ordinary file in the server's WAL summaries directory (pg_wal/summaries). Filenames beginning with a dot, directories, and other special files are excluded.

This function is restricted to superusers and members of the pg_monitor role by default, but other users can be granted EXECUTE to run the function.

pg_ls_archive_statusdir () → setof record ( name text, size bigint, modification timestamp with time zone )

Returns the name, size, and last modification time (mtime) of each ordinary file in the server's WAL archive status directory (pg_wal/archive_status). Filenames beginning with a dot, directories, and other special files are excluded.

This function is restricted to superusers and members of the pg_monitor role by default, but other users can be granted EXECUTE to run the function.

pg_ls_tmpdir ( [ tablespace oid ] ) → setof record ( name text, size bigint, modification timestamp with time zone )

Returns the name, size, and last modification time (mtime) of each ordinary file in the temporary file directory for the specified tablespace. If tablespace is not provided, the pg_default tablespace is examined. Filenames beginning with a dot, directories, and other special files are excluded.

This function is restricted to superusers and members of the pg_monitor role by default, but other users can be granted EXECUTE to run the function.

pg_read_file ( filename text [, offset bigint, length bigint ] [, missing_ok boolean ] ) → text

Returns all or part of a text file, starting at the given byte offset, returning at most length bytes (less if the end of file is reached first). If offset is negative, it is relative to the end of the file. If offset and length are omitted, the entire file is returned. The bytes read from the file are interpreted as a string in the database's encoding; an error is thrown if they are not valid in that encoding.

This function is restricted to superusers by default, but other users can be granted EXECUTE to run the function.

pg_read_binary_file ( filename text [, offset bigint, length bigint ] [, missing_ok boolean ] ) → bytea

Returns all or part of a file. This function is identical to pg_read_file except that it can read arbitrary binary data, returning the result as bytea not text; accordingly, no encoding checks are performed.

This function is restricted to superusers by default, but other users can be granted EXECUTE to run the function.

In combination with the convert_from function, this function can be used to read a text file in a specified encoding and convert to the database's encoding:

pg_stat_file ( filename text [, missing_ok boolean ] ) → record ( size bigint, access timestamp with time zone, modification timestamp with time zone, change timestamp with time zone, creation timestamp with time zone, isdir boolean )

Returns a record containing the file's size, last access time stamp, last modification time stamp, last file status change time stamp (Unix platforms only), file creation time stamp (Windows only), and a flag indicating if it is a directory.

This function is restricted to superusers by default, but other users can be granted EXECUTE to run the function.

The functions shown in Table 9.109 manage advisory locks. For details about proper use of these functions, see Section 13.3.5.

All these functions are intended to be used to lock application-defined resources, which can be identified either by a single 64-bit key value or two 32-bit key values (note that these two key spaces do not overlap). If another session already holds a conflicting lock on the same resource identifier, the functions will either wait until the resource becomes available, or return a false result, as appropriate for the function. Locks can be either shared or exclusive: a shared lock does not conflict with other shared locks on the same resource, only with exclusive locks. Locks can be taken at session level (so that they are held until released or the session ends) or at transaction level (so that they are held until the current transaction ends; there is no provision for manual release). Multiple session-level lock requests stack, so that if the same resource identifier is locked three times there must then be three unlock requests to release the resource in advance of session end.

Table 9.109. Advisory Lock Functions

pg_advisory_lock ( key bigint ) → void

pg_advisory_lock ( key1 integer, key2 integer ) → void

Obtains an exclusive session-level advisory lock, waiting if necessary.

pg_advisory_lock_shared ( key bigint ) → void

pg_advisory_lock_shared ( key1 integer, key2 integer ) → void

Obtains a shared session-level advisory lock, waiting if necessary.

pg_advisory_unlock ( key bigint ) → boolean

pg_advisory_unlock ( key1 integer, key2 integer ) → boolean

Releases a previously-acquired exclusive session-level advisory lock. Returns true if the lock is successfully released. If the lock was not held, false is returned, and in addition, an SQL warning will be reported by the server.

pg_advisory_unlock_all () → void

Releases all session-level advisory locks held by the current session. (This function is implicitly invoked at session end, even if the client disconnects ungracefully.)

pg_advisory_unlock_shared ( key bigint ) → boolean

pg_advisory_unlock_shared ( key1 integer, key2 integer ) → boolean

Releases a previously-acquired shared session-level advisory lock. Returns true if the lock is successfully released. If the lock was not held, false is returned, and in addition, an SQL warning will be reported by the server.

pg_advisory_xact_lock ( key bigint ) → void

pg_advisory_xact_lock ( key1 integer, key2 integer ) → void

Obtains an exclusive transaction-level advisory lock, waiting if necessary.

pg_advisory_xact_lock_shared ( key bigint ) → void

pg_advisory_xact_lock_shared ( key1 integer, key2 integer ) → void

Obtains a shared transaction-level advisory lock, waiting if necessary.

pg_try_advisory_lock ( key bigint ) → boolean

pg_try_advisory_lock ( key1 integer, key2 integer ) → boolean

Obtains an exclusive session-level advisory lock if available. This will either obtain the lock immediately and return true, or return false without waiting if the lock cannot be acquired immediately.

pg_try_advisory_lock_shared ( key bigint ) → boolean

pg_try_advisory_lock_shared ( key1 integer, key2 integer ) → boolean

Obtains a shared session-level advisory lock if available. This will either obtain the lock immediately and return true, or return false without waiting if the lock cannot be acquired immediately.

pg_try_advisory_xact_lock ( key bigint ) → boolean

pg_try_advisory_xact_lock ( key1 integer, key2 integer ) → boolean

Obtains an exclusive transaction-level advisory lock if available. This will either obtain the lock immediately and return true, or return false without waiting if the lock cannot be acquired immediately.

pg_try_advisory_xact_lock_shared ( key bigint ) → boolean

pg_try_advisory_xact_lock_shared ( key1 integer, key2 integer ) → boolean

Obtains a shared transaction-level advisory lock if available. This will either obtain the lock immediately and return true, or return false without waiting if the lock cannot be acquired immediately.

Examples:

Example 1 (unknown):

postgres=# SELECT pg_log_backend_memory_contexts(pg_backend_pid());
 pg_log_backend_memory_contexts
--------------------------------
 t
(1 row)

Example 2 (unknown):

LOG:  logging memory contexts of PID 10377
STATEMENT:  SELECT pg_log_backend_memory_contexts(pg_backend_pid());
LOG:  level: 1; TopMemoryContext: 80800 total in 6 blocks; 14432 free (5 chunks); 66368 used
LOG:  level: 2; pgstat TabStatusArray lookup hash table: 8192 total in 1 blocks; 1408 free (0 chunks); 6784 used
LOG:  level: 2; TopTransactionContext: 8192 total in 1 blocks; 7720 free (1 chunks); 472 used
LOG:  level: 2; RowDescriptionContext: 8192 total in 1 blocks; 6880 free (0 chunks); 1312 used
LOG:  level: 2; MessageContext: 16384 total in 2 blocks; 5152 free (0 chunks); 11232 used
LOG:  level: 2; Operator class cache: 8192 total in 1 blocks; 512 free (0 chunks); 7680 used
LOG:  level: 2; smgr relation table: 16384 total in 2 blocks; 4544 free (3 chunks); 11840 used
LOG:  level: 2; TransactionAbortContext: 32768 total in 1 blocks; 32504 free (0 chunks); 264 used
...
LOG:  level: 2; ErrorContext: 8192 total in 1 blocks; 7928 free (3 chunks); 264 used
LOG:  Grand total: 1651920 bytes in 201 blocks; 622360 free (88 chunks); 1029560 used

Example 3 (unknown):

postgres=# SELECT * FROM pg_walfile_name_offset((pg_backup_stop()).lsn);
        file_name         | file_offset
--------------------------+-------------
 00000001000000000000000D |     4039624
(1 row)

Example 4 (unknown):

postgres=# \set file_name '000000010000000100C000AB'
postgres=# \set offset 256
postgres=# SELECT '0/0'::pg_lsn + pd.segment_number * ps.setting::int + :offset AS lsn
  FROM pg_split_walfile_name(:'file_name') pd,
       pg_show_all_settings() ps
  WHERE ps.name = 'wal_segment_size';
      lsn
---------------
 C001/AB000100
(1 row)

---

PostgreSQL: Documentation: 18: 35.43. routine_sequence_usage

URL: https://www.postgresql.org/docs/current/infoschema-routine-sequence-usage.html

Contents:

• 35.43. routine_sequence_usage #

The view routine_sequence_usage identifies all sequences that are used by a function or procedure, either in the SQL body or in parameter default expressions. (This only works for unquoted SQL bodies, not quoted bodies or functions in other languages.) A sequence is only included if that sequence is owned by a currently enabled role.

Table 35.41. routine_sequence_usage Columns

specific_catalog sql_identifier

Name of the database containing the function (always the current database)

specific_schema sql_identifier

Name of the schema containing the function

specific_name sql_identifier

The “specific name” of the function. See Section 35.45 for more information.

routine_catalog sql_identifier

Name of the database containing the function (always the current database)

routine_schema sql_identifier

Name of the schema containing the function

routine_name sql_identifier

Name of the function (might be duplicated in case of overloading)

schema_catalog sql_identifier

Name of the database that contains the sequence that is used by the function (always the current database)

sequence_schema sql_identifier

Name of the schema that contains the sequence that is used by the function

sequence_name sql_identifier

Name of the sequence that is used by the function

---

PostgreSQL: Documentation: 18: 20.15. OAuth Authorization/Authentication

URL: https://www.postgresql.org/docs/current/auth-oauth.html

Contents:

• 20.15. OAuth Authorization/Authentication #
  • Note
  • Warning
  • Warning

OAuth 2.0 is an industry-standard framework, defined in RFC 6749, to enable third-party applications to obtain limited access to a protected resource. OAuth client support has to be enabled when PostgreSQL is built, see Chapter 17 for more information.

This documentation uses the following terminology when discussing the OAuth ecosystem:

The user or system who owns protected resources and can grant access to them. This documentation also uses the term end user when the resource owner is a person. When you use psql to connect to the database using OAuth, you are the resource owner/end user.

The system which accesses the protected resources using access tokens. Applications using libpq, such as psql, are the OAuth clients when connecting to a PostgreSQL cluster.

The system hosting the protected resources which are accessed by the client. The PostgreSQL cluster being connected to is the resource server.

The organization, product vendor, or other entity which develops and/or administers the OAuth authorization servers and clients for a given application. Different providers typically choose different implementation details for their OAuth systems; a client of one provider is not generally guaranteed to have access to the servers of another.

This use of the term "provider" is not standard, but it seems to be in wide use colloquially. (It should not be confused with OpenID's similar term "Identity Provider". While the implementation of OAuth in PostgreSQL is intended to be interoperable and compatible with OpenID Connect/OIDC, it is not itself an OIDC client and does not require its use.)

The system which receives requests from, and issues access tokens to, the client after the authenticated resource owner has given approval. PostgreSQL does not provide an authorization server; it is the responsibility of the OAuth provider.

An identifier for an authorization server, printed as an https:// URL, which provides a trusted "namespace" for OAuth clients and applications. The issuer identifier allows a single authorization server to talk to the clients of mutually untrusting entities, as long as they maintain separate issuers.

For small deployments, there may not be a meaningful distinction between the "provider", "authorization server", and "issuer". However, for more complicated setups, there may be a one-to-many (or many-to-many) relationship: a provider may rent out multiple issuer identifiers to separate tenants, then provide multiple authorization servers, possibly with different supported feature sets, to interact with their clients.

PostgreSQL supports bearer tokens, defined in RFC 6750, which are a type of access token used with OAuth 2.0 where the token is an opaque string. The format of the access token is implementation specific and is chosen by each authorization server.

The following configuration options are supported for OAuth:

An HTTPS URL which is either the exact issuer identifier of the authorization server, as defined by its discovery document, or a well-known URI that points directly to that discovery document. This parameter is required.

When an OAuth client connects to the server, a URL for the discovery document will be constructed using the issuer identifier. By default, this URL uses the conventions of OpenID Connect Discovery: the path /.well-known/openid-configuration will be appended to the end of the issuer identifier. Alternatively, if the issuer contains a /.well-known/ path segment, that URL will be provided to the client as-is.

The OAuth client in libpq requires the server's issuer setting to exactly match the issuer identifier which is provided in the discovery document, which must in turn match the client's oauth_issuer setting. No variations in case or formatting are permitted.

A space-separated list of the OAuth scopes needed for the server to both authorize the client and authenticate the user. Appropriate values are determined by the authorization server and the OAuth validation module used (see Chapter 50 for more information on validators). This parameter is required.

The library to use for validating bearer tokens. If given, the name must exactly match one of the libraries listed in oauth_validator_libraries. This parameter is optional unless oauth_validator_libraries contains more than one library, in which case it is required.

Allows for mapping between OAuth identity provider and database user names. See Section 20.2 for details. If a map is not specified, the user name associated with the token (as determined by the OAuth validator) must exactly match the role name being requested. This parameter is optional.

An advanced option which is not intended for common use.

When set to 1, standard user mapping with pg_ident.conf is skipped, and the OAuth validator takes full responsibility for mapping end user identities to database roles. If the validator authorizes the token, the server trusts that the user is allowed to connect under the requested role, and the connection is allowed to proceed regardless of the authentication status of the user.

This parameter is incompatible with map.

delegate_ident_mapping provides additional flexibility in the design of the authentication system, but it also requires careful implementation of the OAuth validator, which must determine whether the provided token carries sufficient end-user privileges in addition to the standard checks required of all validators. Use with caution.

---

PostgreSQL: Documentation: 18: Chapter 9. Functions and Operators

URL: https://www.postgresql.org/docs/current/functions.html

Contents:

• Chapter 9. Functions and Operators

PostgreSQL provides a large number of functions and operators for the built-in data types. This chapter describes most of them, although additional special-purpose functions appear in relevant sections of the manual. Users can also define their own functions and operators, as described in Part V. The psql commands \df and \do can be used to list all available functions and operators, respectively.

The notation used throughout this chapter to describe the argument and result data types of a function or operator is like this:

which says that the function repeat takes one text and one integer argument and returns a result of type text. The right arrow is also used to indicate the result of an example, thus:

If you are concerned about portability then note that most of the functions and operators described in this chapter, with the exception of the most trivial arithmetic and comparison operators and some explicitly marked functions, are not specified by the SQL standard. Some of this extended functionality is present in other SQL database management systems, and in many cases this functionality is compatible and consistent between the various implementations.

Examples:

Example 1 (unknown):

repeat ( text, integer ) → text

Example 2 (unknown):

repeat('Pg', 4) → PgPgPgPg

---

PostgreSQL: Documentation: 18: Chapter 49. Archive Modules

URL: https://www.postgresql.org/docs/current/archive-modules.html

Contents:

• Chapter 49. Archive Modules

PostgreSQL provides infrastructure to create custom modules for continuous archiving (see Section 25.3). While archiving via a shell command (i.e., archive_command) is much simpler, a custom archive module will often be considerably more robust and performant.

When a custom archive_library is configured, PostgreSQL will submit completed WAL files to the module, and the server will avoid recycling or removing these WAL files until the module indicates that the files were successfully archived. It is ultimately up to the module to decide what to do with each WAL file, but many recommendations are listed at Section 25.3.1.

Archiving modules must at least consist of an initialization function (see Section 49.1) and the required callbacks (see Section 49.2). However, archive modules are also permitted to do much more (e.g., declare GUCs and register background workers).

The contrib/basic_archive module contains a working example, which demonstrates some useful techniques.

---

PostgreSQL: Documentation: 18: 9.7. Pattern Matching

URL: https://www.postgresql.org/docs/current/functions-matching.html

Contents:

• 9.7. Pattern Matching #
  • Tip
  • Caution
  • 9.7.1. LIKE #
  • Note
  • 9.7.2. SIMILAR TO Regular Expressions #
  • 9.7.3. POSIX Regular Expressions #
  • Tip
  • Tip
  • 9.7.3.1. Regular Expression Details #

There are three separate approaches to pattern matching provided by PostgreSQL: the traditional SQL LIKE operator, the more recent SIMILAR TO operator (added in SQL:1999), and POSIX-style regular expressions. Aside from the basic “does this string match this pattern?” operators, functions are available to extract or replace matching substrings and to split a string at matching locations.

If you have pattern matching needs that go beyond this, consider writing a user-defined function in Perl or Tcl.

While most regular-expression searches can be executed very quickly, regular expressions can be contrived that take arbitrary amounts of time and memory to process. Be wary of accepting regular-expression search patterns from hostile sources. If you must do so, it is advisable to impose a statement timeout.

Searches using SIMILAR TO patterns have the same security hazards, since SIMILAR TO provides many of the same capabilities as POSIX-style regular expressions.

LIKE searches, being much simpler than the other two options, are safer to use with possibly-hostile pattern sources.

SIMILAR TO and POSIX-style regular expressions do not support nondeterministic collations. If required, use LIKE or apply a different collation to the expression to work around this limitation.

The LIKE expression returns true if the string matches the supplied pattern. (As expected, the NOT LIKE expression returns false if LIKE returns true, and vice versa. An equivalent expression is NOT (string LIKE pattern).)

If pattern does not contain percent signs or underscores, then the pattern only represents the string itself; in that case LIKE acts like the equals operator. An underscore (_) in pattern stands for (matches) any single character; a percent sign (%) matches any sequence of zero or more characters.

LIKE pattern matching supports nondeterministic collations (see Section 23.2.2.4), such as case-insensitive collations or collations that, say, ignore punctuation. So with a case-insensitive collation, one could have:

With collations that ignore certain characters or in general that consider strings of different lengths equal, the semantics can become a bit more complicated. Consider these examples:

The way the matching works is that the pattern is partitioned into sequences of wildcards and non-wildcard strings (wildcards being _ and %). For example, the pattern f_o is partitioned into f, _, o, the pattern _oo is partitioned into _, oo. The input string matches the pattern if it can be partitioned in such a way that the wildcards match one character or any number of characters respectively and the non-wildcard partitions are equal under the applicable collation. So for example, '.foo.' LIKE 'f_o' COLLATE ign_punct is true because one can partition .foo. into .f, o, o., and then '.f' = 'f' COLLATE ign_punct, 'o' matches the _ wildcard, and 'o.' = 'o' COLLATE ign_punct. But '.foo.' LIKE '_oo' COLLATE ign_punct is false because .foo. cannot be partitioned in a way that the first character is any character and the rest of the string compares equal to oo. (Note that the single-character wildcard always matches exactly one character, independent of the collation. So in this example, the _ would match ., but then the rest of the input string won't match the rest of the pattern.)

LIKE pattern matching always covers the entire string. Therefore, if it's desired to match a sequence anywhere within a string, the pattern must start and end with a percent sign.

To match a literal underscore or percent sign without matching other characters, the respective character in pattern must be preceded by the escape character. The default escape character is the backslash but a different one can be selected by using the ESCAPE clause. To match the escape character itself, write two escape characters.

If you have standard_conforming_strings turned off, any backslashes you write in literal string constants will need to be doubled. See Section 4.1.2.1 for more information.

It's also possible to select no escape character by writing ESCAPE ''. This effectively disables the escape mechanism, which makes it impossible to turn off the special meaning of underscore and percent signs in the pattern.

According to the SQL standard, omitting ESCAPE means there is no escape character (rather than defaulting to a backslash), and a zero-length ESCAPE value is disallowed. PostgreSQL's behavior in this regard is therefore slightly nonstandard.

The key word ILIKE can be used instead of LIKE to make the match case-insensitive according to the active locale. (But this does not support nondeterministic collations.) This is not in the SQL standard but is a PostgreSQL extension.

The operator ~~ is equivalent to LIKE, and * corresponds to ILIKE. There are also ! and !~~* operators that represent NOT LIKE and NOT ILIKE, respectively. All of these operators are PostgreSQL-specific. You may see these operator names in EXPLAIN output and similar places, since the parser actually translates LIKE et al. to these operators.

The phrases LIKE, ILIKE, NOT LIKE, and NOT ILIKE are generally treated as operators in PostgreSQL syntax; for example they can be used in expression operator ANY (subquery) constructs, although an ESCAPE clause cannot be included there. In some obscure cases it may be necessary to use the underlying operator names instead.

Also see the starts-with operator ^@ and the corresponding starts_with() function, which are useful in cases where simply matching the beginning of a string is needed.

The SIMILAR TO operator returns true or false depending on whether its pattern matches the given string. It is similar to LIKE, except that it interprets the pattern using the SQL standard's definition of a regular expression. SQL regular expressions are a curious cross between LIKE notation and common (POSIX) regular expression notation.

Like LIKE, the SIMILAR TO operator succeeds only if its pattern matches the entire string; this is unlike common regular expression behavior where the pattern can match any part of the string. Also like LIKE, SIMILAR TO uses _ and % as wildcard characters denoting any single character and any string, respectively (these are comparable to . and .* in POSIX regular expressions).

In addition to these facilities borrowed from LIKE, SIMILAR TO supports these pattern-matching metacharacters borrowed from POSIX regular expressions:

| denotes alternation (either of two alternatives).

• denotes repetition of the previous item zero or more times.

• denotes repetition of the previous item one or more times.

? denotes repetition of the previous item zero or one time.

{m} denotes repetition of the previous item exactly m times.

{m,} denotes repetition of the previous item m or more times.

{m,n} denotes repetition of the previous item at least m and not more than n times.

Parentheses () can be used to group items into a single logical item.

A bracket expression [...] specifies a character class, just as in POSIX regular expressions.

Notice that the period (.) is not a metacharacter for SIMILAR TO.

As with LIKE, a backslash disables the special meaning of any of these metacharacters. A different escape character can be specified with ESCAPE, or the escape capability can be disabled by writing ESCAPE ''.

According to the SQL standard, omitting ESCAPE means there is no escape character (rather than defaulting to a backslash), and a zero-length ESCAPE value is disallowed. PostgreSQL's behavior in this regard is therefore slightly nonstandard.

Another nonstandard extension is that following the escape character with a letter or digit provides access to the escape sequences defined for POSIX regular expressions; see Table 9.20, Table 9.21, and Table 9.22 below.

The substring function with three parameters provides extraction of a substring that matches an SQL regular expression pattern. The function can be written according to standard SQL syntax:

or using the now obsolete SQL:1999 syntax:

or as a plain three-argument function:

As with SIMILAR TO, the specified pattern must match the entire data string, or else the function fails and returns null. To indicate the part of the pattern for which the matching data sub-string is of interest, the pattern should contain two occurrences of the escape character followed by a double quote ("). The text matching the portion of the pattern between these separators is returned when the match is successful.

The escape-double-quote separators actually divide substring's pattern into three independent regular expressions; for example, a vertical bar (|) in any of the three sections affects only that section. Also, the first and third of these regular expressions are defined to match the smallest possible amount of text, not the largest, when there is any ambiguity about how much of the data string matches which pattern. (In POSIX parlance, the first and third regular expressions are forced to be non-greedy.)

As an extension to the SQL standard, PostgreSQL allows there to be just one escape-double-quote separator, in which case the third regular expression is taken as empty; or no separators, in which case the first and third regular expressions are taken as empty.

Some examples, with #" delimiting the return string:

Table 9.16 lists the available operators for pattern matching using POSIX regular expressions.

Table 9.16. Regular Expression Match Operators

text ~ text → boolean

String matches regular expression, case sensitively

'thomas' ~ 't.*ma' → t

text ~* text → boolean

String matches regular expression, case-insensitively

'thomas' ~* 'T.*ma' → t

text !~ text → boolean

String does not match regular expression, case sensitively

'thomas' !~ 't.*max' → t

text !~* text → boolean

String does not match regular expression, case-insensitively

'thomas' !~* 'T.*ma' → f

POSIX regular expressions provide a more powerful means for pattern matching than the LIKE and SIMILAR TO operators. Many Unix tools such as egrep, sed, or awk use a pattern matching language that is similar to the one described here.

A regular expression is a character sequence that is an abbreviated definition of a set of strings (a regular set). A string is said to match a regular expression if it is a member of the regular set described by the regular expression. As with LIKE, pattern characters match string characters exactly unless they are special characters in the regular expression language — but regular expressions use different special characters than LIKE does. Unlike LIKE patterns, a regular expression is allowed to match anywhere within a string, unless the regular expression is explicitly anchored to the beginning or end of the string.

The POSIX pattern language is described in much greater detail below.

The substring function with two parameters, substring(string from pattern), provides extraction of a substring that matches a POSIX regular expression pattern. It returns null if there is no match, otherwise the first portion of the text that matched the pattern. But if the pattern contains any parentheses, the portion of the text that matched the first parenthesized subexpression (the one whose left parenthesis comes first) is returned. You can put parentheses around the whole expression if you want to use parentheses within it without triggering this exception. If you need parentheses in the pattern before the subexpression you want to extract, see the non-capturing parentheses described below.

The regexp_count function counts the number of places where a POSIX regular expression pattern matches a string. It has the syntax regexp_count(string, pattern [, start [, flags ]]). pattern is searched for in string, normally from the beginning of the string, but if the start parameter is provided then beginning from that character index. The flags parameter is an optional text string containing zero or more single-letter flags that change the function's behavior. For example, including i in flags specifies case-insensitive matching. Supported flags are described in Table 9.24.

The regexp_instr function returns the starting or ending position of the N'th match of a POSIX regular expression pattern to a string, or zero if there is no such match. It has the syntax regexp_instr(string, pattern [, start [, N [, endoption [, flags [, subexpr ]]]]]). pattern is searched for in string, normally from the beginning of the string, but if the start parameter is provided then beginning from that character index. If N is specified then the N'th match of the pattern is located, otherwise the first match is located. If the endoption parameter is omitted or specified as zero, the function returns the position of the first character of the match. Otherwise, endoption must be one, and the function returns the position of the character following the match. The flags parameter is an optional text string containing zero or more single-letter flags that change the function's behavior. Supported flags are described in Table 9.24. For a pattern containing parenthesized subexpressions, subexpr is an integer indicating which subexpression is of interest: the result identifies the position of the substring matching that subexpression. Subexpressions are numbered in the order of their leading parentheses. When subexpr is omitted or zero, the result identifies the position of the whole match regardless of parenthesized subexpressions.

The regexp_like function checks whether a match of a POSIX regular expression pattern occurs within a string, returning boolean true or false. It has the syntax regexp_like(string, pattern [, flags ]). The flags parameter is an optional text string containing zero or more single-letter flags that change the function's behavior. Supported flags are described in Table 9.24. This function has the same results as the ~ operator if no flags are specified. If only the i flag is specified, it has the same results as the ~* operator.

The regexp_match function returns a text array of matching substring(s) within the first match of a POSIX regular expression pattern to a string. It has the syntax regexp_match(string, pattern [, flags ]). If there is no match, the result is NULL. If a match is found, and the pattern contains no parenthesized subexpressions, then the result is a single-element text array containing the substring matching the whole pattern. If a match is found, and the pattern contains parenthesized subexpressions, then the result is a text array whose n'th element is the substring matching the n'th parenthesized subexpression of the pattern (not counting “non-capturing” parentheses; see below for details). The flags parameter is an optional text string containing zero or more single-letter flags that change the function's behavior. Supported flags are described in Table 9.24.

In the common case where you just want the whole matching substring or NULL for no match, the best solution is to use regexp_substr(). However, regexp_substr() only exists in PostgreSQL version 15 and up. When working in older versions, you can extract the first element of regexp_match()'s result, for example:

The regexp_matches function returns a set of text arrays of matching substring(s) within matches of a POSIX regular expression pattern to a string. It has the same syntax as regexp_match. This function returns no rows if there is no match, one row if there is a match and the g flag is not given, or N rows if there are N matches and the g flag is given. Each returned row is a text array containing the whole matched substring or the substrings matching parenthesized subexpressions of the pattern, just as described above for regexp_match. regexp_matches accepts all the flags shown in Table 9.24, plus the g flag which commands it to return all matches, not just the first one.

In most cases regexp_matches() should be used with the g flag, since if you only want the first match, it's easier and more efficient to use regexp_match(). However, regexp_match() only exists in PostgreSQL version 10 and up. When working in older versions, a common trick is to place a regexp_matches() call in a sub-select, for example:

This produces a text array if there's a match, or NULL if not, the same as regexp_match() would do. Without the sub-select, this query would produce no output at all for table rows without a match, which is typically not the desired behavior.

The regexp_replace function provides substitution of new text for substrings that match POSIX regular expression patterns. It has the syntax regexp_replace(string, pattern, replacement [, flags ]) or regexp_replace(string, pattern, replacement, start [, N [, flags ]]). The source string is returned unchanged if there is no match to the pattern. If there is a match, the string is returned with the replacement string substituted for the matching substring. The replacement string can contain \n, where n is 1 through 9, to indicate that the source substring matching the n'th parenthesized subexpression of the pattern should be inserted, and it can contain & to indicate that the substring matching the entire pattern should be inserted. Write \ if you need to put a literal backslash in the replacement text. pattern is searched for in string, normally from the beginning of the string, but if the start parameter is provided then beginning from that character index. By default, only the first match of the pattern is replaced. If N is specified and is greater than zero, then the N'th match of the pattern is replaced. If the g flag is given, or if N is specified and is zero, then all matches at or after the start position are replaced. (The g flag is ignored when N is specified.) The flags parameter is an optional text string containing zero or more single-letter flags that change the function's behavior. Supported flags (though not g) are described in Table 9.24.

The regexp_split_to_table function splits a string using a POSIX regular expression pattern as a delimiter. It has the syntax regexp_split_to_table(string, pattern [, flags ]). If there is no match to the pattern, the function returns the string. If there is at least one match, for each match it returns the text from the end of the last match (or the beginning of the string) to the beginning of the match. When there are no more matches, it returns the text from the end of the last match to the end of the string. The flags parameter is an optional text string containing zero or more single-letter flags that change the function's behavior. regexp_split_to_table supports the flags described in Table 9.24.

The regexp_split_to_array function behaves the same as regexp_split_to_table, except that regexp_split_to_array returns its result as an array of text. It has the syntax regexp_split_to_array(string, pattern [, flags ]). The parameters are the same as for regexp_split_to_table.

As the last example demonstrates, the regexp split functions ignore zero-length matches that occur at the start or end of the string or immediately after a previous match. This is contrary to the strict definition of regexp matching that is implemented by the other regexp functions, but is usually the most convenient behavior in practice. Other software systems such as Perl use similar definitions.

The regexp_substr function returns the substring that matches a POSIX regular expression pattern, or NULL if there is no match. It has the syntax regexp_substr(string, pattern [, start [, N [, flags [, subexpr ]]]]). pattern is searched for in string, normally from the beginning of the string, but if the start parameter is provided then beginning from that character index. If N is specified then the N'th match of the pattern is returned, otherwise the first match is returned. The flags parameter is an optional text string containing zero or more single-letter flags that change the function's behavior. Supported flags are described in Table 9.24. For a pattern containing parenthesized subexpressions, subexpr is an integer indicating which subexpression is of interest: the result is the substring matching that subexpression. Subexpressions are numbered in the order of their leading parentheses. When subexpr is omitted or zero, the result is the whole match regardless of parenthesized subexpressions.

PostgreSQL's regular expressions are implemented using a software package written by Henry Spencer. Much of the description of regular expressions below is copied verbatim from his manual.

Regular expressions (REs), as defined in POSIX 1003.2, come in two forms: extended REs or EREs (roughly those of egrep), and basic REs or BREs (roughly those of ed). PostgreSQL supports both forms, and also implements some extensions that are not in the POSIX standard, but have become widely used due to their availability in programming languages such as Perl and Tcl. REs using these non-POSIX extensions are called advanced REs or AREs in this documentation. AREs are almost an exact superset of EREs, but BREs have several notational incompatibilities (as well as being much more limited). We first describe the ARE and ERE forms, noting features that apply only to AREs, and then describe how BREs differ.

PostgreSQL always initially presumes that a regular expression follows the ARE rules. However, the more limited ERE or BRE rules can be chosen by prepending an embedded option to the RE pattern, as described in Section 9.7.3.4. This can be useful for compatibility with applications that expect exactly the POSIX 1003.2 rules.

A regular expression is defined as one or more branches, separated by |. It matches anything that matches one of the branches.

A branch is zero or more quantified atoms or constraints, concatenated. It matches a match for the first, followed by a match for the second, etc.; an empty branch matches the empty string.

A quantified atom is an atom possibly followed by a single quantifier. Without a quantifier, it matches a match for the atom. With a quantifier, it can match some number of matches of the atom. An atom can be any of the possibilities shown in Table 9.17. The possible quantifiers and their meanings are shown in Table 9.18.

A constraint matches an empty string, but matches only when specific conditions are met. A constraint can be used where an atom could be used, except it cannot be followed by a quantifier. The simple constraints are shown in Table 9.19; some more constraints are described later.

Table 9.17. Regular Expression Atoms

An RE cannot end with a backslash ().

If you have standard_conforming_strings turned off, any backslashes you write in literal string constants will need to be doubled. See Section 4.1.2.1 for more information.

Table 9.18. Regular Expression Quantifiers

The forms using {...} are known as bounds. The numbers m and n within a bound are unsigned decimal integers with permissible values from 0 to 255 inclusive.

Non-greedy quantifiers (available in AREs only) match the same possibilities as their corresponding normal (greedy) counterparts, but prefer the smallest number rather than the largest number of matches. See Section 9.7.3.5 for more detail.

A quantifier cannot immediately follow another quantifier, e.g., ** is invalid. A quantifier cannot begin an expression or subexpression or follow ^ or |.

Table 9.19. Regular Expression Constraints

Lookahead and lookbehind constraints cannot contain back references (see Section 9.7.3.3), and all parentheses within them are considered non-capturing.

A bracket expression is a list of characters enclosed in []. It normally matches any single character from the list (but see below). If the list begins with ^, it matches any single character not from the rest of the list. If two characters in the list are separated by -, this is shorthand for the full range of characters between those two (inclusive) in the collating sequence, e.g., [0-9] in ASCII matches any decimal digit. It is illegal for two ranges to share an endpoint, e.g., a-c-e. Ranges are very collating-sequence-dependent, so portable programs should avoid relying on them.

To include a literal ] in the list, make it the first character (after ^, if that is used). To include a literal -, make it the first or last character, or the second endpoint of a range. To use a literal - as the first endpoint of a range, enclose it in [. and .] to make it a collating element (see below). With the exception of these characters, some combinations using [ (see next paragraphs), and escapes (AREs only), all other special characters lose their special significance within a bracket expression. In particular, \ is not special when following ERE or BRE rules, though it is special (as introducing an escape) in AREs.

Within a bracket expression, a collating element (a character, a multiple-character sequence that collates as if it were a single character, or a collating-sequence name for either) enclosed in [. and .] stands for the sequence of characters of that collating element. The sequence is treated as a single element of the bracket expression's list. This allows a bracket expression containing a multiple-character collating element to match more than one character, e.g., if the collating sequence includes a ch collating element, then the RE [[.ch.]]*c matches the first five characters of chchcc.

PostgreSQL currently does not support multi-character collating elements. This information describes possible future behavior.

Within a bracket expression, a collating element enclosed in [= and =] is an equivalence class, standing for the sequences of characters of all collating elements equivalent to that one, including itself. (If there are no other equivalent collating elements, the treatment is as if the enclosing delimiters were [. and .].) For example, if o and ^ are the members of an equivalence class, then [[=o=]], [[=^=]], and [o^] are all synonymous. An equivalence class cannot be an endpoint of a range.

Within a bracket expression, the name of a character class enclosed in [: and :] stands for the list of all characters belonging to that class. A character class cannot be used as an endpoint of a range. The POSIX standard defines these character class names: alnum (letters and numeric digits), alpha (letters), blank (space and tab), cntrl (control characters), digit (numeric digits), graph (printable characters except space), lower (lower-case letters), print (printable characters including space), punct (punctuation), space (any white space), upper (upper-case letters), and xdigit (hexadecimal digits). The behavior of these standard character classes is generally consistent across platforms for characters in the 7-bit ASCII set. Whether a given non-ASCII character is considered to belong to one of these classes depends on the collation that is used for the regular-expression function or operator (see Section 23.2), or by default on the database's LCCTYPE locale setting (see Section 23.1). The classification of non-ASCII characters can vary across platforms even in similarly-named locales. (But the C locale never considers any non-ASCII characters to belong to any of these classes.) In addition to these standard character classes, PostgreSQL defines the word character class, which is the same as alnum plus the underscore () character, and the ascii character class, which contains exactly the 7-bit ASCII set.

There are two special cases of bracket expressions: the bracket expressions [[:<:]] and [[:>:]] are constraints, matching empty strings at the beginning and end of a word respectively. A word is defined as a sequence of word characters that is neither preceded nor followed by word characters. A word character is any character belonging to the word character class, that is, any letter, digit, or underscore. This is an extension, compatible with but not specified by POSIX 1003.2, and should be used with caution in software intended to be portable to other systems. The constraint escapes described below are usually preferable; they are no more standard, but are easier to type.

Escapes are special sequences beginning with \ followed by an alphanumeric character. Escapes come in several varieties: character entry, class shorthands, constraint escapes, and back references. A \ followed by an alphanumeric character but not constituting a valid escape is illegal in AREs. In EREs, there are no escapes: outside a bracket expression, a \ followed by an alphanumeric character merely stands for that character as an ordinary character, and inside a bracket expression, \ is an ordinary character. (The latter is the one actual incompatibility between EREs and AREs.)

Character-entry escapes exist to make it easier to specify non-printing and other inconvenient characters in REs. They are shown in Table 9.20.

Class-shorthand escapes provide shorthands for certain commonly-used character classes. They are shown in Table 9.21.

A constraint escape is a constraint, matching the empty string if specific conditions are met, written as an escape. They are shown in Table 9.22.

A back reference (\n) matches the same string matched by the previous parenthesized subexpression specified by the number n (see Table 9.23). For example, ([bc])\1 matches bb or cc but not bc or cb. The subexpression must entirely precede the back reference in the RE. Subexpressions are numbered in the order of their leading parentheses. Non-capturing parentheses do not define subexpressions. The back reference considers only the string characters matched by the referenced subexpression, not any constraints contained in it. For example, (^\d)\1 will match 22.

Table 9.20. Regular Expression Character-Entry Escapes

Hexadecimal digits are 0-9, a-f, and A-F. Octal digits are 0-7.

Numeric character-entry escapes specifying values outside the ASCII range (0–127) have meanings dependent on the database encoding. When the encoding is UTF-8, escape values are equivalent to Unicode code points, for example \u1234 means the character U+1234. For other multibyte encodings, character-entry escapes usually just specify the concatenation of the byte values for the character. If the escape value does not correspond to any legal character in the database encoding, no error will be raised, but it will never match any data.

The character-entry escapes are always taken as ordinary characters. For example, \135 is ] in ASCII, but \135 does not terminate a bracket expression.

Table 9.21. Regular Expression Class-Shorthand Escapes

The class-shorthand escapes also work within bracket expressions, although the definitions shown above are not quite syntactically valid in that context. For example, [a-c\d] is equivalent to [a-c[:digit:]].

Table 9.22. Regular Expression Constraint Escapes

A word is defined as in the specification of [[:<:]] and [[:>:]] above. Constraint escapes are illegal within bracket expressions.

Table 9.23. Regular Expression Back References

There is an inherent ambiguity between octal character-entry escapes and back references, which is resolved by the following heuristics, as hinted at above. A leading zero always indicates an octal escape. A single non-zero digit, not followed by another digit, is always taken as a back reference. A multi-digit sequence not starting with a zero is taken as a back reference if it comes after a suitable subexpression (i.e., the number is in the legal range for a back reference), and otherwise is taken as octal.

In addition to the main syntax described above, there are some special forms and miscellaneous syntactic facilities available.

An RE can begin with one of two special director prefixes. If an RE begins with ***:, the rest of the RE is taken as an ARE. (This normally has no effect in PostgreSQL, since REs are assumed to be AREs; but it does have an effect if ERE or BRE mode had been specified by the flags parameter to a regex function.) If an RE begins with ***=, the rest of the RE is taken to be a literal string, with all characters considered ordinary characters.

An ARE can begin with embedded options: a sequence (?xyz) (where xyz is one or more alphabetic characters) specifies options affecting the rest of the RE. These options override any previously determined options — in particular, they can override the case-sensitivity behavior implied by a regex operator, or the flags parameter to a regex function. The available option letters are shown in Table 9.24. Note that these same option letters are used in the flags parameters of regex functions.

Table 9.24. ARE Embedded-Option Letters

Embedded options take effect at the ) terminating the sequence. They can appear only at the start of an ARE (after the ***: director if any).

In addition to the usual (tight) RE syntax, in which all characters are significant, there is an expanded syntax, available by specifying the embedded x option. In the expanded syntax, white-space characters in the RE are ignored, as are all characters between a # and the following newline (or the end of the RE). This permits paragraphing and commenting a complex RE. There are three exceptions to that basic rule:

a white-space character or # preceded by \ is retained

white space or # within a bracket expression is retained

white space and comments cannot appear within multi-character symbols, such as (?:

For this purpose, white-space characters are blank, tab, newline, and any character that belongs to the space character class.

Finally, in an ARE, outside bracket expressions, the sequence (?#ttt) (where ttt is any text not containing a )) is a comment, completely ignored. Again, this is not allowed between the characters of multi-character symbols, like (?:. Such comments are more a historical artifact than a useful facility, and their use is deprecated; use the expanded syntax instead.

None of these metasyntax extensions is available if an initial ***= director has specified that the user's input be treated as a literal string rather than as an RE.

In the event that an RE could match more than one substring of a given string, the RE matches the one starting earliest in the string. If the RE could match more than one substring starting at that point, either the longest possible match or the shortest possible match will be taken, depending on whether the RE is greedy or non-greedy.

Whether an RE is greedy or not is determined by the following rules:

Most atoms, and all constraints, have no greediness attribute (because they cannot match variable amounts of text anyway).

Adding parentheses around an RE does not change its greediness.

A quantified atom with a fixed-repetition quantifier ({m} or {m}?) has the same greediness (possibly none) as the atom itself.

A quantified atom with other normal quantifiers (including {m,n} with m equal to n) is greedy (prefers longest match).

A quantified atom with a non-greedy quantifier (including {m,n}? with m equal to n) is non-greedy (prefers shortest match).

A branch — that is, an RE that has no top-level | operator — has the same greediness as the first quantified atom in it that has a greediness attribute.

An RE consisting of two or more branches connected by the | operator is always greedy.

The above rules associate greediness attributes not only with individual quantified atoms, but with branches and entire REs that contain quantified atoms. What that means is that the matching is done in such a way that the branch, or whole RE, matches the longest or shortest possible substring as a whole. Once the length of the entire match is determined, the part of it that matches any particular subexpression is determined on the basis of the greediness attribute of that subexpression, with subexpressions starting earlier in the RE taking priority over ones starting later.

An example of what this means:

In the first case, the RE as a whole is greedy because Y* is greedy. It can match beginning at the Y, and it matches the longest possible string starting there, i.e., Y123. The output is the parenthesized part of that, or 123. In the second case, the RE as a whole is non-greedy because Y*? is non-greedy. It can match beginning at the Y, and it matches the shortest possible string starting there, i.e., Y1. The subexpression [0-9]{1,3} is greedy but it cannot change the decision as to the overall match length; so it is forced to match just 1.

In short, when an RE contains both greedy and non-greedy subexpressions, the total match length is either as long as possible or as short as possible, according to the attribute assigned to the whole RE. The attributes assigned to the subexpressions only affect how much of that match they are allowed to “eat” relative to each other.

The quantifiers {1,1} and {1,1}? can be used to force greediness or non-greediness, respectively, on a subexpression or a whole RE. This is useful when you need the whole RE to have a greediness attribute different from what's deduced from its elements. As an example, suppose that we are trying to separate a string containing some digits into the digits and the parts before and after them. We might try to do that like this:

That didn't work: the first .* is greedy so it “eats” as much as it can, leaving the \d+ to match at the last possible place, the last digit. We might try to fix that by making it non-greedy:

That didn't work either, because now the RE as a whole is non-greedy and so it ends the overall match as soon as possible. We can get what we want by forcing the RE as a whole to be greedy:

Controlling the RE's overall greediness separately from its components' greediness allows great flexibility in handling variable-length patterns.

When deciding what is a longer or shorter match, match lengths are measured in characters, not collating elements. An empty string is considered longer than no match at all. For example: bb* matches the three middle characters of abbbc; (week|wee)(night|knights) matches all ten characters of weeknights; when (.). is matched against abc the parenthesized subexpression matches all three characters; and when (a) is matched against bc both the whole RE and the parenthesized subexpression match an empty string.

If case-independent matching is specified, the effect is much as if all case distinctions had vanished from the alphabet. When an alphabetic that exists in multiple cases appears as an ordinary character outside a bracket expression, it is effectively transformed into a bracket expression containing both cases, e.g., x becomes [xX]. When it appears inside a bracket expression, all case counterparts of it are added to the bracket expression, e.g., [x] becomes [xX] and [^x] becomes [^xX].

If newline-sensitive matching is specified, . and bracket expressions using ^ will never match the newline character (so that matches will not cross lines unless the RE explicitly includes a newline) and ^ and $ will match the empty string after and before a newline respectively, in addition to matching at beginning and end of string respectively. But the ARE escapes \A and \Z continue to match beginning or end of string only. Also, the character class shorthands \D and \W will match a newline regardless of this mode. (Before PostgreSQL 14, they did not match newlines when in newline-sensitive mode. Write [^[:digit:]] or [^[:word:]] to get the old behavior.)

If partial newline-sensitive matching is specified, this affects . and bracket expressions as with newline-sensitive matching, but not ^ and $.

If inverse partial newline-sensitive matching is specified, this affects ^ and $ as with newline-sensitive matching, but not . and bracket expressions. This isn't very useful but is provided for symmetry.

No particular limit is imposed on the length of REs in this implementation. However, programs intended to be highly portable should not employ REs longer than 256 bytes, as a POSIX-compliant implementation can refuse to accept such REs.

The only feature of AREs that is actually incompatible with POSIX EREs is that \ does not lose its special significance inside bracket expressions. All other ARE features use syntax which is illegal or has undefined or unspecified effects in POSIX EREs; the *** syntax of directors likewise is outside the POSIX syntax for both BREs and EREs.

Many of the ARE extensions are borrowed from Perl, but some have been changed to clean them up, and a few Perl extensions are not present. Incompatibilities of note include \b, \B, the lack of special treatment for a trailing newline, the addition of complemented bracket expressions to the things affected by newline-sensitive matching, the restrictions on parentheses and back references in lookahead/lookbehind constraints, and the longest/shortest-match (rather than first-match) matching semantics.

BREs differ from EREs in several respects. In BREs, |, +, and ? are ordinary characters and there is no equivalent for their functionality. The delimiters for bounds are { and }, with { and } by themselves ordinary characters. The parentheses for nested subexpressions are ( and ), with ( and ) by themselves ordinary characters. ^ is an ordinary character except at the beginning of the RE or the beginning of a parenthesized subexpression, $ is an ordinary character except at the end of the RE or the end of a parenthesized subexpression, and * is an ordinary character if it appears at the beginning of the RE or the beginning of a parenthesized subexpression (after a possible leading ^). Finally, single-digit back references are available, and < and > are synonyms for [[:<:]] and [[:>:]] respectively; no other escapes are available in BREs.

Since SQL:2008, the SQL standard includes regular expression operators and functions that performs pattern matching according to the XQuery regular expression standard:

PostgreSQL does not currently implement these operators and functions. You can get approximately equivalent functionality in each case as shown in Table 9.25. (Various optional clauses on both sides have been omitted in this table.)

Table 9.25. Regular Expression Functions Equivalencies

Regular expression functions similar to those provided by PostgreSQL are also available in a number of other SQL implementations, whereas the SQL-standard functions are not as widely implemented. Some of the details of the regular expression syntax will likely differ in each implementation.

The SQL-standard operators and functions use XQuery regular expressions, which are quite close to the ARE syntax described above. Notable differences between the existing POSIX-based regular-expression feature and XQuery regular expressions include:

XQuery character class subtraction is not supported. An example of this feature is using the following to match only English consonants: [a-z-[aeiou]].

XQuery character class shorthands \c, \C, \i, and \I are not supported.

XQuery character class elements using \p{UnicodeProperty} or the inverse \P{UnicodeProperty} are not supported.

POSIX interprets character classes such as \w (see Table 9.21) according to the prevailing locale (which you can control by attaching a COLLATE clause to the operator or function). XQuery specifies these classes by reference to Unicode character properties, so equivalent behavior is obtained only with a locale that follows the Unicode rules.

The SQL standard (not XQuery itself) attempts to cater for more variants of “newline” than POSIX does. The newline-sensitive matching options described above consider only ASCII NL (\n) to be a newline, but SQL would have us treat CR (\r), CRLF (\r\n) (a Windows-style newline), and some Unicode-only characters like LINE SEPARATOR (U+2028) as newlines as well. Notably, . and \s should count \r\n as one character not two according to SQL.

Of the character-entry escapes described in Table 9.20, XQuery supports only \n, \r, and \t.

XQuery does not support the [:name:] syntax for character classes within bracket expressions.

XQuery does not have lookahead or lookbehind constraints, nor any of the constraint escapes described in Table 9.22.

The metasyntax forms described in Section 9.7.3.4 do not exist in XQuery.

The regular expression flag letters defined by XQuery are related to but not the same as the option letters for POSIX (Table 9.24). While the i and q options behave the same, others do not:

XQuery's s (allow dot to match newline) and m (allow ^ and $ to match at newlines) flags provide access to the same behaviors as POSIX's n, p and w flags, but they do not match the behavior of POSIX's s and m flags. Note in particular that dot-matches-newline is the default behavior in POSIX but not XQuery.

XQuery's x (ignore whitespace in pattern) flag is noticeably different from POSIX's expanded-mode flag. POSIX's x flag also allows # to begin a comment in the pattern, and POSIX will not ignore a whitespace character after a backslash.

Examples:

Example 1 (unknown):

string LIKE pattern [ESCAPE escape-character]
string NOT LIKE pattern [ESCAPE escape-character]

Example 2 (unknown):

'abc' LIKE 'abc'    true
'abc' LIKE 'a%'     true
'abc' LIKE '_b_'    true
'abc' LIKE 'c'      false

Example 3 (unknown):

'AbC' LIKE 'abc' COLLATE case_insensitive    true
'AbC' LIKE 'a%' COLLATE case_insensitive     true

Example 4 (unknown):

'.foo.' LIKE 'foo' COLLATE ign_punct    true
'.foo.' LIKE 'f_o' COLLATE ign_punct    true
'.foo.' LIKE '_oo' COLLATE ign_punct    false

---

PostgreSQL: Documentation: 18: 5.13. Foreign Data

URL: https://www.postgresql.org/docs/current/ddl-foreign-data.html

Contents:

• 5.13. Foreign Data #

PostgreSQL implements portions of the SQL/MED specification, allowing you to access data that resides outside PostgreSQL using regular SQL queries. Such data is referred to as foreign data. (Note that this usage is not to be confused with foreign keys, which are a type of constraint within the database.)

Foreign data is accessed with help from a foreign data wrapper. A foreign data wrapper is a library that can communicate with an external data source, hiding the details of connecting to the data source and obtaining data from it. There are some foreign data wrappers available as contrib modules; see Appendix F. Other kinds of foreign data wrappers might be found as third party products. If none of the existing foreign data wrappers suit your needs, you can write your own; see Chapter 58.

To access foreign data, you need to create a foreign server object, which defines how to connect to a particular external data source according to the set of options used by its supporting foreign data wrapper. Then you need to create one or more foreign tables, which define the structure of the remote data. A foreign table can be used in queries just like a normal table, but a foreign table has no storage in the PostgreSQL server. Whenever it is used, PostgreSQL asks the foreign data wrapper to fetch data from the external source, or transmit data to the external source in the case of update commands.

Accessing remote data may require authenticating to the external data source. This information can be provided by a user mapping, which can provide additional data such as user names and passwords based on the current PostgreSQL role.

For additional information, see CREATE FOREIGN DATA WRAPPER, CREATE SERVER, CREATE USER MAPPING, CREATE FOREIGN TABLE, and IMPORT FOREIGN SCHEMA.

---

PostgreSQL: Documentation: 18: Chapter 69. How the Planner Uses Statistics

URL: https://www.postgresql.org/docs/current/planner-stats-details.html

Contents:

• Chapter 69. How the Planner Uses Statistics

This chapter builds on the material covered in Section 14.1 and Section 14.2 to show some additional details about how the planner uses the system statistics to estimate the number of rows each part of a query might return. This is a significant part of the planning process, providing much of the raw material for cost calculation.

The intent of this chapter is not to document the code in detail, but to present an overview of how it works. This will perhaps ease the learning curve for someone who subsequently wishes to read the code.

---

PostgreSQL: Documentation: 18: 35.7. character_sets

URL: https://www.postgresql.org/docs/current/infoschema-character-sets.html

Contents:

• 35.7. character_sets #

The view character_sets identifies the character sets available in the current database. Since PostgreSQL does not support multiple character sets within one database, this view only shows one, which is the database encoding.

Take note of how the following terms are used in the SQL standard:

An abstract collection of characters, for example UNICODE, UCS, or LATIN1. Not exposed as an SQL object, but visible in this view.

An encoding of some character repertoire. Most older character repertoires only use one encoding form, and so there are no separate names for them (e.g., LATIN2 is an encoding form applicable to the LATIN2 repertoire). But for example Unicode has the encoding forms UTF8, UTF16, etc. (not all supported by PostgreSQL). Encoding forms are not exposed as an SQL object, but are visible in this view.

A named SQL object that identifies a character repertoire, a character encoding, and a default collation. A predefined character set would typically have the same name as an encoding form, but users could define other names. For example, the character set UTF8 would typically identify the character repertoire UCS, encoding form UTF8, and some default collation.

You can think of an “encoding” in PostgreSQL either as a character set or a character encoding form. They will have the same name, and there can only be one in one database.

Table 35.5. character_sets Columns

character_set_catalog sql_identifier

Character sets are currently not implemented as schema objects, so this column is null.

character_set_schema sql_identifier

Character sets are currently not implemented as schema objects, so this column is null.

character_set_name sql_identifier

Name of the character set, currently implemented as showing the name of the database encoding

character_repertoire sql_identifier

Character repertoire, showing UCS if the encoding is UTF8, else just the encoding name

form_of_use sql_identifier

Character encoding form, same as the database encoding

default_collate_catalog sql_identifier

Name of the database containing the default collation (always the current database, if any collation is identified)

default_collate_schema sql_identifier

Name of the schema containing the default collation

default_collate_name sql_identifier

Name of the default collation. The default collation is identified as the collation that matches the COLLATE and CTYPE settings of the current database. If there is no such collation, then this column and the associated schema and catalog columns are null.

---

PostgreSQL: Documentation: 18: 19.1. Setting Parameters

URL: https://www.postgresql.org/docs/current/config-setting.html

Contents:

• 19.1. Setting Parameters #
  • 19.1.1. Parameter Names and Values #
  • 19.1.2. Parameter Interaction via the Configuration File #
  • 19.1.3. Parameter Interaction via SQL #
  • 19.1.4. Parameter Interaction via the Shell #
  • 19.1.5. Managing Configuration File Contents #

All parameter names are case-insensitive. Every parameter takes a value of one of five types: boolean, string, integer, floating point, or enumerated (enum). The type determines the syntax for setting the parameter:

Boolean: Values can be written as on, off, true, false, yes, no, 1, 0 (all case-insensitive) or any unambiguous prefix of one of these.

String: In general, enclose the value in single quotes, doubling any single quotes within the value. Quotes can usually be omitted if the value is a simple number or identifier, however. (Values that match an SQL keyword require quoting in some contexts.)

Numeric (integer and floating point): Numeric parameters can be specified in the customary integer and floating-point formats; fractional values are rounded to the nearest integer if the parameter is of integer type. Integer parameters additionally accept hexadecimal input (beginning with 0x) and octal input (beginning with 0), but these formats cannot have a fraction. Do not use thousands separators. Quotes are not required, except for hexadecimal input.

Numeric with Unit: Some numeric parameters have an implicit unit, because they describe quantities of memory or time. The unit might be bytes, kilobytes, blocks (typically eight kilobytes), milliseconds, seconds, or minutes. An unadorned numeric value for one of these settings will use the setting's default unit, which can be learned from pg_settings.unit. For convenience, settings can be given with a unit specified explicitly, for example '120 ms' for a time value, and they will be converted to whatever the parameter's actual unit is. Note that the value must be written as a string (with quotes) to use this feature. The unit name is case-sensitive, and there can be whitespace between the numeric value and the unit.

Valid memory units are B (bytes), kB (kilobytes), MB (megabytes), GB (gigabytes), and TB (terabytes). The multiplier for memory units is 1024, not 1000.

Valid time units are us (microseconds), ms (milliseconds), s (seconds), min (minutes), h (hours), and d (days).

If a fractional value is specified with a unit, it will be rounded to a multiple of the next smaller unit if there is one. For example, 30.1 GB will be converted to 30822 MB not 32319628902 B. If the parameter is of integer type, a final rounding to integer occurs after any unit conversion.

Enumerated: Enumerated-type parameters are written in the same way as string parameters, but are restricted to have one of a limited set of values. The values allowable for such a parameter can be found from pg_settings.enumvals. Enum parameter values are case-insensitive.

The most fundamental way to set these parameters is to edit the file postgresql.conf, which is normally kept in the data directory. A default copy is installed when the database cluster directory is initialized. An example of what this file might look like is:

One parameter is specified per line. The equal sign between name and value is optional. Whitespace is insignificant (except within a quoted parameter value) and blank lines are ignored. Hash marks (#) designate the remainder of the line as a comment. Parameter values that are not simple identifiers or numbers must be single-quoted. To embed a single quote in a parameter value, write either two quotes (preferred) or backslash-quote. If the file contains multiple entries for the same parameter, all but the last one are ignored.

Parameters set in this way provide default values for the cluster. The settings seen by active sessions will be these values unless they are overridden. The following sections describe ways in which the administrator or user can override these defaults.

The configuration file is reread whenever the main server process receives a SIGHUP signal; this signal is most easily sent by running pg_ctl reload from the command line or by calling the SQL function pg_reload_conf(). The main server process also propagates this signal to all currently running server processes, so that existing sessions also adopt the new values (this will happen after they complete any currently-executing client command). Alternatively, you can send the signal to a single server process directly. Some parameters can only be set at server start; any changes to their entries in the configuration file will be ignored until the server is restarted. Invalid parameter settings in the configuration file are likewise ignored (but logged) during SIGHUP processing.

In addition to postgresql.conf, a PostgreSQL data directory contains a file postgresql.auto.conf, which has the same format as postgresql.conf but is intended to be edited automatically, not manually. This file holds settings provided through the ALTER SYSTEM command. This file is read whenever postgresql.conf is, and its settings take effect in the same way. Settings in postgresql.auto.conf override those in postgresql.conf.

External tools may also modify postgresql.auto.conf. It is not recommended to do this while the server is running unless allow_alter_system is set to off, since a concurrent ALTER SYSTEM command could overwrite such changes. Such tools might simply append new settings to the end, or they might choose to remove duplicate settings and/or comments (as ALTER SYSTEM will).

The system view pg_file_settings can be helpful for pre-testing changes to the configuration files, or for diagnosing problems if a SIGHUP signal did not have the desired effects.

PostgreSQL provides three SQL commands to establish configuration defaults. The already-mentioned ALTER SYSTEM command provides an SQL-accessible means of changing global defaults; it is functionally equivalent to editing postgresql.conf. In addition, there are two commands that allow setting of defaults on a per-database or per-role basis:

The ALTER DATABASE command allows global settings to be overridden on a per-database basis.

The ALTER ROLE command allows both global and per-database settings to be overridden with user-specific values.

Values set with ALTER DATABASE and ALTER ROLE are applied only when starting a fresh database session. They override values obtained from the configuration files or server command line, and constitute defaults for the rest of the session. Note that some settings cannot be changed after server start, and so cannot be set with these commands (or the ones listed below).

Once a client is connected to the database, PostgreSQL provides two additional SQL commands (and equivalent functions) to interact with session-local configuration settings:

The SHOW command allows inspection of the current value of any parameter. The corresponding SQL function is current_setting(setting_name text) (see Section 9.28.1).

The SET command allows modification of the current value of those parameters that can be set locally to a session; it has no effect on other sessions. Many parameters can be set this way by any user, but some can only be set by superusers and users who have been granted SET privilege on that parameter. The corresponding SQL function is set_config(setting_name, new_value, is_local) (see Section 9.28.1).

In addition, the system view pg_settings can be used to view and change session-local values:

Querying this view is similar to using SHOW ALL but provides more detail. It is also more flexible, since it's possible to specify filter conditions or join against other relations.

Using UPDATE on this view, specifically updating the setting column, is the equivalent of issuing SET commands. For example, the equivalent of

In addition to setting global defaults or attaching overrides at the database or role level, you can pass settings to PostgreSQL via shell facilities. Both the server and libpq client library accept parameter values via the shell.

During server startup, parameter settings can be passed to the postgres command via the -c name=value command-line parameter, or its equivalent --name=value variation. For example,

Settings provided in this way override those set via postgresql.conf or ALTER SYSTEM, so they cannot be changed globally without restarting the server.

When starting a client session via libpq, parameter settings can be specified using the PGOPTIONS environment variable. Settings established in this way constitute defaults for the life of the session, but do not affect other sessions. For historical reasons, the format of PGOPTIONS is similar to that used when launching the postgres command; specifically, the -c, or prepended --, before the name must be specified. For example,

Other clients and libraries might provide their own mechanisms, via the shell or otherwise, that allow the user to alter session settings without direct use of SQL commands.

PostgreSQL provides several features for breaking down complex postgresql.conf files into sub-files. These features are especially useful when managing multiple servers with related, but not identical, configurations.

In addition to individual parameter settings, the postgresql.conf file can contain include directives, which specify another file to read and process as if it were inserted into the configuration file at this point. This feature allows a configuration file to be divided into physically separate parts. Include directives simply look like:

If the file name is not an absolute path, it is taken as relative to the directory containing the referencing configuration file. Inclusions can be nested.

There is also an include_if_exists directive, which acts the same as the include directive, except when the referenced file does not exist or cannot be read. A regular include will consider this an error condition, but include_if_exists merely logs a message and continues processing the referencing configuration file.

The postgresql.conf file can also contain include_dir directives, which specify an entire directory of configuration files to include. These look like

Non-absolute directory names are taken as relative to the directory containing the referencing configuration file. Within the specified directory, only non-directory files whose names end with the suffix .conf will be included. File names that start with the . character are also ignored, to prevent mistakes since such files are hidden on some platforms. Multiple files within an include directory are processed in file name order (according to C locale rules, i.e., numbers before letters, and uppercase letters before lowercase ones).

Include files or directories can be used to logically separate portions of the database configuration, rather than having a single large postgresql.conf file. Consider a company that has two database servers, each with a different amount of memory. There are likely elements of the configuration both will share, for things such as logging. But memory-related parameters on the server will vary between the two. And there might be server specific customizations, too. One way to manage this situation is to break the custom configuration changes for your site into three files. You could add this to the end of your postgresql.conf file to include them:

All systems would have the same shared.conf. Each server with a particular amount of memory could share the same memory.conf; you might have one for all servers with 8GB of RAM, another for those having 16GB. And finally server.conf could have truly server-specific configuration information in it.

Another possibility is to create a configuration file directory and put this information into files there. For example, a conf.d directory could be referenced at the end of postgresql.conf:

Then you could name the files in the conf.d directory like this:

This naming convention establishes a clear order in which these files will be loaded. This is important because only the last setting encountered for a particular parameter while the server is reading configuration files will be used. In this example, something set in conf.d/02server.conf would override a value set in conf.d/01memory.conf.

You might instead use this approach to naming the files descriptively:

This sort of arrangement gives a unique name for each configuration file variation. This can help eliminate ambiguity when several servers have their configurations all stored in one place, such as in a version control repository. (Storing database configuration files under version control is another good practice to consider.)

Examples:

Example 1 (unknown):

# This is a comment
log_connections = all
log_destination = 'syslog'
search_path = '"$user", public'
shared_buffers = 128MB

Example 2 (unknown):

SET configuration_parameter TO DEFAULT;

Example 3 (unknown):

UPDATE pg_settings SET setting = reset_val WHERE name = 'configuration_parameter';

Example 4 (unknown):

postgres -c log_connections=all --log-destination='syslog'

---

PostgreSQL: Documentation: 18: Chapter 39. The Rule System

URL: https://www.postgresql.org/docs/current/rules.html

Contents:

• Chapter 39. The Rule System

This chapter discusses the rule system in PostgreSQL. Production rule systems are conceptually simple, but there are many subtle points involved in actually using them.

Some other database systems define active database rules, which are usually stored procedures and triggers. In PostgreSQL, these can be implemented using functions and triggers as well.

The rule system (more precisely speaking, the query rewrite rule system) is totally different from stored procedures and triggers. It modifies queries to take rules into consideration, and then passes the modified query to the query planner for planning and execution. It is very powerful, and can be used for many things such as query language procedures, views, and versions. The theoretical foundations and the power of this rule system are also discussed in [ston90b] and [ong90].

---

PostgreSQL: Documentation: 18: 34.11. Library Functions

URL: https://www.postgresql.org/docs/current/ecpg-library.html

Contents:

• 34.11. Library Functions #
  • Note
  • Note

The libecpg library primarily contains “hidden” functions that are used to implement the functionality expressed by the embedded SQL commands. But there are some functions that can usefully be called directly. Note that this makes your code unportable.

ECPGdebug(int on, FILE *stream) turns on debug logging if called with the first argument non-zero. Debug logging is done on stream. The log contains all SQL statements with all the input variables inserted, and the results from the PostgreSQL server. This can be very useful when searching for errors in your SQL statements.

On Windows, if the ecpg libraries and an application are compiled with different flags, this function call will crash the application because the internal representation of the FILE pointers differ. Specifically, multithreaded/single-threaded, release/debug, and static/dynamic flags should be the same for the library and all applications using that library.

ECPGget_PGconn(const char *connection_name) returns the library database connection handle identified by the given name. If connection_name is set to NULL, the current connection handle is returned. If no connection handle can be identified, the function returns NULL. The returned connection handle can be used to call any other functions from libpq, if necessary.

It is a bad idea to manipulate database connection handles made from ecpg directly with libpq routines.

ECPGtransactionStatus(const char *connection_name) returns the current transaction status of the given connection identified by connection_name. See Section 32.2 and libpq's PQtransactionStatus for details about the returned status codes.

ECPGstatus(int lineno, const char* connection_name) returns true if you are connected to a database and false if not. connection_name can be NULL if a single connection is being used.

---

PostgreSQL: Documentation: 18: Chapter 65. Built-in Index Access Methods

URL: https://www.postgresql.org/docs/current/indextypes.html

Contents:

• Chapter 65. Built-in Index Access Methods

---

PostgreSQL: Documentation: 18: 5.1. Table Basics

URL: https://www.postgresql.org/docs/current/ddl-basics.html

Contents:

• 5.1. Table Basics #
  • Tip

A table in a relational database is much like a table on paper: It consists of rows and columns. The number and order of the columns is fixed, and each column has a name. The number of rows is variable — it reflects how much data is stored at a given moment. SQL does not make any guarantees about the order of the rows in a table. When a table is read, the rows will appear in an unspecified order, unless sorting is explicitly requested. This is covered in Chapter 7. Furthermore, SQL does not assign unique identifiers to rows, so it is possible to have several completely identical rows in a table. This is a consequence of the mathematical model that underlies SQL but is usually not desirable. Later in this chapter we will see how to deal with this issue.

Each column has a data type. The data type constrains the set of possible values that can be assigned to a column and assigns semantics to the data stored in the column so that it can be used for computations. For instance, a column declared to be of a numerical type will not accept arbitrary text strings, and the data stored in such a column can be used for mathematical computations. By contrast, a column declared to be of a character string type will accept almost any kind of data but it does not lend itself to mathematical calculations, although other operations such as string concatenation are available.

PostgreSQL includes a sizable set of built-in data types that fit many applications. Users can also define their own data types. Most built-in data types have obvious names and semantics, so we defer a detailed explanation to Chapter 8. Some of the frequently used data types are integer for whole numbers, numeric for possibly fractional numbers, text for character strings, date for dates, time for time-of-day values, and timestamp for values containing both date and time.

To create a table, you use the aptly named CREATE TABLE command. In this command you specify at least a name for the new table, the names of the columns and the data type of each column. For example:

This creates a table named my_first_table with two columns. The first column is named first_column and has a data type of text; the second column has the name second_column and the type integer. The table and column names follow the identifier syntax explained in Section 4.1.1. The type names are usually also identifiers, but there are some exceptions. Note that the column list is comma-separated and surrounded by parentheses.

Of course, the previous example was heavily contrived. Normally, you would give names to your tables and columns that convey what kind of data they store. So let's look at a more realistic example:

(The numeric type can store fractional components, as would be typical of monetary amounts.)

When you create many interrelated tables it is wise to choose a consistent naming pattern for the tables and columns. For instance, there is a choice of using singular or plural nouns for table names, both of which are favored by some theorist or other.

There is a limit on how many columns a table can contain. Depending on the column types, it is between 250 and 1600. However, defining a table with anywhere near this many columns is highly unusual and often a questionable design.

If you no longer need a table, you can remove it using the DROP TABLE command. For example:

Attempting to drop a table that does not exist is an error. Nevertheless, it is common in SQL script files to unconditionally try to drop each table before creating it, ignoring any error messages, so that the script works whether or not the table exists. (If you like, you can use the DROP TABLE IF EXISTS variant to avoid the error messages, but this is not standard SQL.)

If you need to modify a table that already exists, see Section 5.7 later in this chapter.

With the tools discussed so far you can create fully functional tables. The remainder of this chapter is concerned with adding features to the table definition to ensure data integrity, security, or convenience. If you are eager to fill your tables with data now you can skip ahead to Chapter 6 and read the rest of this chapter later.

Examples:

Example 1 (unknown):

CREATE TABLE my_first_table (
    first_column text,
    second_column integer
);

Example 2 (unknown):

CREATE TABLE products (
    product_no integer,
    name text,
    price numeric
);

Example 3 (unknown):

DROP TABLE my_first_table;
DROP TABLE products;

---

PostgreSQL: Documentation: 18: DEALLOCATE DESCRIPTOR

URL: https://www.postgresql.org/docs/current/ecpg-sql-deallocate-descriptor.html

Contents:

• DEALLOCATE DESCRIPTOR
• Synopsis
• Description
• Parameters
• Examples
• Compatibility
• See Also

DEALLOCATE DESCRIPTOR — deallocate an SQL descriptor area

DEALLOCATE DESCRIPTOR deallocates a named SQL descriptor area.

The name of the descriptor which is going to be deallocated. It is case sensitive. This can be an SQL identifier or a host variable.

DEALLOCATE DESCRIPTOR is specified in the SQL standard.

Examples:

Example 1 (unknown):

DEALLOCATE DESCRIPTOR name

Example 2 (unknown):

EXEC SQL DEALLOCATE DESCRIPTOR mydesc;

---

PostgreSQL: Documentation: 18: 32.10. Functions Associated with the COPY Command

URL: https://www.postgresql.org/docs/current/libpq-copy.html

Contents:

• 32.10. Functions Associated with the COPY Command #
  • 32.10.1. Functions for Sending COPY Data #
  • 32.10.2. Functions for Receiving COPY Data #
  • 32.10.3. Obsolete Functions for COPY #
  • Note

The COPY command in PostgreSQL has options to read from or write to the network connection used by libpq. The functions described in this section allow applications to take advantage of this capability by supplying or consuming copied data.

The overall process is that the application first issues the SQL COPY command via PQexec or one of the equivalent functions. The response to this (if there is no error in the command) will be a PGresult object bearing a status code of PGRES_COPY_OUT or PGRES_COPY_IN (depending on the specified copy direction). The application should then use the functions of this section to receive or transmit data rows. When the data transfer is complete, another PGresult object is returned to indicate success or failure of the transfer. Its status will be PGRES_COMMAND_OK for success or PGRES_FATAL_ERROR if some problem was encountered. At this point further SQL commands can be issued via PQexec. (It is not possible to execute other SQL commands using the same connection while the COPY operation is in progress.)

If a COPY command is issued via PQexec in a string that could contain additional commands, the application must continue fetching results via PQgetResult after completing the COPY sequence. Only when PQgetResult returns NULL is it certain that the PQexec command string is done and it is safe to issue more commands.

The functions of this section should be executed only after obtaining a result status of PGRES_COPY_OUT or PGRES_COPY_IN from PQexec or PQgetResult.

A PGresult object bearing one of these status values carries some additional data about the COPY operation that is starting. This additional data is available using functions that are also used in connection with query results:

Returns the number of columns (fields) to be copied.

0 indicates the overall copy format is textual (rows separated by newlines, columns separated by separator characters, etc.). 1 indicates the overall copy format is binary. See COPY for more information.

Returns the format code (0 for text, 1 for binary) associated with each column of the copy operation. The per-column format codes will always be zero when the overall copy format is textual, but the binary format can support both text and binary columns. (However, as of the current implementation of COPY, only binary columns appear in a binary copy; so the per-column formats always match the overall format at present.)

These functions are used to send data during COPY FROM STDIN. They will fail if called when the connection is not in COPY_IN state.

Sends data to the server during COPY_IN state.

Transmits the COPY data in the specified buffer, of length nbytes, to the server. The result is 1 if the data was queued, zero if it was not queued because of full buffers (this will only happen in nonblocking mode), or -1 if an error occurred. (Use PQerrorMessage to retrieve details if the return value is -1. If the value is zero, wait for write-ready and try again.)

The application can divide the COPY data stream into buffer loads of any convenient size. Buffer-load boundaries have no semantic significance when sending. The contents of the data stream must match the data format expected by the COPY command; see COPY for details.

Sends end-of-data indication to the server during COPY_IN state.

Ends the COPY_IN operation successfully if errormsg is NULL. If errormsg is not NULL then the COPY is forced to fail, with the string pointed to by errormsg used as the error message. (One should not assume that this exact error message will come back from the server, however, as the server might have already failed the COPY for its own reasons.)

The result is 1 if the termination message was sent; or in nonblocking mode, this may only indicate that the termination message was successfully queued. (In nonblocking mode, to be certain that the data has been sent, you should next wait for write-ready and call PQflush, repeating until it returns zero.) Zero indicates that the function could not queue the termination message because of full buffers; this will only happen in nonblocking mode. (In this case, wait for write-ready and try the PQputCopyEnd call again.) If a hard error occurs, -1 is returned; you can use PQerrorMessage to retrieve details.

After successfully calling PQputCopyEnd, call PQgetResult to obtain the final result status of the COPY command. One can wait for this result to be available in the usual way. Then return to normal operation.

These functions are used to receive data during COPY TO STDOUT. They will fail if called when the connection is not in COPY_OUT state.

Receives data from the server during COPY_OUT state.

Attempts to obtain another row of data from the server during a COPY. Data is always returned one data row at a time; if only a partial row is available, it is not returned. Successful return of a data row involves allocating a chunk of memory to hold the data. The buffer parameter must be non-NULL. *buffer is set to point to the allocated memory, or to NULL in cases where no buffer is returned. A non-NULL result buffer should be freed using PQfreemem when no longer needed.

When a row is successfully returned, the return value is the number of data bytes in the row (this will always be greater than zero). The returned string is always null-terminated, though this is probably only useful for textual COPY. A result of zero indicates that the COPY is still in progress, but no row is yet available (this is only possible when async is true). A result of -1 indicates that the COPY is done. A result of -2 indicates that an error occurred (consult PQerrorMessage for the reason).

When async is true (not zero), PQgetCopyData will not block waiting for input; it will return zero if the COPY is still in progress but no complete row is available. (In this case wait for read-ready and then call PQconsumeInput before calling PQgetCopyData again.) When async is false (zero), PQgetCopyData will block until data is available or the operation completes.

After PQgetCopyData returns -1, call PQgetResult to obtain the final result status of the COPY command. One can wait for this result to be available in the usual way. Then return to normal operation.

These functions represent older methods of handling COPY. Although they still work, they are deprecated due to poor error handling, inconvenient methods of detecting end-of-data, and lack of support for binary or nonblocking transfers.

Reads a newline-terminated line of characters (transmitted by the server) into a buffer string of size length.

This function copies up to length-1 characters into the buffer and converts the terminating newline into a zero byte. PQgetline returns EOF at the end of input, 0 if the entire line has been read, and 1 if the buffer is full but the terminating newline has not yet been read.

Note that the application must check to see if a new line consists of the two characters ., which indicates that the server has finished sending the results of the COPY command. If the application might receive lines that are more than length-1 characters long, care is needed to be sure it recognizes the . line correctly (and does not, for example, mistake the end of a long data line for a terminator line).

Reads a row of COPY data (transmitted by the server) into a buffer without blocking.

This function is similar to PQgetline, but it can be used by applications that must read COPY data asynchronously, that is, without blocking. Having issued the COPY command and gotten a PGRES_COPY_OUT response, the application should call PQconsumeInput and PQgetlineAsync until the end-of-data signal is detected.

Unlike PQgetline, this function takes responsibility for detecting end-of-data.

On each call, PQgetlineAsync will return data if a complete data row is available in libpq's input buffer. Otherwise, no data is returned until the rest of the row arrives. The function returns -1 if the end-of-copy-data marker has been recognized, or 0 if no data is available, or a positive number giving the number of bytes of data returned. If -1 is returned, the caller must next call PQendcopy, and then return to normal processing.

The data returned will not extend beyond a data-row boundary. If possible a whole row will be returned at one time. But if the buffer offered by the caller is too small to hold a row sent by the server, then a partial data row will be returned. With textual data this can be detected by testing whether the last returned byte is \n or not. (In a binary COPY, actual parsing of the COPY data format will be needed to make the equivalent determination.) The returned string is not null-terminated. (If you want to add a terminating null, be sure to pass a bufsize one smaller than the room actually available.)

Sends a null-terminated string to the server. Returns 0 if OK and EOF if unable to send the string.

The COPY data stream sent by a series of calls to PQputline has the same format as that returned by PQgetlineAsync, except that applications are not obliged to send exactly one data row per PQputline call; it is okay to send a partial line or multiple lines per call.

Before PostgreSQL protocol 3.0, it was necessary for the application to explicitly send the two characters . as a final line to indicate to the server that it had finished sending COPY data. While this still works, it is deprecated and the special meaning of . can be expected to be removed in a future release. (It already will misbehave in CSV mode.) It is sufficient to call PQendcopy after having sent the actual data.

Sends a non-null-terminated string to the server. Returns 0 if OK and EOF if unable to send the string.

This is exactly like PQputline, except that the data buffer need not be null-terminated since the number of bytes to send is specified directly. Use this procedure when sending binary data.

Synchronizes with the server.

This function waits until the server has finished the copying. It should either be issued when the last string has been sent to the server using PQputline or when the last string has been received from the server using PQgetline. It must be issued or the server will get “out of sync” with the client. Upon return from this function, the server is ready to receive the next SQL command. The return value is 0 on successful completion, nonzero otherwise. (Use PQerrorMessage to retrieve details if the return value is nonzero.)

When using PQgetResult, the application should respond to a PGRES_COPY_OUT result by executing PQgetline repeatedly, followed by PQendcopy after the terminator line is seen. It should then return to the PQgetResult loop until PQgetResult returns a null pointer. Similarly a PGRES_COPY_IN result is processed by a series of PQputline calls followed by PQendcopy, then return to the PQgetResult loop. This arrangement will ensure that a COPY command embedded in a series of SQL commands will be executed correctly.

Older applications are likely to submit a COPY via PQexec and assume that the transaction is done after PQendcopy. This will work correctly only if the COPY is the only SQL command in the command string.

Examples:

Example 1 (javascript):

int PQputCopyData(PGconn *conn,
                  const char *buffer,
                  int nbytes);

Example 2 (javascript):

int PQputCopyEnd(PGconn *conn,
                 const char *errormsg);

Example 3 (unknown):

int PQgetCopyData(PGconn *conn,
                  char **buffer,
                  int async);

Example 4 (unknown):

int PQgetline(PGconn *conn,
              char *buffer,
              int length);

---

PostgreSQL: Documentation: 18: 9.27. System Information Functions and Operators

URL: https://www.postgresql.org/docs/current/functions-info.html

Contents:

• 9.27. System Information Functions and Operators #
  • 9.27.1. Session Information Functions #
  • Note
  • 9.27.2. Access Privilege Inquiry Functions #
  • 9.27.3. Schema Visibility Inquiry Functions #
  • 9.27.4. System Catalog Information Functions #
  • 9.27.5. Object Information and Addressing Functions #
  • 9.27.6. Comment Information Functions #
  • 9.27.7. Data Validity Checking Functions #
  • 9.27.8. Transaction ID and Snapshot Information Functions #

The functions described in this section are used to obtain various information about a PostgreSQL installation.

Table 9.71 shows several functions that extract session and system information.

In addition to the functions listed in this section, there are a number of functions related to the statistics system that also provide system information. See Section 27.2.26 for more information.

Table 9.71. Session Information Functions

current_catalog → name

current_database () → name

Returns the name of the current database. (Databases are called “catalogs” in the SQL standard, so current_catalog is the standard's spelling.)

current_query () → text

Returns the text of the currently executing query, as submitted by the client (which might contain more than one statement).

This is equivalent to current_user.

current_schema → name

current_schema () → name

Returns the name of the schema that is first in the search path (or a null value if the search path is empty). This is the schema that will be used for any tables or other named objects that are created without specifying a target schema.

current_schemas ( include_implicit boolean ) → name[]

Returns an array of the names of all schemas presently in the effective search path, in their priority order. (Items in the current search_path setting that do not correspond to existing, searchable schemas are omitted.) If the Boolean argument is true, then implicitly-searched system schemas such as pg_catalog are included in the result.

Returns the user name of the current execution context.

inet_client_addr () → inet

Returns the IP address of the current client, or NULL if the current connection is via a Unix-domain socket.

inet_client_port () → integer

Returns the IP port number of the current client, or NULL if the current connection is via a Unix-domain socket.

inet_server_addr () → inet

Returns the IP address on which the server accepted the current connection, or NULL if the current connection is via a Unix-domain socket.

inet_server_port () → integer

Returns the IP port number on which the server accepted the current connection, or NULL if the current connection is via a Unix-domain socket.

pg_backend_pid () → integer

Returns the process ID of the server process attached to the current session.

pg_blocking_pids ( integer ) → integer[]

Returns an array of the process ID(s) of the sessions that are blocking the server process with the specified process ID from acquiring a lock, or an empty array if there is no such server process or it is not blocked.

One server process blocks another if it either holds a lock that conflicts with the blocked process's lock request (hard block), or is waiting for a lock that would conflict with the blocked process's lock request and is ahead of it in the wait queue (soft block). When using parallel queries the result always lists client-visible process IDs (that is, pg_backend_pid results) even if the actual lock is held or awaited by a child worker process. As a result of that, there may be duplicated PIDs in the result. Also note that when a prepared transaction holds a conflicting lock, it will be represented by a zero process ID.

Frequent calls to this function could have some impact on database performance, because it needs exclusive access to the lock manager's shared state for a short time.

pg_conf_load_time () → timestamp with time zone

Returns the time when the server configuration files were last loaded. If the current session was alive at the time, this will be the time when the session itself re-read the configuration files (so the reading will vary a little in different sessions). Otherwise it is the time when the postmaster process re-read the configuration files.

pg_current_logfile ( [ text ] ) → text

Returns the path name of the log file currently in use by the logging collector. The path includes the log_directory directory and the individual log file name. The result is NULL if the logging collector is disabled. When multiple log files exist, each in a different format, pg_current_logfile without an argument returns the path of the file having the first format found in the ordered list: stderr, csvlog, jsonlog. NULL is returned if no log file has any of these formats. To request information about a specific log file format, supply either csvlog, jsonlog or stderr as the value of the optional parameter. The result is NULL if the log format requested is not configured in log_destination. The result reflects the contents of the current_logfiles file.

This function is restricted to superusers and roles with privileges of the pg_monitor role by default, but other users can be granted EXECUTE to run the function.

pg_get_loaded_modules () → setof record ( module_name text, version text, file_name text )

Returns a list of the loadable modules that are loaded into the current server session. The module_name and version fields are NULL unless the module author supplied values for them using the PG_MODULE_MAGIC_EXT macro. The file_name field gives the file name of the module (shared library).

pg_my_temp_schema () → oid

Returns the OID of the current session's temporary schema, or zero if it has none (because it has not created any temporary tables).

pg_is_other_temp_schema ( oid ) → boolean

Returns true if the given OID is the OID of another session's temporary schema. (This can be useful, for example, to exclude other sessions' temporary tables from a catalog display.)

pg_jit_available () → boolean

Returns true if a JIT compiler extension is available (see Chapter 30) and the jit configuration parameter is set to on.

pg_numa_available () → boolean

Returns true if the server has been compiled with NUMA support.

pg_listening_channels () → setof text

Returns the set of names of asynchronous notification channels that the current session is listening to.

pg_notification_queue_usage () → double precision

Returns the fraction (0–1) of the asynchronous notification queue's maximum size that is currently occupied by notifications that are waiting to be processed. See LISTEN and NOTIFY for more information.

pg_postmaster_start_time () → timestamp with time zone

Returns the time when the server started.

pg_safe_snapshot_blocking_pids ( integer ) → integer[]

Returns an array of the process ID(s) of the sessions that are blocking the server process with the specified process ID from acquiring a safe snapshot, or an empty array if there is no such server process or it is not blocked.

A session running a SERIALIZABLE transaction blocks a SERIALIZABLE READ ONLY DEFERRABLE transaction from acquiring a snapshot until the latter determines that it is safe to avoid taking any predicate locks. See Section 13.2.3 for more information about serializable and deferrable transactions.

Frequent calls to this function could have some impact on database performance, because it needs access to the predicate lock manager's shared state for a short time.

pg_trigger_depth () → integer

Returns the current nesting level of PostgreSQL triggers (0 if not called, directly or indirectly, from inside a trigger).

Returns the session user's name.

Returns the authentication method and the identity (if any) that the user presented during the authentication cycle before they were assigned a database role. It is represented as auth_method:identity or NULL if the user has not been authenticated (for example if Trust authentication has been used).

This is equivalent to current_user.

current_catalog, current_role, current_schema, current_user, session_user, and user have special syntactic status in SQL: they must be called without trailing parentheses. In PostgreSQL, parentheses can optionally be used with current_schema, but not with the others.

The session_user is normally the user who initiated the current database connection; but superusers can change this setting with SET SESSION AUTHORIZATION. The current_user is the user identifier that is applicable for permission checking. Normally it is equal to the session user, but it can be changed with SET ROLE. It also changes during the execution of functions with the attribute SECURITY DEFINER. In Unix parlance, the session user is the “real user” and the current user is the “effective user”. current_role and user are synonyms for current_user. (The SQL standard draws a distinction between current_role and current_user, but PostgreSQL does not, since it unifies users and roles into a single kind of entity.)

Table 9.72 lists functions that allow querying object access privileges programmatically. (See Section 5.8 for more information about privileges.) In these functions, the user whose privileges are being inquired about can be specified by name or by OID (pg_authid.oid), or if the name is given as public then the privileges of the PUBLIC pseudo-role are checked. Also, the user argument can be omitted entirely, in which case the current_user is assumed. The object that is being inquired about can be specified either by name or by OID, too. When specifying by name, a schema name can be included if relevant. The access privilege of interest is specified by a text string, which must evaluate to one of the appropriate privilege keywords for the object's type (e.g., SELECT). Optionally, WITH GRANT OPTION can be added to a privilege type to test whether the privilege is held with grant option. Also, multiple privilege types can be listed separated by commas, in which case the result will be true if any of the listed privileges is held. (Case of the privilege string is not significant, and extra whitespace is allowed between but not within privilege names.) Some examples:

Table 9.72. Access Privilege Inquiry Functions

has_any_column_privilege ( [ user name or oid, ] table text or oid, privilege text ) → boolean

Does user have privilege for any column of table? This succeeds either if the privilege is held for the whole table, or if there is a column-level grant of the privilege for at least one column. Allowable privilege types are SELECT, INSERT, UPDATE, and REFERENCES.

has_column_privilege ( [ user name or oid, ] table text or oid, column text or smallint, privilege text ) → boolean

Does user have privilege for the specified table column? This succeeds either if the privilege is held for the whole table, or if there is a column-level grant of the privilege for the column. The column can be specified by name or by attribute number (pg_attribute.attnum). Allowable privilege types are SELECT, INSERT, UPDATE, and REFERENCES.

has_database_privilege ( [ user name or oid, ] database text or oid, privilege text ) → boolean

Does user have privilege for database? Allowable privilege types are CREATE, CONNECT, TEMPORARY, and TEMP (which is equivalent to TEMPORARY).

has_foreign_data_wrapper_privilege ( [ user name or oid, ] fdw text or oid, privilege text ) → boolean

Does user have privilege for foreign-data wrapper? The only allowable privilege type is USAGE.

has_function_privilege ( [ user name or oid, ] function text or oid, privilege text ) → boolean

Does user have privilege for function? The only allowable privilege type is EXECUTE.

When specifying a function by name rather than by OID, the allowed input is the same as for the regprocedure data type (see Section 8.19). An example is:

has_language_privilege ( [ user name or oid, ] language text or oid, privilege text ) → boolean

Does user have privilege for language? The only allowable privilege type is USAGE.

has_largeobject_privilege ( [ user name or oid, ] largeobject oid, privilege text ) → boolean

Does user have privilege for large object? Allowable privilege types are SELECT and UPDATE.

has_parameter_privilege ( [ user name or oid, ] parameter text, privilege text ) → boolean

Does user have privilege for configuration parameter? The parameter name is case-insensitive. Allowable privilege types are SET and ALTER SYSTEM.

has_schema_privilege ( [ user name or oid, ] schema text or oid, privilege text ) → boolean

Does user have privilege for schema? Allowable privilege types are CREATE and USAGE.

has_sequence_privilege ( [ user name or oid, ] sequence text or oid, privilege text ) → boolean

Does user have privilege for sequence? Allowable privilege types are USAGE, SELECT, and UPDATE.

has_server_privilege ( [ user name or oid, ] server text or oid, privilege text ) → boolean

Does user have privilege for foreign server? The only allowable privilege type is USAGE.

has_table_privilege ( [ user name or oid, ] table text or oid, privilege text ) → boolean

Does user have privilege for table? Allowable privilege types are SELECT, INSERT, UPDATE, DELETE, TRUNCATE, REFERENCES, TRIGGER, and MAINTAIN.

has_tablespace_privilege ( [ user name or oid, ] tablespace text or oid, privilege text ) → boolean

Does user have privilege for tablespace? The only allowable privilege type is CREATE.

has_type_privilege ( [ user name or oid, ] type text or oid, privilege text ) → boolean

Does user have privilege for data type? The only allowable privilege type is USAGE. When specifying a type by name rather than by OID, the allowed input is the same as for the regtype data type (see Section 8.19).

pg_has_role ( [ user name or oid, ] role text or oid, privilege text ) → boolean

Does user have privilege for role? Allowable privilege types are MEMBER, USAGE, and SET. MEMBER denotes direct or indirect membership in the role without regard to what specific privileges may be conferred. USAGE denotes whether the privileges of the role are immediately available without doing SET ROLE, while SET denotes whether it is possible to change to the role using the SET ROLE command. WITH ADMIN OPTION or WITH GRANT OPTION can be added to any of these privilege types to test whether the ADMIN privilege is held (all six spellings test the same thing). This function does not allow the special case of setting user to public, because the PUBLIC pseudo-role can never be a member of real roles.

row_security_active ( table text or oid ) → boolean

Is row-level security active for the specified table in the context of the current user and current environment?

Table 9.73 shows the operators available for the aclitem type, which is the catalog representation of access privileges. See Section 5.8 for information about how to read access privilege values.

Table 9.73. aclitem Operators

aclitem = aclitem → boolean

Are aclitems equal? (Notice that type aclitem lacks the usual set of comparison operators; it has only equality. In turn, aclitem arrays can only be compared for equality.)

'calvin=r*w/hobbes'::aclitem = 'calvin=rw/hobbes'::aclitem → f

aclitem[] @> aclitem → boolean

Does array contain the specified privileges? (This is true if there is an array entry that matches the aclitem's grantee and grantor, and has at least the specified set of privileges.)

'{calvin=r*w/hobbes,hobbes=rw/postgres}'::aclitem[] @> 'calvin=r*/hobbes'::aclitem → t

aclitem[] ~ aclitem → boolean

This is a deprecated alias for @>.

'{calvin=r*w/hobbes,hobbes=rw/postgres}'::aclitem[] ~ 'calvin=r*/hobbes'::aclitem → t

Table 9.74 shows some additional functions to manage the aclitem type.

Table 9.74. aclitem Functions

acldefault ( type "char", ownerId oid ) → aclitem[]

Constructs an aclitem array holding the default access privileges for an object of type type belonging to the role with OID ownerId. This represents the access privileges that will be assumed when an object's ACL entry is null. (The default access privileges are described in Section 5.8.) The type parameter must be one of 'c' for COLUMN, 'r' for TABLE and table-like objects, 's' for SEQUENCE, 'd' for DATABASE, 'f' for FUNCTION or PROCEDURE, 'l' for LANGUAGE, 'L' for LARGE OBJECT, 'n' for SCHEMA, 'p' for PARAMETER, 't' for TABLESPACE, 'F' for FOREIGN DATA WRAPPER, 'S' for FOREIGN SERVER, or 'T' for TYPE or DOMAIN.

aclexplode ( aclitem[] ) → setof record ( grantor oid, grantee oid, privilege_type text, is_grantable boolean )

Returns the aclitem array as a set of rows. If the grantee is the pseudo-role PUBLIC, it is represented by zero in the grantee column. Each granted privilege is represented as SELECT, INSERT, etc (see Table 5.1 for a full list). Note that each privilege is broken out as a separate row, so only one keyword appears in the privilege_type column.

makeaclitem ( grantee oid, grantor oid, privileges text, is_grantable boolean ) → aclitem

Constructs an aclitem with the given properties. privileges is a comma-separated list of privilege names such as SELECT, INSERT, etc, all of which are set in the result. (Case of the privilege string is not significant, and extra whitespace is allowed between but not within privilege names.)

Table 9.75 shows functions that determine whether a certain object is visible in the current schema search path. For example, a table is said to be visible if its containing schema is in the search path and no table of the same name appears earlier in the search path. This is equivalent to the statement that the table can be referenced by name without explicit schema qualification. Thus, to list the names of all visible tables:

For functions and operators, an object in the search path is said to be visible if there is no object of the same name and argument data type(s) earlier in the path. For operator classes and families, both the name and the associated index access method are considered.

Table 9.75. Schema Visibility Inquiry Functions

pg_collation_is_visible ( collation oid ) → boolean

Is collation visible in search path?

pg_conversion_is_visible ( conversion oid ) → boolean

Is conversion visible in search path?

pg_function_is_visible ( function oid ) → boolean

Is function visible in search path? (This also works for procedures and aggregates.)

pg_opclass_is_visible ( opclass oid ) → boolean

Is operator class visible in search path?

pg_operator_is_visible ( operator oid ) → boolean

Is operator visible in search path?

pg_opfamily_is_visible ( opclass oid ) → boolean

Is operator family visible in search path?

pg_statistics_obj_is_visible ( stat oid ) → boolean

Is statistics object visible in search path?

pg_table_is_visible ( table oid ) → boolean

Is table visible in search path? (This works for all types of relations, including views, materialized views, indexes, sequences and foreign tables.)

pg_ts_config_is_visible ( config oid ) → boolean

Is text search configuration visible in search path?

pg_ts_dict_is_visible ( dict oid ) → boolean

Is text search dictionary visible in search path?

pg_ts_parser_is_visible ( parser oid ) → boolean

Is text search parser visible in search path?

pg_ts_template_is_visible ( template oid ) → boolean

Is text search template visible in search path?

pg_type_is_visible ( type oid ) → boolean

Is type (or domain) visible in search path?

All these functions require object OIDs to identify the object to be checked. If you want to test an object by name, it is convenient to use the OID alias types (regclass, regtype, regprocedure, regoperator, regconfig, or regdictionary), for example:

Note that it would not make much sense to test a non-schema-qualified type name in this way — if the name can be recognized at all, it must be visible.

Table 9.76 lists functions that extract information from the system catalogs.

Table 9.76. System Catalog Information Functions

format_type ( type oid, typemod integer ) → text

Returns the SQL name for a data type that is identified by its type OID and possibly a type modifier. Pass NULL for the type modifier if no specific modifier is known.

pg_basetype ( regtype ) → regtype

Returns the OID of the base type of a domain identified by its type OID. If the argument is the OID of a non-domain type, returns the argument as-is. Returns NULL if the argument is not a valid type OID. If there's a chain of domain dependencies, it will recurse until finding the base type.

Assuming CREATE DOMAIN mytext AS text:

pg_basetype('mytext'::regtype) → text

pg_char_to_encoding ( encoding name ) → integer

Converts the supplied encoding name into an integer representing the internal identifier used in some system catalog tables. Returns -1 if an unknown encoding name is provided.

pg_encoding_to_char ( encoding integer ) → name

Converts the integer used as the internal identifier of an encoding in some system catalog tables into a human-readable string. Returns an empty string if an invalid encoding number is provided.

pg_get_catalog_foreign_keys () → setof record ( fktable regclass, fkcols text[], pktable regclass, pkcols text[], is_array boolean, is_opt boolean )

Returns a set of records describing the foreign key relationships that exist within the PostgreSQL system catalogs. The fktable column contains the name of the referencing catalog, and the fkcols column contains the name(s) of the referencing column(s). Similarly, the pktable column contains the name of the referenced catalog, and the pkcols column contains the name(s) of the referenced column(s). If is_array is true, the last referencing column is an array, each of whose elements should match some entry in the referenced catalog. If is_opt is true, the referencing column(s) are allowed to contain zeroes instead of a valid reference.

pg_get_constraintdef ( constraint oid [, pretty boolean ] ) → text

Reconstructs the creating command for a constraint. (This is a decompiled reconstruction, not the original text of the command.)

pg_get_expr ( expr pg_node_tree, relation oid [, pretty boolean ] ) → text

Decompiles the internal form of an expression stored in the system catalogs, such as the default value for a column. If the expression might contain Vars, specify the OID of the relation they refer to as the second parameter; if no Vars are expected, passing zero is sufficient.

pg_get_functiondef ( func oid ) → text

Reconstructs the creating command for a function or procedure. (This is a decompiled reconstruction, not the original text of the command.) The result is a complete CREATE OR REPLACE FUNCTION or CREATE OR REPLACE PROCEDURE statement.

pg_get_function_arguments ( func oid ) → text

Reconstructs the argument list of a function or procedure, in the form it would need to appear in within CREATE FUNCTION (including default values).

pg_get_function_identity_arguments ( func oid ) → text

Reconstructs the argument list necessary to identify a function or procedure, in the form it would need to appear in within commands such as ALTER FUNCTION. This form omits default values.

pg_get_function_result ( func oid ) → text

Reconstructs the RETURNS clause of a function, in the form it would need to appear in within CREATE FUNCTION. Returns NULL for a procedure.

pg_get_indexdef ( index oid [, column integer, pretty boolean ] ) → text

Reconstructs the creating command for an index. (This is a decompiled reconstruction, not the original text of the command.) If column is supplied and is not zero, only the definition of that column is reconstructed.

pg_get_keywords () → setof record ( word text, catcode "char", barelabel boolean, catdesc text, baredesc text )

Returns a set of records describing the SQL keywords recognized by the server. The word column contains the keyword. The catcode column contains a category code: U for an unreserved keyword, C for a keyword that can be a column name, T for a keyword that can be a type or function name, or R for a fully reserved keyword. The barelabel column contains true if the keyword can be used as a “bare” column label in SELECT lists, or false if it can only be used after AS. The catdesc column contains a possibly-localized string describing the keyword's category. The baredesc column contains a possibly-localized string describing the keyword's column label status.

pg_get_partkeydef ( table oid ) → text

Reconstructs the definition of a partitioned table's partition key, in the form it would have in the PARTITION BY clause of CREATE TABLE. (This is a decompiled reconstruction, not the original text of the command.)

pg_get_ruledef ( rule oid [, pretty boolean ] ) → text

Reconstructs the creating command for a rule. (This is a decompiled reconstruction, not the original text of the command.)

pg_get_serial_sequence ( table text, column text ) → text

Returns the name of the sequence associated with a column, or NULL if no sequence is associated with the column. If the column is an identity column, the associated sequence is the sequence internally created for that column. For columns created using one of the serial types (serial, smallserial, bigserial), it is the sequence created for that serial column definition. In the latter case, the association can be modified or removed with ALTER SEQUENCE OWNED BY. (This function probably should have been called pg_get_owned_sequence; its current name reflects the fact that it has historically been used with serial-type columns.) The first parameter is a table name with optional schema, and the second parameter is a column name. Because the first parameter potentially contains both schema and table names, it is parsed per usual SQL rules, meaning it is lower-cased by default. The second parameter, being just a column name, is treated literally and so has its case preserved. The result is suitably formatted for passing to the sequence functions (see Section 9.17).

A typical use is in reading the current value of the sequence for an identity or serial column, for example:

pg_get_statisticsobjdef ( statobj oid ) → text

Reconstructs the creating command for an extended statistics object. (This is a decompiled reconstruction, not the original text of the command.)

pg_get_triggerdef ( trigger oid [, pretty boolean ] ) → text

Reconstructs the creating command for a trigger. (This is a decompiled reconstruction, not the original text of the command.)

pg_get_userbyid ( role oid ) → name

Returns a role's name given its OID.

pg_get_viewdef ( view oid [, pretty boolean ] ) → text

Reconstructs the underlying SELECT command for a view or materialized view. (This is a decompiled reconstruction, not the original text of the command.)

pg_get_viewdef ( view oid, wrap_column integer ) → text

Reconstructs the underlying SELECT command for a view or materialized view. (This is a decompiled reconstruction, not the original text of the command.) In this form of the function, pretty-printing is always enabled, and long lines are wrapped to try to keep them shorter than the specified number of columns.

pg_get_viewdef ( view text [, pretty boolean ] ) → text

Reconstructs the underlying SELECT command for a view or materialized view, working from a textual name for the view rather than its OID. (This is deprecated; use the OID variant instead.)

pg_index_column_has_property ( index regclass, column integer, property text ) → boolean

Tests whether an index column has the named property. Common index column properties are listed in Table 9.77. (Note that extension access methods can define additional property names for their indexes.) NULL is returned if the property name is not known or does not apply to the particular object, or if the OID or column number does not identify a valid object.

pg_index_has_property ( index regclass, property text ) → boolean

Tests whether an index has the named property. Common index properties are listed in Table 9.78. (Note that extension access methods can define additional property names for their indexes.) NULL is returned if the property name is not known or does not apply to the particular object, or if the OID does not identify a valid object.

pg_indexam_has_property ( am oid, property text ) → boolean

Tests whether an index access method has the named property. Access method properties are listed in Table 9.79. NULL is returned if the property name is not known or does not apply to the particular object, or if the OID does not identify a valid object.

pg_options_to_table ( options_array text[] ) → setof record ( option_name text, option_value text )

Returns the set of storage options represented by a value from pg_class.reloptions or pg_attribute.attoptions.

pg_settings_get_flags ( guc text ) → text[]

Returns an array of the flags associated with the given GUC, or NULL if it does not exist. The result is an empty array if the GUC exists but there are no flags to show. Only the most useful flags listed in Table 9.80 are exposed.

pg_tablespace_databases ( tablespace oid ) → setof oid

Returns the set of OIDs of databases that have objects stored in the specified tablespace. If this function returns any rows, the tablespace is not empty and cannot be dropped. To identify the specific objects populating the tablespace, you will need to connect to the database(s) identified by pg_tablespace_databases and query their pg_class catalogs.

pg_tablespace_location ( tablespace oid ) → text

Returns the file system path that this tablespace is located in.

pg_typeof ( "any" ) → regtype

Returns the OID of the data type of the value that is passed to it. This can be helpful for troubleshooting or dynamically constructing SQL queries. The function is declared as returning regtype, which is an OID alias type (see Section 8.19); this means that it is the same as an OID for comparison purposes but displays as a type name.

pg_typeof(33) → integer

COLLATION FOR ( "any" ) → text

Returns the name of the collation of the value that is passed to it. The value is quoted and schema-qualified if necessary. If no collation was derived for the argument expression, then NULL is returned. If the argument is not of a collatable data type, then an error is raised.

collation for ('foo'::text) → "default"

collation for ('foo' COLLATE "de_DE") → "de_DE"

to_regclass ( text ) → regclass

Translates a textual relation name to its OID. A similar result is obtained by casting the string to type regclass (see Section 8.19); however, this function will return NULL rather than throwing an error if the name is not found.

to_regcollation ( text ) → regcollation

Translates a textual collation name to its OID. A similar result is obtained by casting the string to type regcollation (see Section 8.19); however, this function will return NULL rather than throwing an error if the name is not found.

to_regnamespace ( text ) → regnamespace

Translates a textual schema name to its OID. A similar result is obtained by casting the string to type regnamespace (see Section 8.19); however, this function will return NULL rather than throwing an error if the name is not found.

to_regoper ( text ) → regoper

Translates a textual operator name to its OID. A similar result is obtained by casting the string to type regoper (see Section 8.19); however, this function will return NULL rather than throwing an error if the name is not found or is ambiguous.

to_regoperator ( text ) → regoperator

Translates a textual operator name (with parameter types) to its OID. A similar result is obtained by casting the string to type regoperator (see Section 8.19); however, this function will return NULL rather than throwing an error if the name is not found.

to_regproc ( text ) → regproc

Translates a textual function or procedure name to its OID. A similar result is obtained by casting the string to type regproc (see Section 8.19); however, this function will return NULL rather than throwing an error if the name is not found or is ambiguous.

to_regprocedure ( text ) → regprocedure

Translates a textual function or procedure name (with argument types) to its OID. A similar result is obtained by casting the string to type regprocedure (see Section 8.19); however, this function will return NULL rather than throwing an error if the name is not found.

to_regrole ( text ) → regrole

Translates a textual role name to its OID. A similar result is obtained by casting the string to type regrole (see Section 8.19); however, this function will return NULL rather than throwing an error if the name is not found.

to_regtype ( text ) → regtype

Parses a string of text, extracts a potential type name from it, and translates that name into a type OID. A syntax error in the string will result in an error; but if the string is a syntactically valid type name that happens not to be found in the catalogs, the result is NULL. A similar result is obtained by casting the string to type regtype (see Section 8.19), except that that will throw error for name not found.

to_regtypemod ( text ) → integer

Parses a string of text, extracts a potential type name from it, and translates its type modifier, if any. A syntax error in the string will result in an error; but if the string is a syntactically valid type name that happens not to be found in the catalogs, the result is NULL. The result is -1 if no type modifier is present.

to_regtypemod can be combined with to_regtype to produce appropriate inputs for format_type, allowing a string representing a type name to be canonicalized.

format_type(to_regtype('varchar(32)'), to_regtypemod('varchar(32)')) → character varying(32)

Most of the functions that reconstruct (decompile) database objects have an optional pretty flag, which if true causes the result to be “pretty-printed”. Pretty-printing suppresses unnecessary parentheses and adds whitespace for legibility. The pretty-printed format is more readable, but the default format is more likely to be interpreted the same way by future versions of PostgreSQL; so avoid using pretty-printed output for dump purposes. Passing false for the pretty parameter yields the same result as omitting the parameter.

Table 9.77. Index Column Properties

Table 9.78. Index Properties

Table 9.79. Index Access Method Properties

Table 9.80. GUC Flags

Table 9.81 lists functions related to database object identification and addressing.

Table 9.81. Object Information and Addressing Functions

pg_get_acl ( classid oid, objid oid, objsubid integer ) → aclitem[]

Returns the ACL for a database object, specified by catalog OID, object OID and sub-object ID. This function returns NULL values for undefined objects.

pg_describe_object ( classid oid, objid oid, objsubid integer ) → text

Returns a textual description of a database object identified by catalog OID, object OID, and sub-object ID (such as a column number within a table; the sub-object ID is zero when referring to a whole object). This description is intended to be human-readable, and might be translated, depending on server configuration. This is especially useful to determine the identity of an object referenced in the pg_depend catalog. This function returns NULL values for undefined objects.

pg_identify_object ( classid oid, objid oid, objsubid integer ) → record ( type text, schema text, name text, identity text )

Returns a row containing enough information to uniquely identify the database object specified by catalog OID, object OID and sub-object ID. This information is intended to be machine-readable, and is never translated. type identifies the type of database object; schema is the schema name that the object belongs in, or NULL for object types that do not belong to schemas; name is the name of the object, quoted if necessary, if the name (along with schema name, if pertinent) is sufficient to uniquely identify the object, otherwise NULL; identity is the complete object identity, with the precise format depending on object type, and each name within the format being schema-qualified and quoted as necessary. Undefined objects are identified with NULL values.

pg_identify_object_as_address ( classid oid, objid oid, objsubid integer ) → record ( type text, object_names text[], object_args text[] )

Returns a row containing enough information to uniquely identify the database object specified by catalog OID, object OID and sub-object ID. The returned information is independent of the current server, that is, it could be used to identify an identically named object in another server. type identifies the type of database object; object_names and object_args are text arrays that together form a reference to the object. These three values can be passed to pg_get_object_address to obtain the internal address of the object.

pg_get_object_address ( type text, object_names text[], object_args text[] ) → record ( classid oid, objid oid, objsubid integer )

Returns a row containing enough information to uniquely identify the database object specified by a type code and object name and argument arrays. The returned values are the ones that would be used in system catalogs such as pg_depend; they can be passed to other system functions such as pg_describe_object or pg_identify_object. classid is the OID of the system catalog containing the object; objid is the OID of the object itself, and objsubid is the sub-object ID, or zero if none. This function is the inverse of pg_identify_object_as_address. Undefined objects are identified with NULL values.

pg_get_acl is useful for retrieving and inspecting the privileges associated with database objects without looking at specific catalogs. For example, to retrieve all the granted privileges on objects in the current database:

The functions shown in Table 9.82 extract comments previously stored with the COMMENT command. A null value is returned if no comment could be found for the specified parameters.

Table 9.82. Comment Information Functions

col_description ( table oid, column integer ) → text

Returns the comment for a table column, which is specified by the OID of its table and its column number. (obj_description cannot be used for table columns, since columns do not have OIDs of their own.)

obj_description ( object oid, catalog name ) → text

Returns the comment for a database object specified by its OID and the name of the containing system catalog. For example, obj_description(123456, 'pg_class') would retrieve the comment for the table with OID 123456.

obj_description ( object oid ) → text

Returns the comment for a database object specified by its OID alone. This is deprecated since there is no guarantee that OIDs are unique across different system catalogs; therefore, the wrong comment might be returned.

shobj_description ( object oid, catalog name ) → text

Returns the comment for a shared database object specified by its OID and the name of the containing system catalog. This is just like obj_description except that it is used for retrieving comments on shared objects (that is, databases, roles, and tablespaces). Some system catalogs are global to all databases within each cluster, and the descriptions for objects in them are stored globally as well.

The functions shown in Table 9.83 can be helpful for checking validity of proposed input data.

Table 9.83. Data Validity Checking Functions

pg_input_is_valid ( string text, type text ) → boolean

Tests whether the given string is valid input for the specified data type, returning true or false.

This function will only work as desired if the data type's input function has been updated to report invalid input as a “soft” error. Otherwise, invalid input will abort the transaction, just as if the string had been cast to the type directly.

pg_input_is_valid('42', 'integer') → t

pg_input_is_valid('42000000000', 'integer') → f

pg_input_is_valid('1234.567', 'numeric(7,4)') → f

pg_input_error_info ( string text, type text ) → record ( message text, detail text, hint text, sql_error_code text )

Tests whether the given string is valid input for the specified data type; if not, return the details of the error that would have been thrown. If the input is valid, the results are NULL. The inputs are the same as for pg_input_is_valid.

This function will only work as desired if the data type's input function has been updated to report invalid input as a “soft” error. Otherwise, invalid input will abort the transaction, just as if the string had been cast to the type directly.

SELECT * FROM pg_input_error_info('42000000000', 'integer') →

The functions shown in Table 9.84 provide server transaction information in an exportable form. The main use of these functions is to determine which transactions were committed between two snapshots.

Table 9.84. Transaction ID and Snapshot Information Functions

age ( xid ) → integer

Returns the number of transactions between the supplied transaction id and the current transaction counter.

mxid_age ( xid ) → integer

Returns the number of multixacts IDs between the supplied multixact ID and the current multixacts counter.

pg_current_xact_id () → xid8

Returns the current transaction's ID. It will assign a new one if the current transaction does not have one already (because it has not performed any database updates); see Section 67.1 for details. If executed in a subtransaction, this will return the top-level transaction ID; see Section 67.3 for details.

pg_current_xact_id_if_assigned () → xid8

Returns the current transaction's ID, or NULL if no ID is assigned yet. (It's best to use this variant if the transaction might otherwise be read-only, to avoid unnecessary consumption of an XID.) If executed in a subtransaction, this will return the top-level transaction ID.

pg_xact_status ( xid8 ) → text

Reports the commit status of a recent transaction. The result is one of in progress, committed, or aborted, provided that the transaction is recent enough that the system retains the commit status of that transaction. If it is old enough that no references to the transaction survive in the system and the commit status information has been discarded, the result is NULL. Applications might use this function, for example, to determine whether their transaction committed or aborted after the application and database server become disconnected while a COMMIT is in progress. Note that prepared transactions are reported as in progress; applications must check pg_prepared_xacts if they need to determine whether a transaction ID belongs to a prepared transaction.

pg_current_snapshot () → pg_snapshot

Returns a current snapshot, a data structure showing which transaction IDs are now in-progress. Only top-level transaction IDs are included in the snapshot; subtransaction IDs are not shown; see Section 67.3 for details.

pg_snapshot_xip ( pg_snapshot ) → setof xid8

Returns the set of in-progress transaction IDs contained in a snapshot.

pg_snapshot_xmax ( pg_snapshot ) → xid8

Returns the xmax of a snapshot.

pg_snapshot_xmin ( pg_snapshot ) → xid8

Returns the xmin of a snapshot.

pg_visible_in_snapshot ( xid8, pg_snapshot ) → boolean

Is the given transaction ID visible according to this snapshot (that is, was it completed before the snapshot was taken)? Note that this function will not give the correct answer for a subtransaction ID (subxid); see Section 67.3 for details.

pg_get_multixact_members ( multixid xid ) → setof record ( xid xid, mode text )

Returns the transaction ID and lock mode for each member of the specified multixact ID. The lock modes forupd, fornokeyupd, sh, and keysh correspond to the row-level locks FOR UPDATE, FOR NO KEY UPDATE, FOR SHARE, and FOR KEY SHARE, respectively, as described in Section 13.3.2. Two additional modes are specific to multixacts: nokeyupd, used by updates that do not modify key columns, and upd, used by updates or deletes that modify key columns.

The internal transaction ID type xid is 32 bits wide and wraps around every 4 billion transactions. However, the functions shown in Table 9.84, except age, mxid_age, and pg_get_multixact_members, use a 64-bit type xid8 that does not wrap around during the life of an installation and can be converted to xid by casting if required; see Section 67.1 for details. The data type pg_snapshot stores information about transaction ID visibility at a particular moment in time. Its components are described in Table 9.85. pg_snapshot's textual representation is xmin:xmax:xip_list. For example 10:20:10,14,15 means xmin=10, xmax=20, xip_list=10, 14, 15.

Table 9.85. Snapshot Components

In releases of PostgreSQL before 13 there was no xid8 type, so variants of these functions were provided that used bigint to represent a 64-bit XID, with a correspondingly distinct snapshot data type txid_snapshot. These older functions have txid in their names. They are still supported for backward compatibility, but may be removed from a future release. See Table 9.86.

Table 9.86. Deprecated Transaction ID and Snapshot Information Functions

txid_current () → bigint

See pg_current_xact_id().

txid_current_if_assigned () → bigint

See pg_current_xact_id_if_assigned().

txid_current_snapshot () → txid_snapshot

See pg_current_snapshot().

txid_snapshot_xip ( txid_snapshot ) → setof bigint

See pg_snapshot_xip().

txid_snapshot_xmax ( txid_snapshot ) → bigint

See pg_snapshot_xmax().

txid_snapshot_xmin ( txid_snapshot ) → bigint

See pg_snapshot_xmin().

txid_visible_in_snapshot ( bigint, txid_snapshot ) → boolean

See pg_visible_in_snapshot().

txid_status ( bigint ) → text

See pg_xact_status().

The functions shown in Table 9.87 provide information about when past transactions were committed. They only provide useful data when the track_commit_timestamp configuration option is enabled, and only for transactions that were committed after it was enabled. Commit timestamp information is routinely removed during vacuum.

Table 9.87. Committed Transaction Information Functions

pg_xact_commit_timestamp ( xid ) → timestamp with time zone

Returns the commit timestamp of a transaction.

pg_xact_commit_timestamp_origin ( xid ) → record ( timestamp timestamp with time zone, roident oid)

Returns the commit timestamp and replication origin of a transaction.

pg_last_committed_xact () → record ( xid xid, timestamp timestamp with time zone, roident oid )

Returns the transaction ID, commit timestamp and replication origin of the latest committed transaction.

The functions shown in Table 9.88 print information initialized during initdb, such as the catalog version. They also show information about write-ahead logging and checkpoint processing. This information is cluster-wide, not specific to any one database. These functions provide most of the same information, from the same source, as the pg_controldata application.

Table 9.88. Control Data Functions

pg_control_checkpoint () → record

Returns information about current checkpoint state, as shown in Table 9.89.

pg_control_system () → record

Returns information about current control file state, as shown in Table 9.90.

pg_control_init () → record

Returns information about cluster initialization state, as shown in Table 9.91.

pg_control_recovery () → record

Returns information about recovery state, as shown in Table 9.92.

Table 9.89. pg_control_checkpoint Output Columns

Table 9.90. pg_control_system Output Columns

Table 9.91. pg_control_init Output Columns

Table 9.92. pg_control_recovery Output Columns

The functions shown in Table 9.93 print version information.

Table 9.93. Version Information Functions

Returns a string describing the PostgreSQL server's version. You can also get this information from server_version, or for a machine-readable version use server_version_num. Software developers should use server_version_num (available since 8.2) or PQserverVersion instead of parsing the text version.

unicode_version () → text

Returns a string representing the version of Unicode used by PostgreSQL.

icu_unicode_version () → text

Returns a string representing the version of Unicode used by ICU, if the server was built with ICU support; otherwise returns NULL

The functions shown in Table 9.94 print information about the status of WAL summarization. See summarize_wal.

Table 9.94. WAL Summarization Information Functions

pg_available_wal_summaries () → setof record ( tli bigint, start_lsn pg_lsn, end_lsn pg_lsn )

Returns information about the WAL summary files present in the data directory, under pg_wal/summaries. One row will be returned per WAL summary file. Each file summarizes WAL on the indicated TLI within the indicated LSN range. This function might be useful to determine whether enough WAL summaries are present on the server to take an incremental backup based on some prior backup whose start LSN is known.

pg_wal_summary_contents ( tli bigint, start_lsn pg_lsn, end_lsn pg_lsn ) → setof record ( relfilenode oid, reltablespace oid, reldatabase oid, relforknumber smallint, relblocknumber bigint, is_limit_block boolean )

Returns one information about the contents of a single WAL summary file identified by TLI and starting and ending LSNs. Each row with is_limit_block false indicates that the block identified by the remaining output columns was modified by at least one WAL record within the range of records summarized by this file. Each row with is_limit_block true indicates either that (a) the relation fork was truncated to the length given by relblocknumber within the relevant range of WAL records or (b) that the relation fork was created or dropped within the relevant range of WAL records; in such cases, relblocknumber will be zero.

pg_get_wal_summarizer_state () → record ( summarized_tli bigint, summarized_lsn pg_lsn, pending_lsn pg_lsn, summarizer_pid int )

Returns information about the progress of the WAL summarizer. If the WAL summarizer has never run since the instance was started, then summarized_tli and summarized_lsn will be 0 and 0/0 respectively; otherwise, they will be the TLI and ending LSN of the last WAL summary file written to disk. If the WAL summarizer is currently running, pending_lsn will be the ending LSN of the last record that it has consumed, which must always be greater than or equal to summarized_lsn; if the WAL summarizer is not running, it will be equal to summarized_lsn. summarizer_pid is the PID of the WAL summarizer process, if it is running, and otherwise NULL.

As a special exception, the WAL summarizer will refuse to generate WAL summary files if run on WAL generated under wal_level=minimal, since such summaries would be unsafe to use as the basis for an incremental backup. In this case, the fields above will continue to advance as if summaries were being generated, but nothing will be written to disk. Once the summarizer reaches WAL generated while wal_level was set to replica or higher, it will resume writing summaries to disk.

Examples:

Example 1 (unknown):

SELECT has_table_privilege('myschema.mytable', 'select');
SELECT has_table_privilege('joe', 'mytable', 'INSERT, SELECT WITH GRANT OPTION');

Example 2 (unknown):

SELECT has_function_privilege('joeuser', 'myfunc(int, text)', 'execute');

Example 3 (unknown):

SELECT relname FROM pg_class WHERE pg_table_is_visible(oid);

Example 4 (unknown):

SELECT pg_type_is_visible('myschema.widget'::regtype);

---

PostgreSQL: Documentation: 18: 32.4. Asynchronous Command Processing

URL: https://www.postgresql.org/docs/current/libpq-async.html

Contents:

• 32.4. Asynchronous Command Processing #
  • Note

The PQexec function is adequate for submitting commands in normal, synchronous applications. It has a few deficiencies, however, that can be of importance to some users:

PQexec waits for the command to be completed. The application might have other work to do (such as maintaining a user interface), in which case it won't want to block waiting for the response.

Since the execution of the client application is suspended while it waits for the result, it is hard for the application to decide that it would like to try to cancel the ongoing command. (It can be done from a signal handler, but not otherwise.)

PQexec can return only one PGresult structure. If the submitted command string contains multiple SQL commands, all but the last PGresult are discarded by PQexec.

PQexec always collects the command's entire result, buffering it in a single PGresult. While this simplifies error-handling logic for the application, it can be impractical for results containing many rows.

Applications that do not like these limitations can instead use the underlying functions that PQexec is built from: PQsendQuery and PQgetResult. There are also PQsendQueryParams, PQsendPrepare, PQsendQueryPrepared, PQsendDescribePrepared, PQsendDescribePortal, PQsendClosePrepared, and PQsendClosePortal, which can be used with PQgetResult to duplicate the functionality of PQexecParams, PQprepare, PQexecPrepared, PQdescribePrepared, PQdescribePortal, PQclosePrepared, and PQclosePortal respectively.

Submits a command to the server without waiting for the result(s). 1 is returned if the command was successfully dispatched and 0 if not (in which case, use PQerrorMessage to get more information about the failure).

After successfully calling PQsendQuery, call PQgetResult one or more times to obtain the results. PQsendQuery cannot be called again (on the same connection) until PQgetResult has returned a null pointer, indicating that the command is done.

In pipeline mode, this function is disallowed.

Submits a command and separate parameters to the server without waiting for the result(s).

This is equivalent to PQsendQuery except that query parameters can be specified separately from the query string. The function's parameters are handled identically to PQexecParams. Like PQexecParams, it allows only one command in the query string.

Sends a request to create a prepared statement with the given parameters, without waiting for completion.

This is an asynchronous version of PQprepare: it returns 1 if it was able to dispatch the request, and 0 if not. After a successful call, call PQgetResult to determine whether the server successfully created the prepared statement. The function's parameters are handled identically to PQprepare.

Sends a request to execute a prepared statement with given parameters, without waiting for the result(s).

This is similar to PQsendQueryParams, but the command to be executed is specified by naming a previously-prepared statement, instead of giving a query string. The function's parameters are handled identically to PQexecPrepared.

Submits a request to obtain information about the specified prepared statement, without waiting for completion.

This is an asynchronous version of PQdescribePrepared: it returns 1 if it was able to dispatch the request, and 0 if not. After a successful call, call PQgetResult to obtain the results. The function's parameters are handled identically to PQdescribePrepared.

Submits a request to obtain information about the specified portal, without waiting for completion.

This is an asynchronous version of PQdescribePortal: it returns 1 if it was able to dispatch the request, and 0 if not. After a successful call, call PQgetResult to obtain the results. The function's parameters are handled identically to PQdescribePortal.

Submits a request to close the specified prepared statement, without waiting for completion.

This is an asynchronous version of PQclosePrepared: it returns 1 if it was able to dispatch the request, and 0 if not. After a successful call, call PQgetResult to obtain the results. The function's parameters are handled identically to PQclosePrepared.

Submits a request to close specified portal, without waiting for completion.

This is an asynchronous version of PQclosePortal: it returns 1 if it was able to dispatch the request, and 0 if not. After a successful call, call PQgetResult to obtain the results. The function's parameters are handled identically to PQclosePortal.

Waits for the next result from a prior PQsendQuery, PQsendQueryParams, PQsendPrepare, PQsendQueryPrepared, PQsendDescribePrepared, PQsendDescribePortal, PQsendClosePrepared, PQsendClosePortal, PQsendPipelineSync, or PQpipelineSync call, and returns it. A null pointer is returned when the command is complete and there will be no more results.

PQgetResult must be called repeatedly until it returns a null pointer, indicating that the command is done. (If called when no command is active, PQgetResult will just return a null pointer at once.) Each non-null result from PQgetResult should be processed using the same PGresult accessor functions previously described. Don't forget to free each result object with PQclear when done with it. Note that PQgetResult will block only if a command is active and the necessary response data has not yet been read by PQconsumeInput .

In pipeline mode, PQgetResult will return normally unless an error occurs; for any subsequent query sent after the one that caused the error until (and excluding) the next synchronization point, a special result of type PGRES_PIPELINE_ABORTED will be returned, and a null pointer will be returned after it. When the pipeline synchronization point is reached, a result of type PGRES_PIPELINE_SYNC will be returned. The result of the next query after the synchronization point follows immediately (that is, no null pointer is returned after the synchronization point).

Even when PQresultStatus indicates a fatal error, PQgetResult should be called until it returns a null pointer, to allow libpq to process the error information completely.

Using PQsendQuery and PQgetResult solves one of PQexec's problems: If a command string contains multiple SQL commands, the results of those commands can be obtained individually. (This allows a simple form of overlapped processing, by the way: the client can be handling the results of one command while the server is still working on later queries in the same command string.)

Another frequently-desired feature that can be obtained with PQsendQuery and PQgetResult is retrieving large query results a limited number of rows at a time. This is discussed in Section 32.6.

By itself, calling PQgetResult will still cause the client to block until the server completes the next SQL command. This can be avoided by proper use of two more functions:

If input is available from the server, consume it.

PQconsumeInput normally returns 1 indicating “no error”, but returns 0 if there was some kind of trouble (in which case PQerrorMessage can be consulted). Note that the result does not say whether any input data was actually collected. After calling PQconsumeInput , the application can check PQisBusy and/or PQnotifies to see if their state has changed.

PQconsumeInput can be called even if the application is not prepared to deal with a result or notification just yet. The function will read available data and save it in a buffer, thereby causing a select() read-ready indication to go away. The application can thus use PQconsumeInput to clear the select() condition immediately, and then examine the results at leisure.

Returns 1 if a command is busy, that is, PQgetResult would block waiting for input. A 0 return indicates that PQgetResult can be called with assurance of not blocking.

PQisBusy will not itself attempt to read data from the server; therefore PQconsumeInput must be invoked first, or the busy state will never end.

A typical application using these functions will have a main loop that uses select() or poll() to wait for all the conditions that it must respond to. One of the conditions will be input available from the server, which in terms of select() means readable data on the file descriptor identified by PQsocket. When the main loop detects input ready, it should call PQconsumeInput to read the input. It can then call PQisBusy, followed by PQgetResult if PQisBusy returns false (0). It can also call PQnotifies to detect NOTIFY messages (see Section 32.9).

A client that uses PQsendQuery/PQgetResult can also attempt to cancel a command that is still being processed by the server; see Section 32.7. But regardless of the return value of PQcancelBlocking, the application must continue with the normal result-reading sequence using PQgetResult. A successful cancellation will simply cause the command to terminate sooner than it would have otherwise.

By using the functions described above, it is possible to avoid blocking while waiting for input from the database server. However, it is still possible that the application will block waiting to send output to the server. This is relatively uncommon but can happen if very long SQL commands or data values are sent. (It is much more probable if the application sends data via COPY IN, however.) To prevent this possibility and achieve completely nonblocking database operation, the following additional functions can be used.

Sets the nonblocking status of the connection.

Sets the state of the connection to nonblocking if arg is 1, or blocking if arg is 0. Returns 0 if OK, -1 if error.

In the nonblocking state, successful calls to PQsendQuery, PQputline, PQputnbytes, PQputCopyData, and PQendcopy will not block; their changes are stored in the local output buffer until they are flushed. Unsuccessful calls will return an error and must be retried.

Note that PQexec does not honor nonblocking mode; if it is called, it will act in blocking fashion anyway.

Returns the blocking status of the database connection.

Returns 1 if the connection is set to nonblocking mode and 0 if blocking.

Attempts to flush any queued output data to the server. Returns 0 if successful (or if the send queue is empty), -1 if it failed for some reason, or 1 if it was unable to send all the data in the send queue yet (this case can only occur if the connection is nonblocking).

After sending any command or data on a nonblocking connection, call PQflush. If it returns 1, wait for the socket to become read- or write-ready. If it becomes write-ready, call PQflush again. If it becomes read-ready, call PQconsumeInput , then call PQflush again. Repeat until PQflush returns 0. (It is necessary to check for read-ready and drain the input with PQconsumeInput , because the server can block trying to send us data, e.g., NOTICE messages, and won't read our data until we read its.) Once PQflush returns 0, wait for the socket to be read-ready and then read the response as described above.

Examples:

Example 1 (javascript):

int PQsendQuery(PGconn *conn, const char *command);

Example 2 (javascript):

int PQsendQueryParams(PGconn *conn,
                      const char *command,
                      int nParams,
                      const Oid *paramTypes,
                      const char * const *paramValues,
                      const int *paramLengths,
                      const int *paramFormats,
                      int resultFormat);

Example 3 (javascript):

int PQsendPrepare(PGconn *conn,
                  const char *stmtName,
                  const char *query,
                  int nParams,
                  const Oid *paramTypes);

Example 4 (javascript):

int PQsendQueryPrepared(PGconn *conn,
                        const char *stmtName,
                        int nParams,
                        const char * const *paramValues,
                        const int *paramLengths,
                        const int *paramFormats,
                        int resultFormat);

---

PostgreSQL: Documentation: 18: Part VII. Internals

URL: https://www.postgresql.org/docs/current/internals.html

Contents:

• Part VII. Internals

This part contains assorted information that might be of use to PostgreSQL developers.

---

PostgreSQL: Documentation: 18: 37.1. Overview of Trigger Behavior

URL: https://www.postgresql.org/docs/current/trigger-definition.html

Contents:

• 37.1. Overview of Trigger Behavior #

A trigger is a specification that the database should automatically execute a particular function whenever a certain type of operation is performed. Triggers can be attached to tables (partitioned or not), views, and foreign tables.

On tables and foreign tables, triggers can be defined to execute either before or after any INSERT, UPDATE, or DELETE operation, either once per modified row, or once per SQL statement. UPDATE triggers can moreover be set to fire only if certain columns are mentioned in the SET clause of the UPDATE statement. Triggers can also fire for TRUNCATE statements. If a trigger event occurs, the trigger's function is called at the appropriate time to handle the event.

On views, triggers can be defined to execute instead of INSERT, UPDATE, or DELETE operations. Such INSTEAD OF triggers are fired once for each row that needs to be modified in the view. It is the responsibility of the trigger's function to perform the necessary modifications to the view's underlying base table(s) and, where appropriate, return the modified row as it will appear in the view. Triggers on views can also be defined to execute once per SQL statement, before or after INSERT, UPDATE, or DELETE operations. However, such triggers are fired only if there is also an INSTEAD OF trigger on the view. Otherwise, any statement targeting the view must be rewritten into a statement affecting its underlying base table(s), and then the triggers that will be fired are the ones attached to the base table(s).

The trigger function must be defined before the trigger itself can be created. The trigger function must be declared as a function taking no arguments and returning type trigger. (The trigger function receives its input through a specially-passed TriggerData structure, not in the form of ordinary function arguments.)

Once a suitable trigger function has been created, the trigger is established with CREATE TRIGGER. The same trigger function can be used for multiple triggers.

PostgreSQL offers both per-row triggers and per-statement triggers. With a per-row trigger, the trigger function is invoked once for each row that is affected by the statement that fired the trigger. In contrast, a per-statement trigger is invoked only once when an appropriate statement is executed, regardless of the number of rows affected by that statement. In particular, a statement that affects zero rows will still result in the execution of any applicable per-statement triggers. These two types of triggers are sometimes called row-level triggers and statement-level triggers, respectively. Triggers on TRUNCATE may only be defined at statement level, not per-row.

Triggers are also classified according to whether they fire before, after, or instead of the operation. These are referred to as BEFORE triggers, AFTER triggers, and INSTEAD OF triggers respectively. Statement-level BEFORE triggers naturally fire before the statement starts to do anything, while statement-level AFTER triggers fire at the very end of the statement. These types of triggers may be defined on tables, views, or foreign tables. Row-level BEFORE triggers fire immediately before a particular row is operated on, while row-level AFTER triggers fire at the end of the statement (but before any statement-level AFTER triggers). These types of triggers may only be defined on tables and foreign tables, not views. INSTEAD OF triggers may only be defined on views, and only at row level; they fire immediately as each row in the view is identified as needing to be operated on.

The execution of an AFTER trigger can be deferred to the end of the transaction, rather than the end of the statement, if it was defined as a constraint trigger. In all cases, a trigger is executed as part of the same transaction as the statement that triggered it, so if either the statement or the trigger causes an error, the effects of both will be rolled back. Also, the trigger will always run as the role that queued the trigger event, unless the trigger function is marked as SECURITY DEFINER, in which case it will run as the function owner.

If an INSERT contains an ON CONFLICT DO UPDATE clause, it is possible for row-level BEFORE INSERT and then BEFORE UPDATE triggers to be executed on triggered rows. Such interactions can be complex if the triggers are not idempotent because change made by BEFORE INSERT triggers will be seen by BEFORE UPDATE triggers, including changes to EXCLUDED columns.

Note that statement-level UPDATE triggers are executed when ON CONFLICT DO UPDATE is specified, regardless of whether or not any rows were affected by the UPDATE (and regardless of whether the alternative UPDATE path was ever taken). An INSERT with an ON CONFLICT DO UPDATE clause will execute statement-level BEFORE INSERT triggers first, then statement-level BEFORE UPDATE triggers, followed by statement-level AFTER UPDATE triggers and finally statement-level AFTER INSERT triggers.

A statement that targets a parent table in an inheritance or partitioning hierarchy does not cause the statement-level triggers of affected child tables to be fired; only the parent table's statement-level triggers are fired. However, row-level triggers of any affected child tables will be fired.

If an UPDATE on a partitioned table causes a row to move to another partition, it will be performed as a DELETE from the original partition followed by an INSERT into the new partition. In this case, all row-level BEFORE UPDATE triggers and all row-level BEFORE DELETE triggers are fired on the original partition. Then all row-level BEFORE INSERT triggers are fired on the destination partition. The possibility of surprising outcomes should be considered when all these triggers affect the row being moved. As far as AFTER ROW triggers are concerned, AFTER DELETE and AFTER INSERT triggers are applied; but AFTER UPDATE triggers are not applied because the UPDATE has been converted to a DELETE and an INSERT. As far as statement-level triggers are concerned, none of the DELETE or INSERT triggers are fired, even if row movement occurs; only the UPDATE triggers defined on the target table used in the UPDATE statement will be fired.

No separate triggers are defined for MERGE. Instead, statement-level or row-level UPDATE, DELETE, and INSERT triggers are fired depending on (for statement-level triggers) what actions are specified in the MERGE query and (for row-level triggers) what actions are performed.

While running a MERGE command, statement-level BEFORE and AFTER triggers are fired for events specified in the actions of the MERGE command, irrespective of whether or not the action is ultimately performed. This is the same as an UPDATE statement that updates no rows, yet statement-level triggers are fired. The row-level triggers are fired only when a row is actually updated, inserted or deleted. So it's perfectly legal that while statement-level triggers are fired for certain types of action, no row-level triggers are fired for the same kind of action.

Trigger functions invoked by per-statement triggers should always return NULL. Trigger functions invoked by per-row triggers can return a table row (a value of type HeapTuple) to the calling executor, if they choose. A row-level trigger fired before an operation has the following choices:

It can return NULL to skip the operation for the current row. This instructs the executor to not perform the row-level operation that invoked the trigger (the insertion, modification, or deletion of a particular table row).

For row-level INSERT and UPDATE triggers only, the returned row becomes the row that will be inserted or will replace the row being updated. This allows the trigger function to modify the row being inserted or updated.

A row-level BEFORE trigger that does not intend to cause either of these behaviors must be careful to return as its result the same row that was passed in (that is, the NEW row for INSERT and UPDATE triggers, the OLD row for DELETE triggers).

A row-level INSTEAD OF trigger should either return NULL to indicate that it did not modify any data from the view's underlying base tables, or it should return the view row that was passed in (the NEW row for INSERT and UPDATE operations, or the OLD row for DELETE operations). A nonnull return value is used to signal that the trigger performed the necessary data modifications in the view. This will cause the count of the number of rows affected by the command to be incremented. For INSERT and UPDATE operations only, the trigger may modify the NEW row before returning it. This will change the data returned by INSERT RETURNING or UPDATE RETURNING, and is useful when the view will not show exactly the same data that was provided.

The return value is ignored for row-level triggers fired after an operation, and so they can return NULL.

Some considerations apply for generated columns. Stored generated columns are computed after BEFORE triggers and before AFTER triggers. Therefore, the generated value can be inspected in AFTER triggers. In BEFORE triggers, the OLD row contains the old generated value, as one would expect, but the NEW row does not yet contain the new generated value and should not be accessed. In the C language interface, the content of the column is undefined at this point; a higher-level programming language should prevent access to a stored generated column in the NEW row in a BEFORE trigger. Changes to the value of a generated column in a BEFORE trigger are ignored and will be overwritten. Virtual generated columns are never computed when triggers fire. In the C language interface, their content is undefined in a trigger function. Higher-level programming languages should prevent access to virtual generated columns in triggers.

If more than one trigger is defined for the same event on the same relation, the triggers will be fired in alphabetical order by trigger name. In the case of BEFORE and INSTEAD OF triggers, the possibly-modified row returned by each trigger becomes the input to the next trigger. If any BEFORE or INSTEAD OF trigger returns NULL, the operation is abandoned for that row and subsequent triggers are not fired (for that row).

A trigger definition can also specify a Boolean WHEN condition, which will be tested to see whether the trigger should be fired. In row-level triggers the WHEN condition can examine the old and/or new values of columns of the row. (Statement-level triggers can also have WHEN conditions, although the feature is not so useful for them.) In a BEFORE trigger, the WHEN condition is evaluated just before the function is or would be executed, so using WHEN is not materially different from testing the same condition at the beginning of the trigger function. However, in an AFTER trigger, the WHEN condition is evaluated just after the row update occurs, and it determines whether an event is queued to fire the trigger at the end of statement. So when an AFTER trigger's WHEN condition does not return true, it is not necessary to queue an event nor to re-fetch the row at end of statement. This can result in significant speedups in statements that modify many rows, if the trigger only needs to be fired for a few of the rows. INSTEAD OF triggers do not support WHEN conditions.

Typically, row-level BEFORE triggers are used for checking or modifying the data that will be inserted or updated. For example, a BEFORE trigger might be used to insert the current time into a timestamp column, or to check that two elements of the row are consistent. Row-level AFTER triggers are most sensibly used to propagate the updates to other tables, or make consistency checks against other tables. The reason for this division of labor is that an AFTER trigger can be certain it is seeing the final value of the row, while a BEFORE trigger cannot; there might be other BEFORE triggers firing after it. If you have no specific reason to make a trigger BEFORE or AFTER, the BEFORE case is more efficient, since the information about the operation doesn't have to be saved until end of statement.

If a trigger function executes SQL commands then these commands might fire triggers again. This is known as cascading triggers. There is no direct limitation on the number of cascade levels. It is possible for cascades to cause a recursive invocation of the same trigger; for example, an INSERT trigger might execute a command that inserts an additional row into the same table, causing the INSERT trigger to be fired again. It is the trigger programmer's responsibility to avoid infinite recursion in such scenarios.

If a foreign key constraint specifies referential actions (that is, cascading updates or deletes), those actions are performed via ordinary SQL UPDATE or DELETE commands on the referencing table. In particular, any triggers that exist on the referencing table will be fired for those changes. If such a trigger modifies or blocks the effect of one of these commands, the end result could be to break referential integrity. It is the trigger programmer's responsibility to avoid that.

When a trigger is being defined, arguments can be specified for it. The purpose of including arguments in the trigger definition is to allow different triggers with similar requirements to call the same function. As an example, there could be a generalized trigger function that takes as its arguments two column names and puts the current user in one and the current time stamp in the other. Properly written, this trigger function would be independent of the specific table it is triggering on. So the same function could be used for INSERT events on any table with suitable columns, to automatically track creation of records in a transaction table for example. It could also be used to track last-update events if defined as an UPDATE trigger.

Each programming language that supports triggers has its own method for making the trigger input data available to the trigger function. This input data includes the type of trigger event (e.g., INSERT or UPDATE) as well as any arguments that were listed in CREATE TRIGGER. For a row-level trigger, the input data also includes the NEW row for INSERT and UPDATE triggers, and/or the OLD row for UPDATE and DELETE triggers.

By default, statement-level triggers do not have any way to examine the individual row(s) modified by the statement. But an AFTER STATEMENT trigger can request that transition tables be created to make the sets of affected rows available to the trigger. AFTER ROW triggers can also request transition tables, so that they can see the total changes in the table as well as the change in the individual row they are currently being fired for. The method for examining the transition tables again depends on the programming language that is being used, but the typical approach is to make the transition tables act like read-only temporary tables that can be accessed by SQL commands issued within the trigger function.

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PostgreSQL: Documentation: 18: 19.8. Error Reporting and Logging

URL: https://www.postgresql.org/docs/current/runtime-config-logging.html

Contents:

• 19.8. Error Reporting and Logging #
  • 19.8.1. Where to Log #
  • Note
  • Note
  • Note
  • 19.8.2. When to Log #
  • Note
  • Note
  • 19.8.3. What to Log #
  • Note

PostgreSQL supports several methods for logging server messages, including stderr, csvlog, jsonlog, and syslog. On Windows, eventlog is also supported. Set this parameter to a list of desired log destinations separated by commas. The default is to log to stderr only. This parameter can only be set in the postgresql.conf file or on the server command line.

If csvlog is included in log_destination, log entries are output in “comma-separated value” (CSV) format, which is convenient for loading logs into programs. See Section 19.8.4 for details. logging_collector must be enabled to generate CSV-format log output.

If jsonlog is included in log_destination, log entries are output in JSON format, which is convenient for loading logs into programs. See Section 19.8.5 for details. logging_collector must be enabled to generate JSON-format log output.

When either stderr, csvlog or jsonlog are included, the file current_logfiles is created to record the location of the log file(s) currently in use by the logging collector and the associated logging destination. This provides a convenient way to find the logs currently in use by the instance. Here is an example of this file's content:

current_logfiles is recreated when a new log file is created as an effect of rotation, and when log_destination is reloaded. It is removed when none of stderr, csvlog or jsonlog are included in log_destination, and when the logging collector is disabled.

On most Unix systems, you will need to alter the configuration of your system's syslog daemon in order to make use of the syslog option for log_destination. PostgreSQL can log to syslog facilities LOCAL0 through LOCAL7 (see syslog_facility), but the default syslog configuration on most platforms will discard all such messages. You will need to add something like:

to the syslog daemon's configuration file to make it work.

On Windows, when you use the eventlog option for log_destination, you should register an event source and its library with the operating system so that the Windows Event Viewer can display event log messages cleanly. See Section 18.12 for details.

This parameter enables the logging collector, which is a background process that captures log messages sent to stderr and redirects them into log files. This approach is often more useful than logging to syslog, since some types of messages might not appear in syslog output. (One common example is dynamic-linker failure messages; another is error messages produced by scripts such as archive_command.) This parameter can only be set at server start.

It is possible to log to stderr without using the logging collector; the log messages will just go to wherever the server's stderr is directed. However, that method is only suitable for low log volumes, since it provides no convenient way to rotate log files. Also, on some platforms not using the logging collector can result in lost or garbled log output, because multiple processes writing concurrently to the same log file can overwrite each other's output.

The logging collector is designed to never lose messages. This means that in case of extremely high load, server processes could be blocked while trying to send additional log messages when the collector has fallen behind. In contrast, syslog prefers to drop messages if it cannot write them, which means it may fail to log some messages in such cases but it will not block the rest of the system.

When logging_collector is enabled, this parameter determines the directory in which log files will be created. It can be specified as an absolute path, or relative to the cluster data directory. This parameter can only be set in the postgresql.conf file or on the server command line. The default is log.

When logging_collector is enabled, this parameter sets the file names of the created log files. The value is treated as a strftime pattern, so %-escapes can be used to specify time-varying file names. (Note that if there are any time-zone-dependent %-escapes, the computation is done in the zone specified by logtimezone.) The supported %-escapes are similar to those listed in the Open Group's strftime specification. Note that the system's strftime is not used directly, so platform-specific (nonstandard) extensions do not work. The default is postgresql-%Y-%m-%d%H%M%S.log.

If you specify a file name without escapes, you should plan to use a log rotation utility to avoid eventually filling the entire disk. In releases prior to 8.4, if no % escapes were present, PostgreSQL would append the epoch of the new log file's creation time, but this is no longer the case.

If CSV-format output is enabled in log_destination, .csv will be appended to the timestamped log file name to create the file name for CSV-format output. (If log_filename ends in .log, the suffix is replaced instead.)

If JSON-format output is enabled in log_destination, .json will be appended to the timestamped log file name to create the file name for JSON-format output. (If log_filename ends in .log, the suffix is replaced instead.)

This parameter can only be set in the postgresql.conf file or on the server command line.

On Unix systems this parameter sets the permissions for log files when logging_collector is enabled. (On Microsoft Windows this parameter is ignored.) The parameter value is expected to be a numeric mode specified in the format accepted by the chmod and umask system calls. (To use the customary octal format the number must start with a 0 (zero).)

The default permissions are 0600, meaning only the server owner can read or write the log files. The other commonly useful setting is 0640, allowing members of the owner's group to read the files. Note however that to make use of such a setting, you'll need to alter log_directory to store the files somewhere outside the cluster data directory. In any case, it's unwise to make the log files world-readable, since they might contain sensitive data.

This parameter can only be set in the postgresql.conf file or on the server command line.

When logging_collector is enabled, this parameter determines the maximum amount of time to use an individual log file, after which a new log file will be created. If this value is specified without units, it is taken as minutes. The default is 24 hours. Set to zero to disable time-based creation of new log files. This parameter can only be set in the postgresql.conf file or on the server command line.

When logging_collector is enabled, this parameter determines the maximum size of an individual log file. After this amount of data has been emitted into a log file, a new log file will be created. If this value is specified without units, it is taken as kilobytes. The default is 10 megabytes. Set to zero to disable size-based creation of new log files. This parameter can only be set in the postgresql.conf file or on the server command line.

When logging_collector is enabled, this parameter will cause PostgreSQL to truncate (overwrite), rather than append to, any existing log file of the same name. However, truncation will occur only when a new file is being opened due to time-based rotation, not during server startup or size-based rotation. When off, pre-existing files will be appended to in all cases. For example, using this setting in combination with a log_filename like postgresql-%H.log would result in generating twenty-four hourly log files and then cyclically overwriting them. This parameter can only be set in the postgresql.conf file or on the server command line.

Example: To keep 7 days of logs, one log file per day named server_log.Mon, server_log.Tue, etc., and automatically overwrite last week's log with this week's log, set log_filename to server_log.%a, log_truncate_on_rotation to on, and log_rotation_age to 1440.

Example: To keep 24 hours of logs, one log file per hour, but also rotate sooner if the log file size exceeds 1GB, set log_filename to server_log.%H%M, log_truncate_on_rotation to on, log_rotation_age to 60, and log_rotation_size to 1000000. Including %M in log_filename allows any size-driven rotations that might occur to select a file name different from the hour's initial file name.

When logging to syslog is enabled, this parameter determines the syslog “facility” to be used. You can choose from LOCAL0, LOCAL1, LOCAL2, LOCAL3, LOCAL4, LOCAL5, LOCAL6, LOCAL7; the default is LOCAL0. See also the documentation of your system's syslog daemon. This parameter can only be set in the postgresql.conf file or on the server command line.

When logging to syslog is enabled, this parameter determines the program name used to identify PostgreSQL messages in syslog logs. The default is postgres. This parameter can only be set in the postgresql.conf file or on the server command line.

When logging to syslog and this is on (the default), then each message will be prefixed by an increasing sequence number (such as [2]). This circumvents the “--- last message repeated N times ---” suppression that many syslog implementations perform by default. In more modern syslog implementations, repeated message suppression can be configured (for example, $RepeatedMsgReduction in rsyslog), so this might not be necessary. Also, you could turn this off if you actually want to suppress repeated messages.

This parameter can only be set in the postgresql.conf file or on the server command line.

When logging to syslog is enabled, this parameter determines how messages are delivered to syslog. When on (the default), messages are split by lines, and long lines are split so that they will fit into 1024 bytes, which is a typical size limit for traditional syslog implementations. When off, PostgreSQL server log messages are delivered to the syslog service as is, and it is up to the syslog service to cope with the potentially bulky messages.

If syslog is ultimately logging to a text file, then the effect will be the same either way, and it is best to leave the setting on, since most syslog implementations either cannot handle large messages or would need to be specially configured to handle them. But if syslog is ultimately writing into some other medium, it might be necessary or more useful to keep messages logically together.

This parameter can only be set in the postgresql.conf file or on the server command line.

When logging to event log is enabled, this parameter determines the program name used to identify PostgreSQL messages in the log. The default is PostgreSQL. This parameter can only be set in the postgresql.conf file or on the server command line.

Controls which message levels are written to the server log. Valid values are DEBUG5, DEBUG4, DEBUG3, DEBUG2, DEBUG1, INFO, NOTICE, WARNING, ERROR, LOG, FATAL, and PANIC. Each level includes all the levels that follow it. The later the level, the fewer messages are sent to the log. The default is WARNING. Note that LOG has a different rank here than in client_min_messages. Only superusers and users with the appropriate SET privilege can change this setting.

Controls which SQL statements that cause an error condition are recorded in the server log. The current SQL statement is included in the log entry for any message of the specified severity or higher. Valid values are DEBUG5, DEBUG4, DEBUG3, DEBUG2, DEBUG1, INFO, NOTICE, WARNING, ERROR, LOG, FATAL, and PANIC. The default is ERROR, which means statements causing errors, log messages, fatal errors, or panics will be logged. To effectively turn off logging of failing statements, set this parameter to PANIC. Only superusers and users with the appropriate SET privilege can change this setting.

Causes the duration of each completed statement to be logged if the statement ran for at least the specified amount of time. For example, if you set it to 250ms then all SQL statements that run 250ms or longer will be logged. Enabling this parameter can be helpful in tracking down unoptimized queries in your applications. If this value is specified without units, it is taken as milliseconds. Setting this to zero prints all statement durations. -1 (the default) disables logging statement durations. Only superusers and users with the appropriate SET privilege can change this setting.

This overrides log_min_duration_sample, meaning that queries with duration exceeding this setting are not subject to sampling and are always logged.

For clients using extended query protocol, durations of the Parse, Bind, and Execute steps are logged independently.

When using this option together with log_statement, the text of statements that are logged because of log_statement will not be repeated in the duration log message. If you are not using syslog, it is recommended that you log the PID or session ID using log_line_prefix so that you can link the statement message to the later duration message using the process ID or session ID.

Allows sampling the duration of completed statements that ran for at least the specified amount of time. This produces the same kind of log entries as log_min_duration_statement, but only for a subset of the executed statements, with sample rate controlled by log_statement_sample_rate. For example, if you set it to 100ms then all SQL statements that run 100ms or longer will be considered for sampling. Enabling this parameter can be helpful when the traffic is too high to log all queries. If this value is specified without units, it is taken as milliseconds. Setting this to zero samples all statement durations. -1 (the default) disables sampling statement durations. Only superusers and users with the appropriate SET privilege can change this setting.

This setting has lower priority than log_min_duration_statement, meaning that statements with durations exceeding log_min_duration_statement are not subject to sampling and are always logged.

Other notes for log_min_duration_statement apply also to this setting.

Determines the fraction of statements with duration exceeding log_min_duration_sample that will be logged. Sampling is stochastic, for example 0.5 means there is statistically one chance in two that any given statement will be logged. The default is 1.0, meaning to log all sampled statements. Setting this to zero disables sampled statement-duration logging, the same as setting log_min_duration_sample to -1. Only superusers and users with the appropriate SET privilege can change this setting.

Sets the fraction of transactions whose statements are all logged, in addition to statements logged for other reasons. It applies to each new transaction regardless of its statements' durations. Sampling is stochastic, for example 0.1 means there is statistically one chance in ten that any given transaction will be logged. log_transaction_sample_rate can be helpful to construct a sample of transactions. The default is 0, meaning not to log statements from any additional transactions. Setting this to 1 logs all statements of all transactions. Only superusers and users with the appropriate SET privilege can change this setting.

Like all statement-logging options, this option can add significant overhead.

Sets the amount of time after which the startup process will log a message about a long-running operation that is still in progress, as well as the interval between further progress messages for that operation. The default is 10 seconds. A setting of 0 disables the feature. If this value is specified without units, it is taken as milliseconds. This setting is applied separately to each operation. This parameter can only be set in the postgresql.conf file or on the server command line.

For example, if syncing the data directory takes 25 seconds and thereafter resetting unlogged relations takes 8 seconds, and if this setting has the default value of 10 seconds, then a messages will be logged for syncing the data directory after it has been in progress for 10 seconds and again after it has been in progress for 20 seconds, but nothing will be logged for resetting unlogged relations.

Table 19.2 explains the message severity levels used by PostgreSQL. If logging output is sent to syslog or Windows' eventlog, the severity levels are translated as shown in the table.

Table 19.2. Message Severity Levels

What you choose to log can have security implications; see Section 24.3.

The application_name can be any string of less than NAMEDATALEN characters (64 characters in a standard build). It is typically set by an application upon connection to the server. The name will be displayed in the pg_stat_activity view and included in CSV log entries. It can also be included in regular log entries via the log_line_prefix parameter. Only printable ASCII characters may be used in the application_name value. Other characters are replaced with C-style hexadecimal escapes.

These parameters enable various debugging output to be emitted. When set, they print the resulting parse tree, the query rewriter output, or the execution plan for each executed query. These messages are emitted at LOG message level, so by default they will appear in the server log but will not be sent to the client. You can change that by adjusting client_min_messages and/or log_min_messages. These parameters are off by default.

When set, debug_pretty_print indents the messages produced by debug_print_parse, debug_print_rewritten, or debug_print_plan. This results in more readable but much longer output than the “compact” format used when it is off. It is on by default.

Causes each action executed by autovacuum to be logged if it ran for at least the specified amount of time. Setting this to zero logs all autovacuum actions. -1 disables logging autovacuum actions. If this value is specified without units, it is taken as milliseconds. For example, if you set this to 250ms then all automatic vacuums and analyzes that run 250ms or longer will be logged. In addition, when this parameter is set to any value other than -1, a message will be logged if an autovacuum action is skipped due to a conflicting lock or a concurrently dropped relation. The default is 10min. Enabling this parameter can be helpful in tracking autovacuum activity. This parameter can only be set in the postgresql.conf file or on the server command line; but the setting can be overridden for individual tables by changing table storage parameters.

Causes checkpoints and restartpoints to be logged in the server log. Some statistics are included in the log messages, including the number of buffers written and the time spent writing them. This parameter can only be set in the postgresql.conf file or on the server command line. The default is on.

Causes aspects of each connection to the server to be logged. The default is the empty string, '', which disables all connection logging. The following options may be specified alone or in a comma-separated list:

Table 19.3. Log Connection Options

Disconnection logging is separately controlled by log_disconnections.

For the purposes of backwards compatibility, on, off, true, false, yes, no, 1, and 0 are still supported. The positive values are equivalent to specifying the receipt, authentication, and authorization options.

Only superusers and users with the appropriate SET privilege can change this parameter at session start, and it cannot be changed at all within a session.

Some client programs, like psql, attempt to connect twice while determining if a password is required, so duplicate “connection received” messages do not necessarily indicate a problem.

Causes session terminations to be logged. The log output provides information similar to log_connections, plus the duration of the session. Only superusers and users with the appropriate SET privilege can change this parameter at session start, and it cannot be changed at all within a session. The default is off.

Causes the duration of every completed statement to be logged. The default is off. Only superusers and users with the appropriate SET privilege can change this setting.

For clients using extended query protocol, durations of the Parse, Bind, and Execute steps are logged independently.

The difference between enabling log_duration and setting log_min_duration_statement to zero is that exceeding log_min_duration_statement forces the text of the query to be logged, but this option doesn't. Thus, if log_duration is on and log_min_duration_statement has a positive value, all durations are logged but the query text is included only for statements exceeding the threshold. This behavior can be useful for gathering statistics in high-load installations.

Controls the amount of detail written in the server log for each message that is logged. Valid values are TERSE, DEFAULT, and VERBOSE, each adding more fields to displayed messages. TERSE excludes the logging of DETAIL, HINT, QUERY, and CONTEXT error information. VERBOSE output includes the SQLSTATE error code (see also Appendix A) and the source code file name, function name, and line number that generated the error. Only superusers and users with the appropriate SET privilege can change this setting.

By default, connection log messages only show the IP address of the connecting host. Turning this parameter on causes logging of the host name as well. Note that depending on your host name resolution setup this might impose a non-negligible performance penalty. This parameter can only be set in the postgresql.conf file or on the server command line.

This is a printf-style string that is output at the beginning of each log line. % characters begin “escape sequences” that are replaced with status information as outlined below. Unrecognized escapes are ignored. Other characters are copied straight to the log line. Some escapes are only recognized by session processes, and will be treated as empty by background processes such as the main server process. Status information may be aligned either left or right by specifying a numeric literal after the % and before the option. A negative value will cause the status information to be padded on the right with spaces to give it a minimum width, whereas a positive value will pad on the left. Padding can be useful to aid human readability in log files.

This parameter can only be set in the postgresql.conf file or on the server command line. The default is '%m [%p] ' which logs a time stamp and the process ID.

The backend type corresponds to the column backend_type in the view pg_stat_activity, but additional types can appear in the log that don't show in that view.

The %c escape prints a quasi-unique session identifier, consisting of two 4-byte hexadecimal numbers (without leading zeros) separated by a dot. The numbers are the process start time and the process ID, so %c can also be used as a space saving way of printing those items. For example, to generate the session identifier from pg_stat_activity, use this query:

If you set a nonempty value for log_line_prefix, you should usually make its last character be a space, to provide visual separation from the rest of the log line. A punctuation character can be used too.

Syslog produces its own time stamp and process ID information, so you probably do not want to include those escapes if you are logging to syslog.

The %q escape is useful when including information that is only available in session (backend) context like user or database name. For example:

The %Q escape always reports a zero identifier for lines output by log_statement because log_statement generates output before an identifier can be calculated, including invalid statements for which an identifier cannot be calculated.

Controls whether a log message is produced when a session waits longer than deadlock_timeout to acquire a lock. This is useful in determining if lock waits are causing poor performance. The default is off. Only superusers and users with the appropriate SET privilege can change this setting.

Controls whether a detailed log message is produced when a lock acquisition fails. This is useful for analyzing the causes of lock failures. Currently, only lock failures due to SELECT NOWAIT is supported. The default is off. Only superusers and users with the appropriate SET privilege can change this setting.

Controls whether a log message is produced when the startup process waits longer than deadlock_timeout for recovery conflicts. This is useful in determining if recovery conflicts prevent the recovery from applying WAL.

The default is off. This parameter can only be set in the postgresql.conf file or on the server command line.

If greater than zero, each bind parameter value logged with a non-error statement-logging message is trimmed to this many bytes. Zero disables logging of bind parameters for non-error statement logs. -1 (the default) allows bind parameters to be logged in full. If this value is specified without units, it is taken as bytes. Only superusers and users with the appropriate SET privilege can change this setting.

This setting only affects log messages printed as a result of log_statement, log_duration, and related settings. Non-zero values of this setting add some overhead, particularly if parameters are sent in binary form, since then conversion to text is required.

If greater than zero, each bind parameter value reported in error messages is trimmed to this many bytes. Zero (the default) disables including bind parameters in error messages. -1 allows bind parameters to be printed in full. If this value is specified without units, it is taken as bytes.

Non-zero values of this setting add overhead, as PostgreSQL will need to store textual representations of parameter values in memory at the start of each statement, whether or not an error eventually occurs. The overhead is greater when bind parameters are sent in binary form than when they are sent as text, since the former case requires data conversion while the latter only requires copying the string.

Controls which SQL statements are logged. Valid values are none (off), ddl, mod, and all (all statements). ddl logs all data definition statements, such as CREATE, ALTER, and DROP statements. mod logs all ddl statements, plus data-modifying statements such as INSERT, UPDATE, DELETE, TRUNCATE, and COPY FROM. PREPARE, EXECUTE, and EXPLAIN ANALYZE statements are also logged if their contained command is of an appropriate type. For clients using extended query protocol, logging occurs when an Execute message is received, and values of the Bind parameters are included (with any embedded single-quote marks doubled).

The default is none. Only superusers and users with the appropriate SET privilege can change this setting.

Statements that contain simple syntax errors are not logged even by the log_statement = all setting, because the log message is emitted only after basic parsing has been done to determine the statement type. In the case of extended query protocol, this setting likewise does not log statements that fail before the Execute phase (i.e., during parse analysis or planning). Set log_min_error_statement to ERROR (or lower) to log such statements.

Logged statements might reveal sensitive data and even contain plaintext passwords.

Causes each replication command and walsender process's replication slot acquisition/release to be logged in the server log. See Section 54.4 for more information about replication command. The default value is off. Only superusers and users with the appropriate SET privilege can change this setting.

Controls logging of temporary file names and sizes. Temporary files can be created for sorts, hashes, and temporary query results. If enabled by this setting, a log entry is emitted for each temporary file, with the file size specified in bytes, when it is deleted. A value of zero logs all temporary file information, while positive values log only files whose size is greater than or equal to the specified amount of data. If this value is specified without units, it is taken as kilobytes. The default setting is -1, which disables such logging. Only superusers and users with the appropriate SET privilege can change this setting.

Sets the time zone used for timestamps written in the server log. Unlike TimeZone, this value is cluster-wide, so that all sessions will report timestamps consistently. The built-in default is GMT, but that is typically overridden in postgresql.conf; initdb will install a setting there corresponding to its system environment. See Section 8.5.3 for more information. This parameter can only be set in the postgresql.conf file or on the server command line.

Including csvlog in the log_destination list provides a convenient way to import log files into a database table. This option emits log lines in comma-separated-values (CSV) format, with these columns: time stamp with milliseconds, user name, database name, process ID, client host:port number, session ID, per-session line number, command tag, session start time, virtual transaction ID, regular transaction ID, error severity, SQLSTATE code, error message, error message detail, hint, internal query that led to the error (if any), character count of the error position therein, error context, user query that led to the error (if any and enabled by log_min_error_statement), character count of the error position therein, location of the error in the PostgreSQL source code (if log_error_verbosity is set to verbose), application name, backend type, process ID of parallel group leader, and query id. Here is a sample table definition for storing CSV-format log output:

To import a log file into this table, use the COPY FROM command:

It is also possible to access the file as a foreign table, using the supplied file_fdw module.

There are a few things you need to do to simplify importing CSV log files:

Set log_filename and log_rotation_age to provide a consistent, predictable naming scheme for your log files. This lets you predict what the file name will be and know when an individual log file is complete and therefore ready to be imported.

Set log_rotation_size to 0 to disable size-based log rotation, as it makes the log file name difficult to predict.

Set log_truncate_on_rotation to on so that old log data isn't mixed with the new in the same file.

The table definition above includes a primary key specification. This is useful to protect against accidentally importing the same information twice. The COPY command commits all of the data it imports at one time, so any error will cause the entire import to fail. If you import a partial log file and later import the file again when it is complete, the primary key violation will cause the import to fail. Wait until the log is complete and closed before importing. This procedure will also protect against accidentally importing a partial line that hasn't been completely written, which would also cause COPY to fail.

Including jsonlog in the log_destination list provides a convenient way to import log files into many different programs. This option emits log lines in JSON format.

String fields with null values are excluded from output. Additional fields may be added in the future. User applications that process jsonlog output should ignore unknown fields.

Each log line is serialized as a JSON object with the set of keys and their associated values shown in Table 19.4.

Table 19.4. Keys and Values of JSON Log Entries

These settings control how process titles of server processes are modified. Process titles are typically viewed using programs like ps or, on Windows, Process Explorer. See Section 27.1 for details.

Sets a name that identifies this database cluster (instance) for various purposes. The cluster name appears in the process title for all server processes in this cluster. Moreover, it is the default application name for a standby connection (see synchronous_standby_names).

The name can be any string of less than NAMEDATALEN characters (64 characters in a standard build). Only printable ASCII characters may be used in the cluster_name value. Other characters are replaced with C-style hexadecimal escapes. No name is shown if this parameter is set to the empty string '' (which is the default). This parameter can only be set at server start.

Enables updating of the process title every time a new SQL command is received by the server. This setting defaults to on on most platforms, but it defaults to off on Windows due to that platform's larger overhead for updating the process title. Only superusers and users with the appropriate SET privilege can change this setting.

Examples:

Example 1 (unknown):

stderr log/postgresql.log
csvlog log/postgresql.csv
jsonlog log/postgresql.json

Example 2 (unknown):

local0.*    /var/log/postgresql

Example 3 (unknown):

SELECT to_hex(trunc(EXTRACT(EPOCH FROM backend_start))::integer) || '.' ||
       to_hex(pid)
FROM pg_stat_activity;

Example 4 (unknown):

log_line_prefix = '%m [%p] %q%u@%d/%a '

---

PostgreSQL: Documentation: 18: 9.23. Merge Support Functions

URL: https://www.postgresql.org/docs/current/functions-merge-support.html

Contents:

• 9.23. Merge Support Functions #

PostgreSQL includes one merge support function that may be used in the RETURNING list of a MERGE command to identify the action taken for each row; see Table 9.68.

Table 9.68. Merge Support Functions

merge_action ( ) → text

Returns the merge action command executed for the current row. This will be 'INSERT', 'UPDATE', or 'DELETE'.

Note that this function can only be used in the RETURNING list of a MERGE command. It is an error to use it in any other part of a query.

Examples:

Example 1 (unknown):

MERGE INTO products p
  USING stock s ON p.product_id = s.product_id
  WHEN MATCHED AND s.quantity > 0 THEN
    UPDATE SET in_stock = true, quantity = s.quantity
  WHEN MATCHED THEN
    UPDATE SET in_stock = false, quantity = 0
  WHEN NOT MATCHED THEN
    INSERT (product_id, in_stock, quantity)
      VALUES (s.product_id, true, s.quantity)
  RETURNING merge_action(), p.*;

 merge_action | product_id | in_stock | quantity
--------------+------------+----------+----------
 UPDATE       |       1001 | t        |       50
 UPDATE       |       1002 | f        |        0
 INSERT       |       1003 | t        |       10

---

PostgreSQL: Documentation: 18: Appendix I. The Source Code Repository

URL: https://www.postgresql.org/docs/current/sourcerepo.html

Contents:

• Appendix I. The Source Code Repository

The PostgreSQL source code is stored and managed using the Git version control system. A public mirror of the master repository is available; it is updated within a minute of any change to the master repository.

Our wiki, https://wiki.postgresql.org/wiki/Working_with_Git, has some discussion on working with Git.

---

PostgreSQL: Documentation: 18: 35.61. user_mapping_options

URL: https://www.postgresql.org/docs/current/infoschema-user-mapping-options.html

Contents:

• 35.61. user_mapping_options #

The view user_mapping_options contains all the options defined for user mappings in the current database. Only those user mappings are shown where the current user has access to the corresponding foreign server (by way of being the owner or having some privilege).

Table 35.59. user_mapping_options Columns

authorization_identifier sql_identifier

Name of the user being mapped, or PUBLIC if the mapping is public

foreign_server_catalog sql_identifier

Name of the database that the foreign server used by this mapping is defined in (always the current database)

foreign_server_name sql_identifier

Name of the foreign server used by this mapping

option_name sql_identifier

option_value character_data

Value of the option. This column will show as null unless the current user is the user being mapped, or the mapping is for PUBLIC and the current user is the server owner, or the current user is a superuser. The intent is to protect password information stored as user mapping option.

---

PostgreSQL: Documentation: 18: 35.33. parameters

URL: https://www.postgresql.org/docs/current/infoschema-parameters.html

Contents:

• 35.33. parameters #

The view parameters contains information about the parameters (arguments) of all functions in the current database. Only those functions are shown that the current user has access to (by way of being the owner or having some privilege).

Table 35.31. parameters Columns

specific_catalog sql_identifier

Name of the database containing the function (always the current database)

specific_schema sql_identifier

Name of the schema containing the function

specific_name sql_identifier

The “specific name” of the function. See Section 35.45 for more information.

ordinal_position cardinal_number

Ordinal position of the parameter in the argument list of the function (count starts at 1)

parameter_mode character_data

IN for input parameter, OUT for output parameter, and INOUT for input/output parameter.

Applies to a feature not available in PostgreSQL

Applies to a feature not available in PostgreSQL

parameter_name sql_identifier

Name of the parameter, or null if the parameter has no name

data_type character_data

Data type of the parameter, if it is a built-in type, or ARRAY if it is some array (in that case, see the view element_types), else USER-DEFINED (in that case, the type is identified in udt_name and associated columns).

character_maximum_length cardinal_number

Always null, since this information is not applied to parameter data types in PostgreSQL

character_octet_length cardinal_number

Always null, since this information is not applied to parameter data types in PostgreSQL

character_set_catalog sql_identifier

Applies to a feature not available in PostgreSQL

character_set_schema sql_identifier

Applies to a feature not available in PostgreSQL

character_set_name sql_identifier

Applies to a feature not available in PostgreSQL

collation_catalog sql_identifier

Always null, since this information is not applied to parameter data types in PostgreSQL

collation_schema sql_identifier

Always null, since this information is not applied to parameter data types in PostgreSQL

collation_name sql_identifier

Always null, since this information is not applied to parameter data types in PostgreSQL

numeric_precision cardinal_number

Always null, since this information is not applied to parameter data types in PostgreSQL

numeric_precision_radix cardinal_number

Always null, since this information is not applied to parameter data types in PostgreSQL

numeric_scale cardinal_number

Always null, since this information is not applied to parameter data types in PostgreSQL

datetime_precision cardinal_number

Always null, since this information is not applied to parameter data types in PostgreSQL

interval_type character_data

Always null, since this information is not applied to parameter data types in PostgreSQL

interval_precision cardinal_number

Always null, since this information is not applied to parameter data types in PostgreSQL

udt_catalog sql_identifier

Name of the database that the data type of the parameter is defined in (always the current database)

udt_schema sql_identifier

Name of the schema that the data type of the parameter is defined in

udt_name sql_identifier

Name of the data type of the parameter

scope_catalog sql_identifier

Applies to a feature not available in PostgreSQL

scope_schema sql_identifier

Applies to a feature not available in PostgreSQL

scope_name sql_identifier

Applies to a feature not available in PostgreSQL

maximum_cardinality cardinal_number

Always null, because arrays always have unlimited maximum cardinality in PostgreSQL

dtd_identifier sql_identifier

An identifier of the data type descriptor of the parameter, unique among the data type descriptors pertaining to the function. This is mainly useful for joining with other instances of such identifiers. (The specific format of the identifier is not defined and not guaranteed to remain the same in future versions.)

parameter_default character_data

The default expression of the parameter, or null if none or if the function is not owned by a currently enabled role.

---

PostgreSQL: Documentation: 18: Appendix K. PostgreSQL Limits

URL: https://www.postgresql.org/docs/current/limits.html

Contents:

• Appendix K. PostgreSQL Limits

Table K.1 describes various hard limits of PostgreSQL. However, practical limits, such as performance limitations or available disk space may apply before absolute hard limits are reached.

Table K.1. PostgreSQL Limitations

The maximum number of columns for a table is further reduced as the tuple being stored must fit in a single 8192-byte heap page. For example, excluding the tuple header, a tuple made up of 1,600 int columns would consume 6400 bytes and could be stored in a heap page, but a tuple of 1,600 bigint columns would consume 12800 bytes and would therefore not fit inside a heap page. Variable-length fields of types such as text, varchar, and char can have their values stored out of line in the table's TOAST table when the values are large enough to require it. Only an 18-byte pointer must remain inside the tuple in the table's heap. For shorter length variable-length fields, either a 4-byte or 1-byte field header is used and the value is stored inside the heap tuple.

Columns that have been dropped from the table also contribute to the maximum column limit. Moreover, although the dropped column values for newly created tuples are internally marked as null in the tuple's null bitmap, the null bitmap also occupies space.

Each table can store a theoretical maximum of 2^32 out-of-line values; see Section 66.2 for a detailed discussion of out-of-line storage. This limit arises from the use of a 32-bit OID to identify each such value. The practical limit is significantly less than the theoretical limit, because as the OID space fills up, finding an OID that is still free can become expensive, in turn slowing down INSERT/UPDATE statements. Typically, this is only an issue for tables containing many terabytes of data; partitioning is a possible workaround.

---

PostgreSQL: Documentation: 18: Chapter 29. Logical Replication

URL: https://www.postgresql.org/docs/current/logical-replication.html

Contents:

• Chapter 29. Logical Replication

Logical replication is a method of replicating data objects and their changes, based upon their replication identity (usually a primary key). We use the term logical in contrast to physical replication, which uses exact block addresses and byte-by-byte replication. PostgreSQL supports both mechanisms concurrently, see Chapter 26. Logical replication allows fine-grained control over both data replication and security.

Logical replication uses a publish and subscribe model with one or more subscribers subscribing to one or more publications on a publisher node. Subscribers pull data from the publications they subscribe to and may subsequently re-publish data to allow cascading replication or more complex configurations.

When logical replication of a table typically starts, PostgreSQL takes a snapshot of the table's data on the publisher database and copies it to the subscriber. Once complete, changes on the publisher since the initial copy are sent continually to the subscriber. The subscriber applies the data in the same order as the publisher so that transactional consistency is guaranteed for publications within a single subscription. This method of data replication is sometimes referred to as transactional replication.

The typical use-cases for logical replication are:

Sending incremental changes in a single database or a subset of a database to subscribers as they occur.

Firing triggers for individual changes as they arrive on the subscriber.

Consolidating multiple databases into a single one (for example for analytical purposes).

Replicating between different major versions of PostgreSQL.

Replicating between PostgreSQL instances on different platforms (for example Linux to Windows)

Giving access to replicated data to different groups of users.

Sharing a subset of the database between multiple databases.

The subscriber database behaves in the same way as any other PostgreSQL instance and can be used as a publisher for other databases by defining its own publications. When the subscriber is treated as read-only by application, there will be no conflicts from a single subscription. On the other hand, if there are other writes done either by an application or by other subscribers to the same set of tables, conflicts can arise.

---

PostgreSQL: Documentation: 18: 35.20. data_type_privileges

URL: https://www.postgresql.org/docs/current/infoschema-data-type-privileges.html

Contents:

• 35.20. data_type_privileges #

The view data_type_privileges identifies all data type descriptors that the current user has access to, by way of being the owner of the described object or having some privilege for it. A data type descriptor is generated whenever a data type is used in the definition of a table column, a domain, or a function (as parameter or return type) and stores some information about how the data type is used in that instance (for example, the declared maximum length, if applicable). Each data type descriptor is assigned an arbitrary identifier that is unique among the data type descriptor identifiers assigned for one object (table, domain, function). This view is probably not useful for applications, but it is used to define some other views in the information schema.

Table 35.18. data_type_privileges Columns

object_catalog sql_identifier

Name of the database that contains the described object (always the current database)

object_schema sql_identifier

Name of the schema that contains the described object

object_name sql_identifier

Name of the described object

object_type character_data

The type of the described object: one of TABLE (the data type descriptor pertains to a column of that table), DOMAIN (the data type descriptors pertains to that domain), ROUTINE (the data type descriptor pertains to a parameter or the return data type of that function).

dtd_identifier sql_identifier

The identifier of the data type descriptor, which is unique among the data type descriptors for that same object.

---

PostgreSQL: Documentation: 18: 18.8. Encryption Options

URL: https://www.postgresql.org/docs/current/encryption-options.html

Contents:

• 18.8. Encryption Options #
  • Warning

PostgreSQL offers encryption at several levels, and provides flexibility in protecting data from disclosure due to database server theft, unscrupulous administrators, and insecure networks. Encryption might also be required to secure sensitive data such as medical records or financial transactions.

Database user passwords are stored as hashes (determined by the setting password_encryption), so the administrator cannot determine the actual password assigned to the user. If SCRAM or MD5 encryption is used for client authentication, the unencrypted password is never even temporarily present on the server because the client encrypts it before being sent across the network. SCRAM is preferred, because it is an Internet standard and is more secure than the PostgreSQL-specific MD5 authentication protocol.

Support for MD5-encrypted passwords is deprecated and will be removed in a future release of PostgreSQL. Refer to Section 20.5 for details about migrating to another password type.

The pgcrypto module allows certain fields to be stored encrypted. This is useful if only some of the data is sensitive. The client supplies the decryption key and the data is decrypted on the server and then sent to the client.

The decrypted data and the decryption key are present on the server for a brief time while it is being decrypted and communicated between the client and server. This presents a brief moment where the data and keys can be intercepted by someone with complete access to the database server, such as the system administrator.

Storage encryption can be performed at the file system level or the block level. Linux file system encryption options include eCryptfs and EncFS, while FreeBSD uses PEFS. Block level or full disk encryption options include dm-crypt + LUKS on Linux and GEOM modules geli and gbde on FreeBSD. Many other operating systems support this functionality, including Windows.

This mechanism prevents unencrypted data from being read from the drives if the drives or the entire computer is stolen. This does not protect against attacks while the file system is mounted, because when mounted, the operating system provides an unencrypted view of the data. However, to mount the file system, you need some way for the encryption key to be passed to the operating system, and sometimes the key is stored somewhere on the host that mounts the disk.

SSL connections encrypt all data sent across the network: the password, the queries, and the data returned. The pg_hba.conf file allows administrators to specify which hosts can use non-encrypted connections (host) and which require SSL-encrypted connections (hostssl). Also, clients can specify that they connect to servers only via SSL.

GSSAPI-encrypted connections encrypt all data sent across the network, including queries and data returned. (No password is sent across the network.) The pg_hba.conf file allows administrators to specify which hosts can use non-encrypted connections (host) and which require GSSAPI-encrypted connections (hostgssenc). Also, clients can specify that they connect to servers only on GSSAPI-encrypted connections (gssencmode=require).

Stunnel or SSH can also be used to encrypt transmissions.

It is possible for both the client and server to provide SSL certificates to each other. It takes some extra configuration on each side, but this provides stronger verification of identity than the mere use of passwords. It prevents a computer from pretending to be the server just long enough to read the password sent by the client. It also helps prevent “man in the middle” attacks where a computer between the client and server pretends to be the server and reads and passes all data between the client and server.

If the system administrator for the server's machine cannot be trusted, it is necessary for the client to encrypt the data; this way, unencrypted data never appears on the database server. Data is encrypted on the client before being sent to the server, and database results have to be decrypted on the client before being used.

---

PostgreSQL: Documentation: 18: Chapter 60. Writing a Custom Scan Provider

URL: https://www.postgresql.org/docs/current/custom-scan.html

Contents:

• Chapter 60. Writing a Custom Scan Provider

PostgreSQL supports a set of experimental facilities which are intended to allow extension modules to add new scan types to the system. Unlike a foreign data wrapper, which is only responsible for knowing how to scan its own foreign tables, a custom scan provider can provide an alternative method of scanning any relation in the system. Typically, the motivation for writing a custom scan provider will be to allow the use of some optimization not supported by the core system, such as caching or some form of hardware acceleration. This chapter outlines how to write a new custom scan provider.

Implementing a new type of custom scan is a three-step process. First, during planning, it is necessary to generate access paths representing a scan using the proposed strategy. Second, if one of those access paths is selected by the planner as the optimal strategy for scanning a particular relation, the access path must be converted to a plan. Finally, it must be possible to execute the plan and generate the same results that would have been generated for any other access path targeting the same relation.

---

PostgreSQL: Documentation: 18: 10.6. SELECT Output Columns

URL: https://www.postgresql.org/docs/current/typeconv-select.html

Contents:

• 10.6. SELECT Output Columns #
  • Note

The rules given in the preceding sections will result in assignment of non-unknown data types to all expressions in an SQL query, except for unspecified-type literals that appear as simple output columns of a SELECT command. For example, in

there is nothing to identify what type the string literal should be taken as. In this situation PostgreSQL will fall back to resolving the literal's type as text.

When the SELECT is one arm of a UNION (or INTERSECT or EXCEPT) construct, or when it appears within INSERT ... SELECT, this rule is not applied since rules given in preceding sections take precedence. The type of an unspecified-type literal can be taken from the other UNION arm in the first case, or from the destination column in the second case.

RETURNING lists are treated the same as SELECT output lists for this purpose.

Prior to PostgreSQL 10, this rule did not exist, and unspecified-type literals in a SELECT output list were left as type unknown. That had assorted bad consequences, so it's been changed.

Examples:

Example 1 (unknown):

SELECT 'Hello World';

---

PostgreSQL: Documentation: 18: 36.7. Function Volatility Categories

URL: https://www.postgresql.org/docs/current/xfunc-volatility.html

Contents:

• 36.7. Function Volatility Categories #
  • Note
  • Note

Every function has a volatility classification, with the possibilities being VOLATILE, STABLE, or IMMUTABLE. VOLATILE is the default if the CREATE FUNCTION command does not specify a category. The volatility category is a promise to the optimizer about the behavior of the function:

A VOLATILE function can do anything, including modifying the database. It can return different results on successive calls with the same arguments. The optimizer makes no assumptions about the behavior of such functions. A query using a volatile function will re-evaluate the function at every row where its value is needed.

A STABLE function cannot modify the database and is guaranteed to return the same results given the same arguments for all rows within a single statement. This category allows the optimizer to optimize multiple calls of the function to a single call. In particular, it is safe to use an expression containing such a function in an index scan condition. (Since an index scan will evaluate the comparison value only once, not once at each row, it is not valid to use a VOLATILE function in an index scan condition.)

An IMMUTABLE function cannot modify the database and is guaranteed to return the same results given the same arguments forever. This category allows the optimizer to pre-evaluate the function when a query calls it with constant arguments. For example, a query like SELECT ... WHERE x = 2 + 2 can be simplified on sight to SELECT ... WHERE x = 4, because the function underlying the integer addition operator is marked IMMUTABLE.

For best optimization results, you should label your functions with the strictest volatility category that is valid for them.

Any function with side-effects must be labeled VOLATILE, so that calls to it cannot be optimized away. Even a function with no side-effects needs to be labeled VOLATILE if its value can change within a single query; some examples are random(), currval(), timeofday().

Another important example is that the current_timestamp family of functions qualify as STABLE, since their values do not change within a transaction.

There is relatively little difference between STABLE and IMMUTABLE categories when considering simple interactive queries that are planned and immediately executed: it doesn't matter a lot whether a function is executed once during planning or once during query execution startup. But there is a big difference if the plan is saved and reused later. Labeling a function IMMUTABLE when it really isn't might allow it to be prematurely folded to a constant during planning, resulting in a stale value being re-used during subsequent uses of the plan. This is a hazard when using prepared statements or when using function languages that cache plans (such as PL/pgSQL).

For functions written in SQL or in any of the standard procedural languages, there is a second important property determined by the volatility category, namely the visibility of any data changes that have been made by the SQL command that is calling the function. A VOLATILE function will see such changes, a STABLE or IMMUTABLE function will not. This behavior is implemented using the snapshotting behavior of MVCC (see Chapter 13): STABLE and IMMUTABLE functions use a snapshot established as of the start of the calling query, whereas VOLATILE functions obtain a fresh snapshot at the start of each query they execute.

Functions written in C can manage snapshots however they want, but it's usually a good idea to make C functions work this way too.

Because of this snapshotting behavior, a function containing only SELECT commands can safely be marked STABLE, even if it selects from tables that might be undergoing modifications by concurrent queries. PostgreSQL will execute all commands of a STABLE function using the snapshot established for the calling query, and so it will see a fixed view of the database throughout that query.

The same snapshotting behavior is used for SELECT commands within IMMUTABLE functions. It is generally unwise to select from database tables within an IMMUTABLE function at all, since the immutability will be broken if the table contents ever change. However, PostgreSQL does not enforce that you do not do that.

A common error is to label a function IMMUTABLE when its results depend on a configuration parameter. For example, a function that manipulates timestamps might well have results that depend on the TimeZone setting. For safety, such functions should be labeled STABLE instead.

PostgreSQL requires that STABLE and IMMUTABLE functions contain no SQL commands other than SELECT to prevent data modification. (This is not a completely bulletproof test, since such functions could still call VOLATILE functions that modify the database. If you do that, you will find that the STABLE or IMMUTABLE function does not notice the database changes applied by the called function, since they are hidden from its snapshot.)

---

PostgreSQL: Documentation: 18: 7.7. VALUES Lists

URL: https://www.postgresql.org/docs/current/queries-values.html

Contents:

• 7.7. VALUES Lists #

VALUES provides a way to generate a “constant table” that can be used in a query without having to actually create and populate a table on-disk. The syntax is

Each parenthesized list of expressions generates a row in the table. The lists must all have the same number of elements (i.e., the number of columns in the table), and corresponding entries in each list must have compatible data types. The actual data type assigned to each column of the result is determined using the same rules as for UNION (see Section 10.5).

will return a table of two columns and three rows. It's effectively equivalent to:

By default, PostgreSQL assigns the names column1, column2, etc. to the columns of a VALUES table. The column names are not specified by the SQL standard and different database systems do it differently, so it's usually better to override the default names with a table alias list, like this:

Syntactically, VALUES followed by expression lists is treated as equivalent to:

and can appear anywhere a SELECT can. For example, you can use it as part of a UNION, or attach a sort_specification (ORDER BY, LIMIT, and/or OFFSET) to it. VALUES is most commonly used as the data source in an INSERT command, and next most commonly as a subquery.

For more information see VALUES.

Examples:

Example 1 (unknown):

VALUES ( expression [, ...] ) [, ...]

Example 2 (unknown):

VALUES (1, 'one'), (2, 'two'), (3, 'three');

Example 3 (unknown):

SELECT 1 AS column1, 'one' AS column2
UNION ALL
SELECT 2, 'two'
UNION ALL
SELECT 3, 'three';

Example 4 (javascript):

=> SELECT * FROM (VALUES (1, 'one'), (2, 'two'), (3, 'three')) AS t (num,letter);
 num | letter
-----+--------
   1 | one
   2 | two
   3 | three
(3 rows)

---

PostgreSQL: Documentation: 18: Chapter 37. Triggers

URL: https://www.postgresql.org/docs/current/triggers.html

Contents:

• Chapter 37. Triggers

This chapter provides general information about writing trigger functions. Trigger functions can be written in most of the available procedural languages, including PL/pgSQL (Chapter 41), PL/Tcl (Chapter 42), PL/Perl (Chapter 43), and PL/Python (PL/Python). After reading this chapter, you should consult the chapter for your favorite procedural language to find out the language-specific details of writing a trigger in it.

It is also possible to write a trigger function in C, although most people find it easier to use one of the procedural languages. It is not currently possible to write a trigger function in the plain SQL function language.

---

PostgreSQL: Documentation: 18: 19.13. Version and Platform Compatibility

URL: https://www.postgresql.org/docs/current/runtime-config-compatible.html

Contents:

• 19.13. Version and Platform Compatibility #
  • 19.13.1. Previous PostgreSQL Versions #
  • 19.13.2. Platform and Client Compatibility #

This controls whether the array input parser recognizes unquoted NULL as specifying a null array element. By default, this is on, allowing array values containing null values to be entered. However, PostgreSQL versions before 8.2 did not support null values in arrays, and therefore would treat NULL as specifying a normal array element with the string value “NULL”. For backward compatibility with applications that require the old behavior, this variable can be turned off.

Note that it is possible to create array values containing null values even when this variable is off.

This controls whether a quote mark can be represented by \' in a string literal. The preferred, SQL-standard way to represent a quote mark is by doubling it ('') but PostgreSQL has historically also accepted \'. However, use of \' creates security risks because in some client character set encodings, there are multibyte characters in which the last byte is numerically equivalent to ASCII . If client-side code does escaping incorrectly then an SQL-injection attack is possible. This risk can be prevented by making the server reject queries in which a quote mark appears to be escaped by a backslash. The allowed values of backslash_quote are on (allow \' always), off (reject always), and safe_encoding (allow only if client encoding does not allow ASCII \ within a multibyte character). safe_encoding is the default setting.

Note that in a standard-conforming string literal, \ just means \ anyway. This parameter only affects the handling of non-standard-conforming literals, including escape string syntax (E'...').

When on, a warning is issued if a backslash () appears in an ordinary string literal ('...' syntax) and standard_conforming_strings is off. The default is on.

Applications that wish to use backslash as escape should be modified to use escape string syntax (E'...'), because the default behavior of ordinary strings is now to treat backslash as an ordinary character, per SQL standard. This variable can be enabled to help locate code that needs to be changed.

In PostgreSQL releases prior to 9.0, large objects did not have access privileges and were, therefore, always readable and writable by all users. Setting this variable to on disables the new privilege checks, for compatibility with prior releases. The default is off. Only superusers and users with the appropriate SET privilege can change this setting.

Setting this variable does not disable all security checks related to large objects — only those for which the default behavior has changed in PostgreSQL 9.0.

When the database generates SQL, force all identifiers to be quoted, even if they are not (currently) keywords. This will affect the output of EXPLAIN as well as the results of functions like pg_get_viewdef. See also the --quote-all-identifiers option of pg_dump and pg_dumpall.

This controls whether ordinary string literals ('...') treat backslashes literally, as specified in the SQL standard. Beginning in PostgreSQL 9.1, the default is on (prior releases defaulted to off). Applications can check this parameter to determine how string literals will be processed. The presence of this parameter can also be taken as an indication that the escape string syntax (E'...') is supported. Escape string syntax (Section 4.1.2.2) should be used if an application desires backslashes to be treated as escape characters.

This allows sequential scans of large tables to synchronize with each other, so that concurrent scans read the same block at about the same time and hence share the I/O workload. When this is enabled, a scan might start in the middle of the table and then “wrap around” the end to cover all rows, so as to synchronize with the activity of scans already in progress. This can result in unpredictable changes in the row ordering returned by queries that have no ORDER BY clause. Setting this parameter to off ensures the pre-8.3 behavior in which a sequential scan always starts from the beginning of the table. The default is on.

When on, expressions of the form expr = NULL (or NULL = expr) are treated as expr IS NULL, that is, they return true if expr evaluates to the null value, and false otherwise. The correct SQL-spec-compliant behavior of expr = NULL is to always return null (unknown). Therefore this parameter defaults to off.

However, filtered forms in Microsoft Access generate queries that appear to use expr = NULL to test for null values, so if you use that interface to access the database you might want to turn this option on. Since expressions of the form expr = NULL always return the null value (using the SQL standard interpretation), they are not very useful and do not appear often in normal applications so this option does little harm in practice. But new users are frequently confused about the semantics of expressions involving null values, so this option is off by default.

Note that this option only affects the exact form = NULL, not other comparison operators or other expressions that are computationally equivalent to some expression involving the equals operator (such as IN). Thus, this option is not a general fix for bad programming.

Refer to Section 9.2 for related information.

When allow_alter_system is set to off, an error is returned if the ALTER SYSTEM command is executed. This parameter can only be set in the postgresql.conf file or on the server command line. The default value is on.

Note that this setting must not be regarded as a security feature. It only disables the ALTER SYSTEM command. It does not prevent a superuser from changing the configuration using other SQL commands. A superuser has many ways of executing shell commands at the operating system level, and can therefore modify postgresql.auto.conf regardless of the value of this setting.

Turning this setting off is intended for environments where the configuration of PostgreSQL is managed by some external tool. In such environments, a well-intentioned superuser might mistakenly use ALTER SYSTEM to change the configuration instead of using the external tool. This might result in unintended behavior, such as the external tool overwriting the change at some later point in time when it updates the configuration. Setting this parameter to off can help avoid such mistakes.

This parameter only controls the use of ALTER SYSTEM. The settings stored in postgresql.auto.conf take effect even if allow_alter_system is set to off.

---

PostgreSQL: Documentation: 18: 33.3. Client Interfaces

URL: https://www.postgresql.org/docs/current/lo-interfaces.html

Contents:

• 33.3. Client Interfaces #
  • 33.3.1. Creating a Large Object #
  • 33.3.2. Importing a Large Object #
  • 33.3.3. Exporting a Large Object #
  • 33.3.4. Opening an Existing Large Object #
  • 33.3.5. Writing Data to a Large Object #
  • 33.3.6. Reading Data from a Large Object #
  • 33.3.7. Seeking in a Large Object #
  • 33.3.8. Obtaining the Seek Position of a Large Object #
  • 33.3.9. Truncating a Large Object #

This section describes the facilities that PostgreSQL's libpq client interface library provides for accessing large objects. The PostgreSQL large object interface is modeled after the Unix file-system interface, with analogues of open, read, write, lseek, etc.

All large object manipulation using these functions must take place within an SQL transaction block, since large object file descriptors are only valid for the duration of a transaction. Write operations, including lo_open with the INV_WRITE mode, are not allowed in a read-only transaction.

If an error occurs while executing any one of these functions, the function will return an otherwise-impossible value, typically 0 or -1. A message describing the error is stored in the connection object and can be retrieved with PQerrorMessage .

Client applications that use these functions should include the header file libpq/libpq-fs.h and link with the libpq library.

Client applications cannot use these functions while a libpq connection is in pipeline mode.

creates a new large object. The OID to be assigned can be specified by lobjId; if so, failure occurs if that OID is already in use for some large object. If lobjId is InvalidOid (zero) then lo_create assigns an unused OID. The return value is the OID that was assigned to the new large object, or InvalidOid (zero) on failure.

also creates a new large object, always assigning an unused OID. The return value is the OID that was assigned to the new large object, or InvalidOid (zero) on failure.

In PostgreSQL releases 8.1 and later, the mode is ignored, so that lo_creat is exactly equivalent to lo_create with a zero second argument. However, there is little reason to use lo_creat unless you need to work with servers older than 8.1. To work with such an old server, you must use lo_creat not lo_create, and you must set mode to one of INV_READ, INV_WRITE, or INV_READ | INV_WRITE. (These symbolic constants are defined in the header file libpq/libpq-fs.h.)

To import an operating system file as a large object, call

filename specifies the operating system name of the file to be imported as a large object. The return value is the OID that was assigned to the new large object, or InvalidOid (zero) on failure. Note that the file is read by the client interface library, not by the server; so it must exist in the client file system and be readable by the client application.

also imports a new large object. The OID to be assigned can be specified by lobjId; if so, failure occurs if that OID is already in use for some large object. If lobjId is InvalidOid (zero) then lo_import_with_oid assigns an unused OID (this is the same behavior as lo_import). The return value is the OID that was assigned to the new large object, or InvalidOid (zero) on failure.

lo_import_with_oid is new as of PostgreSQL 8.4 and uses lo_create internally which is new in 8.1; if this function is run against 8.0 or before, it will fail and return InvalidOid.

To export a large object into an operating system file, call

The lobjId argument specifies the OID of the large object to export and the filename argument specifies the operating system name of the file. Note that the file is written by the client interface library, not by the server. Returns 1 on success, -1 on failure.

To open an existing large object for reading or writing, call

The lobjId argument specifies the OID of the large object to open. The mode bits control whether the object is opened for reading (INV_READ), writing (INV_WRITE), or both. (These symbolic constants are defined in the header file libpq/libpq-fs.h.) lo_open returns a (non-negative) large object descriptor for later use in lo_read, lo_write, lo_lseek, lo_lseek64, lo_tell, lo_tell64, lo_truncate, lo_truncate64, and lo_close. The descriptor is only valid for the duration of the current transaction. On failure, -1 is returned.

The server currently does not distinguish between modes INV_WRITE and INV_READ | INV_WRITE: you are allowed to read from the descriptor in either case. However there is a significant difference between these modes and INV_READ alone: with INV_READ you cannot write on the descriptor, and the data read from it will reflect the contents of the large object at the time of the transaction snapshot that was active when lo_open was executed, regardless of later writes by this or other transactions. Reading from a descriptor opened with INV_WRITE returns data that reflects all writes of other committed transactions as well as writes of the current transaction. This is similar to the behavior of REPEATABLE READ versus READ COMMITTED transaction modes for ordinary SQL SELECT commands.

lo_open will fail if SELECT privilege is not available for the large object, or if INV_WRITE is specified and UPDATE privilege is not available. (Prior to PostgreSQL 11, these privilege checks were instead performed at the first actual read or write call using the descriptor.) These privilege checks can be disabled with the lo_compat_privileges run-time parameter.

writes len bytes from buf (which must be of size len) to large object descriptor fd. The fd argument must have been returned by a previous lo_open. The number of bytes actually written is returned (in the current implementation, this will always equal len unless there is an error). In the event of an error, the return value is -1.

Although the len parameter is declared as size_t, this function will reject length values larger than INT_MAX. In practice, it's best to transfer data in chunks of at most a few megabytes anyway.

reads up to len bytes from large object descriptor fd into buf (which must be of size len). The fd argument must have been returned by a previous lo_open. The number of bytes actually read is returned; this will be less than len if the end of the large object is reached first. In the event of an error, the return value is -1.

Although the len parameter is declared as size_t, this function will reject length values larger than INT_MAX. In practice, it's best to transfer data in chunks of at most a few megabytes anyway.

To change the current read or write location associated with a large object descriptor, call

This function moves the current location pointer for the large object descriptor identified by fd to the new location specified by offset. The valid values for whence are SEEK_SET (seek from object start), SEEK_CUR (seek from current position), and SEEK_END (seek from object end). The return value is the new location pointer, or -1 on error.

When dealing with large objects that might exceed 2GB in size, instead use

This function has the same behavior as lo_lseek, but it can accept an offset larger than 2GB and/or deliver a result larger than 2GB. Note that lo_lseek will fail if the new location pointer would be greater than 2GB.

lo_lseek64 is new as of PostgreSQL 9.3. If this function is run against an older server version, it will fail and return -1.

To obtain the current read or write location of a large object descriptor, call

If there is an error, the return value is -1.

When dealing with large objects that might exceed 2GB in size, instead use

This function has the same behavior as lo_tell, but it can deliver a result larger than 2GB. Note that lo_tell will fail if the current read/write location is greater than 2GB.

lo_tell64 is new as of PostgreSQL 9.3. If this function is run against an older server version, it will fail and return -1.

To truncate a large object to a given length, call

This function truncates the large object descriptor fd to length len. The fd argument must have been returned by a previous lo_open. If len is greater than the large object's current length, the large object is extended to the specified length with null bytes ('\0'). On success, lo_truncate returns zero. On error, the return value is -1.

The read/write location associated with the descriptor fd is not changed.

Although the len parameter is declared as size_t, lo_truncate will reject length values larger than INT_MAX.

When dealing with large objects that might exceed 2GB in size, instead use

This function has the same behavior as lo_truncate, but it can accept a len value exceeding 2GB.

lo_truncate is new as of PostgreSQL 8.3; if this function is run against an older server version, it will fail and return -1.

lo_truncate64 is new as of PostgreSQL 9.3; if this function is run against an older server version, it will fail and return -1.

A large object descriptor can be closed by calling

where fd is a large object descriptor returned by lo_open. On success, lo_close returns zero. On error, the return value is -1.

Any large object descriptors that remain open at the end of a transaction will be closed automatically.

To remove a large object from the database, call

The lobjId argument specifies the OID of the large object to remove. Returns 1 if successful, -1 on failure.

Examples:

Example 1 (unknown):

Oid lo_create(PGconn *conn, Oid lobjId);

Example 2 (unknown):

inv_oid = lo_create(conn, desired_oid);

Example 3 (unknown):

Oid lo_creat(PGconn *conn, int mode);

Example 4 (unknown):

inv_oid = lo_creat(conn, INV_READ|INV_WRITE);

---

PostgreSQL: Documentation: 18: 13.3. Explicit Locking

URL: https://www.postgresql.org/docs/current/explicit-locking.html

Contents:

• 13.3. Explicit Locking #
  • 13.3.1. Table-Level Locks #
  • Tip
  • 13.3.2. Row-Level Locks #
  • 13.3.3. Page-Level Locks #
  • 13.3.4. Deadlocks #
  • 13.3.5. Advisory Locks #

PostgreSQL provides various lock modes to control concurrent access to data in tables. These modes can be used for application-controlled locking in situations where MVCC does not give the desired behavior. Also, most PostgreSQL commands automatically acquire locks of appropriate modes to ensure that referenced tables are not dropped or modified in incompatible ways while the command executes. (For example, TRUNCATE cannot safely be executed concurrently with other operations on the same table, so it obtains an ACCESS EXCLUSIVE lock on the table to enforce that.)

To examine a list of the currently outstanding locks in a database server, use the pg_locks system view. For more information on monitoring the status of the lock manager subsystem, refer to Chapter 27.

The list below shows the available lock modes and the contexts in which they are used automatically by PostgreSQL. You can also acquire any of these locks explicitly with the command LOCK. Remember that all of these lock modes are table-level locks, even if the name contains the word “row”; the names of the lock modes are historical. To some extent the names reflect the typical usage of each lock mode — but the semantics are all the same. The only real difference between one lock mode and another is the set of lock modes with which each conflicts (see Table 13.2). Two transactions cannot hold locks of conflicting modes on the same table at the same time. (However, a transaction never conflicts with itself. For example, it might acquire ACCESS EXCLUSIVE lock and later acquire ACCESS SHARE lock on the same table.) Non-conflicting lock modes can be held concurrently by many transactions. Notice in particular that some lock modes are self-conflicting (for example, an ACCESS EXCLUSIVE lock cannot be held by more than one transaction at a time) while others are not self-conflicting (for example, an ACCESS SHARE lock can be held by multiple transactions).

Table-Level Lock Modes

Conflicts with the ACCESS EXCLUSIVE lock mode only.

The SELECT command acquires a lock of this mode on referenced tables. In general, any query that only reads a table and does not modify it will acquire this lock mode.

Conflicts with the EXCLUSIVE and ACCESS EXCLUSIVE lock modes.

The SELECT command acquires a lock of this mode on all tables on which one of the FOR UPDATE, FOR NO KEY UPDATE, FOR SHARE, or FOR KEY SHARE options is specified (in addition to ACCESS SHARE locks on any other tables that are referenced without any explicit FOR ... locking option).

Conflicts with the SHARE, SHARE ROW EXCLUSIVE, EXCLUSIVE, and ACCESS EXCLUSIVE lock modes.

The commands UPDATE, DELETE, INSERT, and MERGE acquire this lock mode on the target table (in addition to ACCESS SHARE locks on any other referenced tables). In general, this lock mode will be acquired by any command that modifies data in a table.

Conflicts with the SHARE UPDATE EXCLUSIVE, SHARE, SHARE ROW EXCLUSIVE, EXCLUSIVE, and ACCESS EXCLUSIVE lock modes. This mode protects a table against concurrent schema changes and VACUUM runs.

Acquired by VACUUM (without FULL), ANALYZE, CREATE INDEX CONCURRENTLY, CREATE STATISTICS, COMMENT ON, REINDEX CONCURRENTLY, and certain ALTER INDEX and ALTER TABLE variants (for full details see the documentation of these commands).

Conflicts with the ROW EXCLUSIVE, SHARE UPDATE EXCLUSIVE, SHARE ROW EXCLUSIVE, EXCLUSIVE, and ACCESS EXCLUSIVE lock modes. This mode protects a table against concurrent data changes.

Acquired by CREATE INDEX (without CONCURRENTLY).

Conflicts with the ROW EXCLUSIVE, SHARE UPDATE EXCLUSIVE, SHARE, SHARE ROW EXCLUSIVE, EXCLUSIVE, and ACCESS EXCLUSIVE lock modes. This mode protects a table against concurrent data changes, and is self-exclusive so that only one session can hold it at a time.

Acquired by CREATE TRIGGER and some forms of ALTER TABLE.

Conflicts with the ROW SHARE, ROW EXCLUSIVE, SHARE UPDATE EXCLUSIVE, SHARE, SHARE ROW EXCLUSIVE, EXCLUSIVE, and ACCESS EXCLUSIVE lock modes. This mode allows only concurrent ACCESS SHARE locks, i.e., only reads from the table can proceed in parallel with a transaction holding this lock mode.

Acquired by REFRESH MATERIALIZED VIEW CONCURRENTLY.

Conflicts with locks of all modes (ACCESS SHARE, ROW SHARE, ROW EXCLUSIVE, SHARE UPDATE EXCLUSIVE, SHARE, SHARE ROW EXCLUSIVE, EXCLUSIVE, and ACCESS EXCLUSIVE). This mode guarantees that the holder is the only transaction accessing the table in any way.

Acquired by the DROP TABLE, TRUNCATE, REINDEX, CLUSTER, VACUUM FULL, and REFRESH MATERIALIZED VIEW (without CONCURRENTLY) commands. Many forms of ALTER INDEX and ALTER TABLE also acquire a lock at this level. This is also the default lock mode for LOCK TABLE statements that do not specify a mode explicitly.

Only an ACCESS EXCLUSIVE lock blocks a SELECT (without FOR UPDATE/SHARE) statement.

Once acquired, a lock is normally held until the end of the transaction. But if a lock is acquired after establishing a savepoint, the lock is released immediately if the savepoint is rolled back to. This is consistent with the principle that ROLLBACK cancels all effects of the commands since the savepoint. The same holds for locks acquired within a PL/pgSQL exception block: an error escape from the block releases locks acquired within it.

Table 13.2. Conflicting Lock Modes

In addition to table-level locks, there are row-level locks, which are listed as below with the contexts in which they are used automatically by PostgreSQL. See Table 13.3 for a complete table of row-level lock conflicts. Note that a transaction can hold conflicting locks on the same row, even in different subtransactions; but other than that, two transactions can never hold conflicting locks on the same row. Row-level locks do not affect data querying; they block only writers and lockers to the same row. Row-level locks are released at transaction end or during savepoint rollback, just like table-level locks.

FOR UPDATE causes the rows retrieved by the SELECT statement to be locked as though for update. This prevents them from being locked, modified or deleted by other transactions until the current transaction ends. That is, other transactions that attempt UPDATE, DELETE, SELECT FOR UPDATE, SELECT FOR NO KEY UPDATE, SELECT FOR SHARE or SELECT FOR KEY SHARE of these rows will be blocked until the current transaction ends; conversely, SELECT FOR UPDATE will wait for a concurrent transaction that has run any of those commands on the same row, and will then lock and return the updated row (or no row, if the row was deleted). Within a REPEATABLE READ or SERIALIZABLE transaction, however, an error will be thrown if a row to be locked has changed since the transaction started. For further discussion see Section 13.4.

The FOR UPDATE lock mode is also acquired by any DELETE on a row, and also by an UPDATE that modifies the values of certain columns. Currently, the set of columns considered for the UPDATE case are those that have a unique index on them that can be used in a foreign key (so partial indexes and expressional indexes are not considered), but this may change in the future.

Behaves similarly to FOR UPDATE, except that the lock acquired is weaker: this lock will not block SELECT FOR KEY SHARE commands that attempt to acquire a lock on the same rows. This lock mode is also acquired by any UPDATE that does not acquire a FOR UPDATE lock.

Behaves similarly to FOR NO KEY UPDATE, except that it acquires a shared lock rather than exclusive lock on each retrieved row. A shared lock blocks other transactions from performing UPDATE, DELETE, SELECT FOR UPDATE or SELECT FOR NO KEY UPDATE on these rows, but it does not prevent them from performing SELECT FOR SHARE or SELECT FOR KEY SHARE.

Behaves similarly to FOR SHARE, except that the lock is weaker: SELECT FOR UPDATE is blocked, but not SELECT FOR NO KEY UPDATE. A key-shared lock blocks other transactions from performing DELETE or any UPDATE that changes the key values, but not other UPDATE, and neither does it prevent SELECT FOR NO KEY UPDATE, SELECT FOR SHARE, or SELECT FOR KEY SHARE.

PostgreSQL doesn't remember any information about modified rows in memory, so there is no limit on the number of rows locked at one time. However, locking a row might cause a disk write, e.g., SELECT FOR UPDATE modifies selected rows to mark them locked, and so will result in disk writes.

Table 13.3. Conflicting Row-Level Locks

In addition to table and row locks, page-level share/exclusive locks are used to control read/write access to table pages in the shared buffer pool. These locks are released immediately after a row is fetched or updated. Application developers normally need not be concerned with page-level locks, but they are mentioned here for completeness.

The use of explicit locking can increase the likelihood of deadlocks, wherein two (or more) transactions each hold locks that the other wants. For example, if transaction 1 acquires an exclusive lock on table A and then tries to acquire an exclusive lock on table B, while transaction 2 has already exclusive-locked table B and now wants an exclusive lock on table A, then neither one can proceed. PostgreSQL automatically detects deadlock situations and resolves them by aborting one of the transactions involved, allowing the other(s) to complete. (Exactly which transaction will be aborted is difficult to predict and should not be relied upon.)

Note that deadlocks can also occur as the result of row-level locks (and thus, they can occur even if explicit locking is not used). Consider the case in which two concurrent transactions modify a table. The first transaction executes:

This acquires a row-level lock on the row with the specified account number. Then, the second transaction executes:

The first UPDATE statement successfully acquires a row-level lock on the specified row, so it succeeds in updating that row. However, the second UPDATE statement finds that the row it is attempting to update has already been locked, so it waits for the transaction that acquired the lock to complete. Transaction two is now waiting on transaction one to complete before it continues execution. Now, transaction one executes:

Transaction one attempts to acquire a row-level lock on the specified row, but it cannot: transaction two already holds such a lock. So it waits for transaction two to complete. Thus, transaction one is blocked on transaction two, and transaction two is blocked on transaction one: a deadlock condition. PostgreSQL will detect this situation and abort one of the transactions.

The best defense against deadlocks is generally to avoid them by being certain that all applications using a database acquire locks on multiple objects in a consistent order. In the example above, if both transactions had updated the rows in the same order, no deadlock would have occurred. One should also ensure that the first lock acquired on an object in a transaction is the most restrictive mode that will be needed for that object. If it is not feasible to verify this in advance, then deadlocks can be handled on-the-fly by retrying transactions that abort due to deadlocks.

So long as no deadlock situation is detected, a transaction seeking either a table-level or row-level lock will wait indefinitely for conflicting locks to be released. This means it is a bad idea for applications to hold transactions open for long periods of time (e.g., while waiting for user input).

PostgreSQL provides a means for creating locks that have application-defined meanings. These are called advisory locks, because the system does not enforce their use — it is up to the application to use them correctly. Advisory locks can be useful for locking strategies that are an awkward fit for the MVCC model. For example, a common use of advisory locks is to emulate pessimistic locking strategies typical of so-called “flat file” data management systems. While a flag stored in a table could be used for the same purpose, advisory locks are faster, avoid table bloat, and are automatically cleaned up by the server at the end of the session.

There are two ways to acquire an advisory lock in PostgreSQL: at session level or at transaction level. Once acquired at session level, an advisory lock is held until explicitly released or the session ends. Unlike standard lock requests, session-level advisory lock requests do not honor transaction semantics: a lock acquired during a transaction that is later rolled back will still be held following the rollback, and likewise an unlock is effective even if the calling transaction fails later. A lock can be acquired multiple times by its owning process; for each completed lock request there must be a corresponding unlock request before the lock is actually released. Transaction-level lock requests, on the other hand, behave more like regular lock requests: they are automatically released at the end of the transaction, and there is no explicit unlock operation. This behavior is often more convenient than the session-level behavior for short-term usage of an advisory lock. Session-level and transaction-level lock requests for the same advisory lock identifier will block each other in the expected way. If a session already holds a given advisory lock, additional requests by it will always succeed, even if other sessions are awaiting the lock; this statement is true regardless of whether the existing lock hold and new request are at session level or transaction level.

Like all locks in PostgreSQL, a complete list of advisory locks currently held by any session can be found in the pg_locks system view.

Both advisory locks and regular locks are stored in a shared memory pool whose size is defined by the configuration variables max_locks_per_transaction and max_connections. Care must be taken not to exhaust this memory or the server will be unable to grant any locks at all. This imposes an upper limit on the number of advisory locks grantable by the server, typically in the tens to hundreds of thousands depending on how the server is configured.

In certain cases using advisory locking methods, especially in queries involving explicit ordering and LIMIT clauses, care must be taken to control the locks acquired because of the order in which SQL expressions are evaluated. For example:

In the above queries, the second form is dangerous because the LIMIT is not guaranteed to be applied before the locking function is executed. This might cause some locks to be acquired that the application was not expecting, and hence would fail to release (until it ends the session). From the point of view of the application, such locks would be dangling, although still viewable in pg_locks.

The functions provided to manipulate advisory locks are described in Section 9.28.10.

Examples:

Example 1 (unknown):

UPDATE accounts SET balance = balance + 100.00 WHERE acctnum = 11111;

Example 2 (unknown):

UPDATE accounts SET balance = balance + 100.00 WHERE acctnum = 22222;
UPDATE accounts SET balance = balance - 100.00 WHERE acctnum = 11111;

Example 3 (unknown):

UPDATE accounts SET balance = balance - 100.00 WHERE acctnum = 22222;

Example 4 (unknown):

SELECT pg_advisory_lock(id) FROM foo WHERE id = 12345; -- ok
SELECT pg_advisory_lock(id) FROM foo WHERE id > 12345 LIMIT 100; -- danger!
SELECT pg_advisory_lock(q.id) FROM
(
  SELECT id FROM foo WHERE id > 12345 LIMIT 100
) q; -- ok

---

PostgreSQL: Documentation: 18: Chapter 36. Extending SQL

URL: https://www.postgresql.org/docs/current/extend.html

Contents:

• Chapter 36. Extending SQL

In the sections that follow, we will discuss how you can extend the PostgreSQL SQL query language by adding:

functions (starting in Section 36.3)

aggregates (starting in Section 36.12)

data types (starting in Section 36.13)

operators (starting in Section 36.14)

operator classes for indexes (starting in Section 36.16)

packages of related objects (starting in Section 36.17)

---

PostgreSQL: Documentation: 18: 34.6. pgtypes Library

URL: https://www.postgresql.org/docs/current/ecpg-pgtypes.html

Contents:

• 34.6. pgtypes Library #
  • 34.6.1. Character Strings #
  • 34.6.2. The numeric Type #
  • 34.6.3. The date Type #
  • 34.6.4. The timestamp Type #
  • 34.6.5. The interval Type #
  • 34.6.6. The decimal Type #
  • 34.6.7. errno Values of pgtypeslib #
  • 34.6.8. Special Constants of pgtypeslib #

The pgtypes library maps PostgreSQL database types to C equivalents that can be used in C programs. It also offers functions to do basic calculations with those types within C, i.e., without the help of the PostgreSQL server. See the following example:

Some functions such as PGTYPESnumeric_to_asc return a pointer to a freshly allocated character string. These results should be freed with PGTYPESchar_free instead of free. (This is important only on Windows, where memory allocation and release sometimes need to be done by the same library.)

The numeric type offers to do calculations with arbitrary precision. See Section 8.1 for the equivalent type in the PostgreSQL server. Because of the arbitrary precision this variable needs to be able to expand and shrink dynamically. That's why you can only create numeric variables on the heap, by means of the PGTYPESnumeric_new and PGTYPESnumeric_free functions. The decimal type, which is similar but limited in precision, can be created on the stack as well as on the heap.

The following functions can be used to work with the numeric type:

Request a pointer to a newly allocated numeric variable.

Free a numeric type, release all of its memory.

Parse a numeric type from its string notation.

Valid formats are for example: -2, .794, +3.44, 592.49E07 or -32.84e-4. If the value could be parsed successfully, a valid pointer is returned, else the NULL pointer. At the moment ECPG always parses the complete string and so it currently does not support to store the address of the first invalid character in *endptr. You can safely set endptr to NULL.

Returns a pointer to a string allocated by malloc that contains the string representation of the numeric type num.

The numeric value will be printed with dscale decimal digits, with rounding applied if necessary. The result must be freed with PGTYPESchar_free().

Add two numeric variables into a third one.

The function adds the variables var1 and var2 into the result variable result. The function returns 0 on success and -1 in case of error.

Subtract two numeric variables and return the result in a third one.

The function subtracts the variable var2 from the variable var1. The result of the operation is stored in the variable result. The function returns 0 on success and -1 in case of error.

Multiply two numeric variables and return the result in a third one.

The function multiplies the variables var1 and var2. The result of the operation is stored in the variable result. The function returns 0 on success and -1 in case of error.

Divide two numeric variables and return the result in a third one.

The function divides the variables var1 by var2. The result of the operation is stored in the variable result. The function returns 0 on success and -1 in case of error.

Compare two numeric variables.

This function compares two numeric variables. In case of error, INT_MAX is returned. On success, the function returns one of three possible results:

1, if var1 is bigger than var2

-1, if var1 is smaller than var2

0, if var1 and var2 are equal

Convert an int variable to a numeric variable.

This function accepts a variable of type signed int and stores it in the numeric variable var. Upon success, 0 is returned and -1 in case of a failure.

Convert a long int variable to a numeric variable.

This function accepts a variable of type signed long int and stores it in the numeric variable var. Upon success, 0 is returned and -1 in case of a failure.

Copy over one numeric variable into another one.

This function copies over the value of the variable that src points to into the variable that dst points to. It returns 0 on success and -1 if an error occurs.

Convert a variable of type double to a numeric.

This function accepts a variable of type double and stores the result in the variable that dst points to. It returns 0 on success and -1 if an error occurs.

Convert a variable of type numeric to double.

The function converts the numeric value from the variable that nv points to into the double variable that dp points to. It returns 0 on success and -1 if an error occurs, including overflow. On overflow, the global variable errno will be set to PGTYPES_NUM_OVERFLOW additionally.

Convert a variable of type numeric to int.

The function converts the numeric value from the variable that nv points to into the integer variable that ip points to. It returns 0 on success and -1 if an error occurs, including overflow. On overflow, the global variable errno will be set to PGTYPES_NUM_OVERFLOW additionally.

Convert a variable of type numeric to long.

The function converts the numeric value from the variable that nv points to into the long integer variable that lp points to. It returns 0 on success and -1 if an error occurs, including overflow and underflow. On overflow, the global variable errno will be set to PGTYPES_NUM_OVERFLOW and on underflow errno will be set to PGTYPES_NUM_UNDERFLOW.

Convert a variable of type numeric to decimal.

The function converts the numeric value from the variable that src points to into the decimal variable that dst points to. It returns 0 on success and -1 if an error occurs, including overflow. On overflow, the global variable errno will be set to PGTYPES_NUM_OVERFLOW additionally.

Convert a variable of type decimal to numeric.

The function converts the decimal value from the variable that src points to into the numeric variable that dst points to. It returns 0 on success and -1 if an error occurs. Since the decimal type is implemented as a limited version of the numeric type, overflow cannot occur with this conversion.

The date type in C enables your programs to deal with data of the SQL type date. See Section 8.5 for the equivalent type in the PostgreSQL server.

The following functions can be used to work with the date type:

Extract the date part from a timestamp.

The function receives a timestamp as its only argument and returns the extracted date part from this timestamp.

Parse a date from its textual representation.

The function receives a C char* string str and a pointer to a C char* string endptr. At the moment ECPG always parses the complete string and so it currently does not support to store the address of the first invalid character in *endptr. You can safely set endptr to NULL.

Note that the function always assumes MDY-formatted dates and there is currently no variable to change that within ECPG.

Table 34.2 shows the allowed input formats.

Table 34.2. Valid Input Formats for PGTYPESdate_from_asc

Return the textual representation of a date variable.

The function receives the date dDate as its only parameter. It will output the date in the form 1999-01-18, i.e., in the YYYY-MM-DD format. The result must be freed with PGTYPESchar_free().

Extract the values for the day, the month and the year from a variable of type date.

The function receives the date d and a pointer to an array of 3 integer values mdy. The variable name indicates the sequential order: mdy[0] will be set to contain the number of the month, mdy[1] will be set to the value of the day and mdy[2] will contain the year.

Create a date value from an array of 3 integers that specify the day, the month and the year of the date.

The function receives the array of the 3 integers (mdy) as its first argument and as its second argument a pointer to a variable of type date that should hold the result of the operation.

Return a number representing the day of the week for a date value.

The function receives the date variable d as its only argument and returns an integer that indicates the day of the week for this date.

Get the current date.

The function receives a pointer to a date variable (d) that it sets to the current date.

Convert a variable of type date to its textual representation using a format mask.

The function receives the date to convert (dDate), the format mask (fmtstring) and the string that will hold the textual representation of the date (outbuf).

On success, 0 is returned and a negative value if an error occurred.

The following literals are the field specifiers you can use:

dd - The number of the day of the month.

mm - The number of the month of the year.

yy - The number of the year as a two digit number.

yyyy - The number of the year as a four digit number.

ddd - The name of the day (abbreviated).

mmm - The name of the month (abbreviated).

All other characters are copied 1:1 to the output string.

Table 34.3 indicates a few possible formats. This will give you an idea of how to use this function. All output lines are based on the same date: November 23, 1959.

Table 34.3. Valid Input Formats for PGTYPESdate_fmt_asc

Use a format mask to convert a C char* string to a value of type date.

The function receives a pointer to the date value that should hold the result of the operation (d), the format mask to use for parsing the date (fmt) and the C char* string containing the textual representation of the date (str). The textual representation is expected to match the format mask. However you do not need to have a 1:1 mapping of the string to the format mask. The function only analyzes the sequential order and looks for the literals yy or yyyy that indicate the position of the year, mm to indicate the position of the month and dd to indicate the position of the day.

Table 34.4 indicates a few possible formats. This will give you an idea of how to use this function.

Table 34.4. Valid Input Formats for rdefmtdate

The timestamp type in C enables your programs to deal with data of the SQL type timestamp. See Section 8.5 for the equivalent type in the PostgreSQL server.

The following functions can be used to work with the timestamp type:

Parse a timestamp from its textual representation into a timestamp variable.

The function receives the string to parse (str) and a pointer to a C char* (endptr). At the moment ECPG always parses the complete string and so it currently does not support to store the address of the first invalid character in *endptr. You can safely set endptr to NULL.

The function returns the parsed timestamp on success. On error, PGTYPESInvalidTimestamp is returned and errno is set to PGTYPES_TS_BAD_TIMESTAMP. See PGTYPESInvalidTimestamp for important notes on this value.

In general, the input string can contain any combination of an allowed date specification, a whitespace character and an allowed time specification. Note that time zones are not supported by ECPG. It can parse them but does not apply any calculation as the PostgreSQL server does for example. Timezone specifiers are silently discarded.

Table 34.5 contains a few examples for input strings.

Table 34.5. Valid Input Formats for PGTYPEStimestamp_from_asc

Converts a date to a C char* string.

The function receives the timestamp tstamp as its only argument and returns an allocated string that contains the textual representation of the timestamp. The result must be freed with PGTYPESchar_free().

Retrieve the current timestamp.

The function retrieves the current timestamp and saves it into the timestamp variable that ts points to.

Convert a timestamp variable to a C char* using a format mask.

The function receives a pointer to the timestamp to convert as its first argument (ts), a pointer to the output buffer (output), the maximal length that has been allocated for the output buffer (str_len) and the format mask to use for the conversion (fmtstr).

Upon success, the function returns 0 and a negative value if an error occurred.

You can use the following format specifiers for the format mask. The format specifiers are the same ones that are used in the strftime function in libc. Any non-format specifier will be copied into the output buffer.

%A - is replaced by national representation of the full weekday name.

%a - is replaced by national representation of the abbreviated weekday name.

%B - is replaced by national representation of the full month name.

%b - is replaced by national representation of the abbreviated month name.

%C - is replaced by (year / 100) as decimal number; single digits are preceded by a zero.

%c - is replaced by national representation of time and date.

%D - is equivalent to %m/%d/%y.

%d - is replaced by the day of the month as a decimal number (01–31).

%E* %O* - POSIX locale extensions. The sequences %Ec %EC %Ex %EX %Ey %EY %Od %Oe %OH %OI %Om %OM %OS %Ou %OU %OV %Ow %OW %Oy are supposed to provide alternative representations.

Additionally %OB implemented to represent alternative months names (used standalone, without day mentioned).

%e - is replaced by the day of month as a decimal number (1–31); single digits are preceded by a blank.

%F - is equivalent to %Y-%m-%d.

%G - is replaced by a year as a decimal number with century. This year is the one that contains the greater part of the week (Monday as the first day of the week).

%g - is replaced by the same year as in %G, but as a decimal number without century (00–99).

%H - is replaced by the hour (24-hour clock) as a decimal number (00–23).

%I - is replaced by the hour (12-hour clock) as a decimal number (01–12).

%j - is replaced by the day of the year as a decimal number (001–366).

%k - is replaced by the hour (24-hour clock) as a decimal number (0–23); single digits are preceded by a blank.

%l - is replaced by the hour (12-hour clock) as a decimal number (1–12); single digits are preceded by a blank.

%M - is replaced by the minute as a decimal number (00–59).

%m - is replaced by the month as a decimal number (01–12).

%n - is replaced by a newline.

%O* - the same as %E*.

%p - is replaced by national representation of either “ante meridiem” or “post meridiem” as appropriate.

%R - is equivalent to %H:%M.

%r - is equivalent to %I:%M:%S %p.

%S - is replaced by the second as a decimal number (00–60).

%s - is replaced by the number of seconds since the Epoch, UTC.

%T - is equivalent to %H:%M:%S

%t - is replaced by a tab.

%U - is replaced by the week number of the year (Sunday as the first day of the week) as a decimal number (00–53).

%u - is replaced by the weekday (Monday as the first day of the week) as a decimal number (1–7).

%V - is replaced by the week number of the year (Monday as the first day of the week) as a decimal number (01–53). If the week containing January 1 has four or more days in the new year, then it is week 1; otherwise it is the last week of the previous year, and the next week is week 1.

%v - is equivalent to %e-%b-%Y.

%W - is replaced by the week number of the year (Monday as the first day of the week) as a decimal number (00–53).

%w - is replaced by the weekday (Sunday as the first day of the week) as a decimal number (0–6).

%X - is replaced by national representation of the time.

%x - is replaced by national representation of the date.

%Y - is replaced by the year with century as a decimal number.

%y - is replaced by the year without century as a decimal number (00–99).

%Z - is replaced by the time zone name.

%z - is replaced by the time zone offset from UTC; a leading plus sign stands for east of UTC, a minus sign for west of UTC, hours and minutes follow with two digits each and no delimiter between them (common form for RFC 822 date headers).

%+ - is replaced by national representation of the date and time.

%-* - GNU libc extension. Do not do any padding when performing numerical outputs.

$_* - GNU libc extension. Explicitly specify space for padding.

%0* - GNU libc extension. Explicitly specify zero for padding.

%% - is replaced by %.

Subtract one timestamp from another one and save the result in a variable of type interval.

The function will subtract the timestamp variable that ts2 points to from the timestamp variable that ts1 points to and will store the result in the interval variable that iv points to.

Upon success, the function returns 0 and a negative value if an error occurred.

Parse a timestamp value from its textual representation using a formatting mask.

The function receives the textual representation of a timestamp in the variable str as well as the formatting mask to use in the variable fmt. The result will be stored in the variable that d points to.

If the formatting mask fmt is NULL, the function will fall back to the default formatting mask which is %Y-%m-%d %H:%M:%S.

This is the reverse function to PGTYPEStimestamp_fmt_asc. See the documentation there in order to find out about the possible formatting mask entries.

Add an interval variable to a timestamp variable.

The function receives a pointer to a timestamp variable tin and a pointer to an interval variable span. It adds the interval to the timestamp and saves the resulting timestamp in the variable that tout points to.

Upon success, the function returns 0 and a negative value if an error occurred.

Subtract an interval variable from a timestamp variable.

The function subtracts the interval variable that span points to from the timestamp variable that tin points to and saves the result into the variable that tout points to.

Upon success, the function returns 0 and a negative value if an error occurred.

The interval type in C enables your programs to deal with data of the SQL type interval. See Section 8.5 for the equivalent type in the PostgreSQL server.

The following functions can be used to work with the interval type:

Return a pointer to a newly allocated interval variable.

Release the memory of a previously allocated interval variable.

Parse an interval from its textual representation.

The function parses the input string str and returns a pointer to an allocated interval variable. At the moment ECPG always parses the complete string and so it currently does not support to store the address of the first invalid character in *endptr. You can safely set endptr to NULL.

Convert a variable of type interval to its textual representation.

The function converts the interval variable that span points to into a C char*. The output looks like this example: @ 1 day 12 hours 59 mins 10 secs. The result must be freed with PGTYPESchar_free().

Copy a variable of type interval.

The function copies the interval variable that intvlsrc points to into the variable that intvldest points to. Note that you need to allocate the memory for the destination variable before.

The decimal type is similar to the numeric type. However it is limited to a maximum precision of 30 significant digits. In contrast to the numeric type which can be created on the heap only, the decimal type can be created either on the stack or on the heap (by means of the functions PGTYPESdecimal_new and PGTYPESdecimal_free). There are a lot of other functions that deal with the decimal type in the Informix compatibility mode described in Section 34.15.

The following functions can be used to work with the decimal type and are not only contained in the libcompat library.

Request a pointer to a newly allocated decimal variable.

Free a decimal type, release all of its memory.

An argument should contain a numeric variable (or point to a numeric variable) but in fact its in-memory representation was invalid.

An overflow occurred. Since the numeric type can deal with almost arbitrary precision, converting a numeric variable into other types might cause overflow.

An underflow occurred. Since the numeric type can deal with almost arbitrary precision, converting a numeric variable into other types might cause underflow.

A division by zero has been attempted.

An invalid date string was passed to the PGTYPESdate_from_asc function.

Invalid arguments were passed to the PGTYPESdate_defmt_asc function.

An invalid token in the input string was found by the PGTYPESdate_defmt_asc function.

An invalid interval string was passed to the PGTYPESinterval_from_asc function, or an invalid interval value was passed to the PGTYPESinterval_to_asc function.

There was a mismatch in the day/month/year assignment in the PGTYPESdate_defmt_asc function.

An invalid day of the month value was found by the PGTYPESdate_defmt_asc function.

An invalid month value was found by the PGTYPESdate_defmt_asc function.

An invalid timestamp string pass passed to the PGTYPEStimestamp_from_asc function, or an invalid timestamp value was passed to the PGTYPEStimestamp_to_asc function.

An infinite timestamp value was encountered in a context that cannot handle it.

A value of type timestamp representing an invalid time stamp. This is returned by the function PGTYPEStimestamp_from_asc on parse error. Note that due to the internal representation of the timestamp data type, PGTYPESInvalidTimestamp is also a valid timestamp at the same time. It is set to 1899-12-31 23:59:59. In order to detect errors, make sure that your application does not only test for PGTYPESInvalidTimestamp but also for errno != 0 after each call to PGTYPEStimestamp_from_asc.

Examples:

Example 1 (unknown):

EXEC SQL BEGIN DECLARE SECTION;
   date date1;
   timestamp ts1, tsout;
   interval iv1;
   char *out;
EXEC SQL END DECLARE SECTION;

PGTYPESdate_today(&date1);
EXEC SQL SELECT started, duration INTO :ts1, :iv1 FROM datetbl WHERE d=:date1;
PGTYPEStimestamp_add_interval(&ts1, &iv1, &tsout);
out = PGTYPEStimestamp_to_asc(&tsout);
printf("Started + duration: %s\n", out);
PGTYPESchar_free(out);

Example 2 (unknown):

numeric *PGTYPESnumeric_new(void);

Example 3 (unknown):

void PGTYPESnumeric_free(numeric *var);

Example 4 (unknown):

numeric *PGTYPESnumeric_from_asc(char *str, char **endptr);

---

PostgreSQL: Documentation: 18: 19.11. Client Connection Defaults

URL: https://www.postgresql.org/docs/current/runtime-config-client.html

Contents:

• 19.11. Client Connection Defaults #
  • 19.11.1. Statement Behavior #
  • Note
  • 19.11.2. Locale and Formatting #
  • Note
  • 19.11.3. Shared Library Preloading #
  • Note
  • 19.11.4. Other Defaults #

Controls which message levels are sent to the client. Valid values are DEBUG5, DEBUG4, DEBUG3, DEBUG2, DEBUG1, LOG, NOTICE, WARNING, and ERROR. Each level includes all the levels that follow it. The later the level, the fewer messages are sent. The default is NOTICE. Note that LOG has a different rank here than in log_min_messages.

INFO level messages are always sent to the client.

This variable specifies the order in which schemas are searched when an object (table, data type, function, etc.) is referenced by a simple name with no schema specified. When there are objects of identical names in different schemas, the one found first in the search path is used. An object that is not in any of the schemas in the search path can only be referenced by specifying its containing schema with a qualified (dotted) name.

The value for search_path must be a comma-separated list of schema names. Any name that is not an existing schema, or is a schema for which the user does not have USAGE permission, is silently ignored.

If one of the list items is the special name $user, then the schema having the name returned by CURRENT_USER is substituted, if there is such a schema and the user has USAGE permission for it. (If not, $user is ignored.)

The system catalog schema, pg_catalog, is always searched, whether it is mentioned in the path or not. If it is mentioned in the path then it will be searched in the specified order. If pg_catalog is not in the path then it will be searched before searching any of the path items.

Likewise, the current session's temporary-table schema, pg_temp_nnn, is always searched if it exists. It can be explicitly listed in the path by using the alias pg_temp. If it is not listed in the path then it is searched first (even before pg_catalog). However, the temporary schema is only searched for relation (table, view, sequence, etc.) and data type names. It is never searched for function or operator names.

When objects are created without specifying a particular target schema, they will be placed in the first valid schema named in search_path. An error is reported if the search path is empty.

The default value for this parameter is "$user", public. This setting supports shared use of a database (where no users have private schemas, and all share use of public), private per-user schemas, and combinations of these. Other effects can be obtained by altering the default search path setting, either globally or per-user.

For more information on schema handling, see Section 5.10. In particular, the default configuration is suitable only when the database has a single user or a few mutually-trusting users.

The current effective value of the search path can be examined via the SQL function current_schemas (see Section 9.27). This is not quite the same as examining the value of search_path, since current_schemas shows how the items appearing in search_path were resolved.

This variable controls whether to raise an error in lieu of applying a row security policy. When set to on, policies apply normally. When set to off, queries fail which would otherwise apply at least one policy. The default is on. Change to off where limited row visibility could cause incorrect results; for example, pg_dump makes that change by default. This variable has no effect on roles which bypass every row security policy, to wit, superusers and roles with the BYPASSRLS attribute.

For more information on row security policies, see CREATE POLICY.

This parameter specifies the default table access method to use when creating tables or materialized views if the CREATE command does not explicitly specify an access method, or when SELECT ... INTO is used, which does not allow specifying a table access method. The default is heap.

This variable specifies the default tablespace in which to create objects (tables and indexes) when a CREATE command does not explicitly specify a tablespace.

The value is either the name of a tablespace, or an empty string to specify using the default tablespace of the current database. If the value does not match the name of any existing tablespace, PostgreSQL will automatically use the default tablespace of the current database. If a nondefault tablespace is specified, the user must have CREATE privilege for it, or creation attempts will fail.

This variable is not used for temporary tables; for them, temp_tablespaces is consulted instead.

This variable is also not used when creating databases. By default, a new database inherits its tablespace setting from the template database it is copied from.

If this parameter is set to a value other than the empty string when a partitioned table is created, the partitioned table's tablespace will be set to that value, which will be used as the default tablespace for partitions created in the future, even if default_tablespace has changed since then.

For more information on tablespaces, see Section 22.6.

This variable sets the default TOAST compression method for values of compressible columns. (This can be overridden for individual columns by setting the COMPRESSION column option in CREATE TABLE or ALTER TABLE.) The supported compression methods are pglz and (if PostgreSQL was compiled with --with-lz4) lz4. The default is pglz.

This variable specifies tablespaces in which to create temporary objects (temp tables and indexes on temp tables) when a CREATE command does not explicitly specify a tablespace. Temporary files for purposes such as sorting large data sets are also created in these tablespaces.

The value is a list of names of tablespaces. When there is more than one name in the list, PostgreSQL chooses a random member of the list each time a temporary object is to be created; except that within a transaction, successively created temporary objects are placed in successive tablespaces from the list. If the selected element of the list is an empty string, PostgreSQL will automatically use the default tablespace of the current database instead.

When temp_tablespaces is set interactively, specifying a nonexistent tablespace is an error, as is specifying a tablespace for which the user does not have CREATE privilege. However, when using a previously set value, nonexistent tablespaces are ignored, as are tablespaces for which the user lacks CREATE privilege. In particular, this rule applies when using a value set in postgresql.conf.

The default value is an empty string, which results in all temporary objects being created in the default tablespace of the current database.

See also default_tablespace.

This parameter is normally on. When set to off, it disables validation of the routine body string during CREATE FUNCTION and CREATE PROCEDURE. Disabling validation avoids side effects of the validation process, in particular preventing false positives due to problems such as forward references. Set this parameter to off before loading functions on behalf of other users; pg_dump does so automatically.

Each SQL transaction has an isolation level, which can be either “read uncommitted”, “read committed”, “repeatable read”, or “serializable”. This parameter controls the default isolation level of each new transaction. The default is “read committed”.

Consult Chapter 13 and SET TRANSACTION for more information.

A read-only SQL transaction cannot alter non-temporary tables. This parameter controls the default read-only status of each new transaction. The default is off (read/write).

Consult SET TRANSACTION for more information.

When running at the serializable isolation level, a deferrable read-only SQL transaction may be delayed before it is allowed to proceed. However, once it begins executing it does not incur any of the overhead required to ensure serializability; so serialization code will have no reason to force it to abort because of concurrent updates, making this option suitable for long-running read-only transactions.

This parameter controls the default deferrable status of each new transaction. It currently has no effect on read-write transactions or those operating at isolation levels lower than serializable. The default is off.

Consult SET TRANSACTION for more information.

This parameter reflects the current transaction's isolation level. At the beginning of each transaction, it is set to the current value of default_transaction_isolation. Any subsequent attempt to change it is equivalent to a SET TRANSACTION command.

This parameter reflects the current transaction's read-only status. At the beginning of each transaction, it is set to the current value of default_transaction_read_only. Any subsequent attempt to change it is equivalent to a SET TRANSACTION command.

This parameter reflects the current transaction's deferrability status. At the beginning of each transaction, it is set to the current value of default_transaction_deferrable. Any subsequent attempt to change it is equivalent to a SET TRANSACTION command.

Controls firing of replication-related triggers and rules for the current session. Possible values are origin (the default), replica and local. Setting this parameter results in discarding any previously cached query plans. Only superusers and users with the appropriate SET privilege can change this setting.

The intended use of this setting is that logical replication systems set it to replica when they are applying replicated changes. The effect of that will be that triggers and rules (that have not been altered from their default configuration) will not fire on the replica. See the ALTER TABLE clauses ENABLE TRIGGER and ENABLE RULE for more information.

PostgreSQL treats the settings origin and local the same internally. Third-party replication systems may use these two values for their internal purposes, for example using local to designate a session whose changes should not be replicated.

Since foreign keys are implemented as triggers, setting this parameter to replica also disables all foreign key checks, which can leave data in an inconsistent state if improperly used.

Abort any statement that takes more than the specified amount of time. If log_min_error_statement is set to ERROR or lower, the statement that timed out will also be logged. If this value is specified without units, it is taken as milliseconds. A value of zero (the default) disables the timeout.

The timeout is measured from the time a command arrives at the server until it is completed by the server. If multiple SQL statements appear in a single simple-query message, the timeout is applied to each statement separately. (PostgreSQL versions before 13 usually treated the timeout as applying to the whole query string.) In extended query protocol, the timeout starts running when any query-related message (Parse, Bind, Execute, Describe) arrives, and it is canceled by completion of an Execute or Sync message.

Setting statement_timeout in postgresql.conf is not recommended because it would affect all sessions.

Terminate any session that spans longer than the specified amount of time in a transaction. The limit applies both to explicit transactions (started with BEGIN) and to an implicitly started transaction corresponding to a single statement. If this value is specified without units, it is taken as milliseconds. A value of zero (the default) disables the timeout.

If transaction_timeout is shorter or equal to idle_in_transaction_session_timeout or statement_timeout then the longer timeout is ignored.

Setting transaction_timeout in postgresql.conf is not recommended because it would affect all sessions.

Prepared transactions are not subject to this timeout.

Abort any statement that waits longer than the specified amount of time while attempting to acquire a lock on a table, index, row, or other database object. The time limit applies separately to each lock acquisition attempt. The limit applies both to explicit locking requests (such as LOCK TABLE, or SELECT FOR UPDATE without NOWAIT) and to implicitly-acquired locks. If this value is specified without units, it is taken as milliseconds. A value of zero (the default) disables the timeout.

Unlike statement_timeout, this timeout can only occur while waiting for locks. Note that if statement_timeout is nonzero, it is rather pointless to set lock_timeout to the same or larger value, since the statement timeout would always trigger first. If log_min_error_statement is set to ERROR or lower, the statement that timed out will be logged.

Setting lock_timeout in postgresql.conf is not recommended because it would affect all sessions.

Terminate any session that has been idle (that is, waiting for a client query) within an open transaction for longer than the specified amount of time. If this value is specified without units, it is taken as milliseconds. A value of zero (the default) disables the timeout.

This option can be used to ensure that idle sessions do not hold locks for an unreasonable amount of time. Even when no significant locks are held, an open transaction prevents vacuuming away recently-dead tuples that may be visible only to this transaction; so remaining idle for a long time can contribute to table bloat. See Section 24.1 for more details.

Terminate any session that has been idle (that is, waiting for a client query), but not within an open transaction, for longer than the specified amount of time. If this value is specified without units, it is taken as milliseconds. A value of zero (the default) disables the timeout.

Unlike the case with an open transaction, an idle session without a transaction imposes no large costs on the server, so there is less need to enable this timeout than idle_in_transaction_session_timeout.

Be wary of enforcing this timeout on connections made through connection-pooling software or other middleware, as such a layer may not react well to unexpected connection closure. It may be helpful to enable this timeout only for interactive sessions, perhaps by applying it only to particular users.

Sets the output format for values of type bytea. Valid values are hex (the default) and escape (the traditional PostgreSQL format). See Section 8.4 for more information. The bytea type always accepts both formats on input, regardless of this setting.

Sets how binary values are to be encoded in XML. This applies for example when bytea values are converted to XML by the functions xmlelement or xmlforest. Possible values are base64 and hex, which are both defined in the XML Schema standard. The default is base64. For further information about XML-related functions, see Section 9.15.

The actual choice here is mostly a matter of taste, constrained only by possible restrictions in client applications. Both methods support all possible values, although the hex encoding will be somewhat larger than the base64 encoding.

Sets whether DOCUMENT or CONTENT is implicit when converting between XML and character string values. See Section 8.13 for a description of this. Valid values are DOCUMENT and CONTENT. The default is CONTENT.

According to the SQL standard, the command to set this option is

This syntax is also available in PostgreSQL.

Sets the maximum size of a GIN index's pending list, which is used when fastupdate is enabled. If the list grows larger than this maximum size, it is cleaned up by moving the entries in it to the index's main GIN data structure in bulk. If this value is specified without units, it is taken as kilobytes. The default is four megabytes (4MB). This setting can be overridden for individual GIN indexes by changing index storage parameters. See Section 65.4.4.1 and Section 65.4.5 for more information.

If a user who has CREATEROLE but not SUPERUSER creates a role, and if this is set to a non-empty value, the newly-created role will be granted to the creating user with the options specified. The value must be set, inherit, or a comma-separated list of these. The default value is an empty string, which disables the feature.

The purpose of this option is to allow a CREATEROLE user who is not a superuser to automatically inherit, or automatically gain the ability to SET ROLE to, any created users. Since a CREATEROLE user is always implicitly granted ADMIN OPTION on created roles, that user could always execute a GRANT statement that would achieve the same effect as this setting. However, it can be convenient for usability reasons if the grant happens automatically. A superuser automatically inherits the privileges of every role and can always SET ROLE to any role, and this setting can be used to produce a similar behavior for CREATEROLE users for users which they create.

Allow temporarily disabling execution of event triggers in order to troubleshoot and repair faulty event triggers. All event triggers will be disabled by setting it to false. Setting the value to true allows all event triggers to fire, this is the default value. Only superusers and users with the appropriate SET privilege can change this setting.

Set relation kinds for which access to non-system relations is prohibited. The value takes the form of a comma-separated list of relation kinds. Currently, the supported relation kinds are view and foreign-table.

Sets the display format for date and time values, as well as the rules for interpreting ambiguous date input values. For historical reasons, this variable contains two independent components: the output format specification (ISO, Postgres, SQL, or German) and the input/output specification for year/month/day ordering (DMY, MDY, or YMD). These can be set separately or together. The keywords Euro and European are synonyms for DMY; the keywords US, NonEuro, and NonEuropean are synonyms for MDY. See Section 8.5 for more information. The built-in default is ISO, MDY, but initdb will initialize the configuration file with a setting that corresponds to the behavior of the chosen lc_time locale.

Sets the display format for interval values. The value sql_standard will produce output matching SQL standard interval literals. The value postgres (which is the default) will produce output matching PostgreSQL releases prior to 8.4 when the DateStyle parameter was set to ISO. The value postgres_verbose will produce output matching PostgreSQL releases prior to 8.4 when the DateStyle parameter was set to non-ISO output. The value iso_8601 will produce output matching the time interval “format with designators” defined in section 4.4.3.2 of ISO 8601.

The IntervalStyle parameter also affects the interpretation of ambiguous interval input. See Section 8.5.4 for more information.

Sets the time zone for displaying and interpreting time stamps. The built-in default is GMT, but that is typically overridden in postgresql.conf; initdb will install a setting there corresponding to its system environment. See Section 8.5.3 for more information.

Sets the collection of additional time zone abbreviations that will be accepted by the server for datetime input (beyond any abbreviations defined by the current TimeZone setting). The default is 'Default', which is a collection that works in most of the world; there are also 'Australia' and 'India', and other collections can be defined for a particular installation. See Section B.4 for more information.

This parameter adjusts the number of digits used for textual output of floating-point values, including float4, float8, and geometric data types.

If the value is 1 (the default) or above, float values are output in shortest-precise format; see Section 8.1.3. The actual number of digits generated depends only on the value being output, not on the value of this parameter. At most 17 digits are required for float8 values, and 9 for float4 values. This format is both fast and precise, preserving the original binary float value exactly when correctly read. For historical compatibility, values up to 3 are permitted.

If the value is zero or negative, then the output is rounded to a given decimal precision. The precision used is the standard number of digits for the type (FLT_DIG or DBL_DIG as appropriate) reduced according to the value of this parameter. (For example, specifying -1 will cause float4 values to be output rounded to 5 significant digits, and float8 values rounded to 14 digits.) This format is slower and does not preserve all the bits of the binary float value, but may be more human-readable.

The meaning of this parameter, and its default value, changed in PostgreSQL 12; see Section 8.1.3 for further discussion.

Sets the client-side encoding (character set). The default is to use the database encoding. The character sets supported by the PostgreSQL server are described in Section 23.3.1.

Sets the language in which messages are displayed. Acceptable values are system-dependent; see Section 23.1 for more information. If this variable is set to the empty string (which is the default) then the value is inherited from the execution environment of the server in a system-dependent way.

On some systems, this locale category does not exist. Setting this variable will still work, but there will be no effect. Also, there is a chance that no translated messages for the desired language exist. In that case you will continue to see the English messages.

Only superusers and users with the appropriate SET privilege can change this setting.

Sets the locale to use for formatting monetary amounts, for example with the to_char family of functions. Acceptable values are system-dependent; see Section 23.1 for more information. If this variable is set to the empty string (which is the default) then the value is inherited from the execution environment of the server in a system-dependent way.

Sets the locale to use for formatting numbers, for example with the to_char family of functions. Acceptable values are system-dependent; see Section 23.1 for more information. If this variable is set to the empty string (which is the default) then the value is inherited from the execution environment of the server in a system-dependent way.

Sets the locale to use for formatting dates and times, for example with the to_char family of functions. Acceptable values are system-dependent; see Section 23.1 for more information. If this variable is set to the empty string (which is the default) then the value is inherited from the execution environment of the server in a system-dependent way.

When ICU locale validation problems are encountered, controls which message level is used to report the problem. Valid values are DISABLED, DEBUG5, DEBUG4, DEBUG3, DEBUG2, DEBUG1, INFO, NOTICE, WARNING, ERROR, and LOG.

If set to DISABLED, does not report validation problems at all. Otherwise reports problems at the given message level. The default is WARNING.

Selects the text search configuration that is used by those variants of the text search functions that do not have an explicit argument specifying the configuration. See Chapter 12 for further information. The built-in default is pg_catalog.simple, but initdb will initialize the configuration file with a setting that corresponds to the chosen lc_ctype locale, if a configuration matching that locale can be identified.

Several settings are available for preloading shared libraries into the server, in order to load additional functionality or achieve performance benefits. For example, a setting of '$libdir/mylib' would cause mylib.so (or on some platforms, mylib.sl) to be preloaded from the installation's standard library directory. The differences between the settings are when they take effect and what privileges are required to change them.

PostgreSQL procedural language libraries can be preloaded in this way, typically by using the syntax '$libdir/plXXX' where XXX is pgsql, perl, tcl, or python.

Only shared libraries specifically intended to be used with PostgreSQL can be loaded this way. Every PostgreSQL-supported library has a “magic block” that is checked to guarantee compatibility. For this reason, non-PostgreSQL libraries cannot be loaded in this way. You might be able to use operating-system facilities such as LD_PRELOAD for that.

In general, refer to the documentation of a specific module for the recommended way to load that module.

This variable specifies one or more shared libraries that are to be preloaded at connection start. It contains a comma-separated list of library names, where each name is interpreted as for the LOAD command. Whitespace between entries is ignored; surround a library name with double quotes if you need to include whitespace or commas in the name. The parameter value only takes effect at the start of the connection. Subsequent changes have no effect. If a specified library is not found, the connection attempt will fail.

This option can be set by any user. Because of that, the libraries that can be loaded are restricted to those appearing in the plugins subdirectory of the installation's standard library directory. (It is the database administrator's responsibility to ensure that only “safe” libraries are installed there.) Entries in local_preload_libraries can specify this directory explicitly, for example $libdir/plugins/mylib, or just specify the library name — mylib would have the same effect as $libdir/plugins/mylib.

The intent of this feature is to allow unprivileged users to load debugging or performance-measurement libraries into specific sessions without requiring an explicit LOAD command. To that end, it would be typical to set this parameter using the PGOPTIONS environment variable on the client or by using ALTER ROLE SET.

However, unless a module is specifically designed to be used in this way by non-superusers, this is usually not the right setting to use. Look at session_preload_libraries instead.

This variable specifies one or more shared libraries that are to be preloaded at connection start. It contains a comma-separated list of library names, where each name is interpreted as for the LOAD command. Whitespace between entries is ignored; surround a library name with double quotes if you need to include whitespace or commas in the name. The parameter value only takes effect at the start of the connection. Subsequent changes have no effect. If a specified library is not found, the connection attempt will fail. Only superusers and users with the appropriate SET privilege can change this setting.

The intent of this feature is to allow debugging or performance-measurement libraries to be loaded into specific sessions without an explicit LOAD command being given. For example, auto_explain could be enabled for all sessions under a given user name by setting this parameter with ALTER ROLE SET. Also, this parameter can be changed without restarting the server (but changes only take effect when a new session is started), so it is easier to add new modules this way, even if they should apply to all sessions.

Unlike shared_preload_libraries, there is no large performance advantage to loading a library at session start rather than when it is first used. There is some advantage, however, when connection pooling is used.

This variable specifies one or more shared libraries to be preloaded at server start. It contains a comma-separated list of library names, where each name is interpreted as for the LOAD command. Whitespace between entries is ignored; surround a library name with double quotes if you need to include whitespace or commas in the name. This parameter can only be set at server start. If a specified library is not found, the server will fail to start.

Some libraries need to perform certain operations that can only take place at postmaster start, such as allocating shared memory, reserving light-weight locks, or starting background workers. Those libraries must be loaded at server start through this parameter. See the documentation of each library for details.

Other libraries can also be preloaded. By preloading a shared library, the library startup time is avoided when the library is first used. However, the time to start each new server process might increase slightly, even if that process never uses the library. So this parameter is recommended only for libraries that will be used in most sessions. Also, changing this parameter requires a server restart, so this is not the right setting to use for short-term debugging tasks, say. Use session_preload_libraries for that instead.

On Windows hosts, preloading a library at server start will not reduce the time required to start each new server process; each server process will re-load all preload libraries. However, shared_preload_libraries is still useful on Windows hosts for libraries that need to perform operations at postmaster start time.

This variable is the name of the JIT provider library to be used (see Section 30.4.2). The default is llvmjit. This parameter can only be set at server start.

If set to a non-existent library, JIT will not be available, but no error will be raised. This allows JIT support to be installed separately from the main PostgreSQL package.

If a dynamically loadable module needs to be opened and the file name specified in the CREATE FUNCTION or LOAD command does not have a directory component (i.e., the name does not contain a slash), the system will search this path for the required file.

The value for dynamic_library_path must be a list of absolute directory paths separated by colons (or semi-colons on Windows). If a list element starts with the special string $libdir, the compiled-in PostgreSQL package library directory is substituted for $libdir; this is where the modules provided by the standard PostgreSQL distribution are installed. (Use pg_config --pkglibdir to find out the name of this directory.) For example:

or, in a Windows environment:

The default value for this parameter is '$libdir'. If the value is set to an empty string, the automatic path search is turned off.

This parameter can be changed at run time by superusers and users with the appropriate SET privilege, but a setting done that way will only persist until the end of the client connection, so this method should be reserved for development purposes. The recommended way to set this parameter is in the postgresql.conf configuration file.

A path to search for extensions, specifically extension control files (name.control). The remaining extension script and secondary control files are then loaded from the same directory where the primary control file was found. See Section 36.17.1 for details.

The value for extension_control_path must be a list of absolute directory paths separated by colons (or semi-colons on Windows). If a list element starts with the special string $system, the compiled-in PostgreSQL extension directory is substituted for $system; this is where the extensions provided by the standard PostgreSQL distribution are installed. (Use pg_config --sharedir to find out the name of this directory.) For example:

or, in a Windows environment:

Note that the specified paths elements are expected to have a subdirectory extension which will contain the .control and .sql files; the extension suffix is automatically appended to each path element.

The default value for this parameter is '$system'. If the value is set to an empty string, the default '$system' is also assumed.

If extensions with equal names are present in multiple directories in the configured path, only the instance found first in the path will be used.

This parameter can be changed at run time by superusers and users with the appropriate SET privilege, but a setting done that way will only persist until the end of the client connection, so this method should be reserved for development purposes. The recommended way to set this parameter is in the postgresql.conf configuration file.

Note that if you set this parameter to be able to load extensions from nonstandard locations, you will most likely also need to set dynamic_library_path to a correspondent location, for example,

Soft upper limit of the size of the set returned by GIN index scans. For more information see Section 65.4.5.

Examples:

Example 1 (unknown):

SET XML OPTION { DOCUMENT | CONTENT };

Example 2 (unknown):

dynamic_library_path = '/usr/local/lib/postgresql:/home/my_project/lib:$libdir'

Example 3 (unknown):

dynamic_library_path = 'C:\tools\postgresql;H:\my_project\lib;$libdir'

Example 4 (unknown):

extension_control_path = '/usr/local/share/postgresql:/home/my_project/share:$system'

---

PostgreSQL: Documentation: 18: 10.2. Operators

URL: https://www.postgresql.org/docs/current/typeconv-oper.html

Contents:

• 10.2. Operators #

The specific operator that is referenced by an operator expression is determined using the following procedure. Note that this procedure is indirectly affected by the precedence of the operators involved, since that will determine which sub-expressions are taken to be the inputs of which operators. See Section 4.1.6 for more information.

Operator Type Resolution

Select the operators to be considered from the pg_operator system catalog. If a non-schema-qualified operator name was used (the usual case), the operators considered are those with the matching name and argument count that are visible in the current search path (see Section 5.10.3). If a qualified operator name was given, only operators in the specified schema are considered.

If the search path finds multiple operators with identical argument types, only the one appearing earliest in the path is considered. Operators with different argument types are considered on an equal footing regardless of search path position.

Check for an operator accepting exactly the input argument types. If one exists (there can be only one exact match in the set of operators considered), use it. Lack of an exact match creates a security hazard when calling, via qualified name 9, any operator found in a schema that permits untrusted users to create objects. In such situations, cast arguments to force an exact match.

If one argument of a binary operator invocation is of the unknown type, then assume it is the same type as the other argument for this check. Invocations involving two unknown inputs, or a prefix operator with an unknown input, will never find a match at this step.

If one argument of a binary operator invocation is of the unknown type and the other is of a domain type, next check to see if there is an operator accepting exactly the domain's base type on both sides; if so, use it.

Look for the best match.

Discard candidate operators for which the input types do not match and cannot be converted (using an implicit conversion) to match. unknown literals are assumed to be convertible to anything for this purpose. If only one candidate remains, use it; else continue to the next step.

If any input argument is of a domain type, treat it as being of the domain's base type for all subsequent steps. This ensures that domains act like their base types for purposes of ambiguous-operator resolution.

Run through all candidates and keep those with the most exact matches on input types. Keep all candidates if none have exact matches. If only one candidate remains, use it; else continue to the next step.

Run through all candidates and keep those that accept preferred types (of the input data type's type category) at the most positions where type conversion will be required. Keep all candidates if none accept preferred types. If only one candidate remains, use it; else continue to the next step.

If any input arguments are unknown, check the type categories accepted at those argument positions by the remaining candidates. At each position, select the string category if any candidate accepts that category. (This bias towards string is appropriate since an unknown-type literal looks like a string.) Otherwise, if all the remaining candidates accept the same type category, select that category; otherwise fail because the correct choice cannot be deduced without more clues. Now discard candidates that do not accept the selected type category. Furthermore, if any candidate accepts a preferred type in that category, discard candidates that accept non-preferred types for that argument. Keep all candidates if none survive these tests. If only one candidate remains, use it; else continue to the next step.

If there are both unknown and known-type arguments, and all the known-type arguments have the same type, assume that the unknown arguments are also of that type, and check which candidates can accept that type at the unknown-argument positions. If exactly one candidate passes this test, use it. Otherwise, fail.

Some examples follow.

Example 10.1. Square Root Operator Type Resolution

There is only one square root operator (prefix |/) defined in the standard catalog, and it takes an argument of type double precision. The scanner assigns an initial type of integer to the argument in this query expression:

So the parser does a type conversion on the operand and the query is equivalent to:

Example 10.2. String Concatenation Operator Type Resolution

A string-like syntax is used for working with string types and for working with complex extension types. Strings with unspecified type are matched with likely operator candidates.

An example with one unspecified argument:

In this case the parser looks to see if there is an operator taking text for both arguments. Since there is, it assumes that the second argument should be interpreted as type text.

Here is a concatenation of two values of unspecified types:

In this case there is no initial hint for which type to use, since no types are specified in the query. So, the parser looks for all candidate operators and finds that there are candidates accepting both string-category and bit-string-category inputs. Since string category is preferred when available, that category is selected, and then the preferred type for strings, text, is used as the specific type to resolve the unknown-type literals as.

Example 10.3. Absolute-Value and Negation Operator Type Resolution

The PostgreSQL operator catalog has several entries for the prefix operator @, all of which implement absolute-value operations for various numeric data types. One of these entries is for type float8, which is the preferred type in the numeric category. Therefore, PostgreSQL will use that entry when faced with an unknown input:

Here the system has implicitly resolved the unknown-type literal as type float8 before applying the chosen operator. We can verify that float8 and not some other type was used:

On the other hand, the prefix operator ~ (bitwise negation) is defined only for integer data types, not for float8. So, if we try a similar case with ~, we get:

This happens because the system cannot decide which of the several possible ~ operators should be preferred. We can help it out with an explicit cast:

Example 10.4. Array Inclusion Operator Type Resolution

Here is another example of resolving an operator with one known and one unknown input:

The PostgreSQL operator catalog has several entries for the infix operator <@, but the only two that could possibly accept an integer array on the left-hand side are array inclusion (anyarray <@ anyarray) and range inclusion (anyelement <@ anyrange). Since none of these polymorphic pseudo-types (see Section 8.21) are considered preferred, the parser cannot resolve the ambiguity on that basis. However, Step 3.f tells it to assume that the unknown-type literal is of the same type as the other input, that is, integer array. Now only one of the two operators can match, so array inclusion is selected. (Had range inclusion been selected, we would have gotten an error, because the string does not have the right format to be a range literal.)

Example 10.5. Custom Operator on a Domain Type

Users sometimes try to declare operators applying just to a domain type. This is possible but is not nearly as useful as it might seem, because the operator resolution rules are designed to select operators applying to the domain's base type. As an example consider

This query will not use the custom operator. The parser will first see if there is a mytext = mytext operator (Step 2.a), which there is not; then it will consider the domain's base type text, and see if there is a text = text operator (Step 2.b), which there is; so it resolves the unknown-type literal as text and uses the text = text operator. The only way to get the custom operator to be used is to explicitly cast the literal:

so that the mytext = text operator is found immediately according to the exact-match rule. If the best-match rules are reached, they actively discriminate against operators on domain types. If they did not, such an operator would create too many ambiguous-operator failures, because the casting rules always consider a domain as castable to or from its base type, and so the domain operator would be considered usable in all the same cases as a similarly-named operator on the base type.

[9] The hazard does not arise with a non-schema-qualified name, because a search path containing schemas that permit untrusted users to create objects is not a secure schema usage pattern.

Examples:

Example 1 (unknown):

SELECT |/ 40 AS "square root of 40";
 square root of 40
-------------------
 6.324555320336759
(1 row)

Example 2 (unknown):

SELECT |/ CAST(40 AS double precision) AS "square root of 40";

Example 3 (unknown):

SELECT text 'abc' || 'def' AS "text and unknown";

 text and unknown
------------------
 abcdef
(1 row)

Example 4 (unknown):

SELECT 'abc' || 'def' AS "unspecified";

 unspecified
-------------
 abcdef
(1 row)

---

PostgreSQL: Documentation: 18: 8.9. Network Address Types

URL: https://www.postgresql.org/docs/current/datatype-net-types.html

Contents:

• 8.9. Network Address Types #
  • 8.9.1. inet #
  • 8.9.2. cidr #
  • 8.9.3. inet vs. cidr #
  • Tip
  • 8.9.4. macaddr #
  • 8.9.5. macaddr8 #

PostgreSQL offers data types to store IPv4, IPv6, and MAC addresses, as shown in Table 8.21. It is better to use these types instead of plain text types to store network addresses, because these types offer input error checking and specialized operators and functions (see Section 9.12).

Table 8.21. Network Address Types

When sorting inet or cidr data types, IPv4 addresses will always sort before IPv6 addresses, including IPv4 addresses encapsulated or mapped to IPv6 addresses, such as ::10.2.3.4 or ::ffff:10.4.3.2.

The inet type holds an IPv4 or IPv6 host address, and optionally its subnet, all in one field. The subnet is represented by the number of network address bits present in the host address (the “netmask”). If the netmask is 32 and the address is IPv4, then the value does not indicate a subnet, only a single host. In IPv6, the address length is 128 bits, so 128 bits specify a unique host address. Note that if you want to accept only networks, you should use the cidr type rather than inet.

The input format for this type is address/y where address is an IPv4 or IPv6 address and y is the number of bits in the netmask. If the /y portion is omitted, the netmask is taken to be 32 for IPv4 or 128 for IPv6, so the value represents just a single host. On display, the /y portion is suppressed if the netmask specifies a single host.

The cidr type holds an IPv4 or IPv6 network specification. Input and output formats follow Classless Internet Domain Routing conventions. The format for specifying networks is address/y where address is the network's lowest address represented as an IPv4 or IPv6 address, and y is the number of bits in the netmask. If y is omitted, it is calculated using assumptions from the older classful network numbering system, except it will be at least large enough to include all of the octets written in the input. It is an error to specify a network address that has bits set to the right of the specified netmask.

Table 8.22 shows some examples.

Table 8.22. cidr Type Input Examples

The essential difference between inet and cidr data types is that inet accepts values with nonzero bits to the right of the netmask, whereas cidr does not. For example, 192.168.0.1/24 is valid for inet but not for cidr.

If you do not like the output format for inet or cidr values, try the functions host, text, and abbrev.

The macaddr type stores MAC addresses, known for example from Ethernet card hardware addresses (although MAC addresses are used for other purposes as well). Input is accepted in the following formats:

These examples all specify the same address. Upper and lower case is accepted for the digits a through f. Output is always in the first of the forms shown.

IEEE Standard 802-2001 specifies the second form shown (with hyphens) as the canonical form for MAC addresses, and specifies the first form (with colons) as used with bit-reversed, MSB-first notation, so that 08-00-2b-01-02-03 = 10:00:D4:80:40:C0. This convention is widely ignored nowadays, and it is relevant only for obsolete network protocols (such as Token Ring). PostgreSQL makes no provisions for bit reversal; all accepted formats use the canonical LSB order.

The remaining five input formats are not part of any standard.

The macaddr8 type stores MAC addresses in EUI-64 format, known for example from Ethernet card hardware addresses (although MAC addresses are used for other purposes as well). This type can accept both 6 and 8 byte length MAC addresses and stores them in 8 byte length format. MAC addresses given in 6 byte format will be stored in 8 byte length format with the 4th and 5th bytes set to FF and FE, respectively. Note that IPv6 uses a modified EUI-64 format where the 7th bit should be set to one after the conversion from EUI-48. The function macaddr8_set7bit is provided to make this change. Generally speaking, any input which is comprised of pairs of hex digits (on byte boundaries), optionally separated consistently by one of ':', '-' or '.', is accepted. The number of hex digits must be either 16 (8 bytes) or 12 (6 bytes). Leading and trailing whitespace is ignored. The following are examples of input formats that are accepted:

These examples all specify the same address. Upper and lower case is accepted for the digits a through f. Output is always in the first of the forms shown.

The last six input formats shown above are not part of any standard.

To convert a traditional 48 bit MAC address in EUI-48 format to modified EUI-64 format to be included as the host portion of an IPv6 address, use macaddr8_set7bit as shown:

Examples:

Example 1 (unknown):

SELECT macaddr8_set7bit('08:00:2b:01:02:03');

    macaddr8_set7bit
-------------------------
 0a:00:2b:ff:fe:01:02:03
(1 row)

---

PostgreSQL: Documentation: 18: 35.42. routine_routine_usage

URL: https://www.postgresql.org/docs/current/infoschema-routine-routine-usage.html

Contents:

• 35.42. routine_routine_usage #

The view routine_routine_usage identifies all functions or procedures that are used by another (or the same) function or procedure, either in the SQL body or in parameter default expressions. (This only works for unquoted SQL bodies, not quoted bodies or functions in other languages.) An entry is included here only if the used function is owned by a currently enabled role. (There is no such restriction on the using function.)

Note that the entries for both functions in the view refer to the “specific” name of the routine, even though the column names are used in a way that is inconsistent with other information schema views about routines. This is per SQL standard, although it is arguably a misdesign. See Section 35.45 for more information about specific names.

Table 35.40. routine_routine_usage Columns

specific_catalog sql_identifier

Name of the database containing the using function (always the current database)

specific_schema sql_identifier

Name of the schema containing the using function

specific_name sql_identifier

The “specific name” of the using function.

routine_catalog sql_identifier

Name of the database that contains the function that is used by the first function (always the current database)

routine_schema sql_identifier

Name of the schema that contains the function that is used by the first function

routine_name sql_identifier

The “specific name” of the function that is used by the first function.

---

PostgreSQL: Documentation: 18: 32.8. The Fast-Path Interface

URL: https://www.postgresql.org/docs/current/libpq-fastpath.html

Contents:

• 32.8. The Fast-Path Interface #
  • Tip

PostgreSQL provides a fast-path interface to send simple function calls to the server.

This interface is somewhat obsolete, as one can achieve similar performance and greater functionality by setting up a prepared statement to define the function call. Then, executing the statement with binary transmission of parameters and results substitutes for a fast-path function call.

The function PQfn requests execution of a server function via the fast-path interface:

The fnid argument is the OID of the function to be executed. args and nargs define the parameters to be passed to the function; they must match the declared function argument list. When the isint field of a parameter structure is true, the u.integer value is sent to the server as an integer of the indicated length (this must be 2 or 4 bytes); proper byte-swapping occurs. When isint is false, the indicated number of bytes at *u.ptr are sent with no processing; the data must be in the format expected by the server for binary transmission of the function's argument data type. (The declaration of u.ptr as being of type int * is historical; it would be better to consider it void *.) result_buf points to the buffer in which to place the function's return value. The caller must have allocated sufficient space to store the return value. (There is no check!) The actual result length in bytes will be returned in the integer pointed to by result_len. If a 2- or 4-byte integer result is expected, set result_is_int to 1, otherwise set it to 0. Setting result_is_int to 1 causes libpq to byte-swap the value if necessary, so that it is delivered as a proper int value for the client machine; note that a 4-byte integer is delivered into *result_buf for either allowed result size. When result_is_int is 0, the binary-format byte string sent by the server is returned unmodified. (In this case it's better to consider result_buf as being of type void *.)

PQfn always returns a valid PGresult pointer, with status PGRES_COMMAND_OK for success or PGRES_FATAL_ERROR if some problem was encountered. The result status should be checked before the result is used. The caller is responsible for freeing the PGresult with PQclear when it is no longer needed.

To pass a NULL argument to the function, set the len field of that parameter structure to -1; the isint and u fields are then irrelevant.

If the function returns NULL, *result_len is set to -1, and *result_buf is not modified.

Note that it is not possible to handle set-valued results when using this interface. Also, the function must be a plain function, not an aggregate, window function, or procedure.

Examples:

Example 1 (javascript):

PGresult *PQfn(PGconn *conn,
               int fnid,
               int *result_buf,
               int *result_len,
               int result_is_int,
               const PQArgBlock *args,
               int nargs);

typedef struct
{
    int len;
    int isint;
    union
    {
        int *ptr;
        int integer;
    } u;
} PQArgBlock;

---

PostgreSQL: Documentation: 18: 11.11. Indexes and Collations

URL: https://www.postgresql.org/docs/current/indexes-collations.html

Contents:

• 11.11. Indexes and Collations #

An index can support only one collation per index column. If multiple collations are of interest, multiple indexes may be needed.

Consider these statements:

The index automatically uses the collation of the underlying column. So a query of the form

could use the index, because the comparison will by default use the collation of the column. However, this index cannot accelerate queries that involve some other collation. So if queries of the form, say,

are also of interest, an additional index could be created that supports the "y" collation, like this:

Examples:

Example 1 (unknown):

CREATE TABLE test1c (
    id integer,
    content varchar COLLATE "x"
);

CREATE INDEX test1c_content_index ON test1c (content);

Example 2 (unknown):

SELECT * FROM test1c WHERE content > constant;

Example 3 (unknown):

SELECT * FROM test1c WHERE content > constant COLLATE "y";

Example 4 (unknown):

CREATE INDEX test1c_content_y_index ON test1c (content COLLATE "y");

---

PostgreSQL: Documentation: 18: 33.5. Example Program

URL: https://www.postgresql.org/docs/current/lo-examplesect.html

Contents:

• 33.5. Example Program #

Example 33.1 is a sample program which shows how the large object interface in libpq can be used. Parts of the program are commented out but are left in the source for the reader's benefit. This program can also be found in src/test/examples/testlo.c in the source distribution.

Example 33.1. Large Objects with libpq Example Program

Examples:

Example 1 (python):

/*-----------------------------------------------------------------
 *
 * testlo.c
 *    test using large objects with libpq
 *
 * Portions Copyright (c) 1996-2025, PostgreSQL Global Development Group
 * Portions Copyright (c) 1994, Regents of the University of California
 *
 *
 * IDENTIFICATION
 *    src/test/examples/testlo.c
 *
 *-----------------------------------------------------------------
 */
#include <stdio.h>
#include <stdlib.h>

#include <sys/types.h>
#include <sys/stat.h>
#include <fcntl.h>
#include <unistd.h>

#include "libpq-fe.h"
#include "libpq/libpq-fs.h"

#define BUFSIZE         1024

/*
 * importFile -
 *    import file "in_filename" into database as large object "lobjOid"
 *
 */
static Oid
importFile(PGconn *conn, char *filename)
{
    Oid         lobjId;
    int         lobj_fd;
    char        buf[BUFSIZE];
    int         nbytes,
                tmp;
    int         fd;

    /*
     * open the file to be read in
     */
    fd = open(filename, O_RDONLY, 0666);
    if (fd < 0)
    {                           /* error */
        fprintf(stderr, "cannot open unix file\"%s\"\n", filename);
    }

    /*
     * create the large object
     */
    lobjId = lo_creat(conn, INV_READ | INV_WRITE);
    if (lobjId == 0)
        fprintf(stderr, "cannot create large object");

    lobj_fd = lo_open(conn, lobjId, INV_WRITE);

    /*
     * read in from the Unix file and write to the inversion file
     */
    while ((nbytes = read(fd, buf, BUFSIZE)) > 0)
    {
        tmp = lo_write(conn, lobj_fd, buf, nbytes);
        if (tmp < nbytes)
            fprintf(stderr, "error while reading \"%s\"", filename);
    }

    close(fd);
    lo_close(conn, lobj_fd);

    return lobjId;
}

static void
pickout(PGconn *conn, Oid lobjId, int start, int len)
{
    int         lobj_fd;
    char       *buf;
    int         nbytes;
    int         nread;

    lobj_fd = lo_open(conn, lobjId, INV_READ);
    if (lobj_fd < 0)
        fprintf(stderr, "cannot open large object %u", lobjId);

    lo_lseek(conn, lobj_fd, start, SEEK_SET);
    buf = malloc(len + 1);

    nread = 0;
    while (len - nread > 0)
    {
        nbytes = lo_read(conn, lobj_fd, buf, len - nread);
        buf[nbytes] = '\0';
        fprintf(stderr, ">>> %s", buf);
        nread += nbytes;
        if (nbytes <= 0)
            break;              /* no more data? */
    }
    free(buf);
    fprintf(stderr, "\n");
    lo_close(conn, lobj_fd);
}

static void
overwrite(PGconn *conn, Oid lobjId, int start, int len)
{
    int         lobj_fd;
    char       *buf;
    int         nbytes;
    int         nwritten;
    int         i;

    lobj_fd = lo_open(conn, lobjId, INV_WRITE);
    if (lobj_fd < 0)
        fprintf(stderr, "cannot open large object %u", lobjId);

    lo_lseek(conn, lobj_fd, start, SEEK_SET);
    buf = malloc(len + 1);

    for (i = 0; i < len; i++)
        buf[i] = 'X';
    buf[i] = '\0';

    nwritten = 0;
    while (len - nwritten > 0)
    {
        nbytes = lo_write(conn, lobj_fd, buf + nwritten, len - nwritten);
        nwritten += nbytes;
        if (nbytes <= 0)
        {
            fprintf(stderr, "\nWRITE FAILED!\n");
            break;
        }
    }
    free(buf);
    fprintf(stderr, "\n");
    lo_close(conn, lobj_fd);
}


/*
 * exportFile -
 *    export large object "lobjOid" to file "out_filename"
 *
 */
static void
exportFile(PGconn *conn, Oid lobjId, char *filename)
{
    int         lobj_fd;
    char        buf[BUFSIZE];
    int         nbytes,
                tmp;
    int         fd;

    /*
     * open the large object
     */
    lobj_fd = lo_open(conn, lobjId, INV_READ);
    if (lobj_fd < 0)
        fprintf(stderr, "cannot open large object %u", lobjId);

    /*
     * open the file to be written to
     */
    fd = open(filename, O_CREAT | O_WRONLY | O_TRUNC, 0666);
    if (fd < 0)
    {                           /* error */
        fprintf(stderr, "cannot open unix file\"%s\"",
                filename);
    }

    /*
     * read in from the inversion file and write to the Unix file
     */
    while ((nbytes = lo_read(conn, lobj_fd, buf, BUFSIZE)) > 0)
    {
        tmp = write(fd, buf, nbytes);
        if (tmp < nbytes)
        {
            fprintf(stderr, "error while writing \"%s\"",
                    filename);
        }
    }

    lo_close(conn, lobj_fd);
    close(fd);
}

static void
exit_nicely(PGconn *conn)
{
    PQfinish(conn);
    exit(1);
}

int
main(int argc, char **argv)
{
    char       *in_filename,
               *out_filename;
    char       *database;
    Oid         lobjOid;
    PGconn     *conn;
    PGresult   *res;

    if (argc != 4)
    {
        fprintf(stderr, "Usage: %s database_name in_filename out_filename\n",
                argv[0]);
        exit(1);
    }

    database = argv[1];
    in_filename = argv[2];
    out_filename = argv[3];

    /*
     * set up the connection
     */
    conn = PQsetdb(NULL, NULL, NULL, NULL, database);

    /* check to see that the backend connection was successfully made */
    if (PQstatus(conn) != CONNECTION_OK)
    {
        fprintf(stderr, "%s", PQerrorMessage(conn));
        exit_nicely(conn);
    }

    /* Set always-secure search path, so malicious users can't take control. */
    res = PQexec(conn,
                 "SELECT pg_catalog.set_config('search_path', '', false)");
    if (PQresultStatus(res) != PGRES_TUPLES_OK)
    {
        fprintf(stderr, "SET failed: %s", PQerrorMessage(conn));
        PQclear(res);
        exit_nicely(conn);
    }
    PQclear(res);

    res = PQexec(conn, "begin");
    PQclear(res);
    printf("importing file \"%s\" ...\n", in_filename);
/*  lobjOid = importFile(conn, in_filename); */
    lobjOid = lo_import(conn, in_filename);
    if (lobjOid == 0)
        fprintf(stderr, "%s\n", PQerrorMessage(conn));
    else
    {
        printf("\tas large object %u.\n", lobjOid);

        printf("picking out bytes 1000-2000 of the large object\n");
        pickout(conn, lobjOid, 1000, 1000);

        printf("overwriting bytes 1000-2000 of the large object with X's\n");
        overwrite(conn, lobjOid, 1000, 1000);

        printf("exporting large object to file \"%s\" ...\n", out_filename);
/*      exportFile(conn, lobjOid, out_filename); */
        if (lo_export(conn, lobjOid, out_filename) < 0)
            fprintf(stderr, "%s\n", PQerrorMessage(conn));
    }

    res = PQexec(conn, "end");
    PQclear(res);
    PQfinish(conn);
    return 0;
}

---

PostgreSQL: Documentation: 18: 5.6. System Columns

URL: https://www.postgresql.org/docs/current/ddl-system-columns.html

Contents:

• 5.6. System Columns #

Every table has several system columns that are implicitly defined by the system. Therefore, these names cannot be used as names of user-defined columns. (Note that these restrictions are separate from whether the name is a key word or not; quoting a name will not allow you to escape these restrictions.) You do not really need to be concerned about these columns; just know they exist.

The OID of the table containing this row. This column is particularly handy for queries that select from partitioned tables (see Section 5.12) or inheritance hierarchies (see Section 5.11), since without it, it's difficult to tell which individual table a row came from. The tableoid can be joined against the oid column of pg_class to obtain the table name.

The identity (transaction ID) of the inserting transaction for this row version. (A row version is an individual state of a row; each update of a row creates a new row version for the same logical row.)

The command identifier (starting at zero) within the inserting transaction.

The identity (transaction ID) of the deleting transaction, or zero for an undeleted row version. It is possible for this column to be nonzero in a visible row version. That usually indicates that the deleting transaction hasn't committed yet, or that an attempted deletion was rolled back.

The command identifier within the deleting transaction, or zero.

The physical location of the row version within its table. Note that although the ctid can be used to locate the row version very quickly, a row's ctid will change if it is updated or moved by VACUUM FULL. Therefore ctid is useless as a long-term row identifier. A primary key should be used to identify logical rows.

Transaction identifiers are also 32-bit quantities. In a long-lived database it is possible for transaction IDs to wrap around. This is not a fatal problem given appropriate maintenance procedures; see Chapter 24 for details. It is unwise, however, to depend on the uniqueness of transaction IDs over the long term (more than one billion transactions).

Command identifiers are also 32-bit quantities. This creates a hard limit of 232 (4 billion) SQL commands within a single transaction. In practice this limit is not a problem — note that the limit is on the number of SQL commands, not the number of rows processed. Also, only commands that actually modify the database contents will consume a command identifier.

---

PostgreSQL: Documentation: 18: 19.10. Vacuuming

URL: https://www.postgresql.org/docs/current/runtime-config-vacuum.html

Contents:

• 19.10. Vacuuming #
  • 19.10.1. Automatic Vacuuming #
  • 19.10.2. Cost-based Vacuum Delay #
  • Note
  • 19.10.3. Default Behavior #
  • 19.10.4. Freezing #

These parameters control vacuuming behavior. For more information on the purpose and responsibilities of vacuum, see Section 24.1.

These settings control the behavior of the autovacuum feature. Refer to Section 24.1.6 for more information. Note that many of these settings can be overridden on a per-table basis; see Storage Parameters.

Controls whether the server should run the autovacuum launcher daemon. This is on by default; however, track_counts must also be enabled for autovacuum to work. This parameter can only be set in the postgresql.conf file or on the server command line; however, autovacuuming can be disabled for individual tables by changing table storage parameters.

Note that even when this parameter is disabled, the system will launch autovacuum processes if necessary to prevent transaction ID wraparound. See Section 24.1.5 for more information.

Specifies the number of backend slots to reserve for autovacuum worker processes. The default is typically 16 slots, but might be less if your kernel settings will not support it (as determined during initdb). This parameter can only be set at server start.

When changing this value, consider also adjusting autovacuum_max_workers.

Specifies the maximum number of autovacuum processes (other than the autovacuum launcher) that may be running at any one time. The default is 3. This parameter can only be set in the postgresql.conf file or on the server command line.

Note that a setting for this value which is higher than autovacuum_worker_slots will have no effect, since autovacuum workers are taken from the pool of slots established by that setting.

Specifies the minimum delay between autovacuum runs on any given database. In each round the daemon examines the database and issues VACUUM and ANALYZE commands as needed for tables in that database. If this value is specified without units, it is taken as seconds. The default is one minute (1min). This parameter can only be set in the postgresql.conf file or on the server command line.

Specifies the minimum number of updated or deleted tuples needed to trigger a VACUUM in any one table. The default is 50 tuples. This parameter can only be set in the postgresql.conf file or on the server command line; but the setting can be overridden for individual tables by changing table storage parameters.

Specifies the number of inserted tuples needed to trigger a VACUUM in any one table. The default is 1000 tuples. If -1 is specified, autovacuum will not trigger a VACUUM operation on any tables based on the number of inserts. This parameter can only be set in the postgresql.conf file or on the server command line; but the setting can be overridden for individual tables by changing table storage parameters.

Specifies the minimum number of inserted, updated or deleted tuples needed to trigger an ANALYZE in any one table. The default is 50 tuples. This parameter can only be set in the postgresql.conf file or on the server command line; but the setting can be overridden for individual tables by changing table storage parameters.

Specifies a fraction of the table size to add to autovacuum_vacuum_threshold when deciding whether to trigger a VACUUM. The default is 0.2 (20% of table size). This parameter can only be set in the postgresql.conf file or on the server command line; but the setting can be overridden for individual tables by changing table storage parameters.

Specifies a fraction of the unfrozen pages in the table to add to autovacuum_vacuum_insert_threshold when deciding whether to trigger a VACUUM. The default is 0.2 (20% of unfrozen pages in table). This parameter can only be set in the postgresql.conf file or on the server command line; but the setting can be overridden for individual tables by changing table storage parameters.

Specifies a fraction of the table size to add to autovacuum_analyze_threshold when deciding whether to trigger an ANALYZE. The default is 0.1 (10% of table size). This parameter can only be set in the postgresql.conf file or on the server command line; but the setting can be overridden for individual tables by changing table storage parameters.

Specifies the maximum number of updated or deleted tuples needed to trigger a VACUUM in any one table, i.e., a limit on the value calculated with autovacuum_vacuum_threshold and autovacuum_vacuum_scale_factor. The default is 100,000,000 tuples. If -1 is specified, autovacuum will not enforce a maximum number of updated or deleted tuples that will trigger a VACUUM operation. This parameter can only be set in the postgresql.conf file or on the server command line; but the setting can be overridden for individual tables by changing storage parameters.

Specifies the maximum age (in transactions) that a table's pg_class.relfrozenxid field can attain before a VACUUM operation is forced to prevent transaction ID wraparound within the table. Note that the system will launch autovacuum processes to prevent wraparound even when autovacuum is otherwise disabled.

Vacuum also allows removal of old files from the pg_xact subdirectory, which is why the default is a relatively low 200 million transactions. This parameter can only be set at server start, but the setting can be reduced for individual tables by changing table storage parameters. For more information see Section 24.1.5.

Specifies the maximum age (in multixacts) that a table's pg_class.relminmxid field can attain before a VACUUM operation is forced to prevent multixact ID wraparound within the table. Note that the system will launch autovacuum processes to prevent wraparound even when autovacuum is otherwise disabled.

Vacuuming multixacts also allows removal of old files from the pg_multixact/members and pg_multixact/offsets subdirectories, which is why the default is a relatively low 400 million multixacts. This parameter can only be set at server start, but the setting can be reduced for individual tables by changing table storage parameters. For more information see Section 24.1.5.1.

Specifies the cost delay value that will be used in automatic VACUUM operations. If -1 is specified, the regular vacuum_cost_delay value will be used. If this value is specified without units, it is taken as milliseconds. The default value is 2 milliseconds. This parameter can only be set in the postgresql.conf file or on the server command line; but the setting can be overridden for individual tables by changing table storage parameters.

Specifies the cost limit value that will be used in automatic VACUUM operations. If -1 is specified (which is the default), the regular vacuum_cost_limit value will be used. Note that the value is distributed proportionally among the running autovacuum workers, if there is more than one, so that the sum of the limits for each worker does not exceed the value of this variable. This parameter can only be set in the postgresql.conf file or on the server command line; but the setting can be overridden for individual tables by changing table storage parameters.

During the execution of VACUUM and ANALYZE commands, the system maintains an internal counter that keeps track of the estimated cost of the various I/O operations that are performed. When the accumulated cost reaches a limit (specified by vacuum_cost_limit), the process performing the operation will sleep for a short period of time, as specified by vacuum_cost_delay. Then it will reset the counter and continue execution.

The intent of this feature is to allow administrators to reduce the I/O impact of these commands on concurrent database activity. There are many situations where it is not important that maintenance commands like VACUUM and ANALYZE finish quickly; however, it is usually very important that these commands do not significantly interfere with the ability of the system to perform other database operations. Cost-based vacuum delay provides a way for administrators to achieve this.

This feature is disabled by default for manually issued VACUUM commands. To enable it, set the vacuum_cost_delay variable to a nonzero value.

The amount of time that the process will sleep when the cost limit has been exceeded. If this value is specified without units, it is taken as milliseconds. The default value is 0, which disables the cost-based vacuum delay feature. Positive values enable cost-based vacuuming.

When using cost-based vacuuming, appropriate values for vacuum_cost_delay are usually quite small, perhaps less than 1 millisecond. While vacuum_cost_delay can be set to fractional-millisecond values, such delays may not be measured accurately on older platforms. On such platforms, increasing VACUUM's throttled resource consumption above what you get at 1ms will require changing the other vacuum cost parameters. You should, nonetheless, keep vacuum_cost_delay as small as your platform will consistently measure; large delays are not helpful.

The estimated cost for vacuuming a buffer found in the shared buffer cache. It represents the cost to lock the buffer pool, lookup the shared hash table and scan the content of the page. The default value is 1.

The estimated cost for vacuuming a buffer that has to be read from disk. This represents the effort to lock the buffer pool, lookup the shared hash table, read the desired block in from the disk and scan its content. The default value is 2.

The estimated cost charged when vacuum modifies a block that was previously clean. It represents the extra I/O required to flush the dirty block out to disk again. The default value is 20.

This is the accumulated cost that will cause the vacuuming process to sleep for vacuum_cost_delay. The default is 200.

There are certain operations that hold critical locks and should therefore complete as quickly as possible. Cost-based vacuum delays do not occur during such operations. Therefore it is possible that the cost accumulates far higher than the specified limit. To avoid uselessly long delays in such cases, the actual delay is calculated as vacuum_cost_delay * accumulated_balance / vacuum_cost_limit with a maximum of vacuum_cost_delay * 4.

Enables or disables vacuum to try to truncate off any empty pages at the end of the table. The default value is true. If true, VACUUM and autovacuum do the truncation and the disk space for the truncated pages is returned to the operating system. Note that the truncation requires an ACCESS EXCLUSIVE lock on the table. The TRUNCATE parameter of VACUUM, if specified, overrides the value of this parameter. The setting can also be overridden for individual tables by changing table storage parameters.

To maintain correctness even after transaction IDs wrap around, PostgreSQL marks rows that are sufficiently old as frozen. These rows are visible to everyone; other transactions do not need to examine their inserting XID to determine visibility. VACUUM is responsible for marking rows as frozen. The following settings control VACUUM's freezing behavior and should be tuned based on the XID consumption rate of the system and data access patterns of the dominant workloads. See Section 24.1.5 for more information on transaction ID wraparound and tuning these parameters.

VACUUM performs an aggressive scan if the table's pg_class.relfrozenxid field has reached the age specified by this setting. An aggressive scan differs from a regular VACUUM in that it visits every page that might contain unfrozen XIDs or MXIDs, not just those that might contain dead tuples. The default is 150 million transactions. Although users can set this value anywhere from zero to two billion, VACUUM will silently limit the effective value to 95% of autovacuum_freeze_max_age, so that a periodic manual VACUUM has a chance to run before an anti-wraparound autovacuum is launched for the table. For more information see Section 24.1.5.

Specifies the cutoff age (in transactions) that VACUUM should use to decide whether to trigger freezing of pages that have an older XID. The default is 50 million transactions. Although users can set this value anywhere from zero to one billion, VACUUM will silently limit the effective value to half the value of autovacuum_freeze_max_age, so that there is not an unreasonably short time between forced autovacuums. For more information see Section 24.1.5.

Specifies the maximum age (in transactions) that a table's pg_class.relfrozenxid field can attain before VACUUM takes extraordinary measures to avoid system-wide transaction ID wraparound failure. This is VACUUM's strategy of last resort. The failsafe typically triggers when an autovacuum to prevent transaction ID wraparound has already been running for some time, though it's possible for the failsafe to trigger during any VACUUM.

When the failsafe is triggered, any cost-based delay that is in effect will no longer be applied, further non-essential maintenance tasks (such as index vacuuming) are bypassed, and any Buffer Access Strategy in use will be disabled resulting in VACUUM being free to make use of all of shared buffers.

The default is 1.6 billion transactions. Although users can set this value anywhere from zero to 2.1 billion, VACUUM will silently adjust the effective value to no less than 105% of autovacuum_freeze_max_age.

VACUUM performs an aggressive scan if the table's pg_class.relminmxid field has reached the age specified by this setting. An aggressive scan differs from a regular VACUUM in that it visits every page that might contain unfrozen XIDs or MXIDs, not just those that might contain dead tuples. The default is 150 million multixacts. Although users can set this value anywhere from zero to two billion, VACUUM will silently limit the effective value to 95% of autovacuum_multixact_freeze_max_age, so that a periodic manual VACUUM has a chance to run before an anti-wraparound is launched for the table. For more information see Section 24.1.5.1.

Specifies the cutoff age (in multixacts) that VACUUM should use to decide whether to trigger freezing of pages with an older multixact ID. The default is 5 million multixacts. Although users can set this value anywhere from zero to one billion, VACUUM will silently limit the effective value to half the value of autovacuum_multixact_freeze_max_age, so that there is not an unreasonably short time between forced autovacuums. For more information see Section 24.1.5.1.

Specifies the maximum age (in multixacts) that a table's pg_class.relminmxid field can attain before VACUUM takes extraordinary measures to avoid system-wide multixact ID wraparound failure. This is VACUUM's strategy of last resort. The failsafe typically triggers when an autovacuum to prevent transaction ID wraparound has already been running for some time, though it's possible for the failsafe to trigger during any VACUUM.

When the failsafe is triggered, any cost-based delay that is in effect will no longer be applied, and further non-essential maintenance tasks (such as index vacuuming) are bypassed.

The default is 1.6 billion multixacts. Although users can set this value anywhere from zero to 2.1 billion, VACUUM will silently adjust the effective value to no less than 105% of autovacuum_multixact_freeze_max_age.

Specifies the maximum number of pages (as a fraction of total pages in the relation) that VACUUM may scan and fail to set all-frozen in the visibility map before disabling eager scanning. A value of 0 disables eager scanning altogether. The default is 0.03 (3%).

Note that when eager scanning is enabled, only freeze failures count against the cap, not successful freezing. Successful page freezes are capped internally at 20% of the all-visible but not all-frozen pages in the relation. Capping successful page freezes helps amortize the overhead across multiple normal vacuums and limits the potential downside of wasted eager freezes of pages that are modified again before the next aggressive vacuum.

This parameter can only be set in the postgresql.conf file or on the server command line; but the setting can be overridden for individual tables by changing the corresponding table storage parameter. For more information on tuning vacuum's freezing behavior, see Section 24.1.5.

---

PostgreSQL: Documentation: 18: 5.9. Row Security Policies

URL: https://www.postgresql.org/docs/current/ddl-rowsecurity.html

Contents:

• 5.9. Row Security Policies #

In addition to the SQL-standard privilege system available through GRANT, tables can have row security policies that restrict, on a per-user basis, which rows can be returned by normal queries or inserted, updated, or deleted by data modification commands. This feature is also known as Row-Level Security. By default, tables do not have any policies, so that if a user has access privileges to a table according to the SQL privilege system, all rows within it are equally available for querying or updating.

When row security is enabled on a table (with ALTER TABLE ... ENABLE ROW LEVEL SECURITY), all normal access to the table for selecting rows or modifying rows must be allowed by a row security policy. (However, the table's owner is typically not subject to row security policies.) If no policy exists for the table, a default-deny policy is used, meaning that no rows are visible or can be modified. Operations that apply to the whole table, such as TRUNCATE and REFERENCES, are not subject to row security.

Row security policies can be specific to commands, or to roles, or to both. A policy can be specified to apply to ALL commands, or to SELECT, INSERT, UPDATE, or DELETE. Multiple roles can be assigned to a given policy, and normal role membership and inheritance rules apply.

To specify which rows are visible or modifiable according to a policy, an expression is required that returns a Boolean result. This expression will be evaluated for each row prior to any conditions or functions coming from the user's query. (The only exceptions to this rule are leakproof functions, which are guaranteed to not leak information; the optimizer may choose to apply such functions ahead of the row-security check.) Rows for which the expression does not return true will not be processed. Separate expressions may be specified to provide independent control over the rows which are visible and the rows which are allowed to be modified. Policy expressions are run as part of the query and with the privileges of the user running the query, although security-definer functions can be used to access data not available to the calling user.

Superusers and roles with the BYPASSRLS attribute always bypass the row security system when accessing a table. Table owners normally bypass row security as well, though a table owner can choose to be subject to row security with ALTER TABLE ... FORCE ROW LEVEL SECURITY.

Enabling and disabling row security, as well as adding policies to a table, is always the privilege of the table owner only.

Policies are created using the CREATE POLICY command, altered using the ALTER POLICY command, and dropped using the DROP POLICY command. To enable and disable row security for a given table, use the ALTER TABLE command.

Each policy has a name and multiple policies can be defined for a table. As policies are table-specific, each policy for a table must have a unique name. Different tables may have policies with the same name.

When multiple policies apply to a given query, they are combined using either OR (for permissive policies, which are the default) or using AND (for restrictive policies). The OR behavior is similar to the rule that a given role has the privileges of all roles that they are a member of. Permissive vs. restrictive policies are discussed further below.

As a simple example, here is how to create a policy on the account relation to allow only members of the managers role to access rows, and only rows of their accounts:

The policy above implicitly provides a WITH CHECK clause identical to its USING clause, so that the constraint applies both to rows selected by a command (so a manager cannot SELECT, UPDATE, or DELETE existing rows belonging to a different manager) and to rows modified by a command (so rows belonging to a different manager cannot be created via INSERT or UPDATE).

If no role is specified, or the special user name PUBLIC is used, then the policy applies to all users on the system. To allow all users to access only their own row in a users table, a simple policy can be used:

This works similarly to the previous example.

To use a different policy for rows that are being added to the table compared to those rows that are visible, multiple policies can be combined. This pair of policies would allow all users to view all rows in the users table, but only modify their own:

In a SELECT command, these two policies are combined using OR, with the net effect being that all rows can be selected. In other command types, only the second policy applies, so that the effects are the same as before.

Row security can also be disabled with the ALTER TABLE command. Disabling row security does not remove any policies that are defined on the table; they are simply ignored. Then all rows in the table are visible and modifiable, subject to the standard SQL privileges system.

Below is a larger example of how this feature can be used in production environments. The table passwd emulates a Unix password file:

As with any security settings, it's important to test and ensure that the system is behaving as expected. Using the example above, this demonstrates that the permission system is working properly.

All of the policies constructed thus far have been permissive policies, meaning that when multiple policies are applied they are combined using the “OR” Boolean operator. While permissive policies can be constructed to only allow access to rows in the intended cases, it can be simpler to combine permissive policies with restrictive policies (which the records must pass and which are combined using the “AND” Boolean operator). Building on the example above, we add a restrictive policy to require the administrator to be connected over a local Unix socket to access the records of the passwd table:

We can then see that an administrator connecting over a network will not see any records, due to the restrictive policy:

Referential integrity checks, such as unique or primary key constraints and foreign key references, always bypass row security to ensure that data integrity is maintained. Care must be taken when developing schemas and row level policies to avoid “covert channel” leaks of information through such referential integrity checks.

In some contexts it is important to be sure that row security is not being applied. For example, when taking a backup, it could be disastrous if row security silently caused some rows to be omitted from the backup. In such a situation, you can set the row_security configuration parameter to off. This does not in itself bypass row security; what it does is throw an error if any query's results would get filtered by a policy. The reason for the error can then be investigated and fixed.

In the examples above, the policy expressions consider only the current values in the row to be accessed or updated. This is the simplest and best-performing case; when possible, it's best to design row security applications to work this way. If it is necessary to consult other rows or other tables to make a policy decision, that can be accomplished using sub-SELECTs, or functions that contain SELECTs, in the policy expressions. Be aware however that such accesses can create race conditions that could allow information leakage if care is not taken. As an example, consider the following table design:

Now suppose that alice wishes to change the “slightly secret” information, but decides that mallory should not be trusted with the new content of that row, so she does:

That looks safe; there is no window wherein mallory should be able to see the “secret from mallory” string. However, there is a race condition here. If mallory is concurrently doing, say,

and her transaction is in READ COMMITTED mode, it is possible for her to see “secret from mallory”. That happens if her transaction reaches the information row just after alice's does. It blocks waiting for alice's transaction to commit, then fetches the updated row contents thanks to the FOR UPDATE clause. However, it does not fetch an updated row for the implicit SELECT from users, because that sub-SELECT did not have FOR UPDATE; instead the users row is read with the snapshot taken at the start of the query. Therefore, the policy expression tests the old value of mallory's privilege level and allows her to see the updated row.

There are several ways around this problem. One simple answer is to use SELECT ... FOR SHARE in sub-SELECTs in row security policies. However, that requires granting UPDATE privilege on the referenced table (here users) to the affected users, which might be undesirable. (But another row security policy could be applied to prevent them from actually exercising that privilege; or the sub-SELECT could be embedded into a security definer function.) Also, heavy concurrent use of row share locks on the referenced table could pose a performance problem, especially if updates of it are frequent. Another solution, practical if updates of the referenced table are infrequent, is to take an ACCESS EXCLUSIVE lock on the referenced table when updating it, so that no concurrent transactions could be examining old row values. Or one could just wait for all concurrent transactions to end after committing an update of the referenced table and before making changes that rely on the new security situation.

For additional details see CREATE POLICY and ALTER TABLE.

Examples:

Example 1 (unknown):

CREATE TABLE accounts (manager text, company text, contact_email text);

ALTER TABLE accounts ENABLE ROW LEVEL SECURITY;

CREATE POLICY account_managers ON accounts TO managers
    USING (manager = current_user);

Example 2 (unknown):

CREATE POLICY user_policy ON users
    USING (user_name = current_user);

Example 3 (unknown):

CREATE POLICY user_sel_policy ON users
    FOR SELECT
    USING (true);
CREATE POLICY user_mod_policy ON users
    USING (user_name = current_user);

Example 4 (unknown):

-- Simple passwd-file based example
CREATE TABLE passwd (
  user_name             text UNIQUE NOT NULL,
  pwhash                text,
  uid                   int  PRIMARY KEY,
  gid                   int  NOT NULL,
  real_name             text NOT NULL,
  home_phone            text,
  extra_info            text,
  home_dir              text NOT NULL,
  shell                 text NOT NULL
);

CREATE ROLE admin;  -- Administrator
CREATE ROLE bob;    -- Normal user
CREATE ROLE alice;  -- Normal user

-- Populate the table
INSERT INTO passwd VALUES
  ('admin','xxx',0,0,'Admin','111-222-3333',null,'/root','/bin/dash');
INSERT INTO passwd VALUES
  ('bob','xxx',1,1,'Bob','123-456-7890',null,'/home/bob','/bin/zsh');
INSERT INTO passwd VALUES
  ('alice','xxx',2,1,'Alice','098-765-4321',null,'/home/alice','/bin/zsh');

-- Be sure to enable row-level security on the table
ALTER TABLE passwd ENABLE ROW LEVEL SECURITY;

-- Create policies
-- Administrator can see all rows and add any rows
CREATE POLICY admin_all ON passwd TO admin USING (true) WITH CHECK (true);
-- Normal users can view all rows
CREATE POLICY all_view ON passwd FOR SELECT USING (true);
-- Normal users can update their own records, but
-- limit which shells a normal user is allowed to set
CREATE POLICY user_mod ON passwd FOR UPDATE
  USING (current_user = user_name)
  WITH CHECK (
    current_user = user_name AND
    shell IN ('/bin/bash','/bin/sh','/bin/dash','/bin/zsh','/bin/tcsh')
  );

-- Allow admin all normal rights
GRANT SELECT, INSERT, UPDATE, DELETE ON passwd TO admin;
-- Users only get select access on public columns
GRANT SELECT
  (user_name, uid, gid, real_name, home_phone, extra_info, home_dir, shell)
  ON passwd TO public;
-- Allow users to update certain columns
GRANT UPDATE
  (pwhash, real_name, home_phone, extra_info, shell)
  ON passwd TO public;

---

PostgreSQL: Documentation: 18: 27.2. The Cumulative Statistics System

URL: https://www.postgresql.org/docs/current/monitoring-stats.html

Contents:

• 27.2. The Cumulative Statistics System #
  • 27.2.1. Statistics Collection Configuration #
  • 27.2.2. Viewing Statistics #
  • 27.2.3. pg_stat_activity #
  • Note
  • Note
  • 27.2.4. pg_stat_replication #
  • Note
  • 27.2.5. pg_stat_replication_slots #
  • 27.2.6. pg_stat_wal_receiver #

PostgreSQL's cumulative statistics system supports collection and reporting of information about server activity. Presently, accesses to tables and indexes in both disk-block and individual-row terms are counted. The total number of rows in each table, and information about vacuum and analyze actions for each table are also counted. If enabled, calls to user-defined functions and the total time spent in each one are counted as well.

PostgreSQL also supports reporting dynamic information about exactly what is going on in the system right now, such as the exact command currently being executed by other server processes, and which other connections exist in the system. This facility is independent of the cumulative statistics system.

Since collection of statistics adds some overhead to query execution, the system can be configured to collect or not collect information. This is controlled by configuration parameters that are normally set in postgresql.conf. (See Chapter 19 for details about setting configuration parameters.)

The parameter track_activities enables monitoring of the current command being executed by any server process.

The parameter track_cost_delay_timing enables monitoring of cost-based vacuum delay.

The parameter track_counts controls whether cumulative statistics are collected about table and index accesses.

The parameter track_functions enables tracking of usage of user-defined functions.

The parameter track_io_timing enables monitoring of block read, write, extend, and fsync times.

The parameter track_wal_io_timing enables monitoring of WAL read, write and fsync times.

Normally these parameters are set in postgresql.conf so that they apply to all server processes, but it is possible to turn them on or off in individual sessions using the SET command. (To prevent ordinary users from hiding their activity from the administrator, only superusers are allowed to change these parameters with SET.)

Cumulative statistics are collected in shared memory. Every PostgreSQL process collects statistics locally, then updates the shared data at appropriate intervals. When a server, including a physical replica, shuts down cleanly, a permanent copy of the statistics data is stored in the pg_stat subdirectory, so that statistics can be retained across server restarts. In contrast, when starting from an unclean shutdown (e.g., after an immediate shutdown, a server crash, starting from a base backup, and point-in-time recovery), all statistics counters are reset.

Several predefined views, listed in Table 27.1, are available to show the current state of the system. There are also several other views, listed in Table 27.2, available to show the accumulated statistics. Alternatively, one can build custom views using the underlying cumulative statistics functions, as discussed in Section 27.2.26.

When using the cumulative statistics views and functions to monitor collected data, it is important to realize that the information does not update instantaneously. Each individual server process flushes out accumulated statistics to shared memory just before going idle, but not more frequently than once per PGSTAT_MIN_INTERVAL milliseconds (1 second unless altered while building the server); so a query or transaction still in progress does not affect the displayed totals and the displayed information lags behind actual activity. However, current-query information collected by track_activities is always up-to-date.

Another important point is that when a server process is asked to display any of the accumulated statistics, accessed values are cached until the end of its current transaction in the default configuration. So the statistics will show static information as long as you continue the current transaction. Similarly, information about the current queries of all sessions is collected when any such information is first requested within a transaction, and the same information will be displayed throughout the transaction. This is a feature, not a bug, because it allows you to perform several queries on the statistics and correlate the results without worrying that the numbers are changing underneath you. When analyzing statistics interactively, or with expensive queries, the time delta between accesses to individual statistics can lead to significant skew in the cached statistics. To minimize skew, stats_fetch_consistency can be set to snapshot, at the price of increased memory usage for caching not-needed statistics data. Conversely, if it's known that statistics are only accessed once, caching accessed statistics is unnecessary and can be avoided by setting stats_fetch_consistency to none. You can invoke pg_stat_clear_snapshot() to discard the current transaction's statistics snapshot or cached values (if any). The next use of statistical information will (when in snapshot mode) cause a new snapshot to be built or (when in cache mode) accessed statistics to be cached.

A transaction can also see its own statistics (not yet flushed out to the shared memory statistics) in the views pg_stat_xact_all_tables, pg_stat_xact_sys_tables, pg_stat_xact_user_tables, and pg_stat_xact_user_functions. These numbers do not act as stated above; instead they update continuously throughout the transaction.

Some of the information in the dynamic statistics views shown in Table 27.1 is security restricted. Ordinary users can only see all the information about their own sessions (sessions belonging to a role that they are a member of). In rows about other sessions, many columns will be null. Note, however, that the existence of a session and its general properties such as its sessions user and database are visible to all users. Superusers and roles with privileges of built-in role pg_read_all_stats can see all the information about all sessions.

Table 27.1. Dynamic Statistics Views

Table 27.2. Collected Statistics Views

The per-index statistics are particularly useful to determine which indexes are being used and how effective they are.

The pg_stat_io and pgstatio set of views are useful for determining the effectiveness of the buffer cache. They can be used to calculate a cache hit ratio. Note that while PostgreSQL's I/O statistics capture most instances in which the kernel was invoked in order to perform I/O, they do not differentiate between data which had to be fetched from disk and that which already resided in the kernel page cache. Users are advised to use the PostgreSQL statistics views in combination with operating system utilities for a more complete picture of their database's I/O performance.

The pg_stat_activity view will have one row per server process, showing information related to the current activity of that process.

Table 27.3. pg_stat_activity View

OID of the database this backend is connected to

Name of the database this backend is connected to

Process ID of this backend

Process ID of the parallel group leader if this process is a parallel query worker, or process ID of the leader apply worker if this process is a parallel apply worker. NULL indicates that this process is a parallel group leader or leader apply worker, or does not participate in any parallel operation.

OID of the user logged into this backend

Name of the user logged into this backend

application_name text

Name of the application that is connected to this backend

IP address of the client connected to this backend. If this field is null, it indicates either that the client is connected via a Unix socket on the server machine or that this is an internal process such as autovacuum.

Host name of the connected client, as reported by a reverse DNS lookup of client_addr. This field will only be non-null for IP connections, and only when log_hostname is enabled.

TCP port number that the client is using for communication with this backend, or -1 if a Unix socket is used. If this field is null, it indicates that this is an internal server process.

backend_start timestamp with time zone

Time when this process was started. For client backends, this is the time the client connected to the server.

xact_start timestamp with time zone

Time when this process' current transaction was started, or null if no transaction is active. If the current query is the first of its transaction, this column is equal to the query_start column.

query_start timestamp with time zone

Time when the currently active query was started, or if state is not active, when the last query was started

state_change timestamp with time zone

Time when the state was last changed

The type of event for which the backend is waiting, if any; otherwise NULL. See Table 27.4.

Wait event name if backend is currently waiting, otherwise NULL. See Table 27.5 through Table 27.13.

Current overall state of this backend. Possible values are:

starting: The backend is in initial startup. Client authentication is performed during this phase.

active: The backend is executing a query.

idle: The backend is waiting for a new client command.

idle in transaction: The backend is in a transaction, but is not currently executing a query.

idle in transaction (aborted): This state is similar to idle in transaction, except one of the statements in the transaction caused an error.

fastpath function call: The backend is executing a fast-path function.

disabled: This state is reported if track_activities is disabled in this backend.

Top-level transaction identifier of this backend, if any; see Section 67.1.

The current backend's xmin horizon.

Identifier of this backend's most recent query. If state is active this field shows the identifier of the currently executing query. In all other states, it shows the identifier of last query that was executed. Query identifiers are not computed by default so this field will be null unless compute_query_id parameter is enabled or a third-party module that computes query identifiers is configured.

Text of this backend's most recent query. If state is active this field shows the currently executing query. In all other states, it shows the last query that was executed. By default the query text is truncated at 1024 bytes; this value can be changed via the parameter track_activity_query_size.

Type of current backend. Possible types are autovacuum launcher, autovacuum worker, logical replication launcher, logical replication worker, parallel worker, background writer, client backend, checkpointer, archiver, standalone backend, startup, walreceiver, walsender, walwriter and walsummarizer. In addition, background workers registered by extensions may have additional types.

The wait_event and state columns are independent. If a backend is in the active state, it may or may not be waiting on some event. If the state is active and wait_event is non-null, it means that a query is being executed, but is being blocked somewhere in the system. To keep the reporting overhead low, the system does not attempt to synchronize different aspects of activity data for a backend. As a result, ephemeral discrepancies may exist between the view's columns.

Table 27.4. Wait Event Types

Table 27.5. Wait Events of Type Activity

Table 27.6. Wait Events of Type Bufferpin

Table 27.7. Wait Events of Type Client

Table 27.8. Wait Events of Type Extension

Table 27.9. Wait Events of Type Io

Table 27.10. Wait Events of Type Ipc

Table 27.11. Wait Events of Type Lock

Table 27.12. Wait Events of Type Lwlock

Table 27.13. Wait Events of Type Timeout

Here are examples of how wait events can be viewed:

Extensions can add Extension, InjectionPoint, and LWLock events to the lists shown in Table 27.8 and Table 27.12. In some cases, the name of an LWLock assigned by an extension will not be available in all server processes. It might be reported as just “extension” rather than the extension-assigned name.

The pg_stat_replication view will contain one row per WAL sender process, showing statistics about replication to that sender's connected standby server. Only directly connected standbys are listed; no information is available about downstream standby servers.

Table 27.14. pg_stat_replication View

Process ID of a WAL sender process

OID of the user logged into this WAL sender process

Name of the user logged into this WAL sender process

application_name text

Name of the application that is connected to this WAL sender

IP address of the client connected to this WAL sender. If this field is null, it indicates that the client is connected via a Unix socket on the server machine.

Host name of the connected client, as reported by a reverse DNS lookup of client_addr. This field will only be non-null for IP connections, and only when log_hostname is enabled.

TCP port number that the client is using for communication with this WAL sender, or -1 if a Unix socket is used

backend_start timestamp with time zone

Time when this process was started, i.e., when the client connected to this WAL sender

This standby's xmin horizon reported by hot_standby_feedback.

Current WAL sender state. Possible values are:

startup: This WAL sender is starting up.

catchup: This WAL sender's connected standby is catching up with the primary.

streaming: This WAL sender is streaming changes after its connected standby server has caught up with the primary.

backup: This WAL sender is sending a backup.

stopping: This WAL sender is stopping.

Last write-ahead log location sent on this connection

Last write-ahead log location written to disk by this standby server

Last write-ahead log location flushed to disk by this standby server

Last write-ahead log location replayed into the database on this standby server

Time elapsed between flushing recent WAL locally and receiving notification that this standby server has written it (but not yet flushed it or applied it). This can be used to gauge the delay that synchronous_commit level remote_write incurred while committing if this server was configured as a synchronous standby.

Time elapsed between flushing recent WAL locally and receiving notification that this standby server has written and flushed it (but not yet applied it). This can be used to gauge the delay that synchronous_commit level on incurred while committing if this server was configured as a synchronous standby.

Time elapsed between flushing recent WAL locally and receiving notification that this standby server has written, flushed and applied it. This can be used to gauge the delay that synchronous_commit level remote_apply incurred while committing if this server was configured as a synchronous standby.

sync_priority integer

Priority of this standby server for being chosen as the synchronous standby in a priority-based synchronous replication. This has no effect in a quorum-based synchronous replication.

Synchronous state of this standby server. Possible values are:

async: This standby server is asynchronous.

potential: This standby server is now asynchronous, but can potentially become synchronous if one of current synchronous ones fails.

sync: This standby server is synchronous.

quorum: This standby server is considered as a candidate for quorum standbys.

reply_time timestamp with time zone

Send time of last reply message received from standby server

The lag times reported in the pg_stat_replication view are measurements of the time taken for recent WAL to be written, flushed and replayed and for the sender to know about it. These times represent the commit delay that was (or would have been) introduced by each synchronous commit level, if the remote server was configured as a synchronous standby. For an asynchronous standby, the replay_lag column approximates the delay before recent transactions became visible to queries. If the standby server has entirely caught up with the sending server and there is no more WAL activity, the most recently measured lag times will continue to be displayed for a short time and then show NULL.

Lag times work automatically for physical replication. Logical decoding plugins may optionally emit tracking messages; if they do not, the tracking mechanism will simply display NULL lag.

The reported lag times are not predictions of how long it will take for the standby to catch up with the sending server assuming the current rate of replay. Such a system would show similar times while new WAL is being generated, but would differ when the sender becomes idle. In particular, when the standby has caught up completely, pg_stat_replication shows the time taken to write, flush and replay the most recent reported WAL location rather than zero as some users might expect. This is consistent with the goal of measuring synchronous commit and transaction visibility delays for recent write transactions. To reduce confusion for users expecting a different model of lag, the lag columns revert to NULL after a short time on a fully replayed idle system. Monitoring systems should choose whether to represent this as missing data, zero or continue to display the last known value.

The pg_stat_replication_slots view will contain one row per logical replication slot, showing statistics about its usage.

Table 27.15. pg_stat_replication_slots View

A unique, cluster-wide identifier for the replication slot

Number of transactions spilled to disk once the memory used by logical decoding to decode changes from WAL has exceeded logical_decoding_work_mem. The counter gets incremented for both top-level transactions and subtransactions.

Number of times transactions were spilled to disk while decoding changes from WAL for this slot. This counter is incremented each time a transaction is spilled, and the same transaction may be spilled multiple times.

Amount of decoded transaction data spilled to disk while performing decoding of changes from WAL for this slot. This and other spill counters can be used to gauge the I/O which occurred during logical decoding and allow tuning logical_decoding_work_mem.

Number of in-progress transactions streamed to the decoding output plugin after the memory used by logical decoding to decode changes from WAL for this slot has exceeded logical_decoding_work_mem. Streaming only works with top-level transactions (subtransactions can't be streamed independently), so the counter is not incremented for subtransactions.

Number of times in-progress transactions were streamed to the decoding output plugin while decoding changes from WAL for this slot. This counter is incremented each time a transaction is streamed, and the same transaction may be streamed multiple times.

Amount of transaction data decoded for streaming in-progress transactions to the decoding output plugin while decoding changes from WAL for this slot. This and other streaming counters for this slot can be used to tune logical_decoding_work_mem.

Number of decoded transactions sent to the decoding output plugin for this slot. This counts top-level transactions only, and is not incremented for subtransactions. Note that this includes the transactions that are streamed and/or spilled.

Amount of transaction data decoded for sending transactions to the decoding output plugin while decoding changes from WAL for this slot. Note that this includes data that is streamed and/or spilled.

stats_reset timestamp with time zone

Time at which these statistics were last reset

The pg_stat_wal_receiver view will contain only one row, showing statistics about the WAL receiver from that receiver's connected server.

Table 27.16. pg_stat_wal_receiver View

Process ID of the WAL receiver process

Activity status of the WAL receiver process

receive_start_lsn pg_lsn

First write-ahead log location used when WAL receiver is started

receive_start_tli integer

First timeline number used when WAL receiver is started

Last write-ahead log location already received and written to disk, but not flushed. This should not be used for data integrity checks.

Last write-ahead log location already received and flushed to disk, the initial value of this field being the first log location used when WAL receiver is started

Timeline number of last write-ahead log location received and flushed to disk, the initial value of this field being the timeline number of the first log location used when WAL receiver is started

last_msg_send_time timestamp with time zone

Send time of last message received from origin WAL sender

last_msg_receipt_time timestamp with time zone

Receipt time of last message received from origin WAL sender

latest_end_lsn pg_lsn

Last write-ahead log location reported to origin WAL sender

latest_end_time timestamp with time zone

Time of last write-ahead log location reported to origin WAL sender

Replication slot name used by this WAL receiver

Host of the PostgreSQL instance this WAL receiver is connected to. This can be a host name, an IP address, or a directory path if the connection is via Unix socket. (The path case can be distinguished because it will always be an absolute path, beginning with /.)

Port number of the PostgreSQL instance this WAL receiver is connected to.

Connection string used by this WAL receiver, with security-sensitive fields obfuscated.

The pg_stat_recovery_prefetch view will contain only one row. The columns wal_distance, block_distance and io_depth show current values, and the other columns show cumulative counters that can be reset with the pg_stat_reset_shared function.

Table 27.17. pg_stat_recovery_prefetch View

stats_reset timestamp with time zone

Time at which these statistics were last reset

Number of blocks prefetched because they were not in the buffer pool

Number of blocks not prefetched because they were already in the buffer pool

Number of blocks not prefetched because they would be zero-initialized

Number of blocks not prefetched because they didn't exist yet

Number of blocks not prefetched because a full page image was included in the WAL

Number of blocks not prefetched because they were already recently prefetched

How many bytes ahead the prefetcher is looking

How many blocks ahead the prefetcher is looking

How many prefetches have been initiated but are not yet known to have completed

Table 27.18. pg_stat_subscription View

OID of the subscription

Name of the subscription

Type of the subscription worker process. Possible types are apply, parallel apply, and table synchronization.

Process ID of the subscription worker process

Process ID of the leader apply worker if this process is a parallel apply worker; NULL if this process is a leader apply worker or a table synchronization worker

OID of the relation that the worker is synchronizing; NULL for the leader apply worker and parallel apply workers

Last write-ahead log location received, the initial value of this field being 0; NULL for parallel apply workers

last_msg_send_time timestamp with time zone

Send time of last message received from origin WAL sender; NULL for parallel apply workers

last_msg_receipt_time timestamp with time zone

Receipt time of last message received from origin WAL sender; NULL for parallel apply workers

latest_end_lsn pg_lsn

Last write-ahead log location reported to origin WAL sender; NULL for parallel apply workers

latest_end_time timestamp with time zone

Time of last write-ahead log location reported to origin WAL sender; NULL for parallel apply workers

The pg_stat_subscription_stats view will contain one row per subscription.

Table 27.19. pg_stat_subscription_stats View

OID of the subscription

Name of the subscription

apply_error_count bigint

Number of times an error occurred while applying changes. Note that any conflict resulting in an apply error will be counted in both apply_errorcount and the corresponding conflict count (e.g., confl*).

sync_error_count bigint

Number of times an error occurred during the initial table synchronization

confl_insert_exists bigint

Number of times a row insertion violated a NOT DEFERRABLE unique constraint during the application of changes. See insert_exists for details about this conflict.

confl_update_origin_differs bigint

Number of times an update was applied to a row that had been previously modified by another source during the application of changes. See update_origin_differs for details about this conflict.

confl_update_exists bigint

Number of times that an updated row value violated a NOT DEFERRABLE unique constraint during the application of changes. See update_exists for details about this conflict.

confl_update_missing bigint

Number of times the tuple to be updated was not found during the application of changes. See update_missing for details about this conflict.

confl_delete_origin_differs bigint

Number of times a delete operation was applied to row that had been previously modified by another source during the application of changes. See delete_origin_differs for details about this conflict.

confl_delete_missing bigint

Number of times the tuple to be deleted was not found during the application of changes. See delete_missing for details about this conflict.

confl_multiple_unique_conflicts bigint

Number of times a row insertion or an updated row values violated multiple NOT DEFERRABLE unique constraints during the application of changes. See multiple_unique_conflicts for details about this conflict.

stats_reset timestamp with time zone

Time at which these statistics were last reset

The pg_stat_ssl view will contain one row per backend or WAL sender process, showing statistics about SSL usage on this connection. It can be joined to pg_stat_activity or pg_stat_replication on the pid column to get more details about the connection.

Table 27.20. pg_stat_ssl View

Process ID of a backend or WAL sender process

True if SSL is used on this connection

Version of SSL in use, or NULL if SSL is not in use on this connection

Name of SSL cipher in use, or NULL if SSL is not in use on this connection

Number of bits in the encryption algorithm used, or NULL if SSL is not used on this connection

Distinguished Name (DN) field from the client certificate used, or NULL if no client certificate was supplied or if SSL is not in use on this connection. This field is truncated if the DN field is longer than NAMEDATALEN (64 characters in a standard build).

client_serial numeric

Serial number of the client certificate, or NULL if no client certificate was supplied or if SSL is not in use on this connection. The combination of certificate serial number and certificate issuer uniquely identifies a certificate (unless the issuer erroneously reuses serial numbers).

DN of the issuer of the client certificate, or NULL if no client certificate was supplied or if SSL is not in use on this connection. This field is truncated like client_dn.

The pg_stat_gssapi view will contain one row per backend, showing information about GSSAPI usage on this connection. It can be joined to pg_stat_activity or pg_stat_replication on the pid column to get more details about the connection.

Table 27.21. pg_stat_gssapi View

Process ID of a backend

gss_authenticated boolean

True if GSSAPI authentication was used for this connection

Principal used to authenticate this connection, or NULL if GSSAPI was not used to authenticate this connection. This field is truncated if the principal is longer than NAMEDATALEN (64 characters in a standard build).

True if GSSAPI encryption is in use on this connection

credentials_delegated boolean

True if GSSAPI credentials were delegated on this connection.

The pg_stat_archiver view will always have a single row, containing data about the archiver process of the cluster.

Table 27.22. pg_stat_archiver View

archived_count bigint

Number of WAL files that have been successfully archived

last_archived_wal text

Name of the WAL file most recently successfully archived

last_archived_time timestamp with time zone

Time of the most recent successful archive operation

Number of failed attempts for archiving WAL files

Name of the WAL file of the most recent failed archival operation

last_failed_time timestamp with time zone

Time of the most recent failed archival operation

stats_reset timestamp with time zone

Time at which these statistics were last reset

Normally, WAL files are archived in order, oldest to newest, but that is not guaranteed, and does not hold under special circumstances like when promoting a standby or after crash recovery. Therefore it is not safe to assume that all files older than last_archived_wal have also been successfully archived.

The pg_stat_io view will contain one row for each combination of backend type, target I/O object, and I/O context, showing cluster-wide I/O statistics. Combinations which do not make sense are omitted.

Currently, I/O on relations (e.g. tables, indexes) and WAL activity are tracked. However, relation I/O which bypasses shared buffers (e.g. when moving a table from one tablespace to another) is currently not tracked.

Table 27.23. pg_stat_io View

Type of backend (e.g. background worker, autovacuum worker). See pg_stat_activity for more information on backend_types. Some backend_types do not accumulate I/O operation statistics and will not be included in the view.

Target object of an I/O operation. Possible values are:

relation: Permanent relations.

temp relation: Temporary relations.

wal: Write Ahead Logs.

The context of an I/O operation. Possible values are:

normal: The default or standard context for a type of I/O operation. For example, by default, relation data is read into and written out from shared buffers. Thus, reads and writes of relation data to and from shared buffers are tracked in context normal.

init: I/O operations performed while creating the WAL segments are tracked in context init.

vacuum: I/O operations performed outside of shared buffers while vacuuming and analyzing permanent relations. Temporary table vacuums use the same local buffer pool as other temporary table I/O operations and are tracked in context normal.

bulkread: Certain large read I/O operations done outside of shared buffers, for example, a sequential scan of a large table.

bulkwrite: Certain large write I/O operations done outside of shared buffers, such as COPY.

Number of read operations.

The total size of read operations in bytes.

read_time double precision

Time spent waiting for read operations in milliseconds (if track_io_timing is enabled and object is not wal, or if track_wal_io_timing is enabled and object is wal, otherwise zero)

Number of write operations.

The total size of write operations in bytes.

write_time double precision

Time spent waiting for write operations in milliseconds (if track_io_timing is enabled and object is not wal, or if track_wal_io_timing is enabled and object is wal, otherwise zero)

Number of units of size BLCKSZ (typically 8kB) which the process requested the kernel write out to permanent storage.

writeback_time double precision

Time spent waiting for writeback operations in milliseconds (if track_io_timing is enabled, otherwise zero). This includes the time spent queueing write-out requests and, potentially, the time spent to write out the dirty data.

Number of relation extend operations.

The total size of relation extend operations in bytes.

extend_time double precision

Time spent waiting for extend operations in milliseconds. (if track_io_timing is enabled and object is not wal, or if track_wal_io_timing is enabled and object is wal, otherwise zero)

The number of times a desired block was found in a shared buffer.

Number of times a block has been written out from a shared or local buffer in order to make it available for another use.

In context normal, this counts the number of times a block was evicted from a buffer and replaced with another block. In contexts bulkwrite, bulkread, and vacuum, this counts the number of times a block was evicted from shared buffers in order to add the shared buffer to a separate, size-limited ring buffer for use in a bulk I/O operation.

The number of times an existing buffer in a size-limited ring buffer outside of shared buffers was reused as part of an I/O operation in the bulkread, bulkwrite, or vacuum contexts.

Number of fsync calls. These are only tracked in context normal.

fsync_time double precision

Time spent waiting for fsync operations in milliseconds (if track_io_timing is enabled and object is not wal, or if track_wal_io_timing is enabled and object is wal, otherwise zero)

stats_reset timestamp with time zone

Time at which these statistics were last reset.

Some backend types never perform I/O operations on some I/O objects and/or in some I/O contexts. These rows are omitted from the view. For example, the checkpointer does not checkpoint temporary tables, so there will be no rows for backend_type checkpointer and object temp relation.

In addition, some I/O operations will never be performed either by certain backend types or on certain I/O objects and/or in certain I/O contexts. These cells will be NULL. For example, temporary tables are not fsynced, so fsyncs will be NULL for object temp relation. Also, the background writer does not perform reads, so reads will be NULL in rows for backend_type background writer.

For the object wal, fsyncs and fsync_time track the fsync activity of WAL files done in issue_xlog_fsync. writes and write_time track the write activity of WAL files done in XLogWrite. See Section 28.5 for more information.

pg_stat_io can be used to inform database tuning. For example:

A high evictions count can indicate that shared buffers should be increased.

Client backends rely on the checkpointer to ensure data is persisted to permanent storage. Large numbers of fsyncs by client backends could indicate a misconfiguration of shared buffers or of the checkpointer. More information on configuring the checkpointer can be found in Section 28.5.

Normally, client backends should be able to rely on auxiliary processes like the checkpointer and the background writer to write out dirty data as much as possible. Large numbers of writes by client backends could indicate a misconfiguration of shared buffers or of the checkpointer. More information on configuring the checkpointer can be found in Section 28.5.

Columns tracking I/O wait time will only be non-zero when track_io_timing is enabled. The user should be careful when referencing these columns in combination with their corresponding I/O operations in case track_io_timing was not enabled for the entire time since the last stats reset.

The pg_stat_bgwriter view will always have a single row, containing data about the background writer of the cluster.

Table 27.24. pg_stat_bgwriter View

Number of buffers written by the background writer

maxwritten_clean bigint

Number of times the background writer stopped a cleaning scan because it had written too many buffers

Number of buffers allocated

stats_reset timestamp with time zone

Time at which these statistics were last reset

The pg_stat_checkpointer view will always have a single row, containing data about the checkpointer process of the cluster.

Table 27.25. pg_stat_checkpointer View

Number of scheduled checkpoints due to timeout

Number of requested checkpoints

Number of checkpoints that have been performed

restartpoints_timed bigint

Number of scheduled restartpoints due to timeout or after a failed attempt to perform it

restartpoints_req bigint

Number of requested restartpoints

restartpoints_done bigint

Number of restartpoints that have been performed

write_time double precision

Total amount of time that has been spent in the portion of processing checkpoints and restartpoints where files are written to disk, in milliseconds

sync_time double precision

Total amount of time that has been spent in the portion of processing checkpoints and restartpoints where files are synchronized to disk, in milliseconds

buffers_written bigint

Number of shared buffers written during checkpoints and restartpoints

Number of SLRU buffers written during checkpoints and restartpoints

stats_reset timestamp with time zone

Time at which these statistics were last reset

Checkpoints may be skipped if the server has been idle since the last one. num_timed and num_requested count both completed and skipped checkpoints, while num_done tracks only the completed ones. Similarly, restartpoints may be skipped if the last replayed checkpoint record is already the last restartpoint. restartpoints_timed and restartpoints_req count both completed and skipped restartpoints, while restartpoints_done tracks only the completed ones.

The pg_stat_wal view will always have a single row, containing data about WAL activity of the cluster.

Table 27.26. pg_stat_wal View

Total number of WAL records generated

Total number of WAL full page images generated

Total amount of WAL generated in bytes

wal_buffers_full bigint

Number of times WAL data was written to disk because WAL buffers became full

stats_reset timestamp with time zone

Time at which these statistics were last reset

The pg_stat_database view will contain one row for each database in the cluster, plus one for shared objects, showing database-wide statistics.

Table 27.27. pg_stat_database View

OID of this database, or 0 for objects belonging to a shared relation

Name of this database, or NULL for shared objects.

Number of backends currently connected to this database, or NULL for shared objects. This is the only column in this view that returns a value reflecting current state; all other columns return the accumulated values since the last reset.

Number of transactions in this database that have been committed

Number of transactions in this database that have been rolled back

Number of disk blocks read in this database

Number of times disk blocks were found already in the buffer cache, so that a read was not necessary (this only includes hits in the PostgreSQL buffer cache, not the operating system's file system cache)

Number of live rows fetched by sequential scans and index entries returned by index scans in this database

Number of live rows fetched by index scans in this database

Number of rows inserted by queries in this database

Number of rows updated by queries in this database

Number of rows deleted by queries in this database

Number of queries canceled due to conflicts with recovery in this database. (Conflicts occur only on standby servers; see pg_stat_database_conflicts for details.)

Number of temporary files created by queries in this database. All temporary files are counted, regardless of why the temporary file was created (e.g., sorting or hashing), and regardless of the log_temp_files setting.

Total amount of data written to temporary files by queries in this database. All temporary files are counted, regardless of why the temporary file was created, and regardless of the log_temp_files setting.

Number of deadlocks detected in this database

checksum_failures bigint

Number of data page checksum failures detected in this database (or on a shared object), or NULL if data checksums are disabled.

checksum_last_failure timestamp with time zone

Time at which the last data page checksum failure was detected in this database (or on a shared object), or NULL if data checksums are disabled.

blk_read_time double precision

Time spent reading data file blocks by backends in this database, in milliseconds (if track_io_timing is enabled, otherwise zero)

blk_write_time double precision

Time spent writing data file blocks by backends in this database, in milliseconds (if track_io_timing is enabled, otherwise zero)

session_time double precision

Time spent by database sessions in this database, in milliseconds (note that statistics are only updated when the state of a session changes, so if sessions have been idle for a long time, this idle time won't be included)

active_time double precision

Time spent executing SQL statements in this database, in milliseconds (this corresponds to the states active and fastpath function call in pg_stat_activity)

idle_in_transaction_time double precision

Time spent idling while in a transaction in this database, in milliseconds (this corresponds to the states idle in transaction and idle in transaction (aborted) in pg_stat_activity)

Total number of sessions established to this database

sessions_abandoned bigint

Number of database sessions to this database that were terminated because connection to the client was lost

sessions_fatal bigint

Number of database sessions to this database that were terminated by fatal errors

sessions_killed bigint

Number of database sessions to this database that were terminated by operator intervention

parallel_workers_to_launch bigint

Number of parallel workers planned to be launched by queries on this database

parallel_workers_launched bigint

Number of parallel workers launched by queries on this database

stats_reset timestamp with time zone

Time at which these statistics were last reset

The pg_stat_database_conflicts view will contain one row per database, showing database-wide statistics about query cancels occurring due to conflicts with recovery on standby servers. This view will only contain information on standby servers, since conflicts do not occur on primary servers.

Table 27.28. pg_stat_database_conflicts View

Name of this database

confl_tablespace bigint

Number of queries in this database that have been canceled due to dropped tablespaces

Number of queries in this database that have been canceled due to lock timeouts

confl_snapshot bigint

Number of queries in this database that have been canceled due to old snapshots

confl_bufferpin bigint

Number of queries in this database that have been canceled due to pinned buffers

confl_deadlock bigint

Number of queries in this database that have been canceled due to deadlocks

confl_active_logicalslot bigint

Number of uses of logical slots in this database that have been canceled due to old snapshots or too low a wal_level on the primary

The pg_stat_all_tables view will contain one row for each table in the current database (including TOAST tables), showing statistics about accesses to that specific table. The pg_stat_user_tables and pg_stat_sys_tables views contain the same information, but filtered to only show user and system tables respectively.

Table 27.29. pg_stat_all_tables View

Name of the schema that this table is in

Number of sequential scans initiated on this table

last_seq_scan timestamp with time zone

The time of the last sequential scan on this table, based on the most recent transaction stop time

Number of live rows fetched by sequential scans

Number of index scans initiated on this table

last_idx_scan timestamp with time zone

The time of the last index scan on this table, based on the most recent transaction stop time

Number of live rows fetched by index scans

Total number of rows inserted

Total number of rows updated. (This includes row updates counted in n_tup_hot_upd and n_tup_newpage_upd, and remaining non-HOT updates.)

Total number of rows deleted

Number of rows HOT updated. These are updates where no successor versions are required in indexes.

n_tup_newpage_upd bigint

Number of rows updated where the successor version goes onto a new heap page, leaving behind an original version with a t_ctid field that points to a different heap page. These are always non-HOT updates.

Estimated number of live rows

Estimated number of dead rows

n_mod_since_analyze bigint

Estimated number of rows modified since this table was last analyzed

n_ins_since_vacuum bigint

Estimated number of rows inserted since this table was last vacuumed (not counting VACUUM FULL)

last_vacuum timestamp with time zone

Last time at which this table was manually vacuumed (not counting VACUUM FULL)

last_autovacuum timestamp with time zone

Last time at which this table was vacuumed by the autovacuum daemon

last_analyze timestamp with time zone

Last time at which this table was manually analyzed

last_autoanalyze timestamp with time zone

Last time at which this table was analyzed by the autovacuum daemon

Number of times this table has been manually vacuumed (not counting VACUUM FULL)

autovacuum_count bigint

Number of times this table has been vacuumed by the autovacuum daemon

Number of times this table has been manually analyzed

autoanalyze_count bigint

Number of times this table has been analyzed by the autovacuum daemon

total_vacuum_time double precision

Total time this table has been manually vacuumed, in milliseconds (not counting VACUUM FULL). (This includes the time spent sleeping due to cost-based delays.)

total_autovacuum_time double precision

Total time this table has been vacuumed by the autovacuum daemon, in milliseconds. (This includes the time spent sleeping due to cost-based delays.)

total_analyze_time double precision

Total time this table has been manually analyzed, in milliseconds. (This includes the time spent sleeping due to cost-based delays.)

total_autoanalyze_time double precision

Total time this table has been analyzed by the autovacuum daemon, in milliseconds. (This includes the time spent sleeping due to cost-based delays.)

The pg_stat_all_indexes view will contain one row for each index in the current database, showing statistics about accesses to that specific index. The pg_stat_user_indexes and pg_stat_sys_indexes views contain the same information, but filtered to only show user and system indexes respectively.

Table 27.30. pg_stat_all_indexes View

OID of the table for this index

Name of the schema this index is in

Name of the table for this index

Number of index scans initiated on this index

last_idx_scan timestamp with time zone

The time of the last scan on this index, based on the most recent transaction stop time

Number of index entries returned by scans on this index

Number of live table rows fetched by simple index scans using this index

Indexes can be used by simple index scans, “bitmap” index scans, and the optimizer. In a bitmap scan the output of several indexes can be combined via AND or OR rules, so it is difficult to associate individual heap row fetches with specific indexes when a bitmap scan is used. Therefore, a bitmap scan increments the pg_stat_all_indexes.idx_tup_read count(s) for the index(es) it uses, and it increments the pg_stat_all_tables.idx_tup_fetch count for the table, but it does not affect pg_stat_all_indexes.idx_tup_fetch. The optimizer also accesses indexes to check for supplied constants whose values are outside the recorded range of the optimizer statistics because the optimizer statistics might be stale.

The idx_tup_read and idx_tup_fetch counts can be different even without any use of bitmap scans, because idx_tup_read counts index entries retrieved from the index while idx_tup_fetch counts live rows fetched from the table. The latter will be less if any dead or not-yet-committed rows are fetched using the index, or if any heap fetches are avoided by means of an index-only scan.

Index scans may sometimes perform multiple index searches per execution. Each index search increments pg_stat_all_indexes.idx_scan, so it's possible for the count of index scans to significantly exceed the total number of index scan executor node executions.

This can happen with queries that use certain SQL constructs to search for rows matching any value out of a list or array of multiple scalar values (see Section 9.25). It can also happen to queries with a column_name = value1 OR column_name = value2 ... construct, though only when the optimizer transforms the construct into an equivalent multi-valued array representation. Similarly, when B-tree index scans use the skip scan optimization, an index search is performed each time the scan is repositioned to the next index leaf page that might have matching tuples (see Section 11.3).

EXPLAIN ANALYZE outputs the total number of index searches performed by each index scan node. See Section 14.1.2 for an example demonstrating how this works.

The pg_statio_all_tables view will contain one row for each table in the current database (including TOAST tables), showing statistics about I/O on that specific table. The pg_statio_user_tables and pg_statio_sys_tables views contain the same information, but filtered to only show user and system tables respectively.

Table 27.31. pg_statio_all_tables View

Name of the schema that this table is in

heap_blks_read bigint

Number of disk blocks read from this table

Number of buffer hits in this table

Number of disk blocks read from all indexes on this table

Number of buffer hits in all indexes on this table

toast_blks_read bigint

Number of disk blocks read from this table's TOAST table (if any)

toast_blks_hit bigint

Number of buffer hits in this table's TOAST table (if any)

tidx_blks_read bigint

Number of disk blocks read from this table's TOAST table indexes (if any)

Number of buffer hits in this table's TOAST table indexes (if any)

The pg_statio_all_indexes view will contain one row for each index in the current database, showing statistics about I/O on that specific index. The pg_statio_user_indexes and pg_statio_sys_indexes views contain the same information, but filtered to only show user and system indexes respectively.

Table 27.32. pg_statio_all_indexes View

OID of the table for this index

Name of the schema this index is in

Name of the table for this index

Number of disk blocks read from this index

Number of buffer hits in this index

The pg_statio_all_sequences view will contain one row for each sequence in the current database, showing statistics about I/O on that specific sequence.

Table 27.33. pg_statio_all_sequences View

Name of the schema this sequence is in

Name of this sequence

Number of disk blocks read from this sequence

Number of buffer hits in this sequence

The pg_stat_user_functions view will contain one row for each tracked function, showing statistics about executions of that function. The track_functions parameter controls exactly which functions are tracked.

Table 27.34. pg_stat_user_functions View

Name of the schema this function is in

Name of this function

Number of times this function has been called

total_time double precision

Total time spent in this function and all other functions called by it, in milliseconds

self_time double precision

Total time spent in this function itself, not including other functions called by it, in milliseconds

PostgreSQL accesses certain on-disk information via SLRU (simple least-recently-used) caches. The pg_stat_slru view will contain one row for each tracked SLRU cache, showing statistics about access to cached pages.

For each SLRU cache that's part of the core server, there is a configuration parameter that controls its size, with the suffix _buffers appended.

Table 27.35. pg_stat_slru View

Number of blocks zeroed during initializations

Number of times disk blocks were found already in the SLRU, so that a read was not necessary (this only includes hits in the SLRU, not the operating system's file system cache)

Number of disk blocks read for this SLRU

Number of disk blocks written for this SLRU

Number of blocks checked for existence for this SLRU

Number of flushes of dirty data for this SLRU

Number of truncates for this SLRU

stats_reset timestamp with time zone

Time at which these statistics were last reset

Other ways of looking at the statistics can be set up by writing queries that use the same underlying statistics access functions used by the standard views shown above. For details such as the functions' names, consult the definitions of the standard views. (For example, in psql you could issue \d+ pg_stat_activity.) The access functions for per-database statistics take a database OID as an argument to identify which database to report on. The per-table and per-index functions take a table or index OID. The functions for per-function statistics take a function OID. Note that only tables, indexes, and functions in the current database can be seen with these functions.

Additional functions related to the cumulative statistics system are listed in Table 27.36.

Table 27.36. Additional Statistics Functions

pg_backend_pid () → integer

Returns the process ID of the server process attached to the current session.

pg_stat_get_backend_io ( integer ) → setof record

Returns I/O statistics about the backend with the specified process ID. The output fields are exactly the same as the ones in the pg_stat_io view.

The function does not return I/O statistics for the checkpointer, the background writer, the startup process and the autovacuum launcher as they are already visible in the pg_stat_io view and there is only one of each.

pg_stat_get_activity ( integer ) → setof record

Returns a record of information about the backend with the specified process ID, or one record for each active backend in the system if NULL is specified. The fields returned are a subset of those in the pg_stat_activity view.

pg_stat_get_backend_wal ( integer ) → record

Returns WAL statistics about the backend with the specified process ID. The output fields are exactly the same as the ones in the pg_stat_wal view.

The function does not return WAL statistics for the checkpointer, the background writer, the startup process and the autovacuum launcher.

pg_stat_get_snapshot_timestamp () → timestamp with time zone

Returns the timestamp of the current statistics snapshot, or NULL if no statistics snapshot has been taken. A snapshot is taken the first time cumulative statistics are accessed in a transaction if stats_fetch_consistency is set to snapshot

pg_stat_get_xact_blocks_fetched ( oid ) → bigint

Returns the number of block read requests for table or index, in the current transaction. This number minus pg_stat_get_xact_blocks_hit gives the number of kernel read() calls; the number of actual physical reads is usually lower due to kernel-level buffering.

pg_stat_get_xact_blocks_hit ( oid ) → bigint

Returns the number of block read requests for table or index, in the current transaction, found in cache (not triggering kernel read() calls).

pg_stat_clear_snapshot () → void

Discards the current statistics snapshot or cached information.

pg_stat_reset () → void

Resets all statistics counters for the current database to zero.

This function is restricted to superusers by default, but other users can be granted EXECUTE to run the function.

pg_stat_reset_shared ( [ target text DEFAULT NULL ] ) → void

Resets some cluster-wide statistics counters to zero, depending on the argument. target can be:

archiver: Reset all the counters shown in the pg_stat_archiver view.

bgwriter: Reset all the counters shown in the pg_stat_bgwriter view.

checkpointer: Reset all the counters shown in the pg_stat_checkpointer view.

io: Reset all the counters shown in the pg_stat_io view.

recovery_prefetch: Reset all the counters shown in the pg_stat_recovery_prefetch view.

slru: Reset all the counters shown in the pg_stat_slru view.

wal: Reset all the counters shown in the pg_stat_wal view.

NULL or not specified: All the counters from the views listed above are reset.

This function is restricted to superusers by default, but other users can be granted EXECUTE to run the function.

pg_stat_reset_single_table_counters ( oid ) → void

Resets statistics for a single table or index in the current database or shared across all databases in the cluster to zero.

This function is restricted to superusers by default, but other users can be granted EXECUTE to run the function.

pg_stat_reset_backend_stats ( integer ) → void

Resets statistics for a single backend with the specified process ID to zero.

This function is restricted to superusers by default, but other users can be granted EXECUTE to run the function.

pg_stat_reset_single_function_counters ( oid ) → void

Resets statistics for a single function in the current database to zero.

This function is restricted to superusers by default, but other users can be granted EXECUTE to run the function.

pg_stat_reset_slru ( [ target text DEFAULT NULL ] ) → void

Resets statistics to zero for a single SLRU cache, or for all SLRUs in the cluster. If target is NULL or is not specified, all the counters shown in the pg_stat_slru view for all SLRU caches are reset. The argument can be one of commit_timestamp, multixact_member, multixact_offset, notify, serializable, subtransaction, or transaction to reset the counters for only that entry. If the argument is other (or indeed, any unrecognized name), then the counters for all other SLRU caches, such as extension-defined caches, are reset.

This function is restricted to superusers by default, but other users can be granted EXECUTE to run the function.

pg_stat_reset_replication_slot ( text ) → void

Resets statistics of the replication slot defined by the argument. If the argument is NULL, resets statistics for all the replication slots.

This function is restricted to superusers by default, but other users can be granted EXECUTE to run the function.

pg_stat_reset_subscription_stats ( oid ) → void

Resets statistics for a single subscription shown in the pg_stat_subscription_stats view to zero. If the argument is NULL, reset statistics for all subscriptions.

This function is restricted to superusers by default, but other users can be granted EXECUTE to run the function.

Using pg_stat_reset() also resets counters that autovacuum uses to determine when to trigger a vacuum or an analyze. Resetting these counters can cause autovacuum to not perform necessary work, which can cause problems such as table bloat or out-dated table statistics. A database-wide ANALYZE is recommended after the statistics have been reset.

pg_stat_get_activity, the underlying function of the pg_stat_activity view, returns a set of records containing all the available information about each backend process. Sometimes it may be more convenient to obtain just a subset of this information. In such cases, another set of per-backend statistics access functions can be used; these are shown in Table 27.37. These access functions use the session's backend ID number, which is a small integer (>= 0) that is distinct from the backend ID of any concurrent session, although a session's ID can be recycled as soon as it exits. The backend ID is used, among other things, to identify the session's temporary schema if it has one. The function pg_stat_get_backend_idset provides a convenient way to list all the active backends' ID numbers for invoking these functions. For example, to show the PIDs and current queries of all backends:

Table 27.37. Per-Backend Statistics Functions

pg_stat_get_backend_activity ( integer ) → text

Returns the text of this backend's most recent query.

pg_stat_get_backend_activity_start ( integer ) → timestamp with time zone

Returns the time when the backend's most recent query was started.

pg_stat_get_backend_client_addr ( integer ) → inet

Returns the IP address of the client connected to this backend.

pg_stat_get_backend_client_port ( integer ) → integer

Returns the TCP port number that the client is using for communication.

pg_stat_get_backend_dbid ( integer ) → oid

Returns the OID of the database this backend is connected to.

pg_stat_get_backend_idset () → setof integer

Returns the set of currently active backend ID numbers.

pg_stat_get_backend_pid ( integer ) → integer

Returns the process ID of this backend.

pg_stat_get_backend_start ( integer ) → timestamp with time zone

Returns the time when this process was started.

pg_stat_get_backend_subxact ( integer ) → record

Returns a record of information about the subtransactions of the backend with the specified ID. The fields returned are subxact_count, which is the number of subtransactions in the backend's subtransaction cache, and subxact_overflow, which indicates whether the backend's subtransaction cache is overflowed or not.

pg_stat_get_backend_userid ( integer ) → oid

Returns the OID of the user logged into this backend.

pg_stat_get_backend_wait_event ( integer ) → text

Returns the wait event name if this backend is currently waiting, otherwise NULL. See Table 27.5 through Table 27.13.

pg_stat_get_backend_wait_event_type ( integer ) → text

Returns the wait event type name if this backend is currently waiting, otherwise NULL. See Table 27.4 for details.

pg_stat_get_backend_xact_start ( integer ) → timestamp with time zone

Returns the time when the backend's current transaction was started.

Examples:

Example 1 (unknown):

SELECT pid, wait_event_type, wait_event FROM pg_stat_activity WHERE wait_event is NOT NULL;
 pid  | wait_event_type | wait_event
------+-----------------+------------
 2540 | Lock            | relation
 6644 | LWLock          | ProcArray
(2 rows)

Example 2 (unknown):

SELECT a.pid, a.wait_event, w.description
  FROM pg_stat_activity a JOIN
       pg_wait_events w ON (a.wait_event_type = w.type AND
                            a.wait_event = w.name)
  WHERE a.wait_event is NOT NULL and a.state = 'active';
-[ RECORD 1 ]------------------------------------------------------​------------
pid         | 686674
wait_event  | WALInitSync
description | Waiting for a newly initialized WAL file to reach durable storage

Example 3 (unknown):

SELECT pg_stat_get_backend_pid(backendid) AS pid,
       pg_stat_get_backend_activity(backendid) AS query
FROM pg_stat_get_backend_idset() AS backendid;

---

PostgreSQL: Documentation: 18: PREPARE

URL: https://www.postgresql.org/docs/current/ecpg-sql-prepare.html

Contents:

• PREPARE
• Synopsis
• Description
• Parameters
• Notes
• Examples
• Compatibility
• See Also

PREPARE — prepare a statement for execution

PREPARE prepares a statement dynamically specified as a string for execution. This is different from the direct SQL statement PREPARE, which can also be used in embedded programs. The EXECUTE command is used to execute either kind of prepared statement.

An identifier for the prepared query.

A literal string or a host variable containing a preparable SQL statement, one of SELECT, INSERT, UPDATE, or DELETE. Use question marks (?) for parameter values to be supplied at execution.

In typical usage, the string is a host variable reference to a string containing a dynamically-constructed SQL statement. The case of a literal string is not very useful; you might as well just write a direct SQL PREPARE statement.

If you do use a literal string, keep in mind that any double quotes you might wish to include in the SQL statement must be written as octal escapes (\042) not the usual C idiom \". This is because the string is inside an EXEC SQL section, so the ECPG lexer parses it according to SQL rules not C rules. Any embedded backslashes will later be handled according to C rules; but \" causes an immediate syntax error because it is seen as ending the literal.

PREPARE is specified in the SQL standard.

Examples:

Example 1 (unknown):

PREPARE prepared_name FROM string

Example 2 (unknown):

char *stmt = "SELECT * FROM test1 WHERE a = ? AND b = ?";

EXEC SQL ALLOCATE DESCRIPTOR outdesc;
EXEC SQL PREPARE foo FROM :stmt;

EXEC SQL EXECUTE foo USING SQL DESCRIPTOR indesc INTO SQL DESCRIPTOR outdesc;

---

PostgreSQL: Documentation: 18: 35.44. routine_table_usage

URL: https://www.postgresql.org/docs/current/infoschema-routine-table-usage.html

Contents:

• 35.44. routine_table_usage #

The view routine_table_usage is meant to identify all tables that are used by a function or procedure. This information is currently not tracked by PostgreSQL.

Table 35.42. routine_table_usage Columns

specific_catalog sql_identifier

Name of the database containing the function (always the current database)

specific_schema sql_identifier

Name of the schema containing the function

specific_name sql_identifier

The “specific name” of the function. See Section 35.45 for more information.

routine_catalog sql_identifier

Name of the database containing the function (always the current database)

routine_schema sql_identifier

Name of the schema containing the function

routine_name sql_identifier

Name of the function (might be duplicated in case of overloading)

table_catalog sql_identifier

Name of the database that contains the table that is used by the function (always the current database)

table_schema sql_identifier

Name of the schema that contains the table that is used by the function

table_name sql_identifier

Name of the table that is used by the function

---

PostgreSQL: Documentation: 18: 32.16. The Password File

URL: https://www.postgresql.org/docs/current/libpq-pgpass.html

Contents:

• 32.16. The Password File #

The file .pgpass in a user's home directory can contain passwords to be used if the connection requires a password (and no password has been specified otherwise). On Unix systems, the directory can be specified by the HOME environment variable, or if undefined, the home directory of the effective user. On Microsoft Windows the file is named %APPDATA%\postgresql\pgpass.conf (where %APPDATA% refers to the Application Data subdirectory in the user's profile). Alternatively, the password file to use can be specified using the connection parameter passfile or the environment variable PGPASSFILE.

This file should contain lines of the following format:

(You can add a reminder comment to the file by copying the line above and preceding it with #.) Each of the first four fields can be a literal value, or *, which matches anything. The password field from the first line that matches the current connection parameters will be used. (Therefore, put more-specific entries first when you are using wildcards.) If an entry needs to contain : or \, escape this character with . The host name field is matched to the host connection parameter if that is specified, otherwise to the hostaddr parameter if that is specified; if neither are given then the host name localhost is searched for. The host name localhost is also searched for when the connection is a Unix-domain socket connection and the host parameter matches libpq's default socket directory path. In a standby server, a database field of replication matches streaming replication connections made to the primary server. The database field is of limited usefulness otherwise, because users have the same password for all databases in the same cluster.

On Unix systems, the permissions on a password file must disallow any access to world or group; achieve this by a command such as chmod 0600 ~/.pgpass. If the permissions are less strict than this, the file will be ignored. On Microsoft Windows, it is assumed that the file is stored in a directory that is secure, so no special permissions check is made.

Examples:

Example 1 (unknown):

hostname:port:database:username:password

---

PostgreSQL: Documentation: 18: 29.6. Generated Column Replication

URL: https://www.postgresql.org/docs/current/logical-replication-gencols.html

Contents:

• 29.6. Generated Column Replication #
  • Tip
  • Note
  • Warning
  • Note

Typically, a table at the subscriber will be defined the same as the publisher table, so if the publisher table has a GENERATED column then the subscriber table will have a matching generated column. In this case, it is always the subscriber table generated column value that is used.

For example, note below that subscriber table generated column value comes from the subscriber column's calculation.

In fact, prior to version 18.0, logical replication does not publish GENERATED columns at all.

But, replicating a generated column to a regular column can sometimes be desirable.

This feature may be useful when replicating data to a non-PostgreSQL database via output plugin, especially if the target database does not support generated columns.

Generated columns are not published by default, but users can opt to publish stored generated columns just like regular ones.

There are two ways to do this:

Set the PUBLICATION parameter publish_generated_columns to stored. This instructs PostgreSQL logical replication to publish current and future stored generated columns of the publication's tables.

Specify a table column list to explicitly nominate which stored generated columns will be published.

When determining which table columns will be published, a column list takes precedence, overriding the effect of the publish_generated_columns parameter.

The following table summarizes behavior when there are generated columns involved in the logical replication. Results are shown for when publishing generated columns is not enabled, and for when it is enabled.

Table 29.2. Replication Result Summary

There's currently no support for subscriptions comprising several publications where the same table has been published with different column lists. See Section 29.5.

This same situation can occur if one publication is publishing generated columns, while another publication in the same subscription is not publishing generated columns for the same table.

If the subscriber is from a release prior to 18, then initial table synchronization won't copy generated columns even if they are defined in the publisher.

Examples:

Example 1 (unknown):

/* pub # */ CREATE TABLE tab_gen_to_gen (a int, b int GENERATED ALWAYS AS (a + 1) STORED);
/* pub # */ INSERT INTO tab_gen_to_gen VALUES (1),(2),(3);
/* pub # */ CREATE PUBLICATION pub1 FOR TABLE tab_gen_to_gen;
/* pub # */ SELECT * FROM tab_gen_to_gen;
 a | b
---+---
 1 | 2
 2 | 3
 3 | 4
(3 rows)

/* sub # */ CREATE TABLE tab_gen_to_gen (a int, b int GENERATED ALWAYS AS (a * 100) STORED);
/* sub # */ CREATE SUBSCRIPTION sub1 CONNECTION 'dbname=test_pub' PUBLICATION pub1;
/* sub # */ SELECT * from tab_gen_to_gen;
 a | b
---+----
 1 | 100
 2 | 200
 3 | 300
(3 rows)

---

PostgreSQL: Documentation: 18: 35.64. view_routine_usage

URL: https://www.postgresql.org/docs/current/infoschema-view-routine-usage.html

Contents:

• 35.64. view_routine_usage #

The view view_routine_usage identifies all routines (functions and procedures) that are used in the query expression of a view (the SELECT statement that defines the view). A routine is only included if that routine is owned by a currently enabled role.

Table 35.62. view_routine_usage Columns

table_catalog sql_identifier

Name of the database containing the view (always the current database)

table_schema sql_identifier

Name of the schema containing the view

table_name sql_identifier

specific_catalog sql_identifier

Name of the database containing the function (always the current database)

specific_schema sql_identifier

Name of the schema containing the function

specific_name sql_identifier

The “specific name” of the function. See Section 35.45 for more information.

---

PostgreSQL: Documentation: 18: ALLOCATE DESCRIPTOR

URL: https://www.postgresql.org/docs/current/ecpg-sql-allocate-descriptor.html

Contents:

• ALLOCATE DESCRIPTOR
• Synopsis
• Description
• Parameters
• Examples
• Compatibility
• See Also

ALLOCATE DESCRIPTOR — allocate an SQL descriptor area

ALLOCATE DESCRIPTOR allocates a new named SQL descriptor area, which can be used to exchange data between the PostgreSQL server and the host program.

Descriptor areas should be freed after use using the DEALLOCATE DESCRIPTOR command.

A name of SQL descriptor, case sensitive. This can be an SQL identifier or a host variable.

ALLOCATE DESCRIPTOR is specified in the SQL standard.

Examples:

Example 1 (unknown):

ALLOCATE DESCRIPTOR name

Example 2 (unknown):

EXEC SQL ALLOCATE DESCRIPTOR mydesc;

---

PostgreSQL: Documentation: 18: 36.17. Packaging Related Objects into an Extension

URL: https://www.postgresql.org/docs/current/extend-extensions.html

Contents:

• 36.17. Packaging Related Objects into an Extension #
  • 36.17.1. Extension Files #
  • 36.17.2. Extension Relocatability #
  • 36.17.3. Extension Configuration Tables #
  • 36.17.4. Extension Updates #
  • 36.17.5. Installing Extensions Using Update Scripts #
  • 36.17.6. Security Considerations for Extensions #
  • 36.17.6.1. Security Considerations for Extension Functions #
  • 36.17.6.2. Security Considerations for Extension Scripts #
  • 36.17.7. Extension Example #

A useful extension to PostgreSQL typically includes multiple SQL objects; for example, a new data type will require new functions, new operators, and probably new index operator classes. It is helpful to collect all these objects into a single package to simplify database management. PostgreSQL calls such a package an extension. To define an extension, you need at least a script file that contains the SQL commands to create the extension's objects, and a control file that specifies a few basic properties of the extension itself. If the extension includes C code, there will typically also be a shared library file into which the C code has been built. Once you have these files, a simple CREATE EXTENSION command loads the objects into your database.

The main advantage of using an extension, rather than just running the SQL script to load a bunch of “loose” objects into your database, is that PostgreSQL will then understand that the objects of the extension go together. You can drop all the objects with a single DROP EXTENSION command (no need to maintain a separate “uninstall” script). Even more useful, pg_dump knows that it should not dump the individual member objects of the extension — it will just include a CREATE EXTENSION command in dumps, instead. This vastly simplifies migration to a new version of the extension that might contain more or different objects than the old version. Note however that you must have the extension's control, script, and other files available when loading such a dump into a new database.

PostgreSQL will not let you drop an individual object contained in an extension, except by dropping the whole extension. Also, while you can change the definition of an extension member object (for example, via CREATE OR REPLACE FUNCTION for a function), bear in mind that the modified definition will not be dumped by pg_dump. Such a change is usually only sensible if you concurrently make the same change in the extension's script file. (But there are special provisions for tables containing configuration data; see Section 36.17.3.) In production situations, it's generally better to create an extension update script to perform changes to extension member objects.

The extension script may set privileges on objects that are part of the extension, using GRANT and REVOKE statements. The final set of privileges for each object (if any are set) will be stored in the pg_init_privs system catalog. When pg_dump is used, the CREATE EXTENSION command will be included in the dump, followed by the set of GRANT and REVOKE statements necessary to set the privileges on the objects to what they were at the time the dump was taken.

PostgreSQL does not currently support extension scripts issuing CREATE POLICY or SECURITY LABEL statements. These are expected to be set after the extension has been created. All RLS policies and security labels on extension objects will be included in dumps created by pg_dump.

The extension mechanism also has provisions for packaging modification scripts that adjust the definitions of the SQL objects contained in an extension. For example, if version 1.1 of an extension adds one function and changes the body of another function compared to 1.0, the extension author can provide an update script that makes just those two changes. The ALTER EXTENSION UPDATE command can then be used to apply these changes and track which version of the extension is actually installed in a given database.

The kinds of SQL objects that can be members of an extension are shown in the description of ALTER EXTENSION. Notably, objects that are database-cluster-wide, such as databases, roles, and tablespaces, cannot be extension members since an extension is only known within one database. (Although an extension script is not prohibited from creating such objects, if it does so they will not be tracked as part of the extension.) Also notice that while a table can be a member of an extension, its subsidiary objects such as indexes are not directly considered members of the extension. Another important point is that schemas can belong to extensions, but not vice versa: an extension as such has an unqualified name and does not exist “within” any schema. The extension's member objects, however, will belong to schemas whenever appropriate for their object types. It may or may not be appropriate for an extension to own the schema(s) its member objects are within.

If an extension's script creates any temporary objects (such as temp tables), those objects are treated as extension members for the remainder of the current session, but are automatically dropped at session end, as any temporary object would be. This is an exception to the rule that extension member objects cannot be dropped without dropping the whole extension.

The CREATE EXTENSION command relies on a control file for each extension, which must be named the same as the extension with a suffix of .control, and must be placed in the installation's SHAREDIR/extension directory. There must also be at least one SQL script file, which follows the naming pattern extension--version.sql (for example, foo--1.0.sql for version 1.0 of extension foo). By default, the script file(s) are also placed in the SHAREDIR/extension directory; but the control file can specify a different directory for the script file(s).

Additional locations for extension control files can be configured using the parameter extension_control_path.

The file format for an extension control file is the same as for the postgresql.conf file, namely a list of parameter_name = value assignments, one per line. Blank lines and comments introduced by # are allowed. Be sure to quote any value that is not a single word or number.

A control file can set the following parameters:

The directory containing the extension's SQL script file(s). Unless an absolute path is given, the name is relative to the directory where the control file was found. By default, the script files are looked for in the same directory where the control file was found.

The default version of the extension (the one that will be installed if no version is specified in CREATE EXTENSION). Although this can be omitted, that will result in CREATE EXTENSION failing if no VERSION option appears, so you generally don't want to do that.

A comment (any string) about the extension. The comment is applied when initially creating an extension, but not during extension updates (since that might override user-added comments). Alternatively, the extension's comment can be set by writing a COMMENT command in the script file.

The character set encoding used by the script file(s). This should be specified if the script files contain any non-ASCII characters. Otherwise the files will be assumed to be in the database encoding.

The value of this parameter will be substituted for each occurrence of MODULE_PATHNAME in the script file(s). If it is not set, no substitution is made. Typically, this is set to just shared_library_name and then MODULE_PATHNAME is used in CREATE FUNCTION commands for C-language functions, so that the script files do not need to hard-wire the name of the shared library.

A list of names of extensions that this extension depends on, for example requires = 'foo, bar'. Those extensions must be installed before this one can be installed.

A list of names of extensions that this extension depends on that should be barred from changing their schemas via ALTER EXTENSION ... SET SCHEMA. This is needed if this extension's script references the name of a required extension's schema (using the @extschema:name@ syntax) in a way that cannot track renames.

If this parameter is true (which is the default), only superusers can create the extension or update it to a new version (but see also trusted, below). If it is set to false, just the privileges required to execute the commands in the installation or update script are required. This should normally be set to true if any of the script commands require superuser privileges. (Such commands would fail anyway, but it's more user-friendly to give the error up front.)

This parameter, if set to true (which is not the default), allows some non-superusers to install an extension that has superuser set to true. Specifically, installation will be permitted for anyone who has CREATE privilege on the current database. When the user executing CREATE EXTENSION is not a superuser but is allowed to install by virtue of this parameter, then the installation or update script is run as the bootstrap superuser, not as the calling user. This parameter is irrelevant if superuser is false. Generally, this should not be set true for extensions that could allow access to otherwise-superuser-only abilities, such as file system access. Also, marking an extension trusted requires significant extra effort to write the extension's installation and update script(s) securely; see Section 36.17.6.

An extension is relocatable if it is possible to move its contained objects into a different schema after initial creation of the extension. The default is false, i.e., the extension is not relocatable. See Section 36.17.2 for more information.

This parameter can only be set for non-relocatable extensions. It forces the extension to be loaded into exactly the named schema and not any other. The schema parameter is consulted only when initially creating an extension, not during extension updates. See Section 36.17.2 for more information.

In addition to the primary control file extension.control, an extension can have secondary control files named in the style extension--version.control. If supplied, these must be located in the script file directory. Secondary control files follow the same format as the primary control file. Any parameters set in a secondary control file override the primary control file when installing or updating to that version of the extension. However, the parameters directory and default_version cannot be set in a secondary control file.

An extension's SQL script files can contain any SQL commands, except for transaction control commands (BEGIN, COMMIT, etc.) and commands that cannot be executed inside a transaction block (such as VACUUM). This is because the script files are implicitly executed within a transaction block.

An extension's SQL script files can also contain lines beginning with \echo, which will be ignored (treated as comments) by the extension mechanism. This provision is commonly used to throw an error if the script file is fed to psql rather than being loaded via CREATE EXTENSION (see example script in Section 36.17.7). Without that, users might accidentally load the extension's contents as “loose” objects rather than as an extension, a state of affairs that's a bit tedious to recover from.

If the extension script contains the string @extowner@, that string is replaced with the (suitably quoted) name of the user calling CREATE EXTENSION or ALTER EXTENSION. Typically this feature is used by extensions that are marked trusted to assign ownership of selected objects to the calling user rather than the bootstrap superuser. (One should be careful about doing so, however. For example, assigning ownership of a C-language function to a non-superuser would create a privilege escalation path for that user.)

While the script files can contain any characters allowed by the specified encoding, control files should contain only plain ASCII, because there is no way for PostgreSQL to know what encoding a control file is in. In practice this is only an issue if you want to use non-ASCII characters in the extension's comment. Recommended practice in that case is to not use the control file comment parameter, but instead use COMMENT ON EXTENSION within a script file to set the comment.

Users often wish to load the objects contained in an extension into a different schema than the extension's author had in mind. There are three supported levels of relocatability:

A fully relocatable extension can be moved into another schema at any time, even after it's been loaded into a database. This is done with the ALTER EXTENSION SET SCHEMA command, which automatically renames all the member objects into the new schema. Normally, this is only possible if the extension contains no internal assumptions about what schema any of its objects are in. Also, the extension's objects must all be in one schema to begin with (ignoring objects that do not belong to any schema, such as procedural languages). Mark a fully relocatable extension by setting relocatable = true in its control file.

An extension might be relocatable during installation but not afterwards. This is typically the case if the extension's script file needs to reference the target schema explicitly, for example in setting search_path properties for SQL functions. For such an extension, set relocatable = false in its control file, and use @extschema@ to refer to the target schema in the script file. All occurrences of this string will be replaced by the actual target schema's name (double-quoted if necessary) before the script is executed. The user can set the target schema using the SCHEMA option of CREATE EXTENSION.

If the extension does not support relocation at all, set relocatable = false in its control file, and also set schema to the name of the intended target schema. This will prevent use of the SCHEMA option of CREATE EXTENSION, unless it specifies the same schema named in the control file. This choice is typically necessary if the extension contains internal assumptions about its schema name that can't be replaced by uses of @extschema@. The @extschema@ substitution mechanism is available in this case too, although it is of limited use since the schema name is determined by the control file.

In all cases, the script file will be executed with search_path initially set to point to the target schema; that is, CREATE EXTENSION does the equivalent of this:

This allows the objects created by the script file to go into the target schema. The script file can change search_path if it wishes, but that is generally undesirable. search_path is restored to its previous setting upon completion of CREATE EXTENSION.

The target schema is determined by the schema parameter in the control file if that is given, otherwise by the SCHEMA option of CREATE EXTENSION if that is given, otherwise the current default object creation schema (the first one in the caller's search_path). When the control file schema parameter is used, the target schema will be created if it doesn't already exist, but in the other two cases it must already exist.

If any prerequisite extensions are listed in requires in the control file, their target schemas are added to the initial setting of search_path, following the new extension's target schema. This allows their objects to be visible to the new extension's script file.

For security, pg_temp is automatically appended to the end of search_path in all cases.

Although a non-relocatable extension can contain objects spread across multiple schemas, it is usually desirable to place all the objects meant for external use into a single schema, which is considered the extension's target schema. Such an arrangement works conveniently with the default setting of search_path during creation of dependent extensions.

If an extension references objects belonging to another extension, it is recommended to schema-qualify those references. To do that, write @extschema:name@ in the extension's script file, where name is the name of the other extension (which must be listed in this extension's requires list). This string will be replaced by the name (double-quoted if necessary) of that extension's target schema. Although this notation avoids the need to make hard-wired assumptions about schema names in the extension's script file, its use may embed the other extension's schema name into the installed objects of this extension. (Typically, that happens when @extschema:name@ is used inside a string literal, such as a function body or a search_path setting. In other cases, the object reference is reduced to an OID during parsing and does not require subsequent lookups.) If the other extension's schema name is so embedded, you should prevent the other extension from being relocated after yours is installed, by adding the name of the other extension to this one's no_relocate list.

Some extensions include configuration tables, which contain data that might be added or changed by the user after installation of the extension. Ordinarily, if a table is part of an extension, neither the table's definition nor its content will be dumped by pg_dump. But that behavior is undesirable for a configuration table; any data changes made by the user need to be included in dumps, or the extension will behave differently after a dump and restore.

To solve this problem, an extension's script file can mark a table or a sequence it has created as a configuration relation, which will cause pg_dump to include the table's or the sequence's contents (not its definition) in dumps. To do that, call the function pg_extension_config_dump(regclass, text) after creating the table or the sequence, for example

Any number of tables or sequences can be marked this way. Sequences associated with serial or bigserial columns can be marked as well.

When the second argument of pg_extension_config_dump is an empty string, the entire contents of the table are dumped by pg_dump. This is usually only correct if the table is initially empty as created by the extension script. If there is a mixture of initial data and user-provided data in the table, the second argument of pg_extension_config_dump provides a WHERE condition that selects the data to be dumped. For example, you might do

and then make sure that standard_entry is true only in the rows created by the extension's script.

For sequences, the second argument of pg_extension_config_dump has no effect.

More complicated situations, such as initially-provided rows that might be modified by users, can be handled by creating triggers on the configuration table to ensure that modified rows are marked correctly.

You can alter the filter condition associated with a configuration table by calling pg_extension_config_dump again. (This would typically be useful in an extension update script.) The only way to mark a table as no longer a configuration table is to dissociate it from the extension with ALTER EXTENSION ... DROP TABLE.

Note that foreign key relationships between these tables will dictate the order in which the tables are dumped out by pg_dump. Specifically, pg_dump will attempt to dump the referenced-by table before the referencing table. As the foreign key relationships are set up at CREATE EXTENSION time (prior to data being loaded into the tables) circular dependencies are not supported. When circular dependencies exist, the data will still be dumped out but the dump will not be able to be restored directly and user intervention will be required.

Sequences associated with serial or bigserial columns need to be directly marked to dump their state. Marking their parent relation is not enough for this purpose.

One advantage of the extension mechanism is that it provides convenient ways to manage updates to the SQL commands that define an extension's objects. This is done by associating a version name or number with each released version of the extension's installation script. In addition, if you want users to be able to update their databases dynamically from one version to the next, you should provide update scripts that make the necessary changes to go from one version to the next. Update scripts have names following the pattern extension--old_version--target_version.sql (for example, foo--1.0--1.1.sql contains the commands to modify version 1.0 of extension foo into version 1.1).

Given that a suitable update script is available, the command ALTER EXTENSION UPDATE will update an installed extension to the specified new version. The update script is run in the same environment that CREATE EXTENSION provides for installation scripts: in particular, search_path is set up in the same way, and any new objects created by the script are automatically added to the extension. Also, if the script chooses to drop extension member objects, they are automatically dissociated from the extension.

If an extension has secondary control files, the control parameters that are used for an update script are those associated with the script's target (new) version.

ALTER EXTENSION is able to execute sequences of update script files to achieve a requested update. For example, if only foo--1.0--1.1.sql and foo--1.1--2.0.sql are available, ALTER EXTENSION will apply them in sequence if an update to version 2.0 is requested when 1.0 is currently installed.

PostgreSQL doesn't assume anything about the properties of version names: for example, it does not know whether 1.1 follows 1.0. It just matches up the available version names and follows the path that requires applying the fewest update scripts. (A version name can actually be any string that doesn't contain -- or leading or trailing -.)

Sometimes it is useful to provide “downgrade” scripts, for example foo--1.1--1.0.sql to allow reverting the changes associated with version 1.1. If you do that, be careful of the possibility that a downgrade script might unexpectedly get applied because it yields a shorter path. The risky case is where there is a “fast path” update script that jumps ahead several versions as well as a downgrade script to the fast path's start point. It might take fewer steps to apply the downgrade and then the fast path than to move ahead one version at a time. If the downgrade script drops any irreplaceable objects, this will yield undesirable results.

To check for unexpected update paths, use this command:

This shows each pair of distinct known version names for the specified extension, together with the update path sequence that would be taken to get from the source version to the target version, or NULL if there is no available update path. The path is shown in textual form with -- separators. You can use regexp_split_to_array(path,'--') if you prefer an array format.

An extension that has been around for awhile will probably exist in several versions, for which the author will need to write update scripts. For example, if you have released a foo extension in versions 1.0, 1.1, and 1.2, there should be update scripts foo--1.0--1.1.sql and foo--1.1--1.2.sql. Before PostgreSQL 10, it was necessary to also create new script files foo--1.1.sql and foo--1.2.sql that directly build the newer extension versions, or else the newer versions could not be installed directly, only by installing 1.0 and then updating. That was tedious and duplicative, but now it's unnecessary, because CREATE EXTENSION can follow update chains automatically. For example, if only the script files foo--1.0.sql, foo--1.0--1.1.sql, and foo--1.1--1.2.sql are available then a request to install version 1.2 is honored by running those three scripts in sequence. The processing is the same as if you'd first installed 1.0 and then updated to 1.2. (As with ALTER EXTENSION UPDATE, if multiple pathways are available then the shortest is preferred.) Arranging an extension's script files in this style can reduce the amount of maintenance effort needed to produce small updates.

If you use secondary (version-specific) control files with an extension maintained in this style, keep in mind that each version needs a control file even if it has no stand-alone installation script, as that control file will determine how the implicit update to that version is performed. For example, if foo--1.0.control specifies requires = 'bar' but foo's other control files do not, the extension's dependency on bar will be dropped when updating from 1.0 to another version.

Widely-distributed extensions should assume little about the database they occupy. Therefore, it's appropriate to write functions provided by an extension in a secure style that cannot be compromised by search-path-based attacks.

An extension that has the superuser property set to true must also consider security hazards for the actions taken within its installation and update scripts. It is not terribly difficult for a malicious user to create trojan-horse objects that will compromise later execution of a carelessly-written extension script, allowing that user to acquire superuser privileges.

If an extension is marked trusted, then its installation schema can be selected by the installing user, who might intentionally use an insecure schema in hopes of gaining superuser privileges. Therefore, a trusted extension is extremely exposed from a security standpoint, and all its script commands must be carefully examined to ensure that no compromise is possible.

Advice about writing functions securely is provided in Section 36.17.6.1 below, and advice about writing installation scripts securely is provided in Section 36.17.6.2.

SQL-language and PL-language functions provided by extensions are at risk of search-path-based attacks when they are executed, since parsing of these functions occurs at execution time not creation time.

The CREATE FUNCTION reference page contains advice about writing SECURITY DEFINER functions safely. It's good practice to apply those techniques for any function provided by an extension, since the function might be called by a high-privilege user.

If you cannot set the search_path to contain only secure schemas, assume that each unqualified name could resolve to an object that a malicious user has defined. Beware of constructs that depend on search_path implicitly; for example, IN and CASE expression WHEN always select an operator using the search path. In their place, use OPERATOR(schema.=) ANY and CASE WHEN expression.

A general-purpose extension usually should not assume that it's been installed into a secure schema, which means that even schema-qualified references to its own objects are not entirely risk-free. For example, if the extension has defined a function myschema.myfunc(bigint) then a call such as myschema.myfunc(42) could be captured by a hostile function myschema.myfunc(integer). Be careful that the data types of function and operator parameters exactly match the declared argument types, using explicit casts where necessary.

An extension installation or update script should be written to guard against search-path-based attacks occurring when the script executes. If an object reference in the script can be made to resolve to some other object than the script author intended, then a compromise might occur immediately, or later when the mis-defined extension object is used.

DDL commands such as CREATE FUNCTION and CREATE OPERATOR CLASS are generally secure, but beware of any command having a general-purpose expression as a component. For example, CREATE VIEW needs to be vetted, as does a DEFAULT expression in CREATE FUNCTION.

Sometimes an extension script might need to execute general-purpose SQL, for example to make catalog adjustments that aren't possible via DDL. Be careful to execute such commands with a secure search_path; do not trust the path provided by CREATE/ALTER EXTENSION to be secure. Best practice is to temporarily set search_path to pg_catalog, pg_temp and insert references to the extension's installation schema explicitly where needed. (This practice might also be helpful for creating views.) Examples can be found in the contrib modules in the PostgreSQL source code distribution.

Secure cross-extension references typically require schema-qualification of the names of the other extension's objects, using the @extschema:name@ syntax, in addition to careful matching of argument types for functions and operators.

Here is a complete example of an SQL-only extension, a two-element composite type that can store any type of value in its slots, which are named “k” and “v”. Non-text values are automatically coerced to text for storage.

The script file pair--1.0.sql looks like this:

The control file pair.control looks like this:

While you hardly need a makefile to install these two files into the correct directory, you could use a Makefile containing this:

This makefile relies on PGXS, which is described in Section 36.18. The command make install will install the control and script files into the correct directory as reported by pg_config.

Once the files are installed, use the CREATE EXTENSION command to load the objects into any particular database.

Examples:

Example 1 (unknown):

SET LOCAL search_path TO @extschema@, pg_temp;

Example 2 (unknown):

CREATE TABLE my_config (key text, value text);
CREATE SEQUENCE my_config_seq;

SELECT pg_catalog.pg_extension_config_dump('my_config', '');
SELECT pg_catalog.pg_extension_config_dump('my_config_seq', '');

Example 3 (unknown):

CREATE TABLE my_config (key text, value text, standard_entry boolean);

SELECT pg_catalog.pg_extension_config_dump('my_config', 'WHERE NOT standard_entry');

Example 4 (unknown):

SELECT * FROM pg_extension_update_paths('extension_name');

---

PostgreSQL: Documentation: 18: Appendix O. Obsolete or Renamed Features

URL: https://www.postgresql.org/docs/current/appendix-obsolete.html

Contents:

• Appendix O. Obsolete or Renamed Features

Functionality is sometimes removed from PostgreSQL, feature, setting and file names sometimes change, or documentation moves to different places. This section directs users coming from old versions of the documentation or from external links to the appropriate new location for the information they need.

---

PostgreSQL: Documentation: 18: 19.18. Short Options

URL: https://www.postgresql.org/docs/current/runtime-config-short.html

Contents:

• 19.18. Short Options #

For convenience there are also single letter command-line option switches available for some parameters. They are described in Table 19.5. Some of these options exist for historical reasons, and their presence as a single-letter option does not necessarily indicate an endorsement to use the option heavily.

Table 19.5. Short Option Key

---

PostgreSQL: Documentation: 18: 35.65. view_table_usage

URL: https://www.postgresql.org/docs/current/infoschema-view-table-usage.html

Contents:

• 35.65. view_table_usage #
  • Note

The view view_table_usage identifies all tables that are used in the query expression of a view (the SELECT statement that defines the view). A table is only included if that table is owned by a currently enabled role.

System tables are not included. This should be fixed sometime.

Table 35.63. view_table_usage Columns

view_catalog sql_identifier

Name of the database that contains the view (always the current database)

view_schema sql_identifier

Name of the schema that contains the view

view_name sql_identifier

table_catalog sql_identifier

Name of the database that contains the table that is used by the view (always the current database)

table_schema sql_identifier

Name of the schema that contains the table that is used by the view

table_name sql_identifier

Name of the table that is used by the view

---

PostgreSQL: Documentation: 18: 32.13. Notice Processing

URL: https://www.postgresql.org/docs/current/libpq-notice-processing.html

Contents:

• 32.13. Notice Processing #

Notice and warning messages generated by the server are not returned by the query execution functions, since they do not imply failure of the query. Instead they are passed to a notice handling function, and execution continues normally after the handler returns. The default notice handling function prints the message on stderr, but the application can override this behavior by supplying its own handling function.

For historical reasons, there are two levels of notice handling, called the notice receiver and notice processor. The default behavior is for the notice receiver to format the notice and pass a string to the notice processor for printing. However, an application that chooses to provide its own notice receiver will typically ignore the notice processor layer and just do all the work in the notice receiver.

The function PQsetNoticeReceiver sets or examines the current notice receiver for a connection object. Similarly, PQsetNoticeProcessor sets or examines the current notice processor.

Each of these functions returns the previous notice receiver or processor function pointer, and sets the new value. If you supply a null function pointer, no action is taken, but the current pointer is returned.

When a notice or warning message is received from the server, or generated internally by libpq, the notice receiver function is called. It is passed the message in the form of a PGRES_NONFATAL_ERROR PGresult. (This allows the receiver to extract individual fields using PQresultErrorField, or obtain a complete preformatted message using PQresultErrorMessage or PQresultVerboseErrorMessage.) The same void pointer passed to PQsetNoticeReceiver is also passed. (This pointer can be used to access application-specific state if needed.)

The default notice receiver simply extracts the message (using PQresultErrorMessage) and passes it to the notice processor.

The notice processor is responsible for handling a notice or warning message given in text form. It is passed the string text of the message (including a trailing newline), plus a void pointer that is the same one passed to PQsetNoticeProcessor. (This pointer can be used to access application-specific state if needed.)

The default notice processor is simply:

Once you have set a notice receiver or processor, you should expect that that function could be called as long as either the PGconn object or PGresult objects made from it exist. At creation of a PGresult, the PGconn's current notice handling pointers are copied into the PGresult for possible use by functions like PQgetvalue.

Examples:

Example 1 (javascript):

typedef void (*PQnoticeReceiver) (void *arg, const PGresult *res);

PQnoticeReceiver
PQsetNoticeReceiver(PGconn *conn,
                    PQnoticeReceiver proc,
                    void *arg);

typedef void (*PQnoticeProcessor) (void *arg, const char *message);

PQnoticeProcessor
PQsetNoticeProcessor(PGconn *conn,
                     PQnoticeProcessor proc,
                     void *arg);

Example 2 (javascript):

static void
defaultNoticeProcessor(void *arg, const char *message)
{
    fprintf(stderr, "%s", message);
}

---

PostgreSQL: Documentation: 18: 18.2. Creating a Database Cluster

URL: https://www.postgresql.org/docs/current/creating-cluster.html

Contents:

• 18.2. Creating a Database Cluster #
  • Tip
  • 18.2.1. Use of Secondary File Systems #
  • 18.2.2. File Systems #
  • 18.2.2.1. NFS #

Before you can do anything, you must initialize a database storage area on disk. We call this a database cluster. (The SQL standard uses the term catalog cluster.) A database cluster is a collection of databases that is managed by a single instance of a running database server. After initialization, a database cluster will contain a database named postgres, which is meant as a default database for use by utilities, users and third party applications. The database server itself does not require the postgres database to exist, but many external utility programs assume it exists. There are two more databases created within each cluster during initialization, named template1 and template0. As the names suggest, these will be used as templates for subsequently-created databases; they should not be used for actual work. (See Chapter 22 for information about creating new databases within a cluster.)

In file system terms, a database cluster is a single directory under which all data will be stored. We call this the data directory or data area. It is completely up to you where you choose to store your data. There is no default, although locations such as /usr/local/pgsql/data or /var/lib/pgsql/data are popular. The data directory must be initialized before being used, using the program initdb which is installed with PostgreSQL.

If you are using a pre-packaged version of PostgreSQL, it may well have a specific convention for where to place the data directory, and it may also provide a script for creating the data directory. In that case you should use that script in preference to running initdb directly. Consult the package-level documentation for details.

To initialize a database cluster manually, run initdb and specify the desired file system location of the database cluster with the -D option, for example:

Note that you must execute this command while logged into the PostgreSQL user account, which is described in the previous section.

As an alternative to the -D option, you can set the environment variable PGDATA.

Alternatively, you can run initdb via the pg_ctl program like so:

This may be more intuitive if you are using pg_ctl for starting and stopping the server (see Section 18.3), so that pg_ctl would be the sole command you use for managing the database server instance.

initdb will attempt to create the directory you specify if it does not already exist. Of course, this will fail if initdb does not have permissions to write in the parent directory. It's generally recommendable that the PostgreSQL user own not just the data directory but its parent directory as well, so that this should not be a problem. If the desired parent directory doesn't exist either, you will need to create it first, using root privileges if the grandparent directory isn't writable. So the process might look like this:

initdb will refuse to run if the data directory exists and already contains files; this is to prevent accidentally overwriting an existing installation.

Because the data directory contains all the data stored in the database, it is essential that it be secured from unauthorized access. initdb therefore revokes access permissions from everyone but the PostgreSQL user, and optionally, group. Group access, when enabled, is read-only. This allows an unprivileged user in the same group as the cluster owner to take a backup of the cluster data or perform other operations that only require read access.

Note that enabling or disabling group access on an existing cluster requires the cluster to be shut down and the appropriate mode to be set on all directories and files before restarting PostgreSQL. Otherwise, a mix of modes might exist in the data directory. For clusters that allow access only by the owner, the appropriate modes are 0700 for directories and 0600 for files. For clusters that also allow reads by the group, the appropriate modes are 0750 for directories and 0640 for files.

However, while the directory contents are secure, the default client authentication setup allows any local user to connect to the database and even become the database superuser. If you do not trust other local users, we recommend you use one of initdb's -W, --pwprompt or --pwfile options to assign a password to the database superuser. Also, specify -A scram-sha-256 so that the default trust authentication mode is not used; or modify the generated pg_hba.conf file after running initdb, but before you start the server for the first time. (Other reasonable approaches include using peer authentication or file system permissions to restrict connections. See Chapter 20 for more information.)

initdb also initializes the default locale for the database cluster. Normally, it will just take the locale settings in the environment and apply them to the initialized database. It is possible to specify a different locale for the database; more information about that can be found in Section 23.1. The default sort order used within the particular database cluster is set by initdb, and while you can create new databases using different sort order, the order used in the template databases that initdb creates cannot be changed without dropping and recreating them. There is also a performance impact for using locales other than C or POSIX. Therefore, it is important to make this choice correctly the first time.

initdb also sets the default character set encoding for the database cluster. Normally this should be chosen to match the locale setting. For details see Section 23.3.

Non-C and non-POSIX locales rely on the operating system's collation library for character set ordering. This controls the ordering of keys stored in indexes. For this reason, a cluster cannot switch to an incompatible collation library version, either through snapshot restore, binary streaming replication, a different operating system, or an operating system upgrade.

Many installations create their database clusters on file systems (volumes) other than the machine's “root” volume. If you choose to do this, it is not advisable to try to use the secondary volume's topmost directory (mount point) as the data directory. Best practice is to create a directory within the mount-point directory that is owned by the PostgreSQL user, and then create the data directory within that. This avoids permissions problems, particularly for operations such as pg_upgrade, and it also ensures clean failures if the secondary volume is taken offline.

Generally, any file system with POSIX semantics can be used for PostgreSQL. Users prefer different file systems for a variety of reasons, including vendor support, performance, and familiarity. Experience suggests that, all other things being equal, one should not expect major performance or behavior changes merely from switching file systems or making minor file system configuration changes.

It is possible to use an NFS file system for storing the PostgreSQL data directory. PostgreSQL does nothing special for NFS file systems, meaning it assumes NFS behaves exactly like locally-connected drives. PostgreSQL does not use any functionality that is known to have nonstandard behavior on NFS, such as file locking.

The only firm requirement for using NFS with PostgreSQL is that the file system is mounted using the hard option. With the hard option, processes can “hang” indefinitely if there are network problems, so this configuration will require a careful monitoring setup. The soft option will interrupt system calls in case of network problems, but PostgreSQL will not repeat system calls interrupted in this way, so any such interruption will result in an I/O error being reported.

It is not necessary to use the sync mount option. The behavior of the async option is sufficient, since PostgreSQL issues fsync calls at appropriate times to flush the write caches. (This is analogous to how it works on a local file system.) However, it is strongly recommended to use the sync export option on the NFS server on systems where it exists (mainly Linux). Otherwise, an fsync or equivalent on the NFS client is not actually guaranteed to reach permanent storage on the server, which could cause corruption similar to running with the parameter fsync off. The defaults of these mount and export options differ between vendors and versions, so it is recommended to check and perhaps specify them explicitly in any case to avoid any ambiguity.

In some cases, an external storage product can be accessed either via NFS or a lower-level protocol such as iSCSI. In the latter case, the storage appears as a block device and any available file system can be created on it. That approach might relieve the DBA from having to deal with some of the idiosyncrasies of NFS, but of course the complexity of managing remote storage then happens at other levels.

Examples:

Example 1 (unknown):

$ initdb -D /usr/local/pgsql/data

Example 2 (unknown):

$ pg_ctl -D /usr/local/pgsql/data initdb

Example 3 (unknown):

root# mkdir /usr/local/pgsql
root# chown postgres /usr/local/pgsql
root# su postgres
postgres$ initdb -D /usr/local/pgsql/data

---

PostgreSQL: Documentation: 18: 35.36. role_routine_grants

URL: https://www.postgresql.org/docs/current/infoschema-role-routine-grants.html

Contents:

• 35.36. role_routine_grants #

The view role_routine_grants identifies all privileges granted on functions where the grantor or grantee is a currently enabled role. Further information can be found under routine_privileges. The only effective difference between this view and routine_privileges is that this view omits functions that have been made accessible to the current user by way of a grant to PUBLIC.

Table 35.34. role_routine_grants Columns

grantor sql_identifier

Name of the role that granted the privilege

grantee sql_identifier

Name of the role that the privilege was granted to

specific_catalog sql_identifier

Name of the database containing the function (always the current database)

specific_schema sql_identifier

Name of the schema containing the function

specific_name sql_identifier

The “specific name” of the function. See Section 35.45 for more information.

routine_catalog sql_identifier

Name of the database containing the function (always the current database)

routine_schema sql_identifier

Name of the schema containing the function

routine_name sql_identifier

Name of the function (might be duplicated in case of overloading)

privilege_type character_data

Always EXECUTE (the only privilege type for functions)

is_grantable yes_or_no

YES if the privilege is grantable, NO if not

---

PostgreSQL: Documentation: 18: Chapter 41. PL/pgSQL — SQL Procedural Language

URL: https://www.postgresql.org/docs/current/plpgsql.html

Contents:

• Chapter 41. PL/pgSQL — SQL Procedural Language

---

PostgreSQL: Documentation: 18: 5.3. Identity Columns

URL: https://www.postgresql.org/docs/current/ddl-identity-columns.html

Contents:

• 5.3. Identity Columns #

An identity column is a special column that is generated automatically from an implicit sequence. It can be used to generate key values.

To create an identity column, use the GENERATED ... AS IDENTITY clause in CREATE TABLE, for example:

See CREATE TABLE for more details.

If an INSERT command is executed on the table with the identity column and no value is explicitly specified for the identity column, then a value generated by the implicit sequence is inserted. For example, with the above definitions and assuming additional appropriate columns, writing

would generate values for the id column starting at 1 and result in the following table data:

Alternatively, the keyword DEFAULT can be specified in place of a value to explicitly request the sequence-generated value, like

Similarly, the keyword DEFAULT can be used in UPDATE commands.

Thus, in many ways, an identity column behaves like a column with a default value.

The clauses ALWAYS and BY DEFAULT in the column definition determine how explicitly user-specified values are handled in INSERT and UPDATE commands. In an INSERT command, if ALWAYS is selected, a user-specified value is only accepted if the INSERT statement specifies OVERRIDING SYSTEM VALUE. If BY DEFAULT is selected, then the user-specified value takes precedence. Thus, using BY DEFAULT results in a behavior more similar to default values, where the default value can be overridden by an explicit value, whereas ALWAYS provides some more protection against accidentally inserting an explicit value.

The data type of an identity column must be one of the data types supported by sequences. (See CREATE SEQUENCE.) The properties of the associated sequence may be specified when creating an identity column (see CREATE TABLE) or changed afterwards (see ALTER TABLE).

An identity column is automatically marked as NOT NULL. An identity column, however, does not guarantee uniqueness. (A sequence normally returns unique values, but a sequence could be reset, or values could be inserted manually into the identity column, as discussed above.) Uniqueness would need to be enforced using a PRIMARY KEY or UNIQUE constraint.

In table inheritance hierarchies, identity columns and their properties in a child table are independent of those in its parent tables. A child table does not inherit identity columns or their properties automatically from the parent. During INSERT or UPDATE, a column is treated as an identity column if that column is an identity column in the table named in the statement, and the corresponding identity properties are applied.

Partitions inherit identity columns from the partitioned table. They cannot have their own identity columns. The properties of a given identity column are consistent across all the partitions in the partition hierarchy.

Examples:

Example 1 (unknown):

CREATE TABLE people (
    id bigint GENERATED ALWAYS AS IDENTITY,
    ...,
);

Example 2 (unknown):

CREATE TABLE people (
    id bigint GENERATED BY DEFAULT AS IDENTITY,
    ...,
);

Example 3 (unknown):

INSERT INTO people (name, address) VALUES ('A', 'foo');
INSERT INTO people (name, address) VALUES ('B', 'bar');

Example 4 (unknown):

id | name | address
----+------+---------
  1 | A    | foo
  2 | B    | bar

---

PostgreSQL: Documentation: 18: 20.9. Peer Authentication

URL: https://www.postgresql.org/docs/current/auth-peer.html

Contents:

• 20.9. Peer Authentication #

The peer authentication method works by obtaining the client's operating system user name from the kernel and using it as the allowed database user name (with optional user name mapping). This method is only supported on local connections.

The following configuration options are supported for peer:

Allows for mapping between system and database user names. See Section 20.2 for details.

Peer authentication is only available on operating systems providing the getpeereid() function, the SO_PEERCRED socket parameter, or similar mechanisms. Currently that includes Linux, most flavors of BSD including macOS, and Solaris.

---

PostgreSQL: Documentation: 18: 20.12. Certificate Authentication

URL: https://www.postgresql.org/docs/current/auth-cert.html

Contents:

• 20.12. Certificate Authentication #

This authentication method uses SSL client certificates to perform authentication. It is therefore only available for SSL connections; see Section 18.9.2 for SSL configuration instructions. When using this authentication method, the server will require that the client provide a valid, trusted certificate. No password prompt will be sent to the client. The cn (Common Name) attribute of the certificate will be compared to the requested database user name, and if they match the login will be allowed. User name mapping can be used to allow cn to be different from the database user name.

The following configuration options are supported for SSL certificate authentication:

Allows for mapping between system and database user names. See Section 20.2 for details.

It is redundant to use the clientcert option with cert authentication because cert authentication is effectively trust authentication with clientcert=verify-full.

---

PostgreSQL: Documentation: 18: 22.3. Template Databases

URL: https://www.postgresql.org/docs/current/manage-ag-templatedbs.html

Contents:

• 22.3. Template Databases #
  • Note

CREATE DATABASE actually works by copying an existing database. By default, it copies the standard system database named template1. Thus that database is the “template” from which new databases are made. If you add objects to template1, these objects will be copied into subsequently created user databases. This behavior allows site-local modifications to the standard set of objects in databases. For example, if you install the procedural language PL/Perl in template1, it will automatically be available in user databases without any extra action being taken when those databases are created.

However, CREATE DATABASE does not copy database-level GRANT permissions attached to the source database. The new database has default database-level permissions.

There is a second standard system database named template0. This database contains the same data as the initial contents of template1, that is, only the standard objects predefined by your version of PostgreSQL. template0 should never be changed after the database cluster has been initialized. By instructing CREATE DATABASE to copy template0 instead of template1, you can create a “pristine” user database (one where no user-defined objects exist and where the system objects have not been altered) that contains none of the site-local additions in template1. This is particularly handy when restoring a pg_dump dump: the dump script should be restored in a pristine database to ensure that one recreates the correct contents of the dumped database, without conflicting with objects that might have been added to template1 later on.

Another common reason for copying template0 instead of template1 is that new encoding and locale settings can be specified when copying template0, whereas a copy of template1 must use the same settings it does. This is because template1 might contain encoding-specific or locale-specific data, while template0 is known not to.

To create a database by copying template0, use:

from the SQL environment, or:

It is possible to create additional template databases, and indeed one can copy any database in a cluster by specifying its name as the template for CREATE DATABASE. It is important to understand, however, that this is not (yet) intended as a general-purpose “COPY DATABASE” facility. The principal limitation is that no other sessions can be connected to the source database while it is being copied. CREATE DATABASE will fail if any other connection exists when it starts; during the copy operation, new connections to the source database are prevented.

Two useful flags exist in pg_database for each database: the columns datistemplate and datallowconn. datistemplate can be set to indicate that a database is intended as a template for CREATE DATABASE. If this flag is set, the database can be cloned by any user with CREATEDB privileges; if it is not set, only superusers and the owner of the database can clone it. If datallowconn is false, then no new connections to that database will be allowed (but existing sessions are not terminated simply by setting the flag false). The template0 database is normally marked datallowconn = false to prevent its modification. Both template0 and template1 should always be marked with datistemplate = true.

template1 and template0 do not have any special status beyond the fact that the name template1 is the default source database name for CREATE DATABASE. For example, one could drop template1 and recreate it from template0 without any ill effects. This course of action might be advisable if one has carelessly added a bunch of junk in template1. (To delete template1, it must have pg_database.datistemplate = false.)

The postgres database is also created when a database cluster is initialized. This database is meant as a default database for users and applications to connect to. It is simply a copy of template1 and can be dropped and recreated if necessary.

Examples:

Example 1 (unknown):

CREATE DATABASE dbname TEMPLATE template0;

Example 2 (unknown):

createdb -T template0 dbname

---

PostgreSQL: Documentation: 18: 9.8. Data Type Formatting Functions

URL: https://www.postgresql.org/docs/current/functions-formatting.html

Contents:

• 9.8. Data Type Formatting Functions #
  • Tip
  • Tip
  • Caution

The PostgreSQL formatting functions provide a powerful set of tools for converting various data types (date/time, integer, floating point, numeric) to formatted strings and for converting from formatted strings to specific data types. Table 9.26 lists them. These functions all follow a common calling convention: the first argument is the value to be formatted and the second argument is a template that defines the output or input format.

Table 9.26. Formatting Functions

to_char ( timestamp, text ) → text

to_char ( timestamp with time zone, text ) → text

Converts time stamp to string according to the given format.

to_char(timestamp '2002-04-20 17:31:12.66', 'HH12:MI:SS') → 05:31:12

to_char ( interval, text ) → text

Converts interval to string according to the given format.

to_char(interval '15h 2m 12s', 'HH24:MI:SS') → 15:02:12

to_char ( numeric_type, text ) → text

Converts number to string according to the given format; available for integer, bigint, numeric, real, double precision.

to_char(125, '999') → 125

to_char(125.8::real, '999D9') → 125.8

to_char(-125.8, '999D99S') → 125.80-

to_date ( text, text ) → date

Converts string to date according to the given format.

to_date('05 Dec 2000', 'DD Mon YYYY') → 2000-12-05

to_number ( text, text ) → numeric

Converts string to numeric according to the given format.

to_number('12,454.8-', '99G999D9S') → -12454.8

to_timestamp ( text, text ) → timestamp with time zone

Converts string to time stamp according to the given format. (See also to_timestamp(double precision) in Table 9.33.)

to_timestamp('05 Dec 2000', 'DD Mon YYYY') → 2000-12-05 00:00:00-05

to_timestamp and to_date exist to handle input formats that cannot be converted by simple casting. For most standard date/time formats, simply casting the source string to the required data type works, and is much easier. Similarly, to_number is unnecessary for standard numeric representations.

In a to_char output template string, there are certain patterns that are recognized and replaced with appropriately-formatted data based on the given value. Any text that is not a template pattern is simply copied verbatim. Similarly, in an input template string (for the other functions), template patterns identify the values to be supplied by the input data string. If there are characters in the template string that are not template patterns, the corresponding characters in the input data string are simply skipped over (whether or not they are equal to the template string characters).

Table 9.27 shows the template patterns available for formatting date and time values.

Table 9.27. Template Patterns for Date/Time Formatting

Modifiers can be applied to any template pattern to alter its behavior. For example, FMMonth is the Month pattern with the FM modifier. Table 9.28 shows the modifier patterns for date/time formatting.

Table 9.28. Template Pattern Modifiers for Date/Time Formatting

Usage notes for date/time formatting:

FM suppresses leading zeroes and trailing blanks that would otherwise be added to make the output of a pattern be fixed-width. In PostgreSQL, FM modifies only the next specification, while in Oracle FM affects all subsequent specifications, and repeated FM modifiers toggle fill mode on and off.

TM suppresses trailing blanks whether or not FM is specified.

to_timestamp and to_date ignore letter case in the input; so for example MON, Mon, and mon all accept the same strings. When using the TM modifier, case-folding is done according to the rules of the function's input collation (see Section 23.2).

to_timestamp and to_date skip multiple blank spaces at the beginning of the input string and around date and time values unless the FX option is used. For example, to_timestamp(' 2000 JUN', 'YYYY MON') and to_timestamp('2000 - JUN', 'YYYY-MON') work, but to_timestamp('2000 JUN', 'FXYYYY MON') returns an error because to_timestamp expects only a single space. FX must be specified as the first item in the template.

A separator (a space or non-letter/non-digit character) in the template string of to_timestamp and to_date matches any single separator in the input string or is skipped, unless the FX option is used. For example, to_timestamp('2000JUN', 'YYYY///MON') and to_timestamp('2000/JUN', 'YYYY MON') work, but to_timestamp('2000//JUN', 'YYYY/MON') returns an error because the number of separators in the input string exceeds the number of separators in the template.

If FX is specified, a separator in the template string matches exactly one character in the input string. But note that the input string character is not required to be the same as the separator from the template string. For example, to_timestamp('2000/JUN', 'FXYYYY MON') works, but to_timestamp('2000/JUN', 'FXYYYY MON') returns an error because the second space in the template string consumes the letter J from the input string.

A TZH template pattern can match a signed number. Without the FX option, minus signs may be ambiguous, and could be interpreted as a separator. This ambiguity is resolved as follows: If the number of separators before TZH in the template string is less than the number of separators before the minus sign in the input string, the minus sign is interpreted as part of TZH. Otherwise, the minus sign is considered to be a separator between values. For example, to_timestamp('2000 -10', 'YYYY TZH') matches -10 to TZH, but to_timestamp('2000 -10', 'YYYY TZH') matches 10 to TZH.

Ordinary text is allowed in to_char templates and will be output literally. You can put a substring in double quotes to force it to be interpreted as literal text even if it contains template patterns. For example, in '"Hello Year "YYYY', the YYYY will be replaced by the year data, but the single Y in Year will not be. In to_date, to_number, and to_timestamp, literal text and double-quoted strings result in skipping the number of characters contained in the string; for example "XX" skips two input characters (whether or not they are XX).

Prior to PostgreSQL 12, it was possible to skip arbitrary text in the input string using non-letter or non-digit characters. For example, to_timestamp('2000y6m1d', 'yyyy-MM-DD') used to work. Now you can only use letter characters for this purpose. For example, to_timestamp('2000y6m1d', 'yyyytMMtDDt') and to_timestamp('2000y6m1d', 'yyyy"y"MM"m"DD"d"') skip y, m, and d.

If you want to have a double quote in the output you must precede it with a backslash, for example '\"YYYY Month\"'. Backslashes are not otherwise special outside of double-quoted strings. Within a double-quoted string, a backslash causes the next character to be taken literally, whatever it is (but this has no special effect unless the next character is a double quote or another backslash).

In to_timestamp and to_date, if the year format specification is less than four digits, e.g., YYY, and the supplied year is less than four digits, the year will be adjusted to be nearest to the year 2020, e.g., 95 becomes 1995.

In to_timestamp and to_date, negative years are treated as signifying BC. If you write both a negative year and an explicit BC field, you get AD again. An input of year zero is treated as 1 BC.

In to_timestamp and to_date, the YYYY conversion has a restriction when processing years with more than 4 digits. You must use some non-digit character or template after YYYY, otherwise the year is always interpreted as 4 digits. For example (with the year 20000): to_date('200001130', 'YYYYMMDD') will be interpreted as a 4-digit year; instead use a non-digit separator after the year, like to_date('20000-1130', 'YYYY-MMDD') or to_date('20000Nov30', 'YYYYMonDD').

In to_timestamp and to_date, the CC (century) field is accepted but ignored if there is a YYY, YYYY or Y,YYY field. If CC is used with YY or Y then the result is computed as that year in the specified century. If the century is specified but the year is not, the first year of the century is assumed.

In to_timestamp and to_date, weekday names or numbers (DAY, D, and related field types) are accepted but are ignored for purposes of computing the result. The same is true for quarter (Q) fields.

In to_timestamp and to_date, an ISO 8601 week-numbering date (as distinct from a Gregorian date) can be specified in one of two ways:

Year, week number, and weekday: for example to_date('2006-42-4', 'IYYY-IW-ID') returns the date 2006-10-19. If you omit the weekday it is assumed to be 1 (Monday).

Year and day of year: for example to_date('2006-291', 'IYYY-IDDD') also returns 2006-10-19.

Attempting to enter a date using a mixture of ISO 8601 week-numbering fields and Gregorian date fields is nonsensical, and will cause an error. In the context of an ISO 8601 week-numbering year, the concept of a “month” or “day of month” has no meaning. In the context of a Gregorian year, the ISO week has no meaning.

While to_date will reject a mixture of Gregorian and ISO week-numbering date fields, to_char will not, since output format specifications like YYYY-MM-DD (IYYY-IDDD) can be useful. But avoid writing something like IYYY-MM-DD; that would yield surprising results near the start of the year. (See Section 9.9.1 for more information.)

In to_timestamp, millisecond (MS) or microsecond (US) fields are used as the seconds digits after the decimal point. For example to_timestamp('12.3', 'SS.MS') is not 3 milliseconds, but 300, because the conversion treats it as 12 + 0.3 seconds. So, for the format SS.MS, the input values 12.3, 12.30, and 12.300 specify the same number of milliseconds. To get three milliseconds, one must write 12.003, which the conversion treats as 12 + 0.003 = 12.003 seconds.

Here is a more complex example: to_timestamp('15:12:02.020.001230', 'HH24:MI:SS.MS.US') is 15 hours, 12 minutes, and 2 seconds + 20 milliseconds + 1230 microseconds = 2.021230 seconds.

to_char(..., 'ID')'s day of the week numbering matches the extract(isodow from ...) function, but to_char(..., 'D')'s does not match extract(dow from ...)'s day numbering.

to_char(interval) formats HH and HH12 as shown on a 12-hour clock, for example zero hours and 36 hours both output as 12, while HH24 outputs the full hour value, which can exceed 23 in an interval value.

Table 9.29 shows the template patterns available for formatting numeric values.

Table 9.29. Template Patterns for Numeric Formatting

Usage notes for numeric formatting:

0 specifies a digit position that will always be printed, even if it contains a leading/trailing zero. 9 also specifies a digit position, but if it is a leading zero then it will be replaced by a space, while if it is a trailing zero and fill mode is specified then it will be deleted. (For to_number(), these two pattern characters are equivalent.)

If the format provides fewer fractional digits than the number being formatted, to_char() will round the number to the specified number of fractional digits.

The pattern characters S, L, D, and G represent the sign, currency symbol, decimal point, and thousands separator characters defined by the current locale (see lc_monetary and lc_numeric). The pattern characters period and comma represent those exact characters, with the meanings of decimal point and thousands separator, regardless of locale.

If no explicit provision is made for a sign in to_char()'s pattern, one column will be reserved for the sign, and it will be anchored to (appear just left of) the number. If S appears just left of some 9's, it will likewise be anchored to the number.

A sign formatted using SG, PL, or MI is not anchored to the number; for example, to_char(-12, 'MI9999') produces '- 12' but to_char(-12, 'S9999') produces ' -12'. (The Oracle implementation does not allow the use of MI before 9, but rather requires that 9 precede MI.)

TH does not convert values less than zero and does not convert fractional numbers.

PL, SG, and TH are PostgreSQL extensions.

In to_number, if non-data template patterns such as L or TH are used, the corresponding number of input characters are skipped, whether or not they match the template pattern, unless they are data characters (that is, digits, sign, decimal point, or comma). For example, TH would skip two non-data characters.

V with to_char multiplies the input values by 10^n, where n is the number of digits following V. V with to_number divides in a similar manner. The V can be thought of as marking the position of an implicit decimal point in the input or output string. to_char and to_number do not support the use of V combined with a decimal point (e.g., 99.9V99 is not allowed).

EEEE (scientific notation) cannot be used in combination with any of the other formatting patterns or modifiers other than digit and decimal point patterns, and must be at the end of the format string (e.g., 9.99EEEE is a valid pattern).

In to_number(), the RN pattern converts Roman numerals (in standard form) to numbers. Input is case-insensitive, so RN and rn are equivalent. RN cannot be used in combination with any other formatting patterns or modifiers except FM, which is applicable only in to_char() and is ignored in to_number().

Certain modifiers can be applied to any template pattern to alter its behavior. For example, FM99.99 is the 99.99 pattern with the FM modifier. Table 9.30 shows the modifier patterns for numeric formatting.

Table 9.30. Template Pattern Modifiers for Numeric Formatting

Table 9.31 shows some examples of the use of the to_char function.

Table 9.31. to_char Examples

---

PostgreSQL: Documentation: 18: WHENEVER

URL: https://www.postgresql.org/docs/current/ecpg-sql-whenever.html

Contents:

• WHENEVER
• Synopsis
• Description
• Parameters
• Examples
• Compatibility

WHENEVER — specify the action to be taken when an SQL statement causes a specific class condition to be raised

Define a behavior which is called on the special cases (Rows not found, SQL warnings or errors) in the result of SQL execution.

See Section 34.8.1 for a description of the parameters.

A typical application is the use of WHENEVER NOT FOUND BREAK to handle looping through result sets:

WHENEVER is specified in the SQL standard, but most of the actions are PostgreSQL extensions.

Examples:

Example 1 (unknown):

WHENEVER { NOT FOUND | SQLERROR | SQLWARNING } action

Example 2 (unknown):

EXEC SQL WHENEVER NOT FOUND CONTINUE;
EXEC SQL WHENEVER NOT FOUND DO BREAK;
EXEC SQL WHENEVER NOT FOUND DO CONTINUE;
EXEC SQL WHENEVER SQLWARNING SQLPRINT;
EXEC SQL WHENEVER SQLWARNING DO warn();
EXEC SQL WHENEVER SQLERROR sqlprint;
EXEC SQL WHENEVER SQLERROR CALL print2();
EXEC SQL WHENEVER SQLERROR DO handle_error("select");
EXEC SQL WHENEVER SQLERROR DO sqlnotice(NULL, NONO);
EXEC SQL WHENEVER SQLERROR DO sqlprint();
EXEC SQL WHENEVER SQLERROR GOTO error_label;
EXEC SQL WHENEVER SQLERROR STOP;

Example 3 (unknown):

int
main(void)
{
    EXEC SQL CONNECT TO testdb AS con1;
    EXEC SQL SELECT pg_catalog.set_config('search_path', '', false); EXEC SQL COMMIT;
    EXEC SQL ALLOCATE DESCRIPTOR d;
    EXEC SQL DECLARE cur CURSOR FOR SELECT current_database(), 'hoge', 256;
    EXEC SQL OPEN cur;

    /* when end of result set reached, break out of while loop */
    EXEC SQL WHENEVER NOT FOUND DO BREAK;

    while (1)
    {
        EXEC SQL FETCH NEXT FROM cur INTO SQL DESCRIPTOR d;
        ...
    }

    EXEC SQL CLOSE cur;
    EXEC SQL COMMIT;

    EXEC SQL DEALLOCATE DESCRIPTOR d;
    EXEC SQL DISCONNECT ALL;

    return 0;
}

---

PostgreSQL: Documentation: 18: Part III. Server Administration

URL: https://www.postgresql.org/docs/current/admin.html

Contents:

• Part III. Server Administration

This part covers topics that are of interest to a PostgreSQL administrator. This includes installation, configuration of the server, management of users and databases, and maintenance tasks. Anyone running PostgreSQL server, even for personal use, but especially in production, should be familiar with these topics.

The information attempts to be in the order in which a new user should read it. The chapters are self-contained and can be read individually as desired. The information is presented in a narrative form in topical units. Readers looking for a complete description of a command are encouraged to review the Part VI.

The first few chapters are written so they can be understood without prerequisite knowledge, so new users who need to set up their own server can begin their exploration. The rest of this part is about tuning and management; that material assumes that the reader is familiar with the general use of the PostgreSQL database system. Readers are encouraged review the Part I and Part II parts for additional information.

---

PostgreSQL: Documentation: 18: 34.7. Using Descriptor Areas

URL: https://www.postgresql.org/docs/current/ecpg-descriptors.html

Contents:

• 34.7. Using Descriptor Areas #
  • 34.7.1. Named SQL Descriptor Areas #
  • 34.7.2. SQLDA Descriptor Areas #
  • 34.7.2.1. SQLDA Data Structure #
  • Tip
    • 34.7.2.1.1. sqlda_t Structure #
    • 34.7.2.1.2. sqlvar_t Structure #
    • 34.7.2.1.3. struct sqlname Structure #
  • 34.7.2.2. Retrieving a Result Set Using an SQLDA #
  • 34.7.2.3. Passing Query Parameters Using an SQLDA #

An SQL descriptor area is a more sophisticated method for processing the result of a SELECT, FETCH or a DESCRIBE statement. An SQL descriptor area groups the data of one row of data together with metadata items into one data structure. The metadata is particularly useful when executing dynamic SQL statements, where the nature of the result columns might not be known ahead of time. PostgreSQL provides two ways to use Descriptor Areas: the named SQL Descriptor Areas and the C-structure SQLDAs.

A named SQL descriptor area consists of a header, which contains information concerning the entire descriptor, and one or more item descriptor areas, which basically each describe one column in the result row.

Before you can use an SQL descriptor area, you need to allocate one:

The identifier serves as the “variable name” of the descriptor area. When you don't need the descriptor anymore, you should deallocate it:

To use a descriptor area, specify it as the storage target in an INTO clause, instead of listing host variables:

If the result set is empty, the Descriptor Area will still contain the metadata from the query, i.e., the field names.

For not yet executed prepared queries, the DESCRIBE statement can be used to get the metadata of the result set:

Before PostgreSQL 9.0, the SQL keyword was optional, so using DESCRIPTOR and SQL DESCRIPTOR produced named SQL Descriptor Areas. Now it is mandatory, omitting the SQL keyword produces SQLDA Descriptor Areas, see Section 34.7.2.

In DESCRIBE and FETCH statements, the INTO and USING keywords can be used to similarly: they produce the result set and the metadata in a Descriptor Area.

Now how do you get the data out of the descriptor area? You can think of the descriptor area as a structure with named fields. To retrieve the value of a field from the header and store it into a host variable, use the following command:

Currently, there is only one header field defined: COUNT, which tells how many item descriptor areas exist (that is, how many columns are contained in the result). The host variable needs to be of an integer type. To get a field from the item descriptor area, use the following command:

num can be a literal integer or a host variable containing an integer. Possible fields are:

number of rows in the result set

actual data item (therefore, the data type of this field depends on the query)

When TYPE is 9, DATETIME_INTERVAL_CODE will have a value of 1 for DATE, 2 for TIME, 3 for TIMESTAMP, 4 for TIME WITH TIME ZONE, or 5 for TIMESTAMP WITH TIME ZONE.

the indicator (indicating a null value or a value truncation)

length of the datum in characters

length of the character representation of the datum in bytes

precision (for type numeric)

length of the datum in characters

length of the character representation of the datum in bytes

scale (for type numeric)

numeric code of the data type of the column

In EXECUTE, DECLARE and OPEN statements, the effect of the INTO and USING keywords are different. A Descriptor Area can also be manually built to provide the input parameters for a query or a cursor and USING SQL DESCRIPTOR name is the way to pass the input parameters into a parameterized query. The statement to build a named SQL Descriptor Area is below:

PostgreSQL supports retrieving more that one record in one FETCH statement and storing the data in host variables in this case assumes that the variable is an array. E.g.:

An SQLDA Descriptor Area is a C language structure which can be also used to get the result set and the metadata of a query. One structure stores one record from the result set.

Note that the SQL keyword is omitted. The paragraphs about the use cases of the INTO and USING keywords in Section 34.7.1 also apply here with an addition. In a DESCRIBE statement the DESCRIPTOR keyword can be completely omitted if the INTO keyword is used:

The general flow of a program that uses SQLDA is:

Prepare a query, and declare a cursor for it.

Declare an SQLDA for the result rows.

Declare an SQLDA for the input parameters, and initialize them (memory allocation, parameter settings).

Open a cursor with the input SQLDA.

Fetch rows from the cursor, and store them into an output SQLDA.

Read values from the output SQLDA into the host variables (with conversion if necessary).

Free the memory area allocated for the input SQLDA.

SQLDA uses three data structure types: sqlda_t, sqlvar_t, and struct sqlname.

PostgreSQL's SQLDA has a similar data structure to the one in IBM DB2 Universal Database, so some technical information on DB2's SQLDA could help understanding PostgreSQL's one better.

The structure type sqlda_t is the type of the actual SQLDA. It holds one record. And two or more sqlda_t structures can be connected in a linked list with the pointer in the desc_next field, thus representing an ordered collection of rows. So, when two or more rows are fetched, the application can read them by following the desc_next pointer in each sqlda_t node.

The definition of sqlda_t is:

The meaning of the fields is:

It contains the literal string "SQLDA ".

It contains the size of the allocated space in bytes.

It contains the number of input parameters for a parameterized query in case it's passed into OPEN, DECLARE or EXECUTE statements using the USING keyword. In case it's used as output of SELECT, EXECUTE or FETCH statements, its value is the same as sqld statement

It contains the number of fields in a result set.

If the query returns more than one record, multiple linked SQLDA structures are returned, and desc_next holds a pointer to the next entry in the list.

This is the array of the columns in the result set.

The structure type sqlvar_t holds a column value and metadata such as type and length. The definition of the type is:

The meaning of the fields is:

Contains the type identifier of the field. For values, see enum ECPGttype in ecpgtype.h.

Contains the binary length of the field. e.g., 4 bytes for ECPGt_int.

Points to the data. The format of the data is described in Section 34.4.4.

Points to the null indicator. 0 means not null, -1 means null.

The name of the field.

A struct sqlname structure holds a column name. It is used as a member of the sqlvar_t structure. The definition of the structure is:

The meaning of the fields is:

Contains the length of the field name.

Contains the actual field name.

The general steps to retrieve a query result set through an SQLDA are:

Declare an sqlda_t structure to receive the result set.

Execute FETCH/EXECUTE/DESCRIBE commands to process a query specifying the declared SQLDA.

Check the number of records in the result set by looking at sqln, a member of the sqlda_t structure.

Get the values of each column from sqlvar[0], sqlvar[1], etc., members of the sqlda_t structure.

Go to next row (sqlda_t structure) by following the desc_next pointer, a member of the sqlda_t structure.

Repeat above as you need.

Here is an example retrieving a result set through an SQLDA.

First, declare a sqlda_t structure to receive the result set.

Next, specify the SQLDA in a command. This is a FETCH command example.

Run a loop following the linked list to retrieve the rows.

Inside the loop, run another loop to retrieve each column data (sqlvar_t structure) of the row.

To get a column value, check the sqltype value, a member of the sqlvar_t structure. Then, switch to an appropriate way, depending on the column type, to copy data from the sqlvar field to a host variable.

The general steps to use an SQLDA to pass input parameters to a prepared query are:

Create a prepared query (prepared statement)

Declare an sqlda_t structure as an input SQLDA.

Allocate memory area (as sqlda_t structure) for the input SQLDA.

Set (copy) input values in the allocated memory.

Open a cursor with specifying the input SQLDA.

First, create a prepared statement.

Next, allocate memory for an SQLDA, and set the number of input parameters in sqln, a member variable of the sqlda_t structure. When two or more input parameters are required for the prepared query, the application has to allocate additional memory space which is calculated by (nr. of params - 1) * sizeof(sqlvar_t). The example shown here allocates memory space for two input parameters.

After memory allocation, store the parameter values into the sqlvar[] array. (This is same array used for retrieving column values when the SQLDA is receiving a result set.) In this example, the input parameters are "postgres", having a string type, and 1, having an integer type.

By opening a cursor and specifying the SQLDA that was set up beforehand, the input parameters are passed to the prepared statement.

Finally, after using input SQLDAs, the allocated memory space must be freed explicitly, unlike SQLDAs used for receiving query results.

Here is an example program, which describes how to fetch access statistics of the databases, specified by the input parameters, from the system catalogs.

This application joins two system tables, pg_database and pg_stat_database on the database OID, and also fetches and shows the database statistics which are retrieved by two input parameters (a database postgres, and OID 1).

First, declare an SQLDA for input and an SQLDA for output.

Next, connect to the database, prepare a statement, and declare a cursor for the prepared statement.

Next, put some values in the input SQLDA for the input parameters. Allocate memory for the input SQLDA, and set the number of input parameters to sqln. Store type, value, and value length into sqltype, sqldata, and sqllen in the sqlvar structure.

After setting up the input SQLDA, open a cursor with the input SQLDA.

Fetch rows into the output SQLDA from the opened cursor. (Generally, you have to call FETCH repeatedly in the loop, to fetch all rows in the result set.)

Next, retrieve the fetched records from the SQLDA, by following the linked list of the sqlda_t structure.

Read each columns in the first record. The number of columns is stored in sqld, the actual data of the first column is stored in sqlvar[0], both members of the sqlda_t structure.

Now, the column data is stored in the variable v. Copy every datum into host variables, looking at v.sqltype for the type of the column.

Close the cursor after processing all of records, and disconnect from the database.

The whole program is shown in Example 34.1.

Example 34.1. Example SQLDA Program

The output of this example should look something like the following (some numbers will vary).

Examples:

Example 1 (unknown):

EXEC SQL ALLOCATE DESCRIPTOR identifier;

Example 2 (unknown):

EXEC SQL DEALLOCATE DESCRIPTOR identifier;

Example 3 (unknown):

EXEC SQL FETCH NEXT FROM mycursor INTO SQL DESCRIPTOR mydesc;

Example 4 (unknown):

EXEC SQL BEGIN DECLARE SECTION;
char *sql_stmt = "SELECT * FROM table1";
EXEC SQL END DECLARE SECTION;

EXEC SQL PREPARE stmt1 FROM :sql_stmt;
EXEC SQL DESCRIBE stmt1 INTO SQL DESCRIPTOR mydesc;

---

PostgreSQL: Documentation: 18: 32.6. Retrieving Query Results in Chunks

URL: https://www.postgresql.org/docs/current/libpq-single-row-mode.html

Contents:

• 32.6. Retrieving Query Results in Chunks #
  • Caution

Ordinarily, libpq collects an SQL command's entire result and returns it to the application as a single PGresult. This can be unworkable for commands that return a large number of rows. For such cases, applications can use PQsendQuery and PQgetResult in single-row mode or chunked mode. In these modes, result row(s) are returned to the application as they are received from the server, one at a time for single-row mode or in groups for chunked mode.

To enter one of these modes, call PQsetSingleRowMode or PQsetChunkedRowsMode immediately after a successful call of PQsendQuery (or a sibling function). This mode selection is effective only for the currently executing query. Then call PQgetResult repeatedly, until it returns null, as documented in Section 32.4. If the query returns any rows, they are returned as one or more PGresult objects, which look like normal query results except for having status code PGRES_SINGLE_TUPLE for single-row mode or PGRES_TUPLES_CHUNK for chunked mode, instead of PGRES_TUPLES_OK. There is exactly one result row in each PGRES_SINGLE_TUPLE object, while a PGRES_TUPLES_CHUNK object contains at least one row but not more than the specified number of rows per chunk. After the last row, or immediately if the query returns zero rows, a zero-row object with status PGRES_TUPLES_OK is returned; this is the signal that no more rows will arrive. (But note that it is still necessary to continue calling PQgetResult until it returns null.) All of these PGresult objects will contain the same row description data (column names, types, etc.) that an ordinary PGresult object for the query would have. Each object should be freed with PQclear as usual.

When using pipeline mode, single-row or chunked mode needs to be activated for each query in the pipeline before retrieving results for that query with PQgetResult. See Section 32.5 for more information.

Select single-row mode for the currently-executing query.

This function can only be called immediately after PQsendQuery or one of its sibling functions, before any other operation on the connection such as PQconsumeInput or PQgetResult. If called at the correct time, the function activates single-row mode for the current query and returns 1. Otherwise the mode stays unchanged and the function returns 0. In any case, the mode reverts to normal after completion of the current query.

Select chunked mode for the currently-executing query.

This function is similar to PQsetSingleRowMode, except that it specifies retrieval of up to chunkSize rows per PGresult, not necessarily just one row. This function can only be called immediately after PQsendQuery or one of its sibling functions, before any other operation on the connection such as PQconsumeInput or PQgetResult. If called at the correct time, the function activates chunked mode for the current query and returns 1. Otherwise the mode stays unchanged and the function returns 0. In any case, the mode reverts to normal after completion of the current query.

While processing a query, the server may return some rows and then encounter an error, causing the query to be aborted. Ordinarily, libpq discards any such rows and reports only the error. But in single-row or chunked mode, some rows may have already been returned to the application. Hence, the application will see some PGRES_SINGLE_TUPLE or PGRES_TUPLES_CHUNK PGresult objects followed by a PGRES_FATAL_ERROR object. For proper transactional behavior, the application must be designed to discard or undo whatever has been done with the previously-processed rows, if the query ultimately fails.

Examples:

Example 1 (unknown):

int PQsetSingleRowMode(PGconn *conn);

Example 2 (unknown):

int PQsetChunkedRowsMode(PGconn *conn, int chunkSize);

---

PostgreSQL: Documentation: 18: 35.60. user_defined_types

URL: https://www.postgresql.org/docs/current/infoschema-user-defined-types.html

Contents:

• 35.60. user_defined_types #

The view user_defined_types currently contains all composite types defined in the current database. Only those types are shown that the current user has access to (by way of being the owner or having some privilege).

SQL knows about two kinds of user-defined types: structured types (also known as composite types in PostgreSQL) and distinct types (not implemented in PostgreSQL). To be future-proof, use the column user_defined_type_category to differentiate between these. Other user-defined types such as base types and enums, which are PostgreSQL extensions, are not shown here. For domains, see Section 35.23 instead.

Table 35.58. user_defined_types Columns

user_defined_type_catalog sql_identifier

Name of the database that contains the type (always the current database)

user_defined_type_schema sql_identifier

Name of the schema that contains the type

user_defined_type_name sql_identifier

user_defined_type_category character_data

Currently always STRUCTURED

is_instantiable yes_or_no

Applies to a feature not available in PostgreSQL

Applies to a feature not available in PostgreSQL

ordering_form character_data

Applies to a feature not available in PostgreSQL

ordering_category character_data

Applies to a feature not available in PostgreSQL

ordering_routine_catalog sql_identifier

Applies to a feature not available in PostgreSQL

ordering_routine_schema sql_identifier

Applies to a feature not available in PostgreSQL

ordering_routine_name sql_identifier

Applies to a feature not available in PostgreSQL

reference_type character_data

Applies to a feature not available in PostgreSQL

data_type character_data

Applies to a feature not available in PostgreSQL

character_maximum_length cardinal_number

Applies to a feature not available in PostgreSQL

character_octet_length cardinal_number

Applies to a feature not available in PostgreSQL

character_set_catalog sql_identifier

Applies to a feature not available in PostgreSQL

character_set_schema sql_identifier

Applies to a feature not available in PostgreSQL

character_set_name sql_identifier

Applies to a feature not available in PostgreSQL

collation_catalog sql_identifier

Applies to a feature not available in PostgreSQL

collation_schema sql_identifier

Applies to a feature not available in PostgreSQL

collation_name sql_identifier

Applies to a feature not available in PostgreSQL

numeric_precision cardinal_number

Applies to a feature not available in PostgreSQL

numeric_precision_radix cardinal_number

Applies to a feature not available in PostgreSQL

numeric_scale cardinal_number

Applies to a feature not available in PostgreSQL

datetime_precision cardinal_number

Applies to a feature not available in PostgreSQL

interval_type character_data

Applies to a feature not available in PostgreSQL

interval_precision cardinal_number

Applies to a feature not available in PostgreSQL

source_dtd_identifier sql_identifier

Applies to a feature not available in PostgreSQL

ref_dtd_identifier sql_identifier

Applies to a feature not available in PostgreSQL

---

PostgreSQL: Documentation: 18: Chapter 44. PL/Python — Python Procedural Language

URL: https://www.postgresql.org/docs/current/plpython.html

Contents:

• Chapter 44. PL/Python — Python Procedural Language
  • Tip
  • Note

The PL/Python procedural language allows PostgreSQL functions and procedures to be written in the Python language.

To install PL/Python in a particular database, use CREATE EXTENSION plpython3u.

If a language is installed into template1, all subsequently created databases will have the language installed automatically.

PL/Python is only available as an “untrusted” language, meaning it does not offer any way of restricting what users can do in it and is therefore named plpython3u. A trusted variant plpython might become available in the future if a secure execution mechanism is developed in Python. The writer of a function in untrusted PL/Python must take care that the function cannot be used to do anything unwanted, since it will be able to do anything that could be done by a user logged in as the database administrator. Only superusers can create functions in untrusted languages such as plpython3u.

Users of source packages must specially enable the build of PL/Python during the installation process. (Refer to the installation instructions for more information.) Users of binary packages might find PL/Python in a separate subpackage.

---

PostgreSQL: Documentation: 18: 35.59. usage_privileges

URL: https://www.postgresql.org/docs/current/infoschema-usage-privileges.html

Contents:

• 35.59. usage_privileges #

The view usage_privileges identifies USAGE privileges granted on various kinds of objects to a currently enabled role or by a currently enabled role. In PostgreSQL, this currently applies to collations, domains, foreign-data wrappers, foreign servers, and sequences. There is one row for each combination of object, grantor, and grantee.

Since collations do not have real privileges in PostgreSQL, this view shows implicit non-grantable USAGE privileges granted by the owner to PUBLIC for all collations. The other object types, however, show real privileges.

In PostgreSQL, sequences also support SELECT and UPDATE privileges in addition to the USAGE privilege. These are nonstandard and therefore not visible in the information schema.

Table 35.57. usage_privileges Columns

grantor sql_identifier

Name of the role that granted the privilege

grantee sql_identifier

Name of the role that the privilege was granted to

object_catalog sql_identifier

Name of the database containing the object (always the current database)

object_schema sql_identifier

Name of the schema containing the object, if applicable, else an empty string

object_name sql_identifier

object_type character_data

COLLATION or DOMAIN or FOREIGN DATA WRAPPER or FOREIGN SERVER or SEQUENCE

privilege_type character_data

is_grantable yes_or_no

YES if the privilege is grantable, NO if not

---

PostgreSQL: Documentation: 18: Chapter 20. Client Authentication

URL: https://www.postgresql.org/docs/current/client-authentication.html

Contents:

• Chapter 20. Client Authentication
  • Note

When a client application connects to the database server, it specifies which PostgreSQL database user name it wants to connect as, much the same way one logs into a Unix computer as a particular user. Within the SQL environment the active database user name determines access privileges to database objects — see Chapter 21 for more information. Therefore, it is essential to restrict which database users can connect.

As explained in Chapter 21, PostgreSQL actually does privilege management in terms of “roles”. In this chapter, we consistently use database user to mean “role with the LOGIN privilege”.

Authentication is the process by which the database server establishes the identity of the client, and by extension determines whether the client application (or the user who runs the client application) is permitted to connect with the database user name that was requested.

PostgreSQL offers a number of different client authentication methods. The method used to authenticate a particular client connection can be selected on the basis of (client) host address, database, and user.

PostgreSQL database user names are logically separate from user names of the operating system in which the server runs. If all the users of a particular server also have accounts on the server's machine, it makes sense to assign database user names that match their operating system user names. However, a server that accepts remote connections might have many database users who have no local operating system account, and in such cases there need be no connection between database user names and OS user names.

---

PostgreSQL: Documentation: 18: 19.5. Write Ahead Log

URL: https://www.postgresql.org/docs/current/runtime-config-wal.html

Contents:

• 19.5. Write Ahead Log #
  • 19.5.1. Settings #
  • 19.5.2. Checkpoints #
  • 19.5.3. Archiving #
  • 19.5.4. Recovery #
  • 19.5.5. Archive Recovery #
  • 19.5.6. Recovery Target #
  • 19.5.7. WAL Summarization #

For additional information on tuning these settings, see Section 28.5.

wal_level determines how much information is written to the WAL. The default value is replica, which writes enough data to support WAL archiving and replication, including running read-only queries on a standby server. minimal removes all logging except the information required to recover from a crash or immediate shutdown. Finally, logical adds information necessary to support logical decoding. Each level includes the information logged at all lower levels. This parameter can only be set at server start.

The minimal level generates the least WAL volume. It logs no row information for permanent relations in transactions that create or rewrite them. This can make operations much faster (see Section 14.4.7). Operations that initiate this optimization include:

However, minimal WAL does not contain sufficient information for point-in-time recovery, so replica or higher must be used to enable continuous archiving (archive_mode) and streaming binary replication. In fact, the server will not even start in this mode if max_wal_senders is non-zero. Note that changing wal_level to minimal makes previous base backups unusable for point-in-time recovery and standby servers.

In logical level, the same information is logged as with replica, plus information needed to extract logical change sets from the WAL. Using a level of logical will increase the WAL volume, particularly if many tables are configured for REPLICA IDENTITY FULL and many UPDATE and DELETE statements are executed.

In releases prior to 9.6, this parameter also allowed the values archive and hot_standby. These are still accepted but mapped to replica.

If this parameter is on, the PostgreSQL server will try to make sure that updates are physically written to disk, by issuing fsync() system calls or various equivalent methods (see wal_sync_method). This ensures that the database cluster can recover to a consistent state after an operating system or hardware crash.

While turning off fsync is often a performance benefit, this can result in unrecoverable data corruption in the event of a power failure or system crash. Thus it is only advisable to turn off fsync if you can easily recreate your entire database from external data.

Examples of safe circumstances for turning off fsync include the initial loading of a new database cluster from a backup file, using a database cluster for processing a batch of data after which the database will be thrown away and recreated, or for a read-only database clone which gets recreated frequently and is not used for failover. High quality hardware alone is not a sufficient justification for turning off fsync.

For reliable recovery when changing fsync off to on, it is necessary to force all modified buffers in the kernel to durable storage. This can be done while the cluster is shutdown or while fsync is on by running initdb --sync-only, running sync, unmounting the file system, or rebooting the server.

In many situations, turning off synchronous_commit for noncritical transactions can provide much of the potential performance benefit of turning off fsync, without the attendant risks of data corruption.

fsync can only be set in the postgresql.conf file or on the server command line. If you turn this parameter off, also consider turning off full_page_writes.

Specifies how much WAL processing must complete before the database server returns a “success” indication to the client. Valid values are remote_apply, on (the default), remote_write, local, and off.

If synchronous_standby_names is empty, the only meaningful settings are on and off; remote_apply, remote_write and local all provide the same local synchronization level as on. The local behavior of all non-off modes is to wait for local flush of WAL to disk. In off mode, there is no waiting, so there can be a delay between when success is reported to the client and when the transaction is later guaranteed to be safe against a server crash. (The maximum delay is three times wal_writer_delay.) Unlike fsync, setting this parameter to off does not create any risk of database inconsistency: an operating system or database crash might result in some recent allegedly-committed transactions being lost, but the database state will be just the same as if those transactions had been aborted cleanly. So, turning synchronous_commit off can be a useful alternative when performance is more important than exact certainty about the durability of a transaction. For more discussion see Section 28.4.

If synchronous_standby_names is non-empty, synchronous_commit also controls whether transaction commits will wait for their WAL records to be processed on the standby server(s).

When set to remote_apply, commits will wait until replies from the current synchronous standby(s) indicate they have received the commit record of the transaction and applied it, so that it has become visible to queries on the standby(s), and also written to durable storage on the standbys. This will cause much larger commit delays than previous settings since it waits for WAL replay. When set to on, commits wait until replies from the current synchronous standby(s) indicate they have received the commit record of the transaction and flushed it to durable storage. This ensures the transaction will not be lost unless both the primary and all synchronous standbys suffer corruption of their database storage. When set to remote_write, commits will wait until replies from the current synchronous standby(s) indicate they have received the commit record of the transaction and written it to their file systems. This setting ensures data preservation if a standby instance of PostgreSQL crashes, but not if the standby suffers an operating-system-level crash because the data has not necessarily reached durable storage on the standby. The setting local causes commits to wait for local flush to disk, but not for replication. This is usually not desirable when synchronous replication is in use, but is provided for completeness.

This parameter can be changed at any time; the behavior for any one transaction is determined by the setting in effect when it commits. It is therefore possible, and useful, to have some transactions commit synchronously and others asynchronously. For example, to make a single multistatement transaction commit asynchronously when the default is the opposite, issue SET LOCAL synchronous_commit TO OFF within the transaction.

Table 19.1 summarizes the capabilities of the synchronous_commit settings.

Table 19.1. synchronous_commit Modes

Method used for forcing WAL updates out to disk. If fsync is off then this setting is irrelevant, since WAL file updates will not be forced out at all. Possible values are:

open_datasync (write WAL files with open() option O_DSYNC)

fdatasync (call fdatasync() at each commit)

fsync (call fsync() at each commit)

fsync_writethrough (call fsync() at each commit, forcing write-through of any disk write cache)

open_sync (write WAL files with open() option O_SYNC)

Not all of these choices are available on all platforms. The default is the first method in the above list that is supported by the platform, except that fdatasync is the default on Linux and FreeBSD. The default is not necessarily ideal; it might be necessary to change this setting or other aspects of your system configuration in order to create a crash-safe configuration or achieve optimal performance. These aspects are discussed in Section 28.1. This parameter can only be set in the postgresql.conf file or on the server command line.

When this parameter is on, the PostgreSQL server writes the entire content of each disk page to WAL during the first modification of that page after a checkpoint. This is needed because a page write that is in process during an operating system crash might be only partially completed, leading to an on-disk page that contains a mix of old and new data. The row-level change data normally stored in WAL will not be enough to completely restore such a page during post-crash recovery. Storing the full page image guarantees that the page can be correctly restored, but at the price of increasing the amount of data that must be written to WAL. (Because WAL replay always starts from a checkpoint, it is sufficient to do this during the first change of each page after a checkpoint. Therefore, one way to reduce the cost of full-page writes is to increase the checkpoint interval parameters.)

Turning this parameter off speeds normal operation, but might lead to either unrecoverable data corruption, or silent data corruption, after a system failure. The risks are similar to turning off fsync, though smaller, and it should be turned off only based on the same circumstances recommended for that parameter.

Turning off this parameter does not affect use of WAL archiving for point-in-time recovery (PITR) (see Section 25.3).

This parameter can only be set in the postgresql.conf file or on the server command line. The default is on.

When this parameter is on, the PostgreSQL server writes the entire content of each disk page to WAL during the first modification of that page after a checkpoint, even for non-critical modifications of so-called hint bits.

If data checksums are enabled, hint bit updates are always WAL-logged and this setting is ignored. You can use this setting to test how much extra WAL-logging would occur if your database had data checksums enabled.

This parameter can only be set at server start. The default value is off.

This parameter enables compression of WAL using the specified compression method. When enabled, the PostgreSQL server compresses full page images written to WAL (e.g. when full_page_writes is on, during a base backup, etc.). A compressed page image will be decompressed during WAL replay. The supported methods are pglz, lz4 (if PostgreSQL was compiled with --with-lz4) and zstd (if PostgreSQL was compiled with --with-zstd). The default value is off. Only superusers and users with the appropriate SET privilege can change this setting.

Enabling compression can reduce the WAL volume without increasing the risk of unrecoverable data corruption, but at the cost of some extra CPU spent on the compression during WAL logging and on the decompression during WAL replay.

If set to on (the default), this option causes new WAL files to be filled with zeroes. On some file systems, this ensures that space is allocated before we need to write WAL records. However, Copy-On-Write (COW) file systems may not benefit from this technique, so the option is given to skip the unnecessary work. If set to off, only the final byte is written when the file is created so that it has the expected size.

If set to on (the default), this option causes WAL files to be recycled by renaming them, avoiding the need to create new ones. On COW file systems, it may be faster to create new ones, so the option is given to disable this behavior.

The amount of shared memory used for WAL data that has not yet been written to disk. The default setting of -1 selects a size equal to 1/32nd (about 3%) of shared_buffers, but not less than 64kB nor more than the size of one WAL segment, typically 16MB. This value can be set manually if the automatic choice is too large or too small, but any positive value less than 32kB will be treated as 32kB. If this value is specified without units, it is taken as WAL blocks, that is XLOG_BLCKSZ bytes, typically 8kB. This parameter can only be set at server start.

The contents of the WAL buffers are written out to disk at every transaction commit, so extremely large values are unlikely to provide a significant benefit. However, setting this value to at least a few megabytes can improve write performance on a busy server where many clients are committing at once. The auto-tuning selected by the default setting of -1 should give reasonable results in most cases.

Specifies how often the WAL writer flushes WAL, in time terms. After flushing WAL the writer sleeps for the length of time given by wal_writer_delay, unless woken up sooner by an asynchronously committing transaction. If the last flush happened less than wal_writer_delay ago and less than wal_writer_flush_after worth of WAL has been produced since, then WAL is only written to the operating system, not flushed to disk. If this value is specified without units, it is taken as milliseconds. The default value is 200 milliseconds (200ms). Note that on some systems, the effective resolution of sleep delays is 10 milliseconds; setting wal_writer_delay to a value that is not a multiple of 10 might have the same results as setting it to the next higher multiple of 10. This parameter can only be set in the postgresql.conf file or on the server command line.

Specifies how often the WAL writer flushes WAL, in volume terms. If the last flush happened less than wal_writer_delay ago and less than wal_writer_flush_after worth of WAL has been produced since, then WAL is only written to the operating system, not flushed to disk. If wal_writer_flush_after is set to 0 then WAL data is always flushed immediately. If this value is specified without units, it is taken as WAL blocks, that is XLOG_BLCKSZ bytes, typically 8kB. The default is 1MB. This parameter can only be set in the postgresql.conf file or on the server command line.

When wal_level is minimal and a transaction commits after creating or rewriting a permanent relation, this setting determines how to persist the new data. If the data is smaller than this setting, write it to the WAL log; otherwise, use an fsync of affected files. Depending on the properties of your storage, raising or lowering this value might help if such commits are slowing concurrent transactions. If this value is specified without units, it is taken as kilobytes. The default is two megabytes (2MB).

Setting commit_delay adds a time delay before a WAL flush is initiated. This can improve group commit throughput by allowing a larger number of transactions to commit via a single WAL flush, if system load is high enough that additional transactions become ready to commit within the given interval. However, it also increases latency by up to the commit_delay for each WAL flush. Because the delay is just wasted if no other transactions become ready to commit, a delay is only performed if at least commit_siblings other transactions are active when a flush is about to be initiated. Also, no delays are performed if fsync is disabled. If this value is specified without units, it is taken as microseconds. The default commit_delay is zero (no delay). Only superusers and users with the appropriate SET privilege can change this setting.

In PostgreSQL releases prior to 9.3, commit_delay behaved differently and was much less effective: it affected only commits, rather than all WAL flushes, and waited for the entire configured delay even if the WAL flush was completed sooner. Beginning in PostgreSQL 9.3, the first process that becomes ready to flush waits for the configured interval, while subsequent processes wait only until the leader completes the flush operation.

Minimum number of concurrent open transactions to require before performing the commit_delay delay. A larger value makes it more probable that at least one other transaction will become ready to commit during the delay interval. The default is five transactions.

Maximum time between automatic WAL checkpoints. If this value is specified without units, it is taken as seconds. The valid range is between 30 seconds and one day. The default is five minutes (5min). Increasing this parameter can increase the amount of time needed for crash recovery. This parameter can only be set in the postgresql.conf file or on the server command line.

Specifies the target of checkpoint completion, as a fraction of total time between checkpoints. The default is 0.9, which spreads the checkpoint across almost all of the available interval, providing fairly consistent I/O load while also leaving some time for checkpoint completion overhead. Reducing this parameter is not recommended because it causes the checkpoint to complete faster. This results in a higher rate of I/O during the checkpoint followed by a period of less I/O between the checkpoint completion and the next scheduled checkpoint. This parameter can only be set in the postgresql.conf file or on the server command line.

Whenever more than this amount of data has been written while performing a checkpoint, attempt to force the OS to issue these writes to the underlying storage. Doing so will limit the amount of dirty data in the kernel's page cache, reducing the likelihood of stalls when an fsync is issued at the end of the checkpoint, or when the OS writes data back in larger batches in the background. Often that will result in greatly reduced transaction latency, but there also are some cases, especially with workloads that are bigger than shared_buffers, but smaller than the OS's page cache, where performance might degrade. This setting may have no effect on some platforms. If this value is specified without units, it is taken as blocks, that is BLCKSZ bytes, typically 8kB. The valid range is between 0, which disables forced writeback, and 2MB. The default is 256kB on Linux, 0 elsewhere. (If BLCKSZ is not 8kB, the default and maximum values scale proportionally to it.) This parameter can only be set in the postgresql.conf file or on the server command line.

Write a message to the server log if checkpoints caused by the filling of WAL segment files happen closer together than this amount of time (which suggests that max_wal_size ought to be raised). If this value is specified without units, it is taken as seconds. The default is 30 seconds (30s). Zero disables the warning. No warnings will be generated if checkpoint_timeout is less than checkpoint_warning. This parameter can only be set in the postgresql.conf file or on the server command line.

Maximum size to let the WAL grow during automatic checkpoints. This is a soft limit; WAL size can exceed max_wal_size under special circumstances, such as heavy load, a failing archive_command or archive_library, or a high wal_keep_size setting. If this value is specified without units, it is taken as megabytes. The default is 1 GB. Increasing this parameter can increase the amount of time needed for crash recovery. This parameter can only be set in the postgresql.conf file or on the server command line.

As long as WAL disk usage stays below this setting, old WAL files are always recycled for future use at a checkpoint, rather than removed. This can be used to ensure that enough WAL space is reserved to handle spikes in WAL usage, for example when running large batch jobs. If this value is specified without units, it is taken as megabytes. The default is 80 MB. This parameter can only be set in the postgresql.conf file or on the server command line.

When archive_mode is enabled, completed WAL segments are sent to archive storage by setting archive_command or archive_library. In addition to off, to disable, there are two modes: on, and always. During normal operation, there is no difference between the two modes, but when set to always the WAL archiver is enabled also during archive recovery or standby mode. In always mode, all files restored from the archive or streamed with streaming replication will be archived (again). See Section 26.2.9 for details.

archive_mode is a separate setting from archive_command and archive_library so that archive_command and archive_library can be changed without leaving archiving mode. This parameter can only be set at server start. archive_mode cannot be enabled when wal_level is set to minimal.

The local shell command to execute to archive a completed WAL file segment. Any %p in the string is replaced by the path name of the file to archive, and any %f is replaced by only the file name. (The path name is relative to the working directory of the server, i.e., the cluster's data directory.) Use %% to embed an actual % character in the command. It is important for the command to return a zero exit status only if it succeeds. For more information see Section 25.3.1.

This parameter can only be set in the postgresql.conf file or on the server command line. It is only used if archive_mode was enabled at server start and archive_library is set to an empty string. If both archive_command and archive_library are set, an error will be raised. If archive_command is an empty string (the default) while archive_mode is enabled (and archive_library is set to an empty string), WAL archiving is temporarily disabled, but the server continues to accumulate WAL segment files in the expectation that a command will soon be provided. Setting archive_command to a command that does nothing but return true, e.g., /bin/true (REM on Windows), effectively disables archiving, but also breaks the chain of WAL files needed for archive recovery, so it should only be used in unusual circumstances.

The library to use for archiving completed WAL file segments. If set to an empty string (the default), archiving via shell is enabled, and archive_command is used. If both archive_command and archive_library are set, an error will be raised. Otherwise, the specified shared library is used for archiving. The WAL archiver process is restarted by the postmaster when this parameter changes. For more information, see Section 25.3.1 and Chapter 49.

This parameter can only be set in the postgresql.conf file or on the server command line.

The archive_command or archive_library is only invoked for completed WAL segments. Hence, if your server generates little WAL traffic (or has slack periods where it does so), there could be a long delay between the completion of a transaction and its safe recording in archive storage. To limit how old unarchived data can be, you can set archive_timeout to force the server to switch to a new WAL segment file periodically. When this parameter is greater than zero, the server will switch to a new segment file whenever this amount of time has elapsed since the last segment file switch, and there has been any database activity, including a single checkpoint (checkpoints are skipped if there is no database activity). Note that archived files that are closed early due to a forced switch are still the same length as completely full files. Therefore, it is unwise to use a very short archive_timeout — it will bloat your archive storage. archive_timeout settings of a minute or so are usually reasonable. You should consider using streaming replication, instead of archiving, if you want data to be copied off the primary server more quickly than that. If this value is specified without units, it is taken as seconds. This parameter can only be set in the postgresql.conf file or on the server command line.

This section describes the settings that apply to recovery in general, affecting crash recovery, streaming replication and archive-based replication.

Whether to try to prefetch blocks that are referenced in the WAL that are not yet in the buffer pool, during recovery. Valid values are off, on and try (the default). The setting try enables prefetching only if the operating system provides support for issuing read-ahead advice.

Prefetching blocks that will soon be needed can reduce I/O wait times during recovery with some workloads. See also the wal_decode_buffer_size and maintenance_io_concurrency settings, which limit prefetching activity.

A limit on how far ahead the server can look in the WAL, to find blocks to prefetch. If this value is specified without units, it is taken as bytes. The default is 512kB.

This section describes the settings that apply only for the duration of the recovery. They must be reset for any subsequent recovery you wish to perform.

“Recovery” covers using the server as a standby or for executing a targeted recovery. Typically, standby mode would be used to provide high availability and/or read scalability, whereas a targeted recovery is used to recover from data loss.

To start the server in standby mode, create a file called standby.signal in the data directory. The server will enter recovery and will not stop recovery when the end of archived WAL is reached, but will keep trying to continue recovery by connecting to the sending server as specified by the primary_conninfo setting and/or by fetching new WAL segments using restore_command. For this mode, the parameters from this section and Section 19.6.3 are of interest. Parameters from Section 19.5.6 will also be applied but are typically not useful in this mode.

To start the server in targeted recovery mode, create a file called recovery.signal in the data directory. If both standby.signal and recovery.signal files are created, standby mode takes precedence. Targeted recovery mode ends when the archived WAL is fully replayed, or when recovery_target is reached. In this mode, the parameters from both this section and Section 19.5.6 will be used.

The local shell command to execute to retrieve an archived segment of the WAL file series. This parameter is required for archive recovery, but optional for streaming replication. Any %f in the string is replaced by the name of the file to retrieve from the archive, and any %p is replaced by the copy destination path name on the server. (The path name is relative to the current working directory, i.e., the cluster's data directory.) Any %r is replaced by the name of the file containing the last valid restart point. That is the earliest file that must be kept to allow a restore to be restartable, so this information can be used to truncate the archive to just the minimum required to support restarting from the current restore. %r is typically only used by warm-standby configurations (see Section 26.2). Write %% to embed an actual % character.

It is important for the command to return a zero exit status only if it succeeds. The command will be asked for file names that are not present in the archive; it must return nonzero when so asked. Examples:

An exception is that if the command was terminated by a signal (other than SIGTERM, which is used as part of a database server shutdown) or an error by the shell (such as command not found), then recovery will abort and the server will not start up.

This parameter can only be set in the postgresql.conf file or on the server command line.

This optional parameter specifies a shell command that will be executed at every restartpoint. The purpose of archive_cleanup_command is to provide a mechanism for cleaning up old archived WAL files that are no longer needed by the standby server. Any %r is replaced by the name of the file containing the last valid restart point. That is the earliest file that must be kept to allow a restore to be restartable, and so all files earlier than %r may be safely removed. This information can be used to truncate the archive to just the minimum required to support restart from the current restore. The pg_archivecleanup module is often used in archive_cleanup_command for single-standby configurations, for example:

Note however that if multiple standby servers are restoring from the same archive directory, you will need to ensure that you do not delete WAL files until they are no longer needed by any of the servers. archive_cleanup_command would typically be used in a warm-standby configuration (see Section 26.2). Write %% to embed an actual % character in the command.

If the command returns a nonzero exit status then a warning log message will be written. An exception is that if the command was terminated by a signal or an error by the shell (such as command not found), a fatal error will be raised.

This parameter can only be set in the postgresql.conf file or on the server command line.

This parameter specifies a shell command that will be executed once only at the end of recovery. This parameter is optional. The purpose of the recovery_end_command is to provide a mechanism for cleanup following replication or recovery. Any %r is replaced by the name of the file containing the last valid restart point, like in archive_cleanup_command.

If the command returns a nonzero exit status then a warning log message will be written and the database will proceed to start up anyway. An exception is that if the command was terminated by a signal or an error by the shell (such as command not found), the database will not proceed with startup.

This parameter can only be set in the postgresql.conf file or on the server command line.

By default, recovery will recover to the end of the WAL log. The following parameters can be used to specify an earlier stopping point. At most one of recovery_target, recovery_target_lsn, recovery_target_name, recovery_target_time, or recovery_target_xid can be used; if more than one of these is specified in the configuration file, an error will be raised. These parameters can only be set at server start.

This parameter specifies that recovery should end as soon as a consistent state is reached, i.e., as early as possible. When restoring from an online backup, this means the point where taking the backup ended.

Technically, this is a string parameter, but 'immediate' is currently the only allowed value.

This parameter specifies the named restore point (created with pg_create_restore_point()) to which recovery will proceed.

This parameter specifies the time stamp up to which recovery will proceed. The precise stopping point is also influenced by recovery_target_inclusive.

The value of this parameter is a time stamp in the same format accepted by the timestamp with time zone data type, except that you cannot use a time zone abbreviation (unless the timezone_abbreviations variable has been set earlier in the configuration file). Preferred style is to use a numeric offset from UTC, or you can write a full time zone name, e.g., Europe/Helsinki not EEST.

This parameter specifies the transaction ID up to which recovery will proceed. Keep in mind that while transaction IDs are assigned sequentially at transaction start, transactions can complete in a different numeric order. The transactions that will be recovered are those that committed before (and optionally including) the specified one. The precise stopping point is also influenced by recovery_target_inclusive.

This parameter specifies the LSN of the write-ahead log location up to which recovery will proceed. The precise stopping point is also influenced by recovery_target_inclusive. This parameter is parsed using the system data type pg_lsn.

The following options further specify the recovery target, and affect what happens when the target is reached:

Specifies whether to stop just after the specified recovery target (on), or just before the recovery target (off). Applies when recovery_target_lsn, recovery_target_time, or recovery_target_xid is specified. This setting controls whether transactions having exactly the target WAL location (LSN), commit time, or transaction ID, respectively, will be included in the recovery. Default is on.

Specifies recovering into a particular timeline. The value can be a numeric timeline ID or a special value. The value current recovers along the same timeline that was current when the base backup was taken. The value latest recovers to the latest timeline found in the archive, which is useful in a standby server. latest is the default.

To specify a timeline ID in hexadecimal (for example, if extracted from a WAL file name or history file), prefix it with a 0x. For instance, if the WAL file name is 00000011000000A10000004F, then the timeline ID is 0x11 (or 17 decimal).

You usually only need to set this parameter in complex re-recovery situations, where you need to return to a state that itself was reached after a point-in-time recovery. See Section 25.3.6 for discussion.

Specifies what action the server should take once the recovery target is reached. The default is pause, which means recovery will be paused. promote means the recovery process will finish and the server will start to accept connections. Finally shutdown will stop the server after reaching the recovery target.

The intended use of the pause setting is to allow queries to be executed against the database to check if this recovery target is the most desirable point for recovery. The paused state can be resumed by using pg_wal_replay_resume() (see Table 9.99), which then causes recovery to end. If this recovery target is not the desired stopping point, then shut down the server, change the recovery target settings to a later target and restart to continue recovery.

The shutdown setting is useful to have the instance ready at the exact replay point desired. The instance will still be able to replay more WAL records (and in fact will have to replay WAL records since the last checkpoint next time it is started).

Note that because recovery.signal will not be removed when recovery_target_action is set to shutdown, any subsequent start will end with immediate shutdown unless the configuration is changed or the recovery.signal file is removed manually.

This setting has no effect if no recovery target is set. If hot_standby is not enabled, a setting of pause will act the same as shutdown. If the recovery target is reached while a promotion is ongoing, a setting of pause will act the same as promote.

In any case, if a recovery target is configured but the archive recovery ends before the target is reached, the server will shut down with a fatal error.

These settings control WAL summarization, a feature which must be enabled in order to perform an incremental backup.

Enables the WAL summarizer process. Note that WAL summarization can be enabled either on a primary or on a standby. This parameter can only be set in the postgresql.conf file or on the server command line. The default is off.

The server cannot be started with summarize_wal=on if wal_level is set to minimal. If summarize_wal=on is configured after server startup while wal_level=minimal, the summarizer will run but refuse to generate summary files for any WAL generated with wal_level=minimal.

Configures the amount of time after which the WAL summarizer automatically removes old WAL summaries. The file timestamp is used to determine which files are old enough to remove. Typically, you should set this comfortably higher than the time that could pass between a backup and a later incremental backup that depends on it. WAL summaries must be available for the entire range of WAL records between the preceding backup and the new one being taken; if not, the incremental backup will fail. If this parameter is set to zero, WAL summaries will not be automatically deleted, but it is safe to manually remove files that you know will not be required for future incremental backups. This parameter can only be set in the postgresql.conf file or on the server command line. If this value is specified without units, it is taken as minutes. The default is 10 days. If summarize_wal = off, existing WAL summaries will not be removed regardless of the value of this parameter, because the WAL summarizer will not run.

Examples:

Example 1 (unknown):

restore_command = 'cp /mnt/server/archivedir/%f "%p"'
restore_command = 'copy "C:\\server\\archivedir\\%f" "%p"'  # Windows

Example 2 (unknown):

archive_cleanup_command = 'pg_archivecleanup /mnt/server/archivedir %r'

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PostgreSQL: Documentation: 18: Chapter 35. The Information Schema

URL: https://www.postgresql.org/docs/current/information-schema.html

Contents:

• Chapter 35. The Information Schema
  • Note

The information schema consists of a set of views that contain information about the objects defined in the current database. The information schema is defined in the SQL standard and can therefore be expected to be portable and remain stable — unlike the system catalogs, which are specific to PostgreSQL and are modeled after implementation concerns. The information schema views do not, however, contain information about PostgreSQL-specific features; to inquire about those you need to query the system catalogs or other PostgreSQL-specific views.

When querying the database for constraint information, it is possible for a standard-compliant query that expects to return one row to return several. This is because the SQL standard requires constraint names to be unique within a schema, but PostgreSQL does not enforce this restriction. PostgreSQL automatically-generated constraint names avoid duplicates in the same schema, but users can specify such duplicate names.

This problem can appear when querying information schema views such as check_constraint_routine_usage, check_constraints, domain_constraints, and referential_constraints. Some other views have similar issues but contain the table name to help distinguish duplicate rows, e.g., constraint_column_usage, constraint_table_usage, table_constraints.

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PostgreSQL: Documentation: 18: 20.5. Password Authentication

URL: https://www.postgresql.org/docs/current/auth-password.html

Contents:

• 20.5. Password Authentication #
  • Warning

There are several password-based authentication methods. These methods operate similarly but differ in how the users' passwords are stored on the server and how the password provided by a client is sent across the connection.

The method scram-sha-256 performs SCRAM-SHA-256 authentication, as described in RFC 7677. It is a challenge-response scheme that prevents password sniffing on untrusted connections and supports storing passwords on the server in a cryptographically hashed form that is thought to be secure.

This is the most secure of the currently provided methods, but it is not supported by older client libraries.

The method md5 uses a custom less secure challenge-response mechanism. It prevents password sniffing and avoids storing passwords on the server in plain text but provides no protection if an attacker manages to steal the password hash from the server. Also, the MD5 hash algorithm is nowadays no longer considered secure against determined attacks.

To ease transition from the md5 method to the newer SCRAM method, if md5 is specified as a method in pg_hba.conf but the user's password on the server is encrypted for SCRAM (see below), then SCRAM-based authentication will automatically be chosen instead.

Support for MD5-encrypted passwords is deprecated and will be removed in a future release of PostgreSQL. Refer to the text below for details about migrating to another password type.

The method password sends the password in clear-text and is therefore vulnerable to password “sniffing” attacks. It should always be avoided if possible. If the connection is protected by SSL encryption then password can be used safely, though. (Though SSL certificate authentication might be a better choice if one is depending on using SSL).

PostgreSQL database passwords are separate from operating system user passwords. The password for each database user is stored in the pg_authid system catalog. Passwords can be managed with the SQL commands CREATE ROLE and ALTER ROLE, e.g., CREATE ROLE foo WITH LOGIN PASSWORD 'secret', or the psql command \password. If no password has been set up for a user, the stored password is null and password authentication will always fail for that user.

The availability of the different password-based authentication methods depends on how a user's password on the server is encrypted (or hashed, more accurately). This is controlled by the configuration parameter password_encryption at the time the password is set. If a password was encrypted using the scram-sha-256 setting, then it can be used for the authentication methods scram-sha-256 and password (but password transmission will be in plain text in the latter case). The authentication method specification md5 will automatically switch to using the scram-sha-256 method in this case, as explained above, so it will also work. If a password was encrypted using the md5 setting, then it can be used only for the md5 and password authentication method specifications (again, with the password transmitted in plain text in the latter case). (Previous PostgreSQL releases supported storing the password on the server in plain text. This is no longer possible.) To check the currently stored password hashes, see the system catalog pg_authid.

To upgrade an existing installation from md5 to scram-sha-256, after having ensured that all client libraries in use are new enough to support SCRAM, set password_encryption = 'scram-sha-256' in postgresql.conf, make all users set new passwords, and change the authentication method specifications in pg_hba.conf to scram-sha-256.

---

PostgreSQL: Documentation: 18: 19.16. Customized Options

URL: https://www.postgresql.org/docs/current/runtime-config-custom.html

Contents:

• 19.16. Customized Options #

This feature was designed to allow parameters not normally known to PostgreSQL to be added by add-on modules (such as procedural languages). This allows extension modules to be configured in the standard ways.

Custom options have two-part names: an extension name, then a dot, then the parameter name proper, much like qualified names in SQL. An example is plpgsql.variable_conflict.

Because custom options may need to be set in processes that have not loaded the relevant extension module, PostgreSQL will accept a setting for any two-part parameter name. Such variables are treated as placeholders and have no function until the module that defines them is loaded. When an extension module is loaded, it will add its variable definitions and convert any placeholder values according to those definitions. If there are any unrecognized placeholders that begin with its extension name, warnings are issued and those placeholders are removed.

---

PostgreSQL: Documentation: 18: 29.1. Publication

URL: https://www.postgresql.org/docs/current/logical-replication-publication.html

Contents:

• 29.1. Publication #
  • 29.1.1. Replica Identity #

A publication can be defined on any physical replication primary. The node where a publication is defined is referred to as publisher. A publication is a set of changes generated from a table or a group of tables, and might also be described as a change set or replication set. Each publication exists in only one database.

Publications are different from schemas and do not affect how the table is accessed. Each table can be added to multiple publications if needed. Publications may currently only contain tables and all tables in schema. Objects must be added explicitly, except when a publication is created for ALL TABLES.

Publications can choose to limit the changes they produce to any combination of INSERT, UPDATE, DELETE, and TRUNCATE, similar to how triggers are fired by particular event types. By default, all operation types are replicated. These publication specifications apply only for DML operations; they do not affect the initial data synchronization copy. (Row filters have no effect for TRUNCATE. See Section 29.4).

Every publication can have multiple subscribers.

A publication is created using the CREATE PUBLICATION command and may later be altered or dropped using corresponding commands.

The individual tables can be added and removed dynamically using ALTER PUBLICATION. Both the ADD TABLE and DROP TABLE operations are transactional, so the table will start or stop replicating at the correct snapshot once the transaction has committed.

A published table must have a replica identity configured in order to be able to replicate UPDATE and DELETE operations, so that appropriate rows to update or delete can be identified on the subscriber side.

By default, this is the primary key, if there is one. Another unique index (with certain additional requirements) can also be set to be the replica identity. If the table does not have any suitable key, then it can be set to replica identity FULL, which means the entire row becomes the key. When replica identity FULL is specified, indexes can be used on the subscriber side for searching the rows. Candidate indexes must be btree or hash, non-partial, and the leftmost index field must be a column (not an expression) that references the published table column. These restrictions on the non-unique index properties adhere to some of the restrictions that are enforced for primary keys. If there are no such suitable indexes, the search on the subscriber side can be very inefficient, therefore replica identity FULL should only be used as a fallback if no other solution is possible.

If a replica identity other than FULL is set on the publisher side, a replica identity comprising the same or fewer columns must also be set on the subscriber side.

Tables with a replica identity defined as NOTHING, DEFAULT without a primary key, or USING INDEX with a dropped index, cannot support UPDATE or DELETE operations when included in a publication replicating these actions. Attempting such operations will result in an error on the publisher.

INSERT operations can proceed regardless of any replica identity.

See ALTER TABLE...REPLICA IDENTITY for details on how to set the replica identity.

---

PostgreSQL: Documentation: 18: 5. Bug Reporting Guidelines

URL: https://www.postgresql.org/docs/current/bug-reporting.html

Contents:

• 5. Bug Reporting Guidelines #
  • 5.1. Identifying Bugs #
  • 5.2. What to Report #
  • Note
  • Note
  • 5.3. Where to Report Bugs #
  • Note

When you find a bug in PostgreSQL we want to hear about it. Your bug reports play an important part in making PostgreSQL more reliable because even the utmost care cannot guarantee that every part of PostgreSQL will work on every platform under every circumstance.

The following suggestions are intended to assist you in forming bug reports that can be handled in an effective fashion. No one is required to follow them but doing so tends to be to everyone's advantage.

We cannot promise to fix every bug right away. If the bug is obvious, critical, or affects a lot of users, chances are good that someone will look into it. It could also happen that we tell you to update to a newer version to see if the bug happens there. Or we might decide that the bug cannot be fixed before some major rewrite we might be planning is done. Or perhaps it is simply too hard and there are more important things on the agenda. If you need help immediately, consider obtaining a commercial support contract.

Before you report a bug, please read and re-read the documentation to verify that you can really do whatever it is you are trying. If it is not clear from the documentation whether you can do something or not, please report that too; it is a bug in the documentation. If it turns out that a program does something different from what the documentation says, that is a bug. That might include, but is not limited to, the following circumstances:

A program terminates with a fatal signal or an operating system error message that would point to a problem in the program. (A counterexample might be a “disk full” message, since you have to fix that yourself.)

A program produces the wrong output for any given input.

A program refuses to accept valid input (as defined in the documentation).

A program accepts invalid input without a notice or error message. But keep in mind that your idea of invalid input might be our idea of an extension or compatibility with traditional practice.

PostgreSQL fails to compile, build, or install according to the instructions on supported platforms.

Here “program” refers to any executable, not only the backend process.

Being slow or resource-hogging is not necessarily a bug. Read the documentation or ask on one of the mailing lists for help in tuning your applications. Failing to comply to the SQL standard is not necessarily a bug either, unless compliance for the specific feature is explicitly claimed.

Before you continue, check on the TODO list and in the FAQ to see if your bug is already known. If you cannot decode the information on the TODO list, report your problem. The least we can do is make the TODO list clearer.

The most important thing to remember about bug reporting is to state all the facts and only facts. Do not speculate what you think went wrong, what “it seemed to do”, or which part of the program has a fault. If you are not familiar with the implementation you would probably guess wrong and not help us a bit. And even if you are, educated explanations are a great supplement to but no substitute for facts. If we are going to fix the bug we still have to see it happen for ourselves first. Reporting the bare facts is relatively straightforward (you can probably copy and paste them from the screen) but all too often important details are left out because someone thought it does not matter or the report would be understood anyway.

The following items should be contained in every bug report:

The exact sequence of steps from program start-up necessary to reproduce the problem. This should be self-contained; it is not enough to send in a bare SELECT statement without the preceding CREATE TABLE and INSERT statements, if the output should depend on the data in the tables. We do not have the time to reverse-engineer your database schema, and if we are supposed to make up our own data we would probably miss the problem.

The best format for a test case for SQL-related problems is a file that can be run through the psql frontend that shows the problem. (Be sure to not have anything in your ~/.psqlrc start-up file.) An easy way to create this file is to use pg_dump to dump out the table declarations and data needed to set the scene, then add the problem query. You are encouraged to minimize the size of your example, but this is not absolutely necessary. If the bug is reproducible, we will find it either way.

If your application uses some other client interface, such as PHP, then please try to isolate the offending queries. We will probably not set up a web server to reproduce your problem. In any case remember to provide the exact input files; do not guess that the problem happens for “large files” or “midsize databases”, etc. since this information is too inexact to be of use.

The output you got. Please do not say that it “didn't work” or “crashed”. If there is an error message, show it, even if you do not understand it. If the program terminates with an operating system error, say which. If nothing at all happens, say so. Even if the result of your test case is a program crash or otherwise obvious it might not happen on our platform. The easiest thing is to copy the output from the terminal, if possible.

If you are reporting an error message, please obtain the most verbose form of the message. In psql, say \set VERBOSITY verbose beforehand. If you are extracting the message from the server log, set the run-time parameter log_error_verbosity to verbose so that all details are logged.

In case of fatal errors, the error message reported by the client might not contain all the information available. Please also look at the log output of the database server. If you do not keep your server's log output, this would be a good time to start doing so.

The output you expected is very important to state. If you just write “This command gives me that output.” or “This is not what I expected.”, we might run it ourselves, scan the output, and think it looks OK and is exactly what we expected. We should not have to spend the time to decode the exact semantics behind your commands. Especially refrain from merely saying that “This is not what SQL says/Oracle does.” Digging out the correct behavior from SQL is not a fun undertaking, nor do we all know how all the other relational databases out there behave. (If your problem is a program crash, you can obviously omit this item.)

Any command line options and other start-up options, including any relevant environment variables or configuration files that you changed from the default. Again, please provide exact information. If you are using a prepackaged distribution that starts the database server at boot time, you should try to find out how that is done.

Anything you did at all differently from the installation instructions.

The PostgreSQL version. You can run the command SELECT version(); to find out the version of the server you are connected to. Most executable programs also support a --version option; at least postgres --version and psql --version should work. If the function or the options do not exist then your version is more than old enough to warrant an upgrade. If you run a prepackaged version, such as RPMs, say so, including any subversion the package might have. If you are talking about a Git snapshot, mention that, including the commit hash.

If your version is older than 18.0 we will almost certainly tell you to upgrade. There are many bug fixes and improvements in each new release, so it is quite possible that a bug you have encountered in an older release of PostgreSQL has already been fixed. We can only provide limited support for sites using older releases of PostgreSQL; if you require more than we can provide, consider acquiring a commercial support contract.

Platform information. This includes the kernel name and version, C library, processor, memory information, and so on. In most cases it is sufficient to report the vendor and version, but do not assume everyone knows what exactly “Debian” contains or that everyone runs on x86_64. If you have installation problems then information about the toolchain on your machine (compiler, make, and so on) is also necessary.

Do not be afraid if your bug report becomes rather lengthy. That is a fact of life. It is better to report everything the first time than us having to squeeze the facts out of you. On the other hand, if your input files are huge, it is fair to ask first whether somebody is interested in looking into it. Here is an article that outlines some more tips on reporting bugs.

Do not spend all your time to figure out which changes in the input make the problem go away. This will probably not help solving it. If it turns out that the bug cannot be fixed right away, you will still have time to find and share your work-around. Also, once again, do not waste your time guessing why the bug exists. We will find that out soon enough.

When writing a bug report, please avoid confusing terminology. The software package in total is called “PostgreSQL”, sometimes “Postgres” for short. If you are specifically talking about the backend process, mention that, do not just say “PostgreSQL crashes”. A crash of a single backend process is quite different from crash of the parent “postgres” process; please don't say “the server crashed” when you mean a single backend process went down, nor vice versa. Also, client programs such as the interactive frontend “psql” are completely separate from the backend. Please try to be specific about whether the problem is on the client or server side.

In general, send bug reports to the bug report mailing list at pgsql-bugs@lists.postgresql.org. You are requested to use a descriptive subject for your email message, perhaps parts of the error message.

Another method is to fill in the bug report web-form available at the project's web site. Entering a bug report this way causes it to be mailed to the pgsql-bugs@lists.postgresql.org mailing list.

If your bug report has security implications and you'd prefer that it not become immediately visible in public archives, don't send it to pgsql-bugs. Security issues can be reported privately to security@postgresql.org.

Do not send bug reports to any of the user mailing lists, such as pgsql-sql@lists.postgresql.org or pgsql-general@lists.postgresql.org. These mailing lists are for answering user questions, and their subscribers normally do not wish to receive bug reports. More importantly, they are unlikely to fix them.

Also, please do not send reports to the developers' mailing list pgsql-hackers@lists.postgresql.org. This list is for discussing the development of PostgreSQL, and it would be nice if we could keep the bug reports separate. We might choose to take up a discussion about your bug report on pgsql-hackers, if the problem needs more review.

If you have a problem with the documentation, the best place to report it is the documentation mailing list pgsql-docs@lists.postgresql.org. Please be specific about what part of the documentation you are unhappy with.

If your bug is a portability problem on a non-supported platform, send mail to pgsql-hackers@lists.postgresql.org, so we (and you) can work on porting PostgreSQL to your platform.

Due to the unfortunate amount of spam going around, all of the above lists will be moderated unless you are subscribed. That means there will be some delay before the email is delivered. If you wish to subscribe to the lists, please visit https://lists.postgresql.org/ for instructions.

---

PostgreSQL: Documentation: 18: 20.13. PAM Authentication

URL: https://www.postgresql.org/docs/current/auth-pam.html

Contents:

• 20.13. PAM Authentication #
  • Note

This authentication method operates similarly to password except that it uses PAM (Pluggable Authentication Modules) as the authentication mechanism. The default PAM service name is postgresql. PAM is used only to validate user name/password pairs and optionally the connected remote host name or IP address. Therefore the user must already exist in the database before PAM can be used for authentication. For more information about PAM, please read the Linux-PAM Page.

The following configuration options are supported for PAM:

Determines whether the remote IP address or the host name is provided to PAM modules through the PAM_RHOST item. By default, the IP address is used. Set this option to 1 to use the resolved host name instead. Host name resolution can lead to login delays. (Most PAM configurations don't use this information, so it is only necessary to consider this setting if a PAM configuration was specifically created to make use of it.)

If PAM is set up to read /etc/shadow, authentication will fail because the PostgreSQL server is started by a non-root user. However, this is not an issue when PAM is configured to use LDAP or other authentication methods.

---

PostgreSQL: Documentation: 18: 9.14. UUID Functions

URL: https://www.postgresql.org/docs/current/functions-uuid.html

Contents:

• 9.14. UUID Functions #
  • Note

Table 9.45 shows the PostgreSQL functions that can be used to generate UUIDs.

Table 9.45. UUID Generation Functions

gen_random_uuid → uuid

Generate a version 4 (random) UUID.

gen_random_uuid() → 5b30857f-0bfa-48b5-ac0b-5c64e28078

uuidv4() → b42410ee-132f-42ee-9e4f-09a6485c95

uuidv7 ( [ shift interval ] ) → uuid

Generate a version 7 (time-ordered) UUID. The timestamp is computed using UNIX timestamp with millisecond precision + sub-millisecond timestamp + random. The optional parameter shift will shift the computed timestamp by the given interval.

uuidv7() → 019535d9-3df7-79fb-b466-fa907fa17f

The uuid-ossp module provides additional functions that implement other standard algorithms for generating UUIDs.

Table 9.46 shows the PostgreSQL functions that can be used to extract information from UUIDs.

Table 9.46. UUID Extraction Functions

uuid_extract_timestamp ( uuid ) → timestamp with time zone

Extracts a timestamp with time zone from UUID version 1 and 7. For other versions, this function returns null. Note that the extracted timestamp is not necessarily exactly equal to the time the UUID was generated; this depends on the implementation that generated the UUID.

uuid_extract_timestamp('019535d9-3df7-79fb-b466-​fa907fa17f'::uuid) → 2025-02-23 21:46:24.503-05

uuid_extract_version ( uuid ) → smallint

Extracts the version from a UUID of the variant described by RFC 9562. For other variants, this function returns null. For example, for a UUID generated by gen_random_uuid, this function will return 4.

uuid_extract_version('41db1265-8bc1-4ab3-992f-​885799a4af'::uuid) → 4

uuid_extract_version('019535d9-3df7-79fb-b466-​fa907fa17f'::uuid) → 7

PostgreSQL also provides the usual comparison operators shown in Table 9.1 for UUIDs.

See Section 8.12 for details on the data type uuid in PostgreSQL.

---

PostgreSQL: Documentation: 18: Chapter 32. libpq — C Library

URL: https://www.postgresql.org/docs/current/libpq.html

Contents:

• Chapter 32. libpq — C Library

libpq is the C application programmer's interface to PostgreSQL. libpq is a set of library functions that allow client programs to pass queries to the PostgreSQL backend server and to receive the results of these queries.

libpq is also the underlying engine for several other PostgreSQL application interfaces, including those written for C++, Perl, Python, Tcl and ECPG. So some aspects of libpq's behavior will be important to you if you use one of those packages. In particular, Section 32.15, Section 32.16 and Section 32.19 describe behavior that is visible to the user of any application that uses libpq.

Some short programs are included at the end of this chapter (Section 32.23) to show how to write programs that use libpq. There are also several complete examples of libpq applications in the directory src/test/examples in the source code distribution.

Client programs that use libpq must include the header file libpq-fe.h and must link with the libpq library.

---

PostgreSQL: Documentation: 18: 9.16. JSON Functions and Operators

URL: https://www.postgresql.org/docs/current/functions-json.html

Contents:

• 9.16. JSON Functions and Operators #
  • 9.16.1. Processing and Creating JSON Data #
  • Note
  • Note
  • 9.16.2. The SQL/JSON Path Language #
  • 9.16.2.1. Deviations from the SQL Standard #
    • 9.16.2.1.1. Boolean Predicate Check Expressions #
  • Note
    • 9.16.2.1.2. Regular Expression Interpretation #
  • 9.16.2.2. Strict and Lax Modes #

This section describes:

functions and operators for processing and creating JSON data

the SQL/JSON path language

the SQL/JSON query functions

To provide native support for JSON data types within the SQL environment, PostgreSQL implements the SQL/JSON data model. This model comprises sequences of items. Each item can hold SQL scalar values, with an additional SQL/JSON null value, and composite data structures that use JSON arrays and objects. The model is a formalization of the implied data model in the JSON specification RFC 7159.

SQL/JSON allows you to handle JSON data alongside regular SQL data, with transaction support, including:

Uploading JSON data into the database and storing it in regular SQL columns as character or binary strings.

Generating JSON objects and arrays from relational data.

Querying JSON data using SQL/JSON query functions and SQL/JSON path language expressions.

To learn more about the SQL/JSON standard, see [sqltr-19075-6]. For details on JSON types supported in PostgreSQL, see Section 8.14.

Table 9.47 shows the operators that are available for use with JSON data types (see Section 8.14). In addition, the usual comparison operators shown in Table 9.1 are available for jsonb, though not for json. The comparison operators follow the ordering rules for B-tree operations outlined in Section 8.14.4. See also Section 9.21 for the aggregate function json_agg which aggregates record values as JSON, the aggregate function json_object_agg which aggregates pairs of values into a JSON object, and their jsonb equivalents, jsonb_agg and jsonb_object_agg.

Table 9.47. json and jsonb Operators

json -> integer → json

jsonb -> integer → jsonb

Extracts n'th element of JSON array (array elements are indexed from zero, but negative integers count from the end).

'[{"a":"foo"},{"b":"bar"},{"c":"baz"}]'::json -> 2 → {"c":"baz"}

'[{"a":"foo"},{"b":"bar"},{"c":"baz"}]'::json -> -3 → {"a":"foo"}

jsonb -> text → jsonb

Extracts JSON object field with the given key.

'{"a": {"b":"foo"}}'::json -> 'a' → {"b":"foo"}

json ->> integer → text

jsonb ->> integer → text

Extracts n'th element of JSON array, as text.

'[1,2,3]'::json ->> 2 → 3

jsonb ->> text → text

Extracts JSON object field with the given key, as text.

'{"a":1,"b":2}'::json ->> 'b' → 2

json #> text[] → json

jsonb #> text[] → jsonb

Extracts JSON sub-object at the specified path, where path elements can be either field keys or array indexes.

'{"a": {"b": ["foo","bar"]}}'::json #> '{a,b,1}' → "bar"

json #>> text[] → text

jsonb #>> text[] → text

Extracts JSON sub-object at the specified path as text.

'{"a": {"b": ["foo","bar"]}}'::json #>> '{a,b,1}' → bar

The field/element/path extraction operators return NULL, rather than failing, if the JSON input does not have the right structure to match the request; for example if no such key or array element exists.

Some further operators exist only for jsonb, as shown in Table 9.48. Section 8.14.4 describes how these operators can be used to effectively search indexed jsonb data.

Table 9.48. Additional jsonb Operators

jsonb @> jsonb → boolean

Does the first JSON value contain the second? (See Section 8.14.3 for details about containment.)

'{"a":1, "b":2}'::jsonb @> '{"b":2}'::jsonb → t

jsonb <@ jsonb → boolean

Is the first JSON value contained in the second?

'{"b":2}'::jsonb <@ '{"a":1, "b":2}'::jsonb → t

jsonb ? text → boolean

Does the text string exist as a top-level key or array element within the JSON value?

'{"a":1, "b":2}'::jsonb ? 'b' → t

'["a", "b", "c"]'::jsonb ? 'b' → t

jsonb ?| text[] → boolean

Do any of the strings in the text array exist as top-level keys or array elements?

'{"a":1, "b":2, "c":3}'::jsonb ?| array['b', 'd'] → t

jsonb ?& text[] → boolean

Do all of the strings in the text array exist as top-level keys or array elements?

'["a", "b", "c"]'::jsonb ?& array['a', 'b'] → t

jsonb || jsonb → jsonb

Concatenates two jsonb values. Concatenating two arrays generates an array containing all the elements of each input. Concatenating two objects generates an object containing the union of their keys, taking the second object's value when there are duplicate keys. All other cases are treated by converting a non-array input into a single-element array, and then proceeding as for two arrays. Does not operate recursively: only the top-level array or object structure is merged.

'["a", "b"]'::jsonb || '["a", "d"]'::jsonb → ["a", "b", "a", "d"]

'{"a": "b"}'::jsonb || '{"c": "d"}'::jsonb → {"a": "b", "c": "d"}

'[1, 2]'::jsonb || '3'::jsonb → [1, 2, 3]

'{"a": "b"}'::jsonb || '42'::jsonb → [{"a": "b"}, 42]

To append an array to another array as a single entry, wrap it in an additional layer of array, for example:

'[1, 2]'::jsonb || jsonb_build_array('[3, 4]'::jsonb) → [1, 2, [3, 4]]

Deletes a key (and its value) from a JSON object, or matching string value(s) from a JSON array.

'{"a": "b", "c": "d"}'::jsonb - 'a' → {"c": "d"}

'["a", "b", "c", "b"]'::jsonb - 'b' → ["a", "c"]

jsonb - text[] → jsonb

Deletes all matching keys or array elements from the left operand.

'{"a": "b", "c": "d"}'::jsonb - '{a,c}'::text[] → {}

jsonb - integer → jsonb

Deletes the array element with specified index (negative integers count from the end). Throws an error if JSON value is not an array.

'["a", "b"]'::jsonb - 1 → ["a"]

jsonb #- text[] → jsonb

Deletes the field or array element at the specified path, where path elements can be either field keys or array indexes.

'["a", {"b":1}]'::jsonb #- '{1,b}' → ["a", {}]

jsonb @? jsonpath → boolean

Does JSON path return any item for the specified JSON value? (This is useful only with SQL-standard JSON path expressions, not predicate check expressions, since those always return a value.)

'{"a":[1,2,3,4,5]}'::jsonb @? '$.a[*] ? (@ > 2)' → t

jsonb @@ jsonpath → boolean

Returns the result of a JSON path predicate check for the specified JSON value. (This is useful only with predicate check expressions, not SQL-standard JSON path expressions, since it will return NULL if the path result is not a single boolean value.)

'{"a":[1,2,3,4,5]}'::jsonb @@ '$.a[*] > 2' → t

The jsonpath operators @? and @@ suppress the following errors: missing object field or array element, unexpected JSON item type, datetime and numeric errors. The jsonpath-related functions described below can also be told to suppress these types of errors. This behavior might be helpful when searching JSON document collections of varying structure.

Table 9.49 shows the functions that are available for constructing json and jsonb values. Some functions in this table have a RETURNING clause, which specifies the data type returned. It must be one of json, jsonb, bytea, a character string type (text, char, or varchar), or a type that can be cast to json. By default, the json type is returned.

Table 9.49. JSON Creation Functions

to_json ( anyelement ) → json

to_jsonb ( anyelement ) → jsonb

Converts any SQL value to json or jsonb. Arrays and composites are converted recursively to arrays and objects (multidimensional arrays become arrays of arrays in JSON). Otherwise, if there is a cast from the SQL data type to json, the cast function will be used to perform the conversion;[a] otherwise, a scalar JSON value is produced. For any scalar other than a number, a Boolean, or a null value, the text representation will be used, with escaping as necessary to make it a valid JSON string value.

to_json('Fred said "Hi."'::text) → "Fred said \"Hi.\""

to_jsonb(row(42, 'Fred said "Hi."'::text)) → {"f1": 42, "f2": "Fred said \"Hi.\""}

array_to_json ( anyarray [, boolean ] ) → json

Converts an SQL array to a JSON array. The behavior is the same as to_json except that line feeds will be added between top-level array elements if the optional boolean parameter is true.

array_to_json('{{1,5},{99,100}}'::int[]) → [[1,5],[99,100]]

json_array ( [ { value_expression [ FORMAT JSON ] } [, ...] ] [ { NULL | ABSENT } ON NULL ] [ RETURNING data_type [ FORMAT JSON [ ENCODING UTF8 ] ] ])

json_array ( [ query_expression ] [ RETURNING data_type [ FORMAT JSON [ ENCODING UTF8 ] ] ])

Constructs a JSON array from either a series of value_expression parameters or from the results of query_expression, which must be a SELECT query returning a single column. If ABSENT ON NULL is specified, NULL values are ignored. This is always the case if a query_expression is used.

json_array(1,true,json '{"a":null}') → [1, true, {"a":null}]

json_array(SELECT * FROM (VALUES(1),(2)) t) → [1, 2]

row_to_json ( record [, boolean ] ) → json

Converts an SQL composite value to a JSON object. The behavior is the same as to_json except that line feeds will be added between top-level elements if the optional boolean parameter is true.

row_to_json(row(1,'foo')) → {"f1":1,"f2":"foo"}

json_build_array ( VARIADIC "any" ) → json

jsonb_build_array ( VARIADIC "any" ) → jsonb

Builds a possibly-heterogeneously-typed JSON array out of a variadic argument list. Each argument is converted as per to_json or to_jsonb.

json_build_array(1, 2, 'foo', 4, 5) → [1, 2, "foo", 4, 5]

json_build_object ( VARIADIC "any" ) → json

jsonb_build_object ( VARIADIC "any" ) → jsonb

Builds a JSON object out of a variadic argument list. By convention, the argument list consists of alternating keys and values. Key arguments are coerced to text; value arguments are converted as per to_json or to_jsonb.

json_build_object('foo', 1, 2, row(3,'bar')) → {"foo" : 1, "2" : {"f1":3,"f2":"bar"}}

json_object ( [ { key_expression { VALUE | ':' } value_expression [ FORMAT JSON [ ENCODING UTF8 ] ] }[, ...] ] [ { NULL | ABSENT } ON NULL ] [ { WITH | WITHOUT } UNIQUE [ KEYS ] ] [ RETURNING data_type [ FORMAT JSON [ ENCODING UTF8 ] ] ])

Constructs a JSON object of all the key/value pairs given, or an empty object if none are given. key_expression is a scalar expression defining the JSON key, which is converted to the text type. It cannot be NULL nor can it belong to a type that has a cast to the json type. If WITH UNIQUE KEYS is specified, there must not be any duplicate key_expression. Any pair for which the value_expression evaluates to NULL is omitted from the output if ABSENT ON NULL is specified; if NULL ON NULL is specified or the clause omitted, the key is included with value NULL.

json_object('code' VALUE 'P123', 'title': 'Jaws') → {"code" : "P123", "title" : "Jaws"}

json_object ( text[] ) → json

jsonb_object ( text[] ) → jsonb

Builds a JSON object out of a text array. The array must have either exactly one dimension with an even number of members, in which case they are taken as alternating key/value pairs, or two dimensions such that each inner array has exactly two elements, which are taken as a key/value pair. All values are converted to JSON strings.

json_object('{a, 1, b, "def", c, 3.5}') → {"a" : "1", "b" : "def", "c" : "3.5"}

json_object('{{a, 1}, {b, "def"}, {c, 3.5}}') → {"a" : "1", "b" : "def", "c" : "3.5"}

json_object ( keys text[], values text[] ) → json

jsonb_object ( keys text[], values text[] ) → jsonb

This form of json_object takes keys and values pairwise from separate text arrays. Otherwise it is identical to the one-argument form.

json_object('{a,b}', '{1,2}') → {"a": "1", "b": "2"}

json ( expression [ FORMAT JSON [ ENCODING UTF8 ]] [ { WITH | WITHOUT } UNIQUE [ KEYS ]] ) → json

Converts a given expression specified as text or bytea string (in UTF8 encoding) into a JSON value. If expression is NULL, an SQL null value is returned. If WITH UNIQUE is specified, the expression must not contain any duplicate object keys.

json('{"a":123, "b":[true,"foo"], "a":"bar"}') → {"a":123, "b":[true,"foo"], "a":"bar"}

json_scalar ( expression )

Converts a given SQL scalar value into a JSON scalar value. If the input is NULL, an SQL null is returned. If the input is number or a boolean value, a corresponding JSON number or boolean value is returned. For any other value, a JSON string is returned.

json_scalar(123.45) → 123.45

json_scalar(CURRENT_TIMESTAMP) → "2022-05-10T10:51:04.62128-04:00"

json_serialize ( expression [ FORMAT JSON [ ENCODING UTF8 ] ] [ RETURNING data_type [ FORMAT JSON [ ENCODING UTF8 ] ] ] )

Converts an SQL/JSON expression into a character or binary string. The expression can be of any JSON type, any character string type, or bytea in UTF8 encoding. The returned type used in RETURNING can be any character string type or bytea. The default is text.

json_serialize('{ "a" : 1 } ' RETURNING bytea) → \x7b20226122203a2031207d20

[a] For example, the hstore extension has a cast from hstore to json, so that hstore values converted via the JSON creation functions will be represented as JSON objects, not as primitive string values.

Table 9.50 details SQL/JSON facilities for testing JSON.

Table 9.50. SQL/JSON Testing Functions

expression IS [ NOT ] JSON [ { VALUE | SCALAR | ARRAY | OBJECT } ] [ { WITH | WITHOUT } UNIQUE [ KEYS ] ]

This predicate tests whether expression can be parsed as JSON, possibly of a specified type. If SCALAR or ARRAY or OBJECT is specified, the test is whether or not the JSON is of that particular type. If WITH UNIQUE KEYS is specified, then any object in the expression is also tested to see if it has duplicate keys.

Table 9.51 shows the functions that are available for processing json and jsonb values.

Table 9.51. JSON Processing Functions

json_array_elements ( json ) → setof json

jsonb_array_elements ( jsonb ) → setof jsonb

Expands the top-level JSON array into a set of JSON values.

select * from json_array_elements('[1,true, [2,false]]') →

json_array_elements_text ( json ) → setof text

jsonb_array_elements_text ( jsonb ) → setof text

Expands the top-level JSON array into a set of text values.

select * from json_array_elements_text('["foo", "bar"]') →

json_array_length ( json ) → integer

jsonb_array_length ( jsonb ) → integer

Returns the number of elements in the top-level JSON array.

json_array_length('[1,2,3,{"f1":1,"f2":[5,6]},4]') → 5

jsonb_array_length('[]') → 0

json_each ( json ) → setof record ( key text, value json )

jsonb_each ( jsonb ) → setof record ( key text, value jsonb )

Expands the top-level JSON object into a set of key/value pairs.

select * from json_each('{"a":"foo", "b":"bar"}') →

json_each_text ( json ) → setof record ( key text, value text )

jsonb_each_text ( jsonb ) → setof record ( key text, value text )

Expands the top-level JSON object into a set of key/value pairs. The returned values will be of type text.

select * from json_each_text('{"a":"foo", "b":"bar"}') →

json_extract_path ( from_json json, VARIADIC path_elems text[] ) → json

jsonb_extract_path ( from_json jsonb, VARIADIC path_elems text[] ) → jsonb

Extracts JSON sub-object at the specified path. (This is functionally equivalent to the #> operator, but writing the path out as a variadic list can be more convenient in some cases.)

json_extract_path('{"f2":{"f3":1},"f4":{"f5":99,"f6":"foo"}}', 'f4', 'f6') → "foo"

json_extract_path_text ( from_json json, VARIADIC path_elems text[] ) → text

jsonb_extract_path_text ( from_json jsonb, VARIADIC path_elems text[] ) → text

Extracts JSON sub-object at the specified path as text. (This is functionally equivalent to the #>> operator.)

json_extract_path_text('{"f2":{"f3":1},"f4":{"f5":99,"f6":"foo"}}', 'f4', 'f6') → foo

json_object_keys ( json ) → setof text

jsonb_object_keys ( jsonb ) → setof text

Returns the set of keys in the top-level JSON object.

select * from json_object_keys('{"f1":"abc","f2":{"f3":"a", "f4":"b"}}') →

json_populate_record ( base anyelement, from_json json ) → anyelement

jsonb_populate_record ( base anyelement, from_json jsonb ) → anyelement

Expands the top-level JSON object to a row having the composite type of the base argument. The JSON object is scanned for fields whose names match column names of the output row type, and their values are inserted into those columns of the output. (Fields that do not correspond to any output column name are ignored.) In typical use, the value of base is just NULL, which means that any output columns that do not match any object field will be filled with nulls. However, if base isn't NULL then the values it contains will be used for unmatched columns.

To convert a JSON value to the SQL type of an output column, the following rules are applied in sequence:

A JSON null value is converted to an SQL null in all cases.

If the output column is of type json or jsonb, the JSON value is just reproduced exactly.

If the output column is a composite (row) type, and the JSON value is a JSON object, the fields of the object are converted to columns of the output row type by recursive application of these rules.

Likewise, if the output column is an array type and the JSON value is a JSON array, the elements of the JSON array are converted to elements of the output array by recursive application of these rules.

Otherwise, if the JSON value is a string, the contents of the string are fed to the input conversion function for the column's data type.

Otherwise, the ordinary text representation of the JSON value is fed to the input conversion function for the column's data type.

While the example below uses a constant JSON value, typical use would be to reference a json or jsonb column laterally from another table in the query's FROM clause. Writing json_populate_record in the FROM clause is good practice, since all of the extracted columns are available for use without duplicate function calls.

create type subrowtype as (d int, e text); create type myrowtype as (a int, b text[], c subrowtype);

select * from json_populate_record(null::myrowtype, '{"a": 1, "b": ["2", "a b"], "c": {"d": 4, "e": "a b c"}, "x": "foo"}') →

jsonb_populate_record_valid ( base anyelement, from_json json ) → boolean

Function for testing jsonb_populate_record. Returns true if the input jsonb_populate_record would finish without an error for the given input JSON object; that is, it's valid input, false otherwise.

create type jsb_char2 as (a char(2));

select jsonb_populate_record_valid(NULL::jsb_char2, '{"a": "aaa"}'); →

select * from jsonb_populate_record(NULL::jsb_char2, '{"a": "aaa"}') q; →

select jsonb_populate_record_valid(NULL::jsb_char2, '{"a": "aa"}'); →

select * from jsonb_populate_record(NULL::jsb_char2, '{"a": "aa"}') q; →

json_populate_recordset ( base anyelement, from_json json ) → setof anyelement

jsonb_populate_recordset ( base anyelement, from_json jsonb ) → setof anyelement

Expands the top-level JSON array of objects to a set of rows having the composite type of the base argument. Each element of the JSON array is processed as described above for json[b]_populate_record.

create type twoints as (a int, b int);

select * from json_populate_recordset(null::twoints, '[{"a":1,"b":2}, {"a":3,"b":4}]') →

json_to_record ( json ) → record

jsonb_to_record ( jsonb ) → record

Expands the top-level JSON object to a row having the composite type defined by an AS clause. (As with all functions returning record, the calling query must explicitly define the structure of the record with an AS clause.) The output record is filled from fields of the JSON object, in the same way as described above for json[b]_populate_record. Since there is no input record value, unmatched columns are always filled with nulls.

create type myrowtype as (a int, b text);

select * from json_to_record('{"a":1,"b":[1,2,3],"c":[1,2,3],"e":"bar","r": {"a": 123, "b": "a b c"}}') as x(a int, b text, c int[], d text, r myrowtype) →

json_to_recordset ( json ) → setof record

jsonb_to_recordset ( jsonb ) → setof record

Expands the top-level JSON array of objects to a set of rows having the composite type defined by an AS clause. (As with all functions returning record, the calling query must explicitly define the structure of the record with an AS clause.) Each element of the JSON array is processed as described above for json[b]_populate_record.

select * from json_to_recordset('[{"a":1,"b":"foo"}, {"a":"2","c":"bar"}]') as x(a int, b text) →

jsonb_set ( target jsonb, path text[], new_value jsonb [, create_if_missing boolean ] ) → jsonb

Returns target with the item designated by path replaced by new_value, or with new_value added if create_if_missing is true (which is the default) and the item designated by path does not exist. All earlier steps in the path must exist, or the target is returned unchanged. As with the path oriented operators, negative integers that appear in the path count from the end of JSON arrays. If the last path step is an array index that is out of range, and create_if_missing is true, the new value is added at the beginning of the array if the index is negative, or at the end of the array if it is positive.

jsonb_set('[{"f1":1,"f2":null},2,null,3]', '{0,f1}', '[2,3,4]', false) → [{"f1": [2, 3, 4], "f2": null}, 2, null, 3]

jsonb_set('[{"f1":1,"f2":null},2]', '{0,f3}', '[2,3,4]') → [{"f1": 1, "f2": null, "f3": [2, 3, 4]}, 2]

jsonb_set_lax ( target jsonb, path text[], new_value jsonb [, create_if_missing boolean [, null_value_treatment text ]] ) → jsonb

If new_value is not NULL, behaves identically to jsonb_set. Otherwise behaves according to the value of null_value_treatment which must be one of 'raise_exception', 'use_json_null', 'delete_key', or 'return_target'. The default is 'use_json_null'.

jsonb_set_lax('[{"f1":1,"f2":null},2,null,3]', '{0,f1}', null) → [{"f1": null, "f2": null}, 2, null, 3]

jsonb_set_lax('[{"f1":99,"f2":null},2]', '{0,f3}', null, true, 'return_target') → [{"f1": 99, "f2": null}, 2]

jsonb_insert ( target jsonb, path text[], new_value jsonb [, insert_after boolean ] ) → jsonb

Returns target with new_value inserted. If the item designated by the path is an array element, new_value will be inserted before that item if insert_after is false (which is the default), or after it if insert_after is true. If the item designated by the path is an object field, new_value will be inserted only if the object does not already contain that key. All earlier steps in the path must exist, or the target is returned unchanged. As with the path oriented operators, negative integers that appear in the path count from the end of JSON arrays. If the last path step is an array index that is out of range, the new value is added at the beginning of the array if the index is negative, or at the end of the array if it is positive.

jsonb_insert('{"a": [0,1,2]}', '{a, 1}', '"new_value"') → {"a": [0, "new_value", 1, 2]}

jsonb_insert('{"a": [0,1,2]}', '{a, 1}', '"new_value"', true) → {"a": [0, 1, "new_value", 2]}

json_strip_nulls ( target json [,strip_in_arrays boolean ] ) → json

jsonb_strip_nulls ( target jsonb [,strip_in_arrays boolean ] ) → jsonb

Deletes all object fields that have null values from the given JSON value, recursively. If strip_in_arrays is true (the default is false), null array elements are also stripped. Otherwise they are not stripped. Bare null values are never stripped.

json_strip_nulls('[{"f1":1, "f2":null}, 2, null, 3]') → [{"f1":1},2,null,3]

jsonb_strip_nulls('[1,2,null,3,4]', true); → [1,2,3,4]

jsonb_path_exists ( target jsonb, path jsonpath [, vars jsonb [, silent boolean ]] ) → boolean

Checks whether the JSON path returns any item for the specified JSON value. (This is useful only with SQL-standard JSON path expressions, not predicate check expressions, since those always return a value.) If the vars argument is specified, it must be a JSON object, and its fields provide named values to be substituted into the jsonpath expression. If the silent argument is specified and is true, the function suppresses the same errors as the @? and @@ operators do.

jsonb_path_exists('{"a":[1,2,3,4,5]}', '$.a[*] ? (@ >= $min && @ <= $max)', '{"min":2, "max":4}') → t

jsonb_path_match ( target jsonb, path jsonpath [, vars jsonb [, silent boolean ]] ) → boolean

Returns the SQL boolean result of a JSON path predicate check for the specified JSON value. (This is useful only with predicate check expressions, not SQL-standard JSON path expressions, since it will either fail or return NULL if the path result is not a single boolean value.) The optional vars and silent arguments act the same as for jsonb_path_exists.

jsonb_path_match('{"a":[1,2,3,4,5]}', 'exists($.a[*] ? (@ >= $min && @ <= $max))', '{"min":2, "max":4}') → t

jsonb_path_query ( target jsonb, path jsonpath [, vars jsonb [, silent boolean ]] ) → setof jsonb

Returns all JSON items returned by the JSON path for the specified JSON value. For SQL-standard JSON path expressions it returns the JSON values selected from target. For predicate check expressions it returns the result of the predicate check: true, false, or null. The optional vars and silent arguments act the same as for jsonb_path_exists.

select * from jsonb_path_query('{"a":[1,2,3,4,5]}', '$.a[*] ? (@ >= $min && @ <= $max)', '{"min":2, "max":4}') →

jsonb_path_query_array ( target jsonb, path jsonpath [, vars jsonb [, silent boolean ]] ) → jsonb

Returns all JSON items returned by the JSON path for the specified JSON value, as a JSON array. The parameters are the same as for jsonb_path_query.

jsonb_path_query_array('{"a":[1,2,3,4,5]}', '$.a[*] ? (@ >= $min && @ <= $max)', '{"min":2, "max":4}') → [2, 3, 4]

jsonb_path_query_first ( target jsonb, path jsonpath [, vars jsonb [, silent boolean ]] ) → jsonb

Returns the first JSON item returned by the JSON path for the specified JSON value, or NULL if there are no results. The parameters are the same as for jsonb_path_query.

jsonb_path_query_first('{"a":[1,2,3,4,5]}', '$.a[*] ? (@ >= $min && @ <= $max)', '{"min":2, "max":4}') → 2

jsonb_path_exists_tz ( target jsonb, path jsonpath [, vars jsonb [, silent boolean ]] ) → boolean

jsonb_path_match_tz ( target jsonb, path jsonpath [, vars jsonb [, silent boolean ]] ) → boolean

jsonb_path_query_tz ( target jsonb, path jsonpath [, vars jsonb [, silent boolean ]] ) → setof jsonb

jsonb_path_query_array_tz ( target jsonb, path jsonpath [, vars jsonb [, silent boolean ]] ) → jsonb

jsonb_path_query_first_tz ( target jsonb, path jsonpath [, vars jsonb [, silent boolean ]] ) → jsonb

These functions act like their counterparts described above without the _tz suffix, except that these functions support comparisons of date/time values that require timezone-aware conversions. The example below requires interpretation of the date-only value 2015-08-02 as a timestamp with time zone, so the result depends on the current TimeZone setting. Due to this dependency, these functions are marked as stable, which means these functions cannot be used in indexes. Their counterparts are immutable, and so can be used in indexes; but they will throw errors if asked to make such comparisons.

jsonb_path_exists_tz('["2015-08-01 12:00:00-05"]', '$[*] ? (@.datetime() < "2015-08-02".datetime())') → t

jsonb_pretty ( jsonb ) → text

Converts the given JSON value to pretty-printed, indented text.

jsonb_pretty('[{"f1":1,"f2":null}, 2]') →

json_typeof ( json ) → text

jsonb_typeof ( jsonb ) → text

Returns the type of the top-level JSON value as a text string. Possible types are object, array, string, number, boolean, and null. (The null result should not be confused with an SQL NULL; see the examples.)

json_typeof('-123.4') → number

json_typeof('null'::json) → null

json_typeof(NULL::json) IS NULL → t

SQL/JSON path expressions specify item(s) to be retrieved from a JSON value, similarly to XPath expressions used for access to XML content. In PostgreSQL, path expressions are implemented as the jsonpath data type and can use any elements described in Section 8.14.7.

JSON query functions and operators pass the provided path expression to the path engine for evaluation. If the expression matches the queried JSON data, the corresponding JSON item, or set of items, is returned. If there is no match, the result will be NULL, false, or an error, depending on the function. Path expressions are written in the SQL/JSON path language and can include arithmetic expressions and functions.

A path expression consists of a sequence of elements allowed by the jsonpath data type. The path expression is normally evaluated from left to right, but you can use parentheses to change the order of operations. If the evaluation is successful, a sequence of JSON items is produced, and the evaluation result is returned to the JSON query function that completes the specified computation.

To refer to the JSON value being queried (the context item), use the $ variable in the path expression. The first element of a path must always be $. It can be followed by one or more accessor operators, which go down the JSON structure level by level to retrieve sub-items of the context item. Each accessor operator acts on the result(s) of the previous evaluation step, producing zero, one, or more output items from each input item.

For example, suppose you have some JSON data from a GPS tracker that you would like to parse, such as:

(The above example can be copied-and-pasted into psql to set things up for the following examples. Then psql will expand :'json' into a suitably-quoted string constant containing the JSON value.)

To retrieve the available track segments, you need to use the .key accessor operator to descend through surrounding JSON objects, for example:

To retrieve the contents of an array, you typically use the [*] operator. The following example will return the location coordinates for all the available track segments:

Here we started with the whole JSON input value ($), then the .track accessor selected the JSON object associated with the "track" object key, then the .segments accessor selected the JSON array associated with the "segments" key within that object, then the [*] accessor selected each element of that array (producing a series of items), then the .location accessor selected the JSON array associated with the "location" key within each of those objects. In this example, each of those objects had a "location" key; but if any of them did not, the .location accessor would have simply produced no output for that input item.

To return the coordinates of the first segment only, you can specify the corresponding subscript in the [] accessor operator. Recall that JSON array indexes are 0-relative:

The result of each path evaluation step can be processed by one or more of the jsonpath operators and methods listed in Section 9.16.2.3. Each method name must be preceded by a dot. For example, you can get the size of an array:

More examples of using jsonpath operators and methods within path expressions appear below in Section 9.16.2.3.

A path can also contain filter expressions that work similarly to the WHERE clause in SQL. A filter expression begins with a question mark and provides a condition in parentheses:

Filter expressions must be written just after the path evaluation step to which they should apply. The result of that step is filtered to include only those items that satisfy the provided condition. SQL/JSON defines three-valued logic, so the condition can produce true, false, or unknown. The unknown value plays the same role as SQL NULL and can be tested for with the is unknown predicate. Further path evaluation steps use only those items for which the filter expression returned true.

The functions and operators that can be used in filter expressions are listed in Table 9.53. Within a filter expression, the @ variable denotes the value being considered (i.e., one result of the preceding path step). You can write accessor operators after @ to retrieve component items.

For example, suppose you would like to retrieve all heart rate values higher than 130. You can achieve this as follows:

To get the start times of segments with such values, you have to filter out irrelevant segments before selecting the start times, so the filter expression is applied to the previous step, and the path used in the condition is different:

You can use several filter expressions in sequence, if required. The following example selects start times of all segments that contain locations with relevant coordinates and high heart rate values:

Using filter expressions at different nesting levels is also allowed. The following example first filters all segments by location, and then returns high heart rate values for these segments, if available:

You can also nest filter expressions within each other. This example returns the size of the track if it contains any segments with high heart rate values, or an empty sequence otherwise:

PostgreSQL's implementation of the SQL/JSON path language has the following deviations from the SQL/JSON standard.

As an extension to the SQL standard, a PostgreSQL path expression can be a Boolean predicate, whereas the SQL standard allows predicates only within filters. While SQL-standard path expressions return the relevant element(s) of the queried JSON value, predicate check expressions return the single three-valued jsonb result of the predicate: true, false, or null. For example, we could write this SQL-standard filter expression:

The similar predicate check expression simply returns true, indicating that a match exists:

Predicate check expressions are required in the @@ operator (and the jsonb_path_match function), and should not be used with the @? operator (or the jsonb_path_exists function).

There are minor differences in the interpretation of regular expression patterns used in like_regex filters, as described in Section 9.16.2.4.

When you query JSON data, the path expression may not match the actual JSON data structure. An attempt to access a non-existent member of an object or element of an array is defined as a structural error. SQL/JSON path expressions have two modes of handling structural errors:

lax (default) — the path engine implicitly adapts the queried data to the specified path. Any structural errors that cannot be fixed as described below are suppressed, producing no match.

strict — if a structural error occurs, an error is raised.

Lax mode facilitates matching of a JSON document and path expression when the JSON data does not conform to the expected schema. If an operand does not match the requirements of a particular operation, it can be automatically wrapped as an SQL/JSON array, or unwrapped by converting its elements into an SQL/JSON sequence before performing the operation. Also, comparison operators automatically unwrap their operands in lax mode, so you can compare SQL/JSON arrays out-of-the-box. An array of size 1 is considered equal to its sole element. Automatic unwrapping is not performed when:

The path expression contains type() or size() methods that return the type and the number of elements in the array, respectively.

The queried JSON data contain nested arrays. In this case, only the outermost array is unwrapped, while all the inner arrays remain unchanged. Thus, implicit unwrapping can only go one level down within each path evaluation step.

For example, when querying the GPS data listed above, you can abstract from the fact that it stores an array of segments when using lax mode:

In strict mode, the specified path must exactly match the structure of the queried JSON document, so using this path expression will cause an error:

To get the same result as in lax mode, you have to explicitly unwrap the segments array:

The unwrapping behavior of lax mode can lead to surprising results. For instance, the following query using the .** accessor selects every HR value twice:

This happens because the .** accessor selects both the segments array and each of its elements, while the .HR accessor automatically unwraps arrays when using lax mode. To avoid surprising results, we recommend using the .** accessor only in strict mode. The following query selects each HR value just once:

The unwrapping of arrays can also lead to unexpected results. Consider this example, which selects all the location arrays:

As expected it returns the full arrays. But applying a filter expression causes the arrays to be unwrapped to evaluate each item, returning only the items that match the expression:

This despite the fact that the full arrays are selected by the path expression. Use strict mode to restore selecting the arrays:

Table 9.52 shows the operators and methods available in jsonpath. Note that while the unary operators and methods can be applied to multiple values resulting from a preceding path step, the binary operators (addition etc.) can only be applied to single values. In lax mode, methods applied to an array will be executed for each value in the array. The exceptions are .type() and .size(), which apply to the array itself.

Table 9.52. jsonpath Operators and Methods

number + number → number

jsonb_path_query('[2]', '$[0] + 3') → 5

Unary plus (no operation); unlike addition, this can iterate over multiple values

jsonb_path_query_array('{"x": [2,3,4]}', '+ $.x') → [2, 3, 4]

number - number → number

jsonb_path_query('[2]', '7 - $[0]') → 5

Negation; unlike subtraction, this can iterate over multiple values

jsonb_path_query_array('{"x": [2,3,4]}', '- $.x') → [-2, -3, -4]

number * number → number

jsonb_path_query('[4]', '2 * $[0]') → 8

number / number → number

jsonb_path_query('[8.5]', '$[0] / 2') → 4.2500000000000000

number % number → number

jsonb_path_query('[32]', '$[0] % 10') → 2

value . type() → string

Type of the JSON item (see json_typeof)

jsonb_path_query_array('[1, "2", {}]', '$[*].type()') → ["number", "string", "object"]

value . size() → number

Size of the JSON item (number of array elements, or 1 if not an array)

jsonb_path_query('{"m": [11, 15]}', '$.m.size()') → 2

value . boolean() → boolean

Boolean value converted from a JSON boolean, number, or string

jsonb_path_query_array('[1, "yes", false]', '$[*].boolean()') → [true, true, false]

value . string() → string

String value converted from a JSON boolean, number, string, or datetime

jsonb_path_query_array('[1.23, "xyz", false]', '$[*].string()') → ["1.23", "xyz", "false"]

jsonb_path_query('"2023-08-15 12:34:56"', '$.timestamp().string()') → "2023-08-15T12:34:56"

value . double() → number

Approximate floating-point number converted from a JSON number or string

jsonb_path_query('{"len": "1.9"}', '$.len.double() * 2') → 3.8

number . ceiling() → number

Nearest integer greater than or equal to the given number

jsonb_path_query('{"h": 1.3}', '$.h.ceiling()') → 2

number . floor() → number

Nearest integer less than or equal to the given number

jsonb_path_query('{"h": 1.7}', '$.h.floor()') → 1

number . abs() → number

Absolute value of the given number

jsonb_path_query('{"z": -0.3}', '$.z.abs()') → 0.3

value . bigint() → bigint

Big integer value converted from a JSON number or string

jsonb_path_query('{"len": "9876543219"}', '$.len.bigint()') → 9876543219

value . decimal( [ precision [ , scale ] ] ) → decimal

Rounded decimal value converted from a JSON number or string (precision and scale must be integer values)

jsonb_path_query('1234.5678', '$.decimal(6, 2)') → 1234.57

value . integer() → integer

Integer value converted from a JSON number or string

jsonb_path_query('{"len": "12345"}', '$.len.integer()') → 12345

value . number() → numeric

Numeric value converted from a JSON number or string

jsonb_path_query('{"len": "123.45"}', '$.len.number()') → 123.45

string . datetime() → datetime_type (see note)

Date/time value converted from a string

jsonb_path_query('["2015-8-1", "2015-08-12"]', '$[*] ? (@.datetime() < "2015-08-2".datetime())') → "2015-8-1"

string . datetime(template) → datetime_type (see note)

Date/time value converted from a string using the specified to_timestamp template

jsonb_path_query_array('["12:30", "18:40"]', '$[*].datetime("HH24:MI")') → ["12:30:00", "18:40:00"]

string . date() → date

Date value converted from a string

jsonb_path_query('"2023-08-15"', '$.date()') → "2023-08-15"

string . time() → time without time zone

Time without time zone value converted from a string

jsonb_path_query('"12:34:56"', '$.time()') → "12:34:56"

string . time(precision) → time without time zone

Time without time zone value converted from a string, with fractional seconds adjusted to the given precision

jsonb_path_query('"12:34:56.789"', '$.time(2)') → "12:34:56.79"

string . time_tz() → time with time zone

Time with time zone value converted from a string

jsonb_path_query('"12:34:56 +05:30"', '$.time_tz()') → "12:34:56+05:30"

string . time_tz(precision) → time with time zone

Time with time zone value converted from a string, with fractional seconds adjusted to the given precision

jsonb_path_query('"12:34:56.789 +05:30"', '$.time_tz(2)') → "12:34:56.79+05:30"

string . timestamp() → timestamp without time zone

Timestamp without time zone value converted from a string

jsonb_path_query('"2023-08-15 12:34:56"', '$.timestamp()') → "2023-08-15T12:34:56"

string . timestamp(precision) → timestamp without time zone

Timestamp without time zone value converted from a string, with fractional seconds adjusted to the given precision

jsonb_path_query('"2023-08-15 12:34:56.789"', '$.timestamp(2)') → "2023-08-15T12:34:56.79"

string . timestamp_tz() → timestamp with time zone

Timestamp with time zone value converted from a string

jsonb_path_query('"2023-08-15 12:34:56 +05:30"', '$.timestamp_tz()') → "2023-08-15T12:34:56+05:30"

string . timestamp_tz(precision) → timestamp with time zone

Timestamp with time zone value converted from a string, with fractional seconds adjusted to the given precision

jsonb_path_query('"2023-08-15 12:34:56.789 +05:30"', '$.timestamp_tz(2)') → "2023-08-15T12:34:56.79+05:30"

object . keyvalue() → array

The object's key-value pairs, represented as an array of objects containing three fields: "key", "value", and "id"; "id" is a unique identifier of the object the key-value pair belongs to

jsonb_path_query_array('{"x": "20", "y": 32}', '$.keyvalue()') → [{"id": 0, "key": "x", "value": "20"}, {"id": 0, "key": "y", "value": 32}]

The result type of the datetime() and datetime(template) methods can be date, timetz, time, timestamptz, or timestamp. Both methods determine their result type dynamically.

The datetime() method sequentially tries to match its input string to the ISO formats for date, timetz, time, timestamptz, and timestamp. It stops on the first matching format and emits the corresponding data type.

The datetime(template) method determines the result type according to the fields used in the provided template string.

The datetime() and datetime(template) methods use the same parsing rules as the to_timestamp SQL function does (see Section 9.8), with three exceptions. First, these methods don't allow unmatched template patterns. Second, only the following separators are allowed in the template string: minus sign, period, solidus (slash), comma, apostrophe, semicolon, colon and space. Third, separators in the template string must exactly match the input string.

If different date/time types need to be compared, an implicit cast is applied. A date value can be cast to timestamp or timestamptz, timestamp can be cast to timestamptz, and time to timetz. However, all but the first of these conversions depend on the current TimeZone setting, and thus can only be performed within timezone-aware jsonpath functions. Similarly, other date/time-related methods that convert strings to date/time types also do this casting, which may involve the current TimeZone setting. Therefore, these conversions can also only be performed within timezone-aware jsonpath functions.

Table 9.53 shows the available filter expression elements.

Table 9.53. jsonpath Filter Expression Elements

value == value → boolean

Equality comparison (this, and the other comparison operators, work on all JSON scalar values)

jsonb_path_query_array('[1, "a", 1, 3]', '$[*] ? (@ == 1)') → [1, 1]

jsonb_path_query_array('[1, "a", 1, 3]', '$[*] ? (@ == "a")') → ["a"]

value != value → boolean

value <> value → boolean

Non-equality comparison

jsonb_path_query_array('[1, 2, 1, 3]', '$[*] ? (@ != 1)') → [2, 3]

jsonb_path_query_array('["a", "b", "c"]', '$[*] ? (@ <> "b")') → ["a", "c"]

value < value → boolean

jsonb_path_query_array('[1, 2, 3]', '$[*] ? (@ < 2)') → [1]

value <= value → boolean

Less-than-or-equal-to comparison

jsonb_path_query_array('["a", "b", "c"]', '$[*] ? (@ <= "b")') → ["a", "b"]

value > value → boolean

Greater-than comparison

jsonb_path_query_array('[1, 2, 3]', '$[*] ? (@ > 2)') → [3]

value >= value → boolean

Greater-than-or-equal-to comparison

jsonb_path_query_array('[1, 2, 3]', '$[*] ? (@ >= 2)') → [2, 3]

jsonb_path_query('[{"name": "John", "parent": false}, {"name": "Chris", "parent": true}]', '$[*] ? (@.parent == true)') → {"name": "Chris", "parent": true}

jsonb_path_query('[{"name": "John", "parent": false}, {"name": "Chris", "parent": true}]', '$[*] ? (@.parent == false)') → {"name": "John", "parent": false}

JSON constant null (note that, unlike in SQL, comparison to null works normally)

jsonb_path_query('[{"name": "Mary", "job": null}, {"name": "Michael", "job": "driver"}]', '$[*] ? (@.job == null) .name') → "Mary"

boolean && boolean → boolean

jsonb_path_query('[1, 3, 7]', '$[*] ? (@ > 1 && @ < 5)') → 3

boolean || boolean → boolean

jsonb_path_query('[1, 3, 7]', '$[*] ? (@ < 1 || @ > 5)') → 7

jsonb_path_query('[1, 3, 7]', '$[*] ? (!(@ < 5))') → 7

boolean is unknown → boolean

Tests whether a Boolean condition is unknown.

jsonb_path_query('[-1, 2, 7, "foo"]', '$[*] ? ((@ > 0) is unknown)') → "foo"

string like_regex string [ flag string ] → boolean

Tests whether the first operand matches the regular expression given by the second operand, optionally with modifications described by a string of flag characters (see Section 9.16.2.4).

jsonb_path_query_array('["abc", "abd", "aBdC", "abdacb", "babc"]', '$[*] ? (@ like_regex "^ab.*c")') → ["abc", "abdacb"]

jsonb_path_query_array('["abc", "abd", "aBdC", "abdacb", "babc"]', '$[*] ? (@ like_regex "^ab.*c" flag "i")') → ["abc", "aBdC", "abdacb"]

string starts with string → boolean

Tests whether the second operand is an initial substring of the first operand.

jsonb_path_query('["John Smith", "Mary Stone", "Bob Johnson"]', '$[*] ? (@ starts with "John")') → "John Smith"

exists ( path_expression ) → boolean

Tests whether a path expression matches at least one SQL/JSON item. Returns unknown if the path expression would result in an error; the second example uses this to avoid a no-such-key error in strict mode.

jsonb_path_query('{"x": [1, 2], "y": [2, 4]}', 'strict $.* ? (exists (@ ? (@[*] > 2)))') → [2, 4]

jsonb_path_query_array('{"value": 41}', 'strict $ ? (exists (@.name)) .name') → []

SQL/JSON path expressions allow matching text to a regular expression with the like_regex filter. For example, the following SQL/JSON path query would case-insensitively match all strings in an array that start with an English vowel:

The optional flag string may include one or more of the characters i for case-insensitive match, m to allow ^ and $ to match at newlines, s to allow . to match a newline, and q to quote the whole pattern (reducing the behavior to a simple substring match).

The SQL/JSON standard borrows its definition for regular expressions from the LIKE_REGEX operator, which in turn uses the XQuery standard. PostgreSQL does not currently support the LIKE_REGEX operator. Therefore, the like_regex filter is implemented using the POSIX regular expression engine described in Section 9.7.3. This leads to various minor discrepancies from standard SQL/JSON behavior, which are cataloged in Section 9.7.3.8. Note, however, that the flag-letter incompatibilities described there do not apply to SQL/JSON, as it translates the XQuery flag letters to match what the POSIX engine expects.

Keep in mind that the pattern argument of like_regex is a JSON path string literal, written according to the rules given in Section 8.14.7. This means in particular that any backslashes you want to use in the regular expression must be doubled. For example, to match string values of the root document that contain only digits:

SQL/JSON functions JSON_EXISTS(), JSON_QUERY(), and JSON_VALUE() described in Table 9.54 can be used to query JSON documents. Each of these functions apply a path_expression (an SQL/JSON path query) to a context_item (the document). See Section 9.16.2 for more details on what the path_expression can contain. The path_expression can also reference variables, whose values are specified with their respective names in the PASSING clause that is supported by each function. context_item can be a jsonb value or a character string that can be successfully cast to jsonb.

Table 9.54. SQL/JSON Query Functions

Returns true if the SQL/JSON path_expression applied to the context_item yields any items, false otherwise.

The ON ERROR clause specifies the behavior if an error occurs during path_expression evaluation. Specifying ERROR will cause an error to be thrown with the appropriate message. Other options include returning boolean values FALSE or TRUE or the value UNKNOWN which is actually an SQL NULL. The default when no ON ERROR clause is specified is to return the boolean value FALSE.

JSON_EXISTS(jsonb '{"key1": [1,2,3]}', 'strict $.key1[*] ? (@ > $x)' PASSING 2 AS x) → t

JSON_EXISTS(jsonb '{"a": [1,2,3]}', 'lax $.a[5]' ERROR ON ERROR) → f

JSON_EXISTS(jsonb '{"a": [1,2,3]}', 'strict $.a[5]' ERROR ON ERROR) →

Returns the result of applying the SQL/JSON path_expression to the context_item.

By default, the result is returned as a value of type jsonb, though the RETURNING clause can be used to return as some other type to which it can be successfully coerced.

If the path expression may return multiple values, it might be necessary to wrap those values using the WITH WRAPPER clause to make it a valid JSON string, because the default behavior is to not wrap them, as if WITHOUT WRAPPER were specified. The WITH WRAPPER clause is by default taken to mean WITH UNCONDITIONAL WRAPPER, which means that even a single result value will be wrapped. To apply the wrapper only when multiple values are present, specify WITH CONDITIONAL WRAPPER. Getting multiple values in result will be treated as an error if WITHOUT WRAPPER is specified.

If the result is a scalar string, by default, the returned value will be surrounded by quotes, making it a valid JSON value. It can be made explicit by specifying KEEP QUOTES. Conversely, quotes can be omitted by specifying OMIT QUOTES. To ensure that the result is a valid JSON value, OMIT QUOTES cannot be specified when WITH WRAPPER is also specified.

The ON EMPTY clause specifies the behavior if evaluating path_expression yields an empty set. The ON ERROR clause specifies the behavior if an error occurs when evaluating path_expression, when coercing the result value to the RETURNING type, or when evaluating the ON EMPTY expression if the path_expression evaluation returns an empty set.

For both ON EMPTY and ON ERROR, specifying ERROR will cause an error to be thrown with the appropriate message. Other options include returning an SQL NULL, an empty array (EMPTY [ARRAY]), an empty object (EMPTY OBJECT), or a user-specified expression (DEFAULT expression) that can be coerced to jsonb or the type specified in RETURNING. The default when ON EMPTY or ON ERROR is not specified is to return an SQL NULL value.

JSON_QUERY(jsonb '[1,[2,3],null]', 'lax $[*][$off]' PASSING 1 AS off WITH CONDITIONAL WRAPPER) → 3

JSON_QUERY(jsonb '{"a": "[1, 2]"}', 'lax $.a' OMIT QUOTES) → [1, 2]

JSON_QUERY(jsonb '{"a": "[1, 2]"}', 'lax $.a' RETURNING int[] OMIT QUOTES ERROR ON ERROR) →

Returns the result of applying the SQL/JSON path_expression to the context_item.

Only use JSON_VALUE() if the extracted value is expected to be a single SQL/JSON scalar item; getting multiple values will be treated as an error. If you expect that extracted value might be an object or an array, use the JSON_QUERY function instead.

By default, the result, which must be a single scalar value, is returned as a value of type text, though the RETURNING clause can be used to return as some other type to which it can be successfully coerced.

The ON ERROR and ON EMPTY clauses have similar semantics as mentioned in the description of JSON_QUERY, except the set of values returned in lieu of throwing an error is different.

Note that scalar strings returned by JSON_VALUE always have their quotes removed, equivalent to specifying OMIT QUOTES in JSON_QUERY.

JSON_VALUE(jsonb '"123.45"', '$' RETURNING float) → 123.45

JSON_VALUE(jsonb '"03:04 2015-02-01"', '$.datetime("HH24:MI YYYY-MM-DD")' RETURNING date) → 2015-02-01

JSON_VALUE(jsonb '[1,2]', 'strict $[$off]' PASSING 1 as off) → 2

JSON_VALUE(jsonb '[1,2]', 'strict $[*]' DEFAULT 9 ON ERROR) → 9

The context_item expression is converted to jsonb by an implicit cast if the expression is not already of type jsonb. Note, however, that any parsing errors that occur during that conversion are thrown unconditionally, that is, are not handled according to the (specified or implicit) ON ERROR clause.

JSON_VALUE() returns an SQL NULL if path_expression returns a JSON null, whereas JSON_QUERY() returns the JSON null as is.

JSON_TABLE is an SQL/JSON function which queries JSON data and presents the results as a relational view, which can be accessed as a regular SQL table. You can use JSON_TABLE inside the FROM clause of a SELECT, UPDATE, or DELETE and as data source in a MERGE statement.

Taking JSON data as input, JSON_TABLE uses a JSON path expression to extract a part of the provided data to use as a row pattern for the constructed view. Each SQL/JSON value given by the row pattern serves as source for a separate row in the constructed view.

To split the row pattern into columns, JSON_TABLE provides the COLUMNS clause that defines the schema of the created view. For each column, a separate JSON path expression can be specified to be evaluated against the row pattern to get an SQL/JSON value that will become the value for the specified column in a given output row.

JSON data stored at a nested level of the row pattern can be extracted using the NESTED PATH clause. Each NESTED PATH clause can be used to generate one or more columns using the data from a nested level of the row pattern. Those columns can be specified using a COLUMNS clause that looks similar to the top-level COLUMNS clause. Rows constructed from NESTED COLUMNS are called child rows and are joined against the row constructed from the columns specified in the parent COLUMNS clause to get the row in the final view. Child columns themselves may contain a NESTED PATH specification thus allowing to extract data located at arbitrary nesting levels. Columns produced by multiple NESTED PATHs at the same level are considered to be siblings of each other and their rows after joining with the parent row are combined using UNION.

The rows produced by JSON_TABLE are laterally joined to the row that generated them, so you do not have to explicitly join the constructed view with the original table holding JSON data.

Each syntax element is described below in more detail.

The context_item specifies the input document to query, the path_expression is an SQL/JSON path expression defining the query, and json_path_name is an optional name for the path_expression. The optional PASSING clause provides data values for the variables mentioned in the path_expression. The result of the input data evaluation using the aforementioned elements is called the row pattern, which is used as the source for row values in the constructed view.

The COLUMNS clause defining the schema of the constructed view. In this clause, you can specify each column to be filled with an SQL/JSON value obtained by applying a JSON path expression against the row pattern. json_table_column has the following variants:

Adds an ordinality column that provides sequential row numbering starting from 1. Each NESTED PATH (see below) gets its own counter for any nested ordinality columns.

Inserts an SQL/JSON value obtained by applying path_expression against the row pattern into the view's output row after coercing it to specified type.

Specifying FORMAT JSON makes it explicit that you expect the value to be a valid json object. It only makes sense to specify FORMAT JSON if type is one of bpchar, bytea, character varying, name, json, jsonb, text, or a domain over these types.

Optionally, you can specify WRAPPER and QUOTES clauses to format the output. Note that specifying OMIT QUOTES overrides FORMAT JSON if also specified, because unquoted literals do not constitute valid json values.

Optionally, you can use ON EMPTY and ON ERROR clauses to specify whether to throw the error or return the specified value when the result of JSON path evaluation is empty and when an error occurs during JSON path evaluation or when coercing the SQL/JSON value to the specified type, respectively. The default for both is to return a NULL value.

This clause is internally turned into and has the same semantics as JSON_VALUE or JSON_QUERY. The latter if the specified type is not a scalar type or if either of FORMAT JSON, WRAPPER, or QUOTES clause is present.

Inserts a boolean value obtained by applying path_expression against the row pattern into the view's output row after coercing it to specified type.

The value corresponds to whether applying the PATH expression to the row pattern yields any values.

The specified type should have a cast from the boolean type.

Optionally, you can use ON ERROR to specify whether to throw the error or return the specified value when an error occurs during JSON path evaluation or when coercing SQL/JSON value to the specified type. The default is to return a boolean value FALSE.

This clause is internally turned into and has the same semantics as JSON_EXISTS.

Extracts SQL/JSON values from nested levels of the row pattern, generates one or more columns as defined by the COLUMNS subclause, and inserts the extracted SQL/JSON values into those columns. The json_table_column expression in the COLUMNS subclause uses the same syntax as in the parent COLUMNS clause.

The NESTED PATH syntax is recursive, so you can go down multiple nested levels by specifying several NESTED PATH subclauses within each other. It allows to unnest the hierarchy of JSON objects and arrays in a single function invocation rather than chaining several JSON_TABLE expressions in an SQL statement.

In each variant of json_table_column described above, if the PATH clause is omitted, path expression $.name is used, where name is the provided column name.

The optional json_path_name serves as an identifier of the provided path_expression. The name must be unique and distinct from the column names.

The optional ON ERROR can be used to specify how to handle errors when evaluating the top-level path_expression. Use ERROR if you want the errors to be thrown and EMPTY to return an empty table, that is, a table containing 0 rows. Note that this clause does not affect the errors that occur when evaluating columns, for which the behavior depends on whether the ON ERROR clause is specified against a given column.

In the examples that follow, the following table containing JSON data will be used:

The following query shows how to use JSON_TABLE to turn the JSON objects in the my_films table to a view containing columns for the keys kind, title, and director contained in the original JSON along with an ordinality column:

The following is a modified version of the above query to show the usage of PASSING arguments in the filter specified in the top-level JSON path expression and the various options for the individual columns:

The following is a modified version of the above query to show the usage of NESTED PATH for populating title and director columns, illustrating how they are joined to the parent columns id and kind:

The following is the same query but without the filter in the root path:

The following shows another query using a different JSON object as input. It shows the UNION "sibling join" between NESTED paths $.movies[] and $.books[] and also the usage of FOR ORDINALITY column at NESTED levels (columns movie_id, book_id, and author_id):

Examples:

Example 1 (unknown):

SELECT js,
  js IS JSON "json?",
  js IS JSON SCALAR "scalar?",
  js IS JSON OBJECT "object?",
  js IS JSON ARRAY "array?"
FROM (VALUES
      ('123'), ('"abc"'), ('{"a": "b"}'), ('[1,2]'),('abc')) foo(js);
     js     | json? | scalar? | object? | array?
------------+-------+---------+---------+--------
 123        | t     | t       | f       | f
 "abc"      | t     | t       | f       | f
 {"a": "b"} | t     | f       | t       | f
 [1,2]      | t     | f       | f       | t
 abc        | f     | f       | f       | f

Example 2 (unknown):

SELECT js,
  js IS JSON OBJECT "object?",
  js IS JSON ARRAY "array?",
  js IS JSON ARRAY WITH UNIQUE KEYS "array w. UK?",
  js IS JSON ARRAY WITHOUT UNIQUE KEYS "array w/o UK?"
FROM (VALUES ('[{"a":"1"},
 {"b":"2","b":"3"}]')) foo(js);
-[ RECORD 1 ]-+--------------------
js            | [{"a":"1"},        +
              |  {"b":"2","b":"3"}]
object?       | f
array?        | t
array w. UK?  | f
array w/o UK? | t

Example 3 (unknown):

value
-----------
 1
 true
 [2,false]

Example 4 (unknown):

value
-----------
 foo
 bar

---

PostgreSQL: Documentation: 18: 29.9. Architecture

URL: https://www.postgresql.org/docs/current/logical-replication-architecture.html

Contents:

• 29.9. Architecture #
  • 29.9.1. Initial Snapshot #
  • Note
  • Note

Logical replication is built with an architecture similar to physical streaming replication (see Section 26.2.5). It is implemented by walsender and apply processes. The walsender process starts logical decoding (described in Chapter 47) of the WAL and loads the standard logical decoding output plugin (pgoutput). The plugin transforms the changes read from WAL to the logical replication protocol (see Section 54.5) and filters the data according to the publication specification. The data is then continuously transferred using the streaming replication protocol to the apply worker, which maps the data to local tables and applies the individual changes as they are received, in correct transactional order.

The apply process on the subscriber database always runs with session_replication_role set to replica. This means that, by default, triggers and rules will not fire on a subscriber. Users can optionally choose to enable triggers and rules on a table using the ALTER TABLE command and the ENABLE TRIGGER and ENABLE RULE clauses.

The logical replication apply process currently only fires row triggers, not statement triggers. The initial table synchronization, however, is implemented like a COPY command and thus fires both row and statement triggers for INSERT.

The initial data in existing subscribed tables are snapshotted and copied in parallel instances of a special kind of apply process. These special apply processes are dedicated table synchronization workers, spawned for each table to be synchronized. Each table synchronization process will create its own replication slot and copy the existing data. As soon as the copy is finished the table contents will become visible to other backends. Once existing data is copied, the worker enters synchronization mode, which ensures that the table is brought up to a synchronized state with the main apply process by streaming any changes that happened during the initial data copy using standard logical replication. During this synchronization phase, the changes are applied and committed in the same order as they happened on the publisher. Once synchronization is done, control of the replication of the table is given back to the main apply process where replication continues as normal.

The publication publish parameter only affects what DML operations will be replicated. The initial data synchronization does not take this parameter into account when copying the existing table data.

If a table synchronization worker fails during copy, the apply worker detects the failure and respawns the table synchronization worker to continue the synchronization process. This behaviour ensures that transient errors do not permanently disrupt the replication setup. See also wal_retrieve_retry_interval.

---

PostgreSQL: Documentation: 18: Chapter 45. Server Programming Interface

URL: https://www.postgresql.org/docs/current/spi.html

Contents:

• Chapter 45. Server Programming Interface
  • Note

The Server Programming Interface (SPI) gives writers of user-defined C functions the ability to run SQL commands inside their functions or procedures. SPI is a set of interface functions to simplify access to the parser, planner, and executor. SPI also does some memory management.

The available procedural languages provide various means to execute SQL commands from functions. Most of these facilities are based on SPI, so this documentation might be of use for users of those languages as well.

Note that if a command invoked via SPI fails, then control will not be returned to your C function. Rather, the transaction or subtransaction in which your C function executes will be rolled back. (This might seem surprising given that the SPI functions mostly have documented error-return conventions. Those conventions only apply for errors detected within the SPI functions themselves, however.) It is possible to recover control after an error by establishing your own subtransaction surrounding SPI calls that might fail.

SPI functions return a nonnegative result on success (either via a returned integer value or in the global variable SPI_result, as described below). On error, a negative result or NULL will be returned.

Source code files that use SPI must include the header file executor/spi.h.

---

PostgreSQL: Documentation: 18: DISCONNECT

URL: https://www.postgresql.org/docs/current/ecpg-sql-disconnect.html

Contents:

• DISCONNECT
• Synopsis
• Description
• Parameters
• Examples
• Compatibility
• See Also

DISCONNECT — terminate a database connection

DISCONNECT closes a connection (or all connections) to the database.

A database connection name established by the CONNECT command.

Close the “current” connection, which is either the most recently opened connection, or the connection set by the SET CONNECTION command. This is also the default if no argument is given to the DISCONNECT command.

Close all open connections.

DISCONNECT is specified in the SQL standard.

Examples:

Example 1 (unknown):

DISCONNECT connection_name
DISCONNECT [ CURRENT ]
DISCONNECT ALL

Example 2 (unknown):

int
main(void)
{
    EXEC SQL CONNECT TO testdb AS con1 USER testuser;
    EXEC SQL CONNECT TO testdb AS con2 USER testuser;
    EXEC SQL CONNECT TO testdb AS con3 USER testuser;

    EXEC SQL DISCONNECT CURRENT;  /* close con3          */
    EXEC SQL DISCONNECT ALL;      /* close con2 and con1 */

    return 0;
}

---

PostgreSQL: Documentation: 18: 35.50. sql_parts

URL: https://www.postgresql.org/docs/current/infoschema-sql-parts.html

Contents:

• 35.50. sql_parts #

The table sql_parts contains information about which of the several parts of the SQL standard are supported by PostgreSQL.

Table 35.48. sql_parts Columns

feature_id character_data

An identifier string containing the number of the part

feature_name character_data

Descriptive name of the part

is_supported yes_or_no

YES if the part is fully supported by the current version of PostgreSQL, NO if not

is_verified_by character_data

Always null, since the PostgreSQL development group does not perform formal testing of feature conformance

comments character_data

Possibly a comment about the supported status of the part

---

PostgreSQL: Documentation: 18: 9.22. Window Functions

URL: https://www.postgresql.org/docs/current/functions-window.html

Contents:

• 9.22. Window Functions #
  • Note

Window functions provide the ability to perform calculations across sets of rows that are related to the current query row. See Section 3.5 for an introduction to this feature, and Section 4.2.8 for syntax details.

The built-in window functions are listed in Table 9.67. Note that these functions must be invoked using window function syntax, i.e., an OVER clause is required.

In addition to these functions, any built-in or user-defined ordinary aggregate (i.e., not ordered-set or hypothetical-set aggregates) can be used as a window function; see Section 9.21 for a list of the built-in aggregates. Aggregate functions act as window functions only when an OVER clause follows the call; otherwise they act as plain aggregates and return a single row for the entire set.

Table 9.67. General-Purpose Window Functions

row_number () → bigint

Returns the number of the current row within its partition, counting from 1.

Returns the rank of the current row, with gaps; that is, the row_number of the first row in its peer group.

dense_rank () → bigint

Returns the rank of the current row, without gaps; this function effectively counts peer groups.

percent_rank () → double precision

Returns the relative rank of the current row, that is (rank - 1) / (total partition rows - 1). The value thus ranges from 0 to 1 inclusive.

cume_dist () → double precision

Returns the cumulative distribution, that is (number of partition rows preceding or peers with current row) / (total partition rows). The value thus ranges from 1/N to 1.

ntile ( num_buckets integer ) → integer

Returns an integer ranging from 1 to the argument value, dividing the partition as equally as possible.

lag ( value anycompatible [, offset integer [, default anycompatible ]] ) → anycompatible

Returns value evaluated at the row that is offset rows before the current row within the partition; if there is no such row, instead returns default (which must be of a type compatible with value). Both offset and default are evaluated with respect to the current row. If omitted, offset defaults to 1 and default to NULL.

lead ( value anycompatible [, offset integer [, default anycompatible ]] ) → anycompatible

Returns value evaluated at the row that is offset rows after the current row within the partition; if there is no such row, instead returns default (which must be of a type compatible with value). Both offset and default are evaluated with respect to the current row. If omitted, offset defaults to 1 and default to NULL.

first_value ( value anyelement ) → anyelement

Returns value evaluated at the row that is the first row of the window frame.

last_value ( value anyelement ) → anyelement

Returns value evaluated at the row that is the last row of the window frame.

nth_value ( value anyelement, n integer ) → anyelement

Returns value evaluated at the row that is the n'th row of the window frame (counting from 1); returns NULL if there is no such row.

All of the functions listed in Table 9.67 depend on the sort ordering specified by the ORDER BY clause of the associated window definition. Rows that are not distinct when considering only the ORDER BY columns are said to be peers. The four ranking functions (including cume_dist) are defined so that they give the same answer for all rows of a peer group.

Note that first_value, last_value, and nth_value consider only the rows within the “window frame”, which by default contains the rows from the start of the partition through the last peer of the current row. This is likely to give unhelpful results for last_value and sometimes also nth_value. You can redefine the frame by adding a suitable frame specification (RANGE, ROWS or GROUPS) to the OVER clause. See Section 4.2.8 for more information about frame specifications.

When an aggregate function is used as a window function, it aggregates over the rows within the current row's window frame. An aggregate used with ORDER BY and the default window frame definition produces a “running sum” type of behavior, which may or may not be what's wanted. To obtain aggregation over the whole partition, omit ORDER BY or use ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING. Other frame specifications can be used to obtain other effects.

The SQL standard defines a RESPECT NULLS or IGNORE NULLS option for lead, lag, first_value, last_value, and nth_value. This is not implemented in PostgreSQL: the behavior is always the same as the standard's default, namely RESPECT NULLS. Likewise, the standard's FROM FIRST or FROM LAST option for nth_value is not implemented: only the default FROM FIRST behavior is supported. (You can achieve the result of FROM LAST by reversing the ORDER BY ordering.)

---

PostgreSQL: Documentation: 18: 27.3. Viewing Locks

URL: https://www.postgresql.org/docs/current/monitoring-locks.html

Contents:

• 27.3. Viewing Locks #

Another useful tool for monitoring database activity is the pg_locks system table. It allows the database administrator to view information about the outstanding locks in the lock manager. For example, this capability can be used to:

View all the locks currently outstanding, all the locks on relations in a particular database, all the locks on a particular relation, or all the locks held by a particular PostgreSQL session.

Determine the relation in the current database with the most ungranted locks (which might be a source of contention among database clients).

Determine the effect of lock contention on overall database performance, as well as the extent to which contention varies with overall database traffic.

Details of the pg_locks view appear in Section 53.13. For more information on locking and managing concurrency with PostgreSQL, refer to Chapter 13.

---

PostgreSQL: Documentation: 18: 35.21. domain_constraints

URL: https://www.postgresql.org/docs/current/infoschema-domain-constraints.html

Contents:

• 35.21. domain_constraints #

The view domain_constraints contains all constraints belonging to domains defined in the current database. Only those domains are shown that the current user has access to (by way of being the owner or having some privilege).

Table 35.19. domain_constraints Columns

constraint_catalog sql_identifier

Name of the database that contains the constraint (always the current database)

constraint_schema sql_identifier

Name of the schema that contains the constraint

constraint_name sql_identifier

Name of the constraint

domain_catalog sql_identifier

Name of the database that contains the domain (always the current database)

domain_schema sql_identifier

Name of the schema that contains the domain

domain_name sql_identifier

is_deferrable yes_or_no

YES if the constraint is deferrable, NO if not

initially_deferred yes_or_no

YES if the constraint is deferrable and initially deferred, NO if not

---

PostgreSQL: Documentation: 18: DECLARE

URL: https://www.postgresql.org/docs/current/ecpg-sql-declare.html

Contents:

• DECLARE
• Synopsis
• Description
• Parameters
• Examples
• Compatibility
• See Also

DECLARE — define a cursor

DECLARE declares a cursor for iterating over the result set of a prepared statement. This command has slightly different semantics from the direct SQL command DECLARE: Whereas the latter executes a query and prepares the result set for retrieval, this embedded SQL command merely declares a name as a “loop variable” for iterating over the result set of a query; the actual execution happens when the cursor is opened with the OPEN command.

A cursor name, case sensitive. This can be an SQL identifier or a host variable.

The name of a prepared query, either as an SQL identifier or a host variable.

A SELECT or VALUES command which will provide the rows to be returned by the cursor.

For the meaning of the cursor options, see DECLARE.

Examples declaring a cursor for a query:

An example declaring a cursor for a prepared statement:

DECLARE is specified in the SQL standard.

Examples:

Example 1 (unknown):

DECLARE cursor_name [ BINARY ] [ ASENSITIVE | INSENSITIVE ] [ [ NO ] SCROLL ] CURSOR [ { WITH | WITHOUT } HOLD ] FOR prepared_name
DECLARE cursor_name [ BINARY ] [ ASENSITIVE | INSENSITIVE ] [ [ NO ] SCROLL ] CURSOR [ { WITH | WITHOUT } HOLD ] FOR query

Example 2 (unknown):

EXEC SQL DECLARE C CURSOR FOR SELECT * FROM My_Table;
EXEC SQL DECLARE C CURSOR FOR SELECT Item1 FROM T;
EXEC SQL DECLARE cur1 CURSOR FOR SELECT version();

Example 3 (unknown):

EXEC SQL PREPARE stmt1 AS SELECT version();
EXEC SQL DECLARE cur1 CURSOR FOR stmt1;

---

PostgreSQL: Documentation: 18: 29.10. Monitoring

URL: https://www.postgresql.org/docs/current/logical-replication-monitoring.html

Contents:

• 29.10. Monitoring #

Because logical replication is based on a similar architecture as physical streaming replication, the monitoring on a publication node is similar to monitoring of a physical replication primary (see Section 26.2.5.2).

The monitoring information about subscription is visible in pg_stat_subscription. This view contains one row for every subscription worker. A subscription can have zero or more active subscription workers depending on its state.

Normally, there is a single apply process running for an enabled subscription. A disabled subscription or a crashed subscription will have zero rows in this view. If the initial data synchronization of any table is in progress, there will be additional workers for the tables being synchronized. Moreover, if the streaming transaction is applied in parallel, there may be additional parallel apply workers.

---

PostgreSQL: Documentation: 18: 29.12. Configuration Settings

URL: https://www.postgresql.org/docs/current/logical-replication-config.html

Contents:

• 29.12. Configuration Settings #
  • 29.12.1. Publishers #
  • 29.12.2. Subscribers #

Logical replication requires several configuration options to be set. These options are relevant only on one side of the replication.

wal_level must be set to logical.

max_replication_slots must be set to at least the number of subscriptions expected to connect, plus some reserve for table synchronization.

Logical replication slots are also affected by idle_replication_slot_timeout.

max_wal_senders should be set to at least the same as max_replication_slots, plus the number of physical replicas that are connected at the same time.

Logical replication walsender is also affected by wal_sender_timeout.

max_active_replication_origins must be set to at least the number of subscriptions that will be added to the subscriber, plus some reserve for table synchronization.

max_logical_replication_workers must be set to at least the number of subscriptions (for leader apply workers), plus some reserve for the table synchronization workers and parallel apply workers.

max_worker_processes may need to be adjusted to accommodate for replication workers, at least (max_logical_replication_workers + 1). Note, some extensions and parallel queries also take worker slots from max_worker_processes.

max_sync_workers_per_subscription controls the amount of parallelism of the initial data copy during the subscription initialization or when new tables are added.

max_parallel_apply_workers_per_subscription controls the amount of parallelism for streaming of in-progress transactions with subscription parameter streaming = parallel.

Logical replication workers are also affected by wal_receiver_timeout, wal_receiver_status_interval and wal_retrieve_retry_interval.

---

PostgreSQL: Documentation: 18: Chapter 15. Parallel Query

URL: https://www.postgresql.org/docs/current/parallel-query.html

Contents:

• Chapter 15. Parallel Query

PostgreSQL can devise query plans that can leverage multiple CPUs in order to answer queries faster. This feature is known as parallel query. Many queries cannot benefit from parallel query, either due to limitations of the current implementation or because there is no imaginable query plan that is any faster than the serial query plan. However, for queries that can benefit, the speedup from parallel query is often very significant. Many queries can run more than twice as fast when using parallel query, and some queries can run four times faster or even more. Queries that touch a large amount of data but return only a few rows to the user will typically benefit most. This chapter explains some details of how parallel query works and in which situations it can be used so that users who wish to make use of it can understand what to expect.

---

PostgreSQL: Documentation: 18: 31.2. Test Evaluation

URL: https://www.postgresql.org/docs/current/regress-evaluation.html

Contents:

• 31.2. Test Evaluation #
  • 31.2.1. Error Message Differences #
  • 31.2.2. Locale Differences #
  • 31.2.3. Date and Time Differences #
  • 31.2.4. Floating-Point Differences #
  • 31.2.5. Row Ordering Differences #
  • 31.2.6. Insufficient Stack Depth #
  • 31.2.7. The “random” Test #
  • 31.2.8. Configuration Parameters #

Some properly installed and fully functional PostgreSQL installations can “fail” some of these regression tests due to platform-specific artifacts such as varying floating-point representation and message wording. The tests are currently evaluated using a simple diff comparison against the outputs generated on a reference system, so the results are sensitive to small system differences. When a test is reported as “failed”, always examine the differences between expected and actual results; you might find that the differences are not significant. Nonetheless, we still strive to maintain accurate reference files across all supported platforms, so it can be expected that all tests pass.

The actual outputs of the regression tests are in files in the src/test/regress/results directory. The test script uses diff to compare each output file against the reference outputs stored in the src/test/regress/expected directory. Any differences are saved for your inspection in src/test/regress/regression.diffs. (When running a test suite other than the core tests, these files of course appear in the relevant subdirectory, not src/test/regress.)

If you don't like the diff options that are used by default, set the environment variable PG_REGRESS_DIFF_OPTS, for instance PG_REGRESS_DIFF_OPTS='-c'. (Or you can run diff yourself, if you prefer.)

If for some reason a particular platform generates a “failure” for a given test, but inspection of the output convinces you that the result is valid, you can add a new comparison file to silence the failure report in future test runs. See Section 31.3 for details.

Some of the regression tests involve intentional invalid input values. Error messages can come from either the PostgreSQL code or from the host platform system routines. In the latter case, the messages can vary between platforms, but should reflect similar information. These differences in messages will result in a “failed” regression test that can be validated by inspection.

If you run the tests against a server that was initialized with a collation-order locale other than C, then there might be differences due to sort order and subsequent failures. The regression test suite is set up to handle this problem by providing alternate result files that together are known to handle a large number of locales.

To run the tests in a different locale when using the temporary-installation method, pass the appropriate locale-related environment variables on the make command line, for example:

(The regression test driver unsets LC_ALL, so it does not work to choose the locale using that variable.) To use no locale, either unset all locale-related environment variables (or set them to C) or use the following special invocation:

When running the tests against an existing installation, the locale setup is determined by the existing installation. To change it, initialize the database cluster with a different locale by passing the appropriate options to initdb.

In general, it is advisable to try to run the regression tests in the locale setup that is wanted for production use, as this will exercise the locale- and encoding-related code portions that will actually be used in production. Depending on the operating system environment, you might get failures, but then you will at least know what locale-specific behaviors to expect when running real applications.

Most of the date and time results are dependent on the time zone environment. The reference files are generated for time zone America/Los_Angeles, and there will be apparent failures if the tests are not run with that time zone setting. The regression test driver sets environment variable PGTZ to America/Los_Angeles, which normally ensures proper results.

Some of the tests involve computing 64-bit floating-point numbers (double precision) from table columns. Differences in results involving mathematical functions of double precision columns have been observed. The float8 and geometry tests are particularly prone to small differences across platforms, or even with different compiler optimization settings. Human eyeball comparison is needed to determine the real significance of these differences which are usually 10 places to the right of the decimal point.

Some systems display minus zero as -0, while others just show 0.

Some systems signal errors from pow() and exp() differently from the mechanism expected by the current PostgreSQL code.

You might see differences in which the same rows are output in a different order than what appears in the expected file. In most cases this is not, strictly speaking, a bug. Most of the regression test scripts are not so pedantic as to use an ORDER BY for every single SELECT, and so their result row orderings are not well-defined according to the SQL specification. In practice, since we are looking at the same queries being executed on the same data by the same software, we usually get the same result ordering on all platforms, so the lack of ORDER BY is not a problem. Some queries do exhibit cross-platform ordering differences, however. When testing against an already-installed server, ordering differences can also be caused by non-C locale settings or non-default parameter settings, such as custom values of work_mem or the planner cost parameters.

Therefore, if you see an ordering difference, it's not something to worry about, unless the query does have an ORDER BY that your result is violating. However, please report it anyway, so that we can add an ORDER BY to that particular query to eliminate the bogus “failure” in future releases.

You might wonder why we don't order all the regression test queries explicitly to get rid of this issue once and for all. The reason is that that would make the regression tests less useful, not more, since they'd tend to exercise query plan types that produce ordered results to the exclusion of those that don't.

If the errors test results in a server crash at the select infinite_recurse() command, it means that the platform's limit on process stack size is smaller than the max_stack_depth parameter indicates. This can be fixed by running the server under a higher stack size limit (4MB is recommended with the default value of max_stack_depth). If you are unable to do that, an alternative is to reduce the value of max_stack_depth.

On platforms supporting getrlimit(), the server should automatically choose a safe value of max_stack_depth; so unless you've manually overridden this setting, a failure of this kind is a reportable bug.

The random test script is intended to produce random results. In very rare cases, this causes that regression test to fail. Typing:

should produce only one or a few lines of differences. You need not worry unless the random test fails repeatedly.

When running the tests against an existing installation, some non-default parameter settings could cause the tests to fail. For example, changing parameters such as enable_seqscan or enable_indexscan could cause plan changes that would affect the results of tests that use EXPLAIN.

Examples:

Example 1 (unknown):

make check LANG=de_DE.utf8

Example 2 (unknown):

make check NO_LOCALE=1

Example 3 (unknown):

diff results/random.out expected/random.out

---

PostgreSQL: Documentation: 18: 7.3. Select Lists

URL: https://www.postgresql.org/docs/current/queries-select-lists.html

Contents:

• 7.3. Select Lists #
  • 7.3.1. Select-List Items #
  • 7.3.2. Column Labels #
  • Note
  • 7.3.3. DISTINCT #

As shown in the previous section, the table expression in the SELECT command constructs an intermediate virtual table by possibly combining tables, views, eliminating rows, grouping, etc. This table is finally passed on to processing by the select list. The select list determines which columns of the intermediate table are actually output.

The simplest kind of select list is * which emits all columns that the table expression produces. Otherwise, a select list is a comma-separated list of value expressions (as defined in Section 4.2). For instance, it could be a list of column names:

The columns names a, b, and c are either the actual names of the columns of tables referenced in the FROM clause, or the aliases given to them as explained in Section 7.2.1.2. The name space available in the select list is the same as in the WHERE clause, unless grouping is used, in which case it is the same as in the HAVING clause.

If more than one table has a column of the same name, the table name must also be given, as in:

When working with multiple tables, it can also be useful to ask for all the columns of a particular table:

See Section 8.16.5 for more about the table_name.* notation.

If an arbitrary value expression is used in the select list, it conceptually adds a new virtual column to the returned table. The value expression is evaluated once for each result row, with the row's values substituted for any column references. But the expressions in the select list do not have to reference any columns in the table expression of the FROM clause; they can be constant arithmetic expressions, for instance.

The entries in the select list can be assigned names for subsequent processing, such as for use in an ORDER BY clause or for display by the client application. For example:

If no output column name is specified using AS, the system assigns a default column name. For simple column references, this is the name of the referenced column. For function calls, this is the name of the function. For complex expressions, the system will generate a generic name.

The AS key word is usually optional, but in some cases where the desired column name matches a PostgreSQL key word, you must write AS or double-quote the column name in order to avoid ambiguity. (Appendix C shows which key words require AS to be used as a column label.) For example, FROM is one such key word, so this does not work:

but either of these do:

For greatest safety against possible future key word additions, it is recommended that you always either write AS or double-quote the output column name.

The naming of output columns here is different from that done in the FROM clause (see Section 7.2.1.2). It is possible to rename the same column twice, but the name assigned in the select list is the one that will be passed on.

After the select list has been processed, the result table can optionally be subject to the elimination of duplicate rows. The DISTINCT key word is written directly after SELECT to specify this:

(Instead of DISTINCT the key word ALL can be used to specify the default behavior of retaining all rows.)

Obviously, two rows are considered distinct if they differ in at least one column value. Null values are considered equal in this comparison.

Alternatively, an arbitrary expression can determine what rows are to be considered distinct:

Here expression is an arbitrary value expression that is evaluated for all rows. A set of rows for which all the expressions are equal are considered duplicates, and only the first row of the set is kept in the output. Note that the “first row” of a set is unpredictable unless the query is sorted on enough columns to guarantee a unique ordering of the rows arriving at the DISTINCT filter. (DISTINCT ON processing occurs after ORDER BY sorting.)

The DISTINCT ON clause is not part of the SQL standard and is sometimes considered bad style because of the potentially indeterminate nature of its results. With judicious use of GROUP BY and subqueries in FROM, this construct can be avoided, but it is often the most convenient alternative.

Examples:

Example 1 (unknown):

SELECT a, b, c FROM ...

Example 2 (unknown):

SELECT tbl1.a, tbl2.a, tbl1.b FROM ...

Example 3 (unknown):

SELECT tbl1.*, tbl2.a FROM ...

Example 4 (unknown):

SELECT a AS value, b + c AS sum FROM ...

---

PostgreSQL: Documentation: 18: Appendix L. Acronyms

URL: https://www.postgresql.org/docs/current/acronyms.html

Contents:

• Appendix L. Acronyms

This is a list of acronyms commonly used in the PostgreSQL documentation and in discussions about PostgreSQL.

American National Standards Institute

Application Programming Interface

American Standard Code for Information Interchange

Certificate Authority

Classless Inter-Domain Routing

Comprehensive Perl Archive Network

Certificate Revocation List

Comma Separated Values

Common Table Expression

Common Vulnerabilities and Exposures

Database Administrator

Database Interface (Perl)

Database Management System

Data Definition Language, SQL commands such as CREATE TABLE, ALTER USER

Data Manipulation Language, SQL commands such as INSERT, UPDATE, DELETE

Embedded C for PostgreSQL

Frequently Asked Questions

Genetic Query Optimizer

Generalized Inverted Index

Generalized Search Tree

Generic Security Services Application Programming Interface

Grand Unified Configuration, the PostgreSQL subsystem that handles server configuration

Host-Based Authentication

International Electrotechnical Commission

Institute of Electrical and Electronics Engineers

Inter-Process Communication

International Organization for Standardization

International Standard Serial Number

Java Database Connectivity

Just-in-Time compilation

JavaScript Object Notation

Lightweight Directory Access Protocol

Most Common Frequency, that is the frequency associated with some Most Common Value

Most Common Value, one of the values appearing most often within a particular table column

Man-in-the-middle attack

Multi-Version Concurrency Control

National Language Support

Open Database Connectivity

Online Analytical Processing

Online Transaction Processing

Object-Relational Database Management System

Pluggable Authentication Modules

PostgreSQL Extension System

Point-In-Time Recovery (Continuous Archiving)

Procedural Languages (server-side)

Portable Operating System Interface

Relational Database Management System

Standard Generalized Markup Language

Server Name Indication, RFC 6066

Server Programming Interface

Space-Partitioned Generalized Search Tree

Structured Query Language

Set-Returning Function

Security Support Provider Interface

Transmission Control Protocol (TCP) / Internet Protocol (IP)

Transport Layer Security

The Oversized-Attribute Storage Technique

Transaction Processing Performance Council

Uniform Resource Locator

Coordinated Universal Time

Unicode Transformation Format

Eight-Bit Unicode Transformation Format

Universally Unique Identifier

Transaction Identifier

Extensible Markup Language

---

PostgreSQL: Documentation: 18: 18.6. Upgrading a PostgreSQL Cluster

URL: https://www.postgresql.org/docs/current/upgrading.html

Contents:

• 18.6. Upgrading a PostgreSQL Cluster #
  • 18.6.1. Upgrading Data via pg_dumpall #
  • 18.6.2. Upgrading Data via pg_upgrade #
  • 18.6.3. Upgrading Data via Replication #

This section discusses how to upgrade your database data from one PostgreSQL release to a newer one.

Current PostgreSQL version numbers consist of a major and a minor version number. For example, in the version number 10.1, the 10 is the major version number and the 1 is the minor version number, meaning this would be the first minor release of the major release 10. For releases before PostgreSQL version 10.0, version numbers consist of three numbers, for example, 9.5.3. In those cases, the major version consists of the first two digit groups of the version number, e.g., 9.5, and the minor version is the third number, e.g., 3, meaning this would be the third minor release of the major release 9.5.

Minor releases never change the internal storage format and are always compatible with earlier and later minor releases of the same major version number. For example, version 10.1 is compatible with version 10.0 and version 10.6. Similarly, for example, 9.5.3 is compatible with 9.5.0, 9.5.1, and 9.5.6. To update between compatible versions, you simply replace the executables while the server is down and restart the server. The data directory remains unchanged — minor upgrades are that simple.

For major releases of PostgreSQL, the internal data storage format is subject to change, thus complicating upgrades. The traditional method for moving data to a new major version is to dump and restore the database, though this can be slow. A faster method is pg_upgrade. Replication methods are also available, as discussed below. (If you are using a pre-packaged version of PostgreSQL, it may provide scripts to assist with major version upgrades. Consult the package-level documentation for details.)

New major versions also typically introduce some user-visible incompatibilities, so application programming changes might be required. All user-visible changes are listed in the release notes (Appendix E); pay particular attention to the section labeled "Migration". Though you can upgrade from one major version to another without upgrading to intervening versions, you should read the major release notes of all intervening versions.

Cautious users will want to test their client applications on the new version before switching over fully; therefore, it's often a good idea to set up concurrent installations of old and new versions. When testing a PostgreSQL major upgrade, consider the following categories of possible changes:

The capabilities available for administrators to monitor and control the server often change and improve in each major release.

Typically this includes new SQL command capabilities and not changes in behavior, unless specifically mentioned in the release notes.

Typically libraries like libpq only add new functionality, again unless mentioned in the release notes.

System catalog changes usually only affect database management tools.

This involves changes in the backend function API, which is written in the C programming language. Such changes affect code that references backend functions deep inside the server.

One upgrade method is to dump data from one major version of PostgreSQL and restore it in another — to do this, you must use a logical backup tool like pg_dumpall; file system level backup methods will not work. (There are checks in place that prevent you from using a data directory with an incompatible version of PostgreSQL, so no great harm can be done by trying to start the wrong server version on a data directory.)

It is recommended that you use the pg_dump and pg_dumpall programs from the newer version of PostgreSQL, to take advantage of enhancements that might have been made in these programs. Current releases of the dump programs can read data from any server version back to 9.2.

These instructions assume that your existing installation is under the /usr/local/pgsql directory, and that the data area is in /usr/local/pgsql/data. Substitute your paths appropriately.

If making a backup, make sure that your database is not being updated. This does not affect the integrity of the backup, but the changed data would of course not be included. If necessary, edit the permissions in the file /usr/local/pgsql/data/pg_hba.conf (or equivalent) to disallow access from everyone except you. See Chapter 20 for additional information on access control.

To back up your database installation, type:

To make the backup, you can use the pg_dumpall command from the version you are currently running; see Section 25.1.2 for more details. For best results, however, try to use the pg_dumpall command from PostgreSQL 18.0, since this version contains bug fixes and improvements over older versions. While this advice might seem idiosyncratic since you haven't installed the new version yet, it is advisable to follow it if you plan to install the new version in parallel with the old version. In that case you can complete the installation normally and transfer the data later. This will also decrease the downtime.

Shut down the old server:

On systems that have PostgreSQL started at boot time, there is probably a start-up file that will accomplish the same thing. For example, on a Red Hat Linux system one might find that this works:

See Chapter 18 for details about starting and stopping the server.

If restoring from backup, rename or delete the old installation directory if it is not version-specific. It is a good idea to rename the directory, rather than delete it, in case you have trouble and need to revert to it. Keep in mind the directory might consume significant disk space. To rename the directory, use a command like this:

(Be sure to move the directory as a single unit so relative paths remain unchanged.)

Install the new version of PostgreSQL as outlined in Chapter 17.

Create a new database cluster if needed. Remember that you must execute these commands while logged in to the special database user account (which you already have if you are upgrading).

Restore your previous pg_hba.conf and any postgresql.conf modifications.

Start the database server, again using the special database user account:

Finally, restore your data from backup with:

The least downtime can be achieved by installing the new server in a different directory and running both the old and the new servers in parallel, on different ports. Then you can use something like:

to transfer your data.

The pg_upgrade module allows an installation to be migrated in-place from one major PostgreSQL version to another. Upgrades can be performed in minutes, particularly with --link mode. It requires steps similar to pg_dumpall above, e.g., starting/stopping the server, running initdb. The pg_upgrade documentation outlines the necessary steps.

It is also possible to use logical replication methods to create a standby server with the updated version of PostgreSQL. This is possible because logical replication supports replication between different major versions of PostgreSQL. The standby can be on the same computer or a different computer. Once it has synced up with the primary server (running the older version of PostgreSQL), you can switch primaries and make the standby the primary and shut down the older database instance. Such a switch-over results in only several seconds of downtime for an upgrade.

This method of upgrading can be performed using the built-in logical replication facilities as well as using external logical replication systems such as pglogical, Slony, Londiste, and Bucardo.

Examples:

Example 1 (unknown):

pg_dumpall > outputfile

Example 2 (unknown):

pg_ctl stop

Example 3 (unknown):

/etc/rc.d/init.d/postgresql stop

Example 4 (unknown):

mv /usr/local/pgsql /usr/local/pgsql.old

---

PostgreSQL: Documentation: 18: 5.2. Default Values

URL: https://www.postgresql.org/docs/current/ddl-default.html

Contents:

• 5.2. Default Values #

A column can be assigned a default value. When a new row is created and no values are specified for some of the columns, those columns will be filled with their respective default values. A data manipulation command can also request explicitly that a column be set to its default value, without having to know what that value is. (Details about data manipulation commands are in Chapter 6.)

If no default value is declared explicitly, the default value is the null value. This usually makes sense because a null value can be considered to represent unknown data.

In a table definition, default values are listed after the column data type. For example:

The default value can be an expression, which will be evaluated whenever the default value is inserted (not when the table is created). A common example is for a timestamp column to have a default of CURRENT_TIMESTAMP, so that it gets set to the time of row insertion. Another common example is generating a “serial number” for each row. In PostgreSQL this is typically done by something like:

where the nextval() function supplies successive values from a sequence object (see Section 9.17). This arrangement is sufficiently common that there's a special shorthand for it:

The SERIAL shorthand is discussed further in Section 8.1.4.

Examples:

Example 1 (unknown):

CREATE TABLE products (
    product_no integer,
    name text,
    price numeric DEFAULT 9.99
);

Example 2 (unknown):

CREATE TABLE products (
    product_no integer DEFAULT nextval('products_product_no_seq'),
    ...
);

Example 3 (unknown):

CREATE TABLE products (
    product_no SERIAL,
    ...
);

---

PostgreSQL: Documentation: 18: 10.4. Value Storage

URL: https://www.postgresql.org/docs/current/typeconv-query.html

Contents:

• 10.4. Value Storage #

Values to be inserted into a table are converted to the destination column's data type according to the following steps.

Value Storage Type Conversion

Check for an exact match with the target.

Otherwise, try to convert the expression to the target type. This is possible if an assignment cast between the two types is registered in the pg_cast catalog (see CREATE CAST). Alternatively, if the expression is an unknown-type literal, the contents of the literal string will be fed to the input conversion routine for the target type.

Check to see if there is a sizing cast for the target type. A sizing cast is a cast from that type to itself. If one is found in the pg_cast catalog, apply it to the expression before storing into the destination column. The implementation function for such a cast always takes an extra parameter of type integer, which receives the destination column's atttypmod value (typically its declared length, although the interpretation of atttypmod varies for different data types), and it may take a third boolean parameter that says whether the cast is explicit or implicit. The cast function is responsible for applying any length-dependent semantics such as size checking or truncation.

Example 10.9. character Storage Type Conversion

For a target column declared as character(20) the following statement shows that the stored value is sized correctly:

What has really happened here is that the two unknown literals are resolved to text by default, allowing the || operator to be resolved as text concatenation. Then the text result of the operator is converted to bpchar (“blank-padded char”, the internal name of the character data type) to match the target column type. (Since the conversion from text to bpchar is binary-coercible, this conversion does not insert any real function call.) Finally, the sizing function bpchar(bpchar, integer, boolean) is found in the system catalog and applied to the operator's result and the stored column length. This type-specific function performs the required length check and addition of padding spaces.

Examples:

Example 1 (unknown):

CREATE TABLE vv (v character(20));
INSERT INTO vv SELECT 'abc' || 'def';
SELECT v, octet_length(v) FROM vv;

          v           | octet_length
----------------------+--------------
 abcdef               |           20
(1 row)

---

PostgreSQL: Documentation: 18: 35.26. foreign_data_wrapper_options

URL: https://www.postgresql.org/docs/current/infoschema-foreign-data-wrapper-options.html

Contents:

• 35.26. foreign_data_wrapper_options #

The view foreign_data_wrapper_options contains all the options defined for foreign-data wrappers in the current database. Only those foreign-data wrappers are shown that the current user has access to (by way of being the owner or having some privilege).

Table 35.24. foreign_data_wrapper_options Columns

foreign_data_wrapper_catalog sql_identifier

Name of the database that the foreign-data wrapper is defined in (always the current database)

foreign_data_wrapper_name sql_identifier

Name of the foreign-data wrapper

option_name sql_identifier

option_value character_data

---

PostgreSQL: Documentation: 18: 23.2. Collation Support

URL: https://www.postgresql.org/docs/current/collation.html

Contents:

• 23.2. Collation Support #
  • 23.2.1. Concepts #
  • 23.2.2. Managing Collations #
  • 23.2.2.1. Standard Collations #
  • 23.2.2.2. Predefined Collations #
    • 23.2.2.2.1. libc Collations #
    • 23.2.2.2.2. ICU Collations #
  • 23.2.2.3. Creating New Collation Objects #
    • 23.2.2.3.1. libc Collations #
    • 23.2.2.3.2. ICU Collations #

The collation feature allows specifying the sort order and character classification behavior of data per-column, or even per-operation. This alleviates the restriction that the LC_COLLATE and LC_CTYPE settings of a database cannot be changed after its creation.

Conceptually, every expression of a collatable data type has a collation. (The built-in collatable data types are text, varchar, and char. User-defined base types can also be marked collatable, and of course a domain over a collatable data type is collatable.) If the expression is a column reference, the collation of the expression is the defined collation of the column. If the expression is a constant, the collation is the default collation of the data type of the constant. The collation of a more complex expression is derived from the collations of its inputs, as described below.

The collation of an expression can be the “default” collation, which means the locale settings defined for the database. It is also possible for an expression's collation to be indeterminate. In such cases, ordering operations and other operations that need to know the collation will fail.

When the database system has to perform an ordering or a character classification, it uses the collation of the input expression. This happens, for example, with ORDER BY clauses and function or operator calls such as <. The collation to apply for an ORDER BY clause is simply the collation of the sort key. The collation to apply for a function or operator call is derived from the arguments, as described below. In addition to comparison operators, collations are taken into account by functions that convert between lower and upper case letters, such as lower, upper, and initcap; by pattern matching operators; and by to_char and related functions.

For a function or operator call, the collation that is derived by examining the argument collations is used at run time for performing the specified operation. If the result of the function or operator call is of a collatable data type, the collation is also used at parse time as the defined collation of the function or operator expression, in case there is a surrounding expression that requires knowledge of its collation.

The collation derivation of an expression can be implicit or explicit. This distinction affects how collations are combined when multiple different collations appear in an expression. An explicit collation derivation occurs when a COLLATE clause is used; all other collation derivations are implicit. When multiple collations need to be combined, for example in a function call, the following rules are used:

If any input expression has an explicit collation derivation, then all explicitly derived collations among the input expressions must be the same, otherwise an error is raised. If any explicitly derived collation is present, that is the result of the collation combination.

Otherwise, all input expressions must have the same implicit collation derivation or the default collation. If any non-default collation is present, that is the result of the collation combination. Otherwise, the result is the default collation.

If there are conflicting non-default implicit collations among the input expressions, then the combination is deemed to have indeterminate collation. This is not an error condition unless the particular function being invoked requires knowledge of the collation it should apply. If it does, an error will be raised at run-time.

For example, consider this table definition:

the < comparison is performed according to de_DE rules, because the expression combines an implicitly derived collation with the default collation. But in

the comparison is performed using fr_FR rules, because the explicit collation derivation overrides the implicit one. Furthermore, given

the parser cannot determine which collation to apply, since the a and b columns have conflicting implicit collations. Since the < operator does need to know which collation to use, this will result in an error. The error can be resolved by attaching an explicit collation specifier to either input expression, thus:

On the other hand, the structurally similar case

does not result in an error, because the || operator does not care about collations: its result is the same regardless of the collation.

The collation assigned to a function or operator's combined input expressions is also considered to apply to the function or operator's result, if the function or operator delivers a result of a collatable data type. So, in

the ordering will be done according to de_DE rules. But this query:

results in an error, because even though the || operator doesn't need to know a collation, the ORDER BY clause does. As before, the conflict can be resolved with an explicit collation specifier:

A collation is an SQL schema object that maps an SQL name to locales provided by libraries installed in the operating system. A collation definition has a provider that specifies which library supplies the locale data. One standard provider name is libc, which uses the locales provided by the operating system C library. These are the locales used by most tools provided by the operating system. Another provider is icu, which uses the external ICU library. ICU locales can only be used if support for ICU was configured when PostgreSQL was built.

A collation object provided by libc maps to a combination of LC_COLLATE and LC_CTYPE settings, as accepted by the setlocale() system library call. (As the name would suggest, the main purpose of a collation is to set LC_COLLATE, which controls the sort order. But it is rarely necessary in practice to have an LC_CTYPE setting that is different from LC_COLLATE, so it is more convenient to collect these under one concept than to create another infrastructure for setting LC_CTYPE per expression.) Also, a libc collation is tied to a character set encoding (see Section 23.3). The same collation name may exist for different encodings.

A collation object provided by icu maps to a named collator provided by the ICU library. ICU does not support separate “collate” and “ctype” settings, so they are always the same. Also, ICU collations are independent of the encoding, so there is always only one ICU collation of a given name in a database.

On all platforms, the following collations are supported:

This SQL standard collation sorts using the Unicode Collation Algorithm with the Default Unicode Collation Element Table. It is available in all encodings. ICU support is required to use this collation, and behavior may change if PostgreSQL is built with a different version of ICU. (This collation has the same behavior as the ICU root locale; see und-x-icu (for “undefined”).)

This SQL standard collation sorts using the Unicode code point values rather than natural language order, and only the ASCII letters “A” through “Z” are treated as letters. The behavior is efficient and stable across all versions. Only available for encoding UTF8. (This collation has the same behavior as the libc locale specification C in UTF8 encoding.)

This collation sorts by Unicode code point values rather than natural language order. For the functions lower, initcap, and upper it uses Unicode full case mapping. For pattern matching (including regular expressions), it uses the Standard variant of Unicode Compatibility Properties. Behavior is efficient and stable within a Postgres major version. It is only available for encoding UTF8.

This collation sorts by Unicode code point values rather than natural language order. For the functions lower, initcap, and upper, it uses Unicode simple case mapping. For pattern matching (including regular expressions), it uses the POSIX Compatible variant of Unicode Compatibility Properties. Behavior is efficient and stable within a PostgreSQL major version. This collation is only available for encoding UTF8.

The C and POSIX collations are based on “traditional C” behavior. They sort by byte values rather than natural language order, and only the ASCII letters “A” through “Z” are treated as letters. The behavior is efficient and stable across all versions for a given database encoding, but behavior may vary between different database encodings.

The default collation selects the locale specified at database creation time.

Additional collations may be available depending on operating system support. The efficiency and stability of these additional collations depend on the collation provider, the provider version, and the locale.

If the operating system provides support for using multiple locales within a single program (newlocale and related functions), or if support for ICU is configured, then when a database cluster is initialized, initdb populates the system catalog pg_collation with collations based on all the locales it finds in the operating system at the time.

To inspect the currently available locales, use the query SELECT * FROM pg_collation, or the command \dOS+ in psql.

For example, the operating system might provide a locale named de_DE.utf8. initdb would then create a collation named de_DE.utf8 for encoding UTF8 that has both LC_COLLATE and LC_CTYPE set to de_DE.utf8. It will also create a collation with the .utf8 tag stripped off the name. So you could also use the collation under the name de_DE, which is less cumbersome to write and makes the name less encoding-dependent. Note that, nevertheless, the initial set of collation names is platform-dependent.

The default set of collations provided by libc map directly to the locales installed in the operating system, which can be listed using the command locale -a. In case a libc collation is needed that has different values for LC_COLLATE and LC_CTYPE, or if new locales are installed in the operating system after the database system was initialized, then a new collation may be created using the CREATE COLLATION command. New operating system locales can also be imported en masse using the pg_import_system_collations() function.

Within any particular database, only collations that use that database's encoding are of interest. Other entries in pg_collation are ignored. Thus, a stripped collation name such as de_DE can be considered unique within a given database even though it would not be unique globally. Use of the stripped collation names is recommended, since it will make one fewer thing you need to change if you decide to change to another database encoding. Note however that the default, C, and POSIX collations can be used regardless of the database encoding.

PostgreSQL considers distinct collation objects to be incompatible even when they have identical properties. Thus for example,

will draw an error even though the C and POSIX collations have identical behaviors. Mixing stripped and non-stripped collation names is therefore not recommended.

With ICU, it is not sensible to enumerate all possible locale names. ICU uses a particular naming system for locales, but there are many more ways to name a locale than there are actually distinct locales. initdb uses the ICU APIs to extract a set of distinct locales to populate the initial set of collations. Collations provided by ICU are created in the SQL environment with names in BCP 47 language tag format, with a “private use” extension -x-icu appended, to distinguish them from libc locales.

Here are some example collations that might be created:

German collation, default variant

German collation for Austria, default variant

(There are also, say, de-DE-x-icu or de-CH-x-icu, but as of this writing, they are equivalent to de-x-icu.)

ICU “root” collation. Use this to get a reasonable language-agnostic sort order.

Some (less frequently used) encodings are not supported by ICU. When the database encoding is one of these, ICU collation entries in pg_collation are ignored. Attempting to use one will draw an error along the lines of “collation "de-x-icu" for encoding "WIN874" does not exist”.

If the standard and predefined collations are not sufficient, users can create their own collation objects using the SQL command CREATE COLLATION.

The standard and predefined collations are in the schema pg_catalog, like all predefined objects. User-defined collations should be created in user schemas. This also ensures that they are saved by pg_dump.

New libc collations can be created like this:

The exact values that are acceptable for the locale clause in this command depend on the operating system. On Unix-like systems, the command locale -a will show a list.

Since the predefined libc collations already include all collations defined in the operating system when the database instance is initialized, it is not often necessary to manually create new ones. Reasons might be if a different naming system is desired (in which case see also Section 23.2.2.3.3) or if the operating system has been upgraded to provide new locale definitions (in which case see also pg_import_system_collations()).

ICU collations can be created like:

ICU locales are specified as a BCP 47 Language Tag, but can also accept most libc-style locale names. If possible, libc-style locale names are transformed into language tags.

New ICU collations can customize collation behavior extensively by including collation attributes in the language tag. See Section 23.2.3 for details and examples.

The command CREATE COLLATION can also be used to create a new collation from an existing collation, which can be useful to be able to use operating-system-independent collation names in applications, create compatibility names, or use an ICU-provided collation under a more readable name. For example:

A collation is either deterministic or nondeterministic. A deterministic collation uses deterministic comparisons, which means that it considers strings to be equal only if they consist of the same byte sequence. Nondeterministic comparison may determine strings to be equal even if they consist of different bytes. Typical situations include case-insensitive comparison, accent-insensitive comparison, as well as comparison of strings in different Unicode normal forms. It is up to the collation provider to actually implement such insensitive comparisons; the deterministic flag only determines whether ties are to be broken using bytewise comparison. See also Unicode Technical Standard 10 for more information on the terminology.

To create a nondeterministic collation, specify the property deterministic = false to CREATE COLLATION, for example:

This example would use the standard Unicode collation in a nondeterministic way. In particular, this would allow strings in different normal forms to be compared correctly. More interesting examples make use of the ICU customization facilities explained above. For example:

All standard and predefined collations are deterministic, all user-defined collations are deterministic by default. While nondeterministic collations give a more “correct” behavior, especially when considering the full power of Unicode and its many special cases, they also have some drawbacks. Foremost, their use leads to a performance penalty. Note, in particular, that B-tree cannot use deduplication with indexes that use a nondeterministic collation. Also, certain operations are not possible with nondeterministic collations, such as some pattern matching operations. Therefore, they should be used only in cases where they are specifically wanted.

To deal with text in different Unicode normalization forms, it is also an option to use the functions/expressions normalize and is normalized to preprocess or check the strings, instead of using nondeterministic collations. There are different trade-offs for each approach.

ICU allows extensive control over collation behavior by defining new collations with collation settings as a part of the language tag. These settings can modify the collation order to suit a variety of needs. For instance:

Many of the available options are described in Section 23.2.3.2, or see Section 23.2.3.5 for more details.

Comparison of two strings (collation) in ICU is determined by a multi-level process, where textual features are grouped into "levels". Treatment of each level is controlled by the collation settings. Higher levels correspond to finer textual features.

Table 23.1 shows which textual feature differences are considered significant when determining equality at the given level. The Unicode character U+2063 is an invisible separator, and as seen in the table, is ignored for at all levels of comparison less than identic.

Table 23.1. ICU Collation Levels

[a] only with ka-shifted; see Table 23.2

At every level, even with full normalization off, basic normalization is performed. For example, 'á' may be composed of the code points U&'\0061\0301' or the single code point U&'\00E1', and those sequences will be considered equal even at the identic level. To treat any difference in code point representation as distinct, use a collation created with deterministic set to true.

Table 23.2 shows the available collation settings, which can be used as part of a language tag to customize a collation.

Table 23.2. ICU Collation Settings

Separates case into a "level 2.5" that falls between accents and other level 3 features.

If set to true and ks is set to level1, will ignore accents but take case into account.

Enable full normalization; may affect performance. Basic normalization is performed even when set to false. Locales for languages that require full normalization typically enable it by default.

Full normalization is important in some cases, such as when multiple accents are applied to a single character. For example, the code point sequences U&'\0065\0323\0302' and U&'\0065\0302\0323' represent an e with circumflex and dot-below accents applied in different orders. With full normalization on, these code point sequences are treated as equal; otherwise they are unequal.

Set to one or more of the valid values, or any BCP 47 script-id, e.g. latn ("Latin") or grek ("Greek"). Multiple values are separated by "-".

Redefines the ordering of classes of characters; those characters belonging to a class earlier in the list sort before characters belonging to a class later in the list. For instance, the value digit-currency-space (as part of a language tag like und-u-kr-digit-currency-space) sorts punctuation before digits and spaces.

Defaults may depend on locale. The above table is not meant to be complete. See Section 23.2.3.5 for additional options and details.

For many collation settings, you must create the collation with deterministic set to false for the setting to have the desired effect (see Section 23.2.2.4). Additionally, some settings only take effect when the key ka is set to shifted (see Table 23.2).

German collation with phone book collation type

Root collation with Emoji collation type, per Unicode Technical Standard #51

Sort Greek letters before Latin ones. (The default is Latin before Greek.)

Sort upper-case letters before lower-case letters. (The default is lower-case letters first.)

Combines both of the above options.

If the options provided by the collation settings shown above are not sufficient, the order of collation elements can be changed with tailoring rules, whose syntax is detailed at https://unicode-org.github.io/icu/userguide/collation/customization/.

This small example creates a collation based on the root locale with a tailoring rule:

With this rule, the letter “W” is sorted after “V”, but is treated as a secondary difference similar to an accent. Rules like this are contained in the locale definitions of some languages. (Of course, if a locale definition already contains the desired rules, then they don't need to be specified again explicitly.)

Here is a more complex example. The following statement sets up a collation named ebcdic with rules to sort US-ASCII characters in the order of the EBCDIC encoding.

This section (Section 23.2.3) is only a brief overview of ICU behavior and language tags. Refer to the following documents for technical details, additional options, and new behavior:

Unicode Technical Standard #35

https://unicode-org.github.io/icu/userguide/locale/

https://unicode-org.github.io/icu/userguide/collation/

Examples:

Example 1 (unknown):

CREATE TABLE test1 (
    a text COLLATE "de_DE",
    b text COLLATE "es_ES",
    ...
);

Example 2 (unknown):

SELECT a < 'foo' FROM test1;

Example 3 (unknown):

SELECT a < ('foo' COLLATE "fr_FR") FROM test1;

Example 4 (unknown):

SELECT a < b FROM test1;

---

PostgreSQL: Documentation: 18: Chapter 30. Just-in-Time Compilation (JIT)

URL: https://www.postgresql.org/docs/current/jit.html

Contents:

• Chapter 30. Just-in-Time Compilation (JIT)

This chapter explains what just-in-time compilation is, and how it can be configured in PostgreSQL.

---

PostgreSQL: Documentation: 18: 36.10. C-Language Functions

URL: https://www.postgresql.org/docs/current/xfunc-c.html

Contents:

• 36.10. C-Language Functions #
  • 36.10.1. Dynamic Loading #
  • Note
  • 36.10.2. Base Types in C-Language Functions #
  • Warning
  • 36.10.3. Version 1 Calling Conventions #
  • 36.10.4. Writing Code #
  • 36.10.5. Compiling and Linking Dynamically-Loaded Functions #
  • Tip
  • 36.10.6. Server API and ABI Stability Guidance #

User-defined functions can be written in C (or a language that can be made compatible with C, such as C++). Such functions are compiled into dynamically loadable objects (also called shared libraries) and are loaded by the server on demand. The dynamic loading feature is what distinguishes “C language” functions from “internal” functions — the actual coding conventions are essentially the same for both. (Hence, the standard internal function library is a rich source of coding examples for user-defined C functions.)

Currently only one calling convention is used for C functions (“version 1”). Support for that calling convention is indicated by writing a PG_FUNCTION_INFO_V1() macro call for the function, as illustrated below.

The first time a user-defined function in a particular loadable object file is called in a session, the dynamic loader loads that object file into memory so that the function can be called. The CREATE FUNCTION for a user-defined C function must therefore specify two pieces of information for the function: the name of the loadable object file, and the C name (link symbol) of the specific function to call within that object file. If the C name is not explicitly specified then it is assumed to be the same as the SQL function name.

The following algorithm is used to locate the shared object file based on the name given in the CREATE FUNCTION command:

If the name is an absolute path, the given file is loaded.

If the name starts with the string $libdir, that part is replaced by the PostgreSQL package library directory name, which is determined at build time.

If the name does not contain a directory part, the file is searched for in the path specified by the configuration variable dynamic_library_path.

Otherwise (the file was not found in the path, or it contains a non-absolute directory part), the dynamic loader will try to take the name as given, which will most likely fail. (It is unreliable to depend on the current working directory.)

If this sequence does not work, the platform-specific shared library file name extension (often .so) is appended to the given name and this sequence is tried again. If that fails as well, the load will fail.

It is recommended to locate shared libraries either relative to $libdir or through the dynamic library path. This simplifies version upgrades if the new installation is at a different location. The actual directory that $libdir stands for can be found out with the command pg_config --pkglibdir.

The user ID the PostgreSQL server runs as must be able to traverse the path to the file you intend to load. Making the file or a higher-level directory not readable and/or not executable by the postgres user is a common mistake.

In any case, the file name that is given in the CREATE FUNCTION command is recorded literally in the system catalogs, so if the file needs to be loaded again the same procedure is applied.

PostgreSQL will not compile a C function automatically. The object file must be compiled before it is referenced in a CREATE FUNCTION command. See Section 36.10.5 for additional information.

To ensure that a dynamically loaded object file is not loaded into an incompatible server, PostgreSQL checks that the file contains a “magic block” with the appropriate contents. This allows the server to detect obvious incompatibilities, such as code compiled for a different major version of PostgreSQL. To include a magic block, write this in one (and only one) of the module source files, after having included the header fmgr.h:

The PG_MODULE_MAGIC_EXT variant allows the specification of additional information about the module; currently, a name and/or a version string can be added. (More fields might be allowed in future.) Write something like this:

Subsequently the name and version can be examined via the pg_get_loaded_modules() function. The meaning of the version string is not restricted by PostgreSQL, but use of semantic versioning rules is recommended.

After it is used for the first time, a dynamically loaded object file is retained in memory. Future calls in the same session to the function(s) in that file will only incur the small overhead of a symbol table lookup. If you need to force a reload of an object file, for example after recompiling it, begin a fresh session.

Optionally, a dynamically loaded file can contain an initialization function. If the file includes a function named _PG_init, that function will be called immediately after loading the file. The function receives no parameters and should return void. There is presently no way to unload a dynamically loaded file.

To know how to write C-language functions, you need to know how PostgreSQL internally represents base data types and how they can be passed to and from functions. Internally, PostgreSQL regards a base type as a “blob of memory”. The user-defined functions that you define over a type in turn define the way that PostgreSQL can operate on it. That is, PostgreSQL will only store and retrieve the data from disk and use your user-defined functions to input, process, and output the data.

Base types can have one of three internal formats:

pass by value, fixed-length

pass by reference, fixed-length

pass by reference, variable-length

By-value types can only be 1, 2, or 4 bytes in length (also 8 bytes, if sizeof(Datum) is 8 on your machine). You should be careful to define your types such that they will be the same size (in bytes) on all architectures. For example, the long type is dangerous because it is 4 bytes on some machines and 8 bytes on others, whereas int type is 4 bytes on most Unix machines. A reasonable implementation of the int4 type on Unix machines might be:

(The actual PostgreSQL C code calls this type int32, because it is a convention in C that intXX means XX bits. Note therefore also that the C type int8 is 1 byte in size. The SQL type int8 is called int64 in C. See also Table 36.2.)

On the other hand, fixed-length types of any size can be passed by-reference. For example, here is a sample implementation of a PostgreSQL type:

Only pointers to such types can be used when passing them in and out of PostgreSQL functions. To return a value of such a type, allocate the right amount of memory with palloc, fill in the allocated memory, and return a pointer to it. (Also, if you just want to return the same value as one of your input arguments that's of the same data type, you can skip the extra palloc and just return the pointer to the input value.)

Finally, all variable-length types must also be passed by reference. All variable-length types must begin with an opaque length field of exactly 4 bytes, which will be set by SET_VARSIZE; never set this field directly! All data to be stored within that type must be located in the memory immediately following that length field. The length field contains the total length of the structure, that is, it includes the size of the length field itself.

Another important point is to avoid leaving any uninitialized bits within data type values; for example, take care to zero out any alignment padding bytes that might be present in structs. Without this, logically-equivalent constants of your data type might be seen as unequal by the planner, leading to inefficient (though not incorrect) plans.

Never modify the contents of a pass-by-reference input value. If you do so you are likely to corrupt on-disk data, since the pointer you are given might point directly into a disk buffer. The sole exception to this rule is explained in Section 36.12.

As an example, we can define the type text as follows:

The [FLEXIBLE_ARRAY_MEMBER] notation means that the actual length of the data part is not specified by this declaration.

When manipulating variable-length types, we must be careful to allocate the correct amount of memory and set the length field correctly. For example, if we wanted to store 40 bytes in a text structure, we might use a code fragment like this:

VARHDRSZ is the same as sizeof(int32), but it's considered good style to use the macro VARHDRSZ to refer to the size of the overhead for a variable-length type. Also, the length field must be set using the SET_VARSIZE macro, not by simple assignment.

Table 36.2 shows the C types corresponding to many of the built-in SQL data types of PostgreSQL. The “Defined In” column gives the header file that needs to be included to get the type definition. (The actual definition might be in a different file that is included by the listed file. It is recommended that users stick to the defined interface.) Note that you should always include postgres.h first in any source file of server code, because it declares a number of things that you will need anyway, and because including other headers first can cause portability issues.

Table 36.2. Equivalent C Types for Built-in SQL Types

Now that we've gone over all of the possible structures for base types, we can show some examples of real functions.

The version-1 calling convention relies on macros to suppress most of the complexity of passing arguments and results. The C declaration of a version-1 function is always:

In addition, the macro call:

must appear in the same source file. (Conventionally, it's written just before the function itself.) This macro call is not needed for internal-language functions, since PostgreSQL assumes that all internal functions use the version-1 convention. It is, however, required for dynamically-loaded functions.

In a version-1 function, each actual argument is fetched using a PG_GETARG_xxx() macro that corresponds to the argument's data type. (In non-strict functions there needs to be a previous check about argument null-ness using PG_ARGISNULL(); see below.) The result is returned using a PG_RETURN_xxx() macro for the return type. PG_GETARG_xxx() takes as its argument the number of the function argument to fetch, where the count starts at 0. PG_RETURN_xxx() takes as its argument the actual value to return.

To call another version-1 function, you can use DirectFunctionCalln(func, arg1, ..., argn). This is particularly useful when you want to call functions defined in the standard internal library, by using an interface similar to their SQL signature.

These convenience functions and similar ones can be found in fmgr.h. The DirectFunctionCalln family expect a C function name as their first argument. There are also OidFunctionCalln which take the OID of the target function, and some other variants. All of these expect the function's arguments to be supplied as Datums, and likewise they return Datum. Note that neither arguments nor result are allowed to be NULL when using these convenience functions.

For example, to call the starts_with(text, text) function from C, you can search through the catalog and find out that its C implementation is the Datum text_starts_with(PG_FUNCTION_ARGS) function. Typically you would use DirectFunctionCall2(text_starts_with, ...) to call such a function. However, starts_with(text, text) requires collation information, so it will fail with “could not determine which collation to use for string comparison” if called that way. Instead you must use DirectFunctionCall2Coll(text_starts_with, ...) and provide the desired collation, which typically is just passed through from PG_GET_COLLATION(), as shown in the example below.

fmgr.h also supplies macros that facilitate conversions between C types and Datum. For example to turn Datum into text, you can use DatumGetTextPP(X). While some types have macros named like TypeGetDatum(X) for the reverse conversion, text does not; it's sufficient to use the generic macro PointerGetDatum(X) for that. If your extension defines additional types, it is usually convenient to define similar macros for your types too.

Here are some examples using the version-1 calling convention:

Supposing that the above code has been prepared in file funcs.c and compiled into a shared object, we could define the functions to PostgreSQL with commands like this:

Here, DIRECTORY stands for the directory of the shared library file (for instance the PostgreSQL tutorial directory, which contains the code for the examples used in this section). (Better style would be to use just 'funcs' in the AS clause, after having added DIRECTORY to the search path. In any case, we can omit the system-specific extension for a shared library, commonly .so.)

Notice that we have specified the functions as “strict”, meaning that the system should automatically assume a null result if any input value is null. By doing this, we avoid having to check for null inputs in the function code. Without this, we'd have to check for null values explicitly, using PG_ARGISNULL().

The macro PG_ARGISNULL(n) allows a function to test whether each input is null. (Of course, doing this is only necessary in functions not declared “strict”.) As with the PG_GETARG_xxx() macros, the input arguments are counted beginning at zero. Note that one should refrain from executing PG_GETARG_xxx() until one has verified that the argument isn't null. To return a null result, execute PG_RETURN_NULL(); this works in both strict and nonstrict functions.

At first glance, the version-1 coding conventions might appear to be just pointless obscurantism, compared to using plain C calling conventions. They do however allow us to deal with NULLable arguments/return values, and “toasted” (compressed or out-of-line) values.

Other options provided by the version-1 interface are two variants of the PG_GETARG_xxx() macros. The first of these, PG_GETARG_xxx_COPY(), guarantees to return a copy of the specified argument that is safe for writing into. (The normal macros will sometimes return a pointer to a value that is physically stored in a table, which must not be written to. Using the PG_GETARG_xxx_COPY() macros guarantees a writable result.) The second variant consists of the PG_GETARG_xxx_SLICE() macros which take three arguments. The first is the number of the function argument (as above). The second and third are the offset and length of the segment to be returned. Offsets are counted from zero, and a negative length requests that the remainder of the value be returned. These macros provide more efficient access to parts of large values in the case where they have storage type “external”. (The storage type of a column can be specified using ALTER TABLE tablename ALTER COLUMN colname SET STORAGE storagetype. storagetype is one of plain, external, extended, or main.)

Finally, the version-1 function call conventions make it possible to return set results (Section 36.10.9) and implement trigger functions (Chapter 37) and procedural-language call handlers (Chapter 57). For more details see src/backend/utils/fmgr/README in the source distribution.

Before we turn to the more advanced topics, we should discuss some coding rules for PostgreSQL C-language functions. While it might be possible to load functions written in languages other than C into PostgreSQL, this is usually difficult (when it is possible at all) because other languages, such as C++, FORTRAN, or Pascal often do not follow the same calling convention as C. That is, other languages do not pass argument and return values between functions in the same way. For this reason, we will assume that your C-language functions are actually written in C.

The basic rules for writing and building C functions are as follows:

Use pg_config --includedir-server to find out where the PostgreSQL server header files are installed on your system (or the system that your users will be running on).

Compiling and linking your code so that it can be dynamically loaded into PostgreSQL always requires special flags. See Section 36.10.5 for a detailed explanation of how to do it for your particular operating system.

Remember to define a “magic block” for your shared library, as described in Section 36.10.1.

When allocating memory, use the PostgreSQL functions palloc and pfree instead of the corresponding C library functions malloc and free. The memory allocated by palloc will be freed automatically at the end of each transaction, preventing memory leaks.

Always zero the bytes of your structures using memset (or allocate them with palloc0 in the first place). Even if you assign to each field of your structure, there might be alignment padding (holes in the structure) that contain garbage values. Without this, it's difficult to support hash indexes or hash joins, as you must pick out only the significant bits of your data structure to compute a hash. The planner also sometimes relies on comparing constants via bitwise equality, so you can get undesirable planning results if logically-equivalent values aren't bitwise equal.

Most of the internal PostgreSQL types are declared in postgres.h, while the function manager interfaces (PG_FUNCTION_ARGS, etc.) are in fmgr.h, so you will need to include at least these two files. For portability reasons it's best to include postgres.h first, before any other system or user header files. Including postgres.h will also include elog.h and palloc.h for you.

Symbol names defined within object files must not conflict with each other or with symbols defined in the PostgreSQL server executable. You will have to rename your functions or variables if you get error messages to this effect.

Before you are able to use your PostgreSQL extension functions written in C, they must be compiled and linked in a special way to produce a file that can be dynamically loaded by the server. To be precise, a shared library needs to be created.

For information beyond what is contained in this section you should read the documentation of your operating system, in particular the manual pages for the C compiler, cc, and the link editor, ld. In addition, the PostgreSQL source code contains several working examples in the contrib directory. If you rely on these examples you will make your modules dependent on the availability of the PostgreSQL source code, however.

Creating shared libraries is generally analogous to linking executables: first the source files are compiled into object files, then the object files are linked together. The object files need to be created as position-independent code (PIC), which conceptually means that they can be placed at an arbitrary location in memory when they are loaded by the executable. (Object files intended for executables are usually not compiled that way.) The command to link a shared library contains special flags to distinguish it from linking an executable (at least in theory — on some systems the practice is much uglier).

In the following examples we assume that your source code is in a file foo.c and we will create a shared library foo.so. The intermediate object file will be called foo.o unless otherwise noted. A shared library can contain more than one object file, but we only use one here.

The compiler flag to create PIC is -fPIC. To create shared libraries the compiler flag is -shared.

This is applicable as of version 13.0 of FreeBSD, older versions used the gcc compiler.

The compiler flag to create PIC is -fPIC. The compiler flag to create a shared library is -shared. A complete example looks like this:

Here is an example. It assumes the developer tools are installed.

The compiler flag to create PIC is -fPIC. For ELF systems, the compiler with the flag -shared is used to link shared libraries. On the older non-ELF systems, ld -Bshareable is used.

The compiler flag to create PIC is -fPIC. ld -Bshareable is used to link shared libraries.

The compiler flag to create PIC is -KPIC with the Sun compiler and -fPIC with GCC. To link shared libraries, the compiler option is -G with either compiler or alternatively -shared with GCC.

If this is too complicated for you, you should consider using GNU Libtool, which hides the platform differences behind a uniform interface.

The resulting shared library file can then be loaded into PostgreSQL. When specifying the file name to the CREATE FUNCTION command, one must give it the name of the shared library file, not the intermediate object file. Note that the system's standard shared-library extension (usually .so or .sl) can be omitted from the CREATE FUNCTION command, and normally should be omitted for best portability.

Refer back to Section 36.10.1 about where the server expects to find the shared library files.

This section contains guidance to authors of extensions and other server plugins about API and ABI stability in the PostgreSQL server.

The PostgreSQL server contains several well-demarcated APIs for server plugins, such as the function manager (fmgr, described in this chapter), SPI (Chapter 45), and various hooks specifically designed for extensions. These interfaces are carefully managed for long-term stability and compatibility. However, the entire set of global functions and variables in the server effectively constitutes the publicly usable API, and most of it was not designed with extensibility and long-term stability in mind.

Therefore, while taking advantage of these interfaces is valid, the further one strays from the well-trodden path, the likelier it will be that one might encounter API or ABI compatibility issues at some point. Extension authors are encouraged to provide feedback about their requirements, so that over time, as new use patterns arise, certain interfaces can be considered more stabilized or new, better-designed interfaces can be added.

The API, or application programming interface, is the interface used at compile time.

There is no promise of API compatibility between PostgreSQL major versions. Extension code therefore might require source code changes to work with multiple major versions. These can usually be managed with preprocessor conditions such as #if PG_VERSION_NUM >= 160000. Sophisticated extensions that use interfaces beyond the well-demarcated ones usually require a few such changes for each major server version.

PostgreSQL makes an effort to avoid server API breaks in minor releases. In general, extension code that compiles and works with a minor release should also compile and work with any other minor release of the same major version, past or future.

When a change is required, it will be carefully managed, taking the requirements of extensions into account. Such changes will be communicated in the release notes (Appendix E).

The ABI, or application binary interface, is the interface used at run time.

Servers of different major versions have intentionally incompatible ABIs. Extensions that use server APIs must therefore be re-compiled for each major release. The inclusion of PG_MODULE_MAGIC (see Section 36.10.1) ensures that code compiled for one major version will be rejected by other major versions.

PostgreSQL makes an effort to avoid server ABI breaks in minor releases. In general, an extension compiled against any minor release should work with any other minor release of the same major version, past or future.

When a change is required, PostgreSQL will choose the least invasive change possible, for example by squeezing a new field into padding space or appending it to the end of a struct. These sorts of changes should not impact extensions unless they use very unusual code patterns.

In rare cases, however, even such non-invasive changes may be impractical or impossible. In such an event, the change will be carefully managed, taking the requirements of extensions into account. Such changes will also be documented in the release notes (Appendix E).

Note, however, that many parts of the server are not designed or maintained as publicly-consumable APIs (and that, in most cases, the actual boundary is also not well-defined). If urgent needs arise, changes in those parts will naturally be made with less consideration for extension code than changes in well-defined and widely used interfaces.

Also, in the absence of automated detection of such changes, this is not a guarantee, but historically such breaking changes have been extremely rare.

Composite types do not have a fixed layout like C structures. Instances of a composite type can contain null fields. In addition, composite types that are part of an inheritance hierarchy can have different fields than other members of the same inheritance hierarchy. Therefore, PostgreSQL provides a function interface for accessing fields of composite types from C.

Suppose we want to write a function to answer the query:

Using the version-1 calling conventions, we can define c_overpaid as:

GetAttributeByName is the PostgreSQL system function that returns attributes out of the specified row. It has three arguments: the argument of type HeapTupleHeader passed into the function, the name of the desired attribute, and a return parameter that tells whether the attribute is null. GetAttributeByName returns a Datum value that you can convert to the proper data type by using the appropriate DatumGetXXX() function. Note that the return value is meaningless if the null flag is set; always check the null flag before trying to do anything with the result.

There is also GetAttributeByNum, which selects the target attribute by column number instead of name.

The following command declares the function c_overpaid in SQL:

Notice we have used STRICT so that we did not have to check whether the input arguments were NULL.

To return a row or composite-type value from a C-language function, you can use a special API that provides macros and functions to hide most of the complexity of building composite data types. To use this API, the source file must include:

There are two ways you can build a composite data value (henceforth a “tuple”): you can build it from an array of Datum values, or from an array of C strings that can be passed to the input conversion functions of the tuple's column data types. In either case, you first need to obtain or construct a TupleDesc descriptor for the tuple structure. When working with Datums, you pass the TupleDesc to BlessTupleDesc, and then call heap_form_tuple for each row. When working with C strings, you pass the TupleDesc to TupleDescGetAttInMetadata, and then call BuildTupleFromCStrings for each row. In the case of a function returning a set of tuples, the setup steps can all be done once during the first call of the function.

Several helper functions are available for setting up the needed TupleDesc. The recommended way to do this in most functions returning composite values is to call:

passing the same fcinfo struct passed to the calling function itself. (This of course requires that you use the version-1 calling conventions.) resultTypeId can be specified as NULL or as the address of a local variable to receive the function's result type OID. resultTupleDesc should be the address of a local TupleDesc variable. Check that the result is TYPEFUNC_COMPOSITE; if so, resultTupleDesc has been filled with the needed TupleDesc. (If it is not, you can report an error along the lines of “function returning record called in context that cannot accept type record”.)

get_call_result_type can resolve the actual type of a polymorphic function result; so it is useful in functions that return scalar polymorphic results, not only functions that return composites. The resultTypeId output is primarily useful for functions returning polymorphic scalars.

get_call_result_type has a sibling get_expr_result_type, which can be used to resolve the expected output type for a function call represented by an expression tree. This can be used when trying to determine the result type from outside the function itself. There is also get_func_result_type, which can be used when only the function's OID is available. However these functions are not able to deal with functions declared to return record, and get_func_result_type cannot resolve polymorphic types, so you should preferentially use get_call_result_type.

Older, now-deprecated functions for obtaining TupleDescs are:

to get a TupleDesc for the row type of a named relation, and:

to get a TupleDesc based on a type OID. This can be used to get a TupleDesc for a base or composite type. It will not work for a function that returns record, however, and it cannot resolve polymorphic types.

Once you have a TupleDesc, call:

if you plan to work with Datums, or:

if you plan to work with C strings. If you are writing a function returning set, you can save the results of these functions in the FuncCallContext structure — use the tuple_desc or attinmeta field respectively.

When working with Datums, use:

to build a HeapTuple given user data in Datum form.

When working with C strings, use:

to build a HeapTuple given user data in C string form. values is an array of C strings, one for each attribute of the return row. Each C string should be in the form expected by the input function of the attribute data type. In order to return a null value for one of the attributes, the corresponding pointer in the values array should be set to NULL. This function will need to be called again for each row you return.

Once you have built a tuple to return from your function, it must be converted into a Datum. Use:

to convert a HeapTuple into a valid Datum. This Datum can be returned directly if you intend to return just a single row, or it can be used as the current return value in a set-returning function.

An example appears in the next section.

C-language functions have two options for returning sets (multiple rows). In one method, called ValuePerCall mode, a set-returning function is called repeatedly (passing the same arguments each time) and it returns one new row on each call, until it has no more rows to return and signals that by returning NULL. The set-returning function (SRF) must therefore save enough state across calls to remember what it was doing and return the correct next item on each call. In the other method, called Materialize mode, an SRF fills and returns a tuplestore object containing its entire result; then only one call occurs for the whole result, and no inter-call state is needed.

When using ValuePerCall mode, it is important to remember that the query is not guaranteed to be run to completion; that is, due to options such as LIMIT, the executor might stop making calls to the set-returning function before all rows have been fetched. This means it is not safe to perform cleanup activities in the last call, because that might not ever happen. It's recommended to use Materialize mode for functions that need access to external resources, such as file descriptors.

The remainder of this section documents a set of helper macros that are commonly used (though not required to be used) for SRFs using ValuePerCall mode. Additional details about Materialize mode can be found in src/backend/utils/fmgr/README. Also, the contrib modules in the PostgreSQL source distribution contain many examples of SRFs using both ValuePerCall and Materialize mode.

To use the ValuePerCall support macros described here, include funcapi.h. These macros work with a structure FuncCallContext that contains the state that needs to be saved across calls. Within the calling SRF, fcinfo->flinfo->fn_extra is used to hold a pointer to FuncCallContext across calls. The macros automatically fill that field on first use, and expect to find the same pointer there on subsequent uses.

The macros to be used by an SRF using this infrastructure are:

Use this to determine if your function is being called for the first or a subsequent time. On the first call (only), call:

to initialize the FuncCallContext. On every function call, including the first, call:

to set up for using the FuncCallContext.

If your function has data to return in the current call, use:

to return it to the caller. (result must be of type Datum, either a single value or a tuple prepared as described above.) Finally, when your function is finished returning data, use:

to clean up and end the SRF.

The memory context that is current when the SRF is called is a transient context that will be cleared between calls. This means that you do not need to call pfree on everything you allocated using palloc; it will go away anyway. However, if you want to allocate any data structures to live across calls, you need to put them somewhere else. The memory context referenced by multi_call_memory_ctx is a suitable location for any data that needs to survive until the SRF is finished running. In most cases, this means that you should switch into multi_call_memory_ctx while doing the first-call setup. Use funcctx->user_fctx to hold a pointer to any such cross-call data structures. (Data you allocate in multi_call_memory_ctx will go away automatically when the query ends, so it is not necessary to free that data manually, either.)

While the actual arguments to the function remain unchanged between calls, if you detoast the argument values (which is normally done transparently by the PG_GETARG_xxx macro) in the transient context then the detoasted copies will be freed on each cycle. Accordingly, if you keep references to such values in your user_fctx, you must either copy them into the multi_call_memory_ctx after detoasting, or ensure that you detoast the values only in that context.

A complete pseudo-code example looks like the following:

A complete example of a simple SRF returning a composite type looks like:

One way to declare this function in SQL is:

A different way is to use OUT parameters:

Notice that in this method the output type of the function is formally an anonymous record type.

C-language functions can be declared to accept and return the polymorphic types described in Section 36.2.5. When a function's arguments or return types are defined as polymorphic types, the function author cannot know in advance what data type it will be called with, or need to return. There are two routines provided in fmgr.h to allow a version-1 C function to discover the actual data types of its arguments and the type it is expected to return. The routines are called get_fn_expr_rettype(FmgrInfo *flinfo) and get_fn_expr_argtype(FmgrInfo *flinfo, int argnum). They return the result or argument type OID, or InvalidOid if the information is not available. The structure flinfo is normally accessed as fcinfo->flinfo. The parameter argnum is zero based. get_call_result_type can also be used as an alternative to get_fn_expr_rettype. There is also get_fn_expr_variadic, which can be used to find out whether variadic arguments have been merged into an array. This is primarily useful for VARIADIC "any" functions, since such merging will always have occurred for variadic functions taking ordinary array types.

For example, suppose we want to write a function to accept a single element of any type, and return a one-dimensional array of that type:

The following command declares the function make_array in SQL:

There is a variant of polymorphism that is only available to C-language functions: they can be declared to take parameters of type "any". (Note that this type name must be double-quoted, since it's also an SQL reserved word.) This works like anyelement except that it does not constrain different "any" arguments to be the same type, nor do they help determine the function's result type. A C-language function can also declare its final parameter to be VARIADIC "any". This will match one or more actual arguments of any type (not necessarily the same type). These arguments will not be gathered into an array as happens with normal variadic functions; they will just be passed to the function separately. The PG_NARGS() macro and the methods described above must be used to determine the number of actual arguments and their types when using this feature. Also, users of such a function might wish to use the VARIADIC keyword in their function call, with the expectation that the function would treat the array elements as separate arguments. The function itself must implement that behavior if wanted, after using get_fn_expr_variadic to detect that the actual argument was marked with VARIADIC.

Add-ins can reserve shared memory on server startup. To do so, the add-in's shared library must be preloaded by specifying it in shared_preload_libraries. The shared library should also register a shmem_request_hook in its _PG_init function. This shmem_request_hook can reserve shared memory by calling:

Each backend should obtain a pointer to the reserved shared memory by calling:

If this function sets foundPtr to false, the caller should proceed to initialize the contents of the reserved shared memory. If foundPtr is set to true, the shared memory was already initialized by another backend, and the caller need not initialize further.

To avoid race conditions, each backend should use the LWLock AddinShmemInitLock when initializing its allocation of shared memory, as shown here:

shmem_startup_hook provides a convenient place for the initialization code, but it is not strictly required that all such code be placed in this hook. On Windows (and anywhere else where EXEC_BACKEND is defined), each backend executes the registered shmem_startup_hook shortly after it attaches to shared memory, so add-ins should still acquire AddinShmemInitLock within this hook, as shown in the example above. On other platforms, only the postmaster process executes the shmem_startup_hook, and each backend automatically inherits the pointers to shared memory.

An example of a shmem_request_hook and shmem_startup_hook can be found in contrib/pg_stat_statements/pg_stat_statements.c in the PostgreSQL source tree.

There is another, more flexible method of reserving shared memory that can be done after server startup and outside a shmem_request_hook. To do so, each backend that will use the shared memory should obtain a pointer to it by calling:

If a dynamic shared memory segment with the given name does not yet exist, this function will allocate it and initialize it with the provided init_callback callback function. If the segment has already been allocated and initialized by another backend, this function simply attaches the existing dynamic shared memory segment to the current backend.

Unlike shared memory reserved at server startup, there is no need to acquire AddinShmemInitLock or otherwise take action to avoid race conditions when reserving shared memory with GetNamedDSMSegment. This function ensures that only one backend allocates and initializes the segment and that all other backends receive a pointer to the fully allocated and initialized segment.

A complete usage example of GetNamedDSMSegment can be found in src/test/modules/test_dsm_registry/test_dsm_registry.c in the PostgreSQL source tree.

Add-ins can reserve LWLocks on server startup. As with shared memory reserved at server startup, the add-in's shared library must be preloaded by specifying it in shared_preload_libraries, and the shared library should register a shmem_request_hook in its _PG_init function. This shmem_request_hook can reserve LWLocks by calling:

This ensures that an array of num_lwlocks LWLocks is available under the name tranche_name. A pointer to this array can be obtained by calling:

There is another, more flexible method of obtaining LWLocks that can be done after server startup and outside a shmem_request_hook. To do so, first allocate a tranche_id by calling:

Next, initialize each LWLock, passing the new tranche_id as an argument:

Similar to shared memory, each backend should ensure that only one process allocates a new tranche_id and initializes each new LWLock. One way to do this is to only call these functions in your shared memory initialization code with the AddinShmemInitLock held exclusively. If using GetNamedDSMSegment, calling these functions in the init_callback callback function is sufficient to avoid race conditions.

Finally, each backend using the tranche_id should associate it with a tranche_name by calling:

A complete usage example of LWLockNewTrancheId, LWLockInitialize, and LWLockRegisterTranche can be found in contrib/pg_prewarm/autoprewarm.c in the PostgreSQL source tree.

Add-ins can define custom wait events under the wait event type Extension by calling:

The wait event is associated to a user-facing custom string. An example can be found in src/test/modules/worker_spi in the PostgreSQL source tree.

Custom wait events can be viewed in pg_stat_activity:

An injection point with a given name is declared using macro:

There are a few injection points already declared at strategic points within the server code. After adding a new injection point the code needs to be compiled in order for that injection point to be available in the binary. Add-ins written in C-language can declare injection points in their own code using the same macro. The injection point names should use lower-case characters, with terms separated by dashes. arg is an optional argument value given to the callback at run-time.

Executing an injection point can require allocating a small amount of memory, which can fail. If you need to have an injection point in a critical section where dynamic allocations are not allowed, you can use a two-step approach with the following macros:

Before entering the critical section, call INJECTION_POINT_LOAD. It checks the shared memory state, and loads the callback into backend-private memory if it is active. Inside the critical section, use INJECTION_POINT_CACHED to execute the callback.

Add-ins can attach callbacks to an already-declared injection point by calling:

name is the name of the injection point, which when reached during execution will execute the function loaded from library. private_data is a private area of data of size private_data_size given as argument to the callback when executed.

Here is an example of callback for InjectionPointCallback:

This callback prints a message to server error log with severity NOTICE, but callbacks may implement more complex logic.

An alternative way to define the action to take when an injection point is reached is to add the testing code alongside the normal source code. This can be useful if the action e.g. depends on local variables that are not accessible to loaded modules. The IS_INJECTION_POINT_ATTACHED macro can then be used to check if an injection point is attached, for example:

Note that the callback attached to the injection point will not be executed by the IS_INJECTION_POINT_ATTACHED macro. If you want to execute the callback, you must also call INJECTION_POINT_CACHED like in the above example.

Optionally, it is possible to detach an injection point by calling:

On success, true is returned, false otherwise.

A callback attached to an injection point is available across all the backends including the backends started after InjectionPointAttach is called. It remains attached while the server is running or until the injection point is detached using InjectionPointDetach.

An example can be found in src/test/modules/injection_points in the PostgreSQL source tree.

Enabling injections points requires --enable-injection-points with configure or -Dinjection_points=true with Meson.

It is possible for add-ins written in C-language to use custom types of cumulative statistics registered in the Cumulative Statistics System.

First, define a PgStat_KindInfo that includes all the information related to the custom type registered. For example:

Then, each backend that needs to use this custom type needs to register it with pgstat_register_kind and a unique ID used to store the entries related to this type of statistics:

While developing a new extension, use PGSTAT_KIND_EXPERIMENTAL for kind. When you are ready to release the extension to users, reserve a kind ID at the Custom Cumulative Statistics page.

The details of the API for PgStat_KindInfo can be found in src/include/utils/pgstat_internal.h.

The type of statistics registered is associated with a name and a unique ID shared across the server in shared memory. Each backend using a custom type of statistics maintains a local cache storing the information of each custom PgStat_KindInfo.

Place the extension module implementing the custom cumulative statistics type in shared_preload_libraries so that it will be loaded early during PostgreSQL startup.

An example describing how to register and use custom statistics can be found in src/test/modules/injection_points.

Although the PostgreSQL backend is written in C, it is possible to write extensions in C++ if these guidelines are followed:

All functions accessed by the backend must present a C interface to the backend; these C functions can then call C++ functions. For example, extern C linkage is required for backend-accessed functions. This is also necessary for any functions that are passed as pointers between the backend and C++ code.

Free memory using the appropriate deallocation method. For example, most backend memory is allocated using palloc(), so use pfree() to free it. Using C++ delete in such cases will fail.

Prevent exceptions from propagating into the C code (use a catch-all block at the top level of all extern C functions). This is necessary even if the C++ code does not explicitly throw any exceptions, because events like out-of-memory can still throw exceptions. Any exceptions must be caught and appropriate errors passed back to the C interface. If possible, compile C++ with -fno-exceptions to eliminate exceptions entirely; in such cases, you must check for failures in your C++ code, e.g., check for NULL returned by new().

If calling backend functions from C++ code, be sure that the C++ call stack contains only plain old data structures (POD). This is necessary because backend errors generate a distant longjmp() that does not properly unroll a C++ call stack with non-POD objects.

In summary, it is best to place C++ code behind a wall of extern C functions that interface to the backend, and avoid exception, memory, and call stack leakage.

Examples:

Example 1 (unknown):

PG_MODULE_MAGIC;

Example 2 (unknown):

PG_MODULE_MAGIC_EXT(parameters);

Example 3 (unknown):

PG_MODULE_MAGIC_EXT(
    .name = "my_module_name",
    .version = "1.2.3"
);

Example 4 (unknown):

/* 4-byte integer, passed by value */
typedef int int4;

---

PostgreSQL: Documentation: 18: Chapter 61. Genetic Query Optimizer

URL: https://www.postgresql.org/docs/current/geqo.html

Contents:

• Chapter 61. Genetic Query Optimizer
  • Author

Written by Martin Utesch (utesch@aut.tu-freiberg.de) for the Institute of Automatic Control at the University of Mining and Technology in Freiberg, Germany.

---

PostgreSQL: Documentation: 18: Chapter 19. Server Configuration

URL: https://www.postgresql.org/docs/current/runtime-config.html

Contents:

• Chapter 19. Server Configuration

There are many configuration parameters that affect the behavior of the database system. In the first section of this chapter we describe how to interact with configuration parameters. The subsequent sections discuss each parameter in detail.

---

PostgreSQL: Documentation: 18: VAR

URL: https://www.postgresql.org/docs/current/ecpg-sql-var.html

Contents:

• VAR
• Synopsis
• Description
• Parameters
• Examples
• Compatibility

VAR — define a variable

The VAR command assigns a new C data type to a host variable. The host variable must be previously declared in a declare section.

A C type specification.

The VAR command is a PostgreSQL extension.

Examples:

Example 1 (unknown):

VAR varname IS ctype

Example 2 (unknown):

Exec sql begin declare section;
short a;
exec sql end declare section;
EXEC SQL VAR a IS int;

---

PostgreSQL: Documentation: 18: 26.4. Hot Standby

URL: https://www.postgresql.org/docs/current/hot-standby.html

Contents:

• 26.4. Hot Standby #
  • 26.4.1. User's Overview #
  • 26.4.2. Handling Query Conflicts #
  • 26.4.3. Administrator's Overview #
  • 26.4.4. Hot Standby Parameter Reference #
  • 26.4.5. Caveats #

Hot standby is the term used to describe the ability to connect to the server and run read-only queries while the server is in archive recovery or standby mode. This is useful both for replication purposes and for restoring a backup to a desired state with great precision. The term hot standby also refers to the ability of the server to move from recovery through to normal operation while users continue running queries and/or keep their connections open.

Running queries in hot standby mode is similar to normal query operation, though there are several usage and administrative differences explained below.

When the hot_standby parameter is set to true on a standby server, it will begin accepting connections once the recovery has brought the system to a consistent state and be ready for hot standby. All such connections are strictly read-only; not even temporary tables may be written.

The data on the standby takes some time to arrive from the primary server so there will be a measurable delay between primary and standby. Running the same query nearly simultaneously on both primary and standby might therefore return differing results. We say that data on the standby is eventually consistent with the primary. Once the commit record for a transaction is replayed on the standby, the changes made by that transaction will be visible to any new snapshots taken on the standby. Snapshots may be taken at the start of each query or at the start of each transaction, depending on the current transaction isolation level. For more details, see Section 13.2.

Transactions started during hot standby may issue the following commands:

Query access: SELECT, COPY TO

Cursor commands: DECLARE, FETCH, CLOSE

Settings: SHOW, SET, RESET

Transaction management commands:

BEGIN, END, ABORT, START TRANSACTION

SAVEPOINT, RELEASE, ROLLBACK TO SAVEPOINT

EXCEPTION blocks and other internal subtransactions

LOCK TABLE, though only when explicitly in one of these modes: ACCESS SHARE, ROW SHARE or ROW EXCLUSIVE.

Plans and resources: PREPARE, EXECUTE, DEALLOCATE, DISCARD

Plugins and extensions: LOAD

Transactions started during hot standby will never be assigned a transaction ID and cannot write to the system write-ahead log. Therefore, the following actions will produce error messages:

Data Manipulation Language (DML): INSERT, UPDATE, DELETE, MERGE, COPY FROM, TRUNCATE. Note that there are no allowed actions that result in a trigger being executed during recovery. This restriction applies even to temporary tables, because table rows cannot be read or written without assigning a transaction ID, which is currently not possible in a hot standby environment.

Data Definition Language (DDL): CREATE, DROP, ALTER, COMMENT. This restriction applies even to temporary tables, because carrying out these operations would require updating the system catalog tables.

SELECT ... FOR SHARE | UPDATE, because row locks cannot be taken without updating the underlying data files.

Rules on SELECT statements that generate DML commands.

LOCK that explicitly requests a mode higher than ROW EXCLUSIVE MODE.

LOCK in short default form, since it requests ACCESS EXCLUSIVE MODE.

Transaction management commands that explicitly set non-read-only state:

BEGIN READ WRITE, START TRANSACTION READ WRITE

SET TRANSACTION READ WRITE, SET SESSION CHARACTERISTICS AS TRANSACTION READ WRITE

SET transaction_read_only = off

Two-phase commit commands: PREPARE TRANSACTION, COMMIT PREPARED, ROLLBACK PREPARED because even read-only transactions need to write WAL in the prepare phase (the first phase of two phase commit).

Sequence updates: nextval(), setval()

In normal operation, “read-only” transactions are allowed to use LISTEN and NOTIFY, so hot standby sessions operate under slightly tighter restrictions than ordinary read-only sessions. It is possible that some of these restrictions might be loosened in a future release.

During hot standby, the parameter transaction_read_only is always true and may not be changed. But as long as no attempt is made to modify the database, connections during hot standby will act much like any other database connection. If failover or switchover occurs, the database will switch to normal processing mode. Sessions will remain connected while the server changes mode. Once hot standby finishes, it will be possible to initiate read-write transactions (even from a session begun during hot standby).

Users can determine whether hot standby is currently active for their session by issuing SHOW in_hot_standby. (In server versions before 14, the in_hot_standby parameter did not exist; a workable substitute method for older servers is SHOW transaction_read_only.) In addition, a set of functions (Table 9.98) allow users to access information about the standby server. These allow you to write programs that are aware of the current state of the database. These can be used to monitor the progress of recovery, or to allow you to write complex programs that restore the database to particular states.

The primary and standby servers are in many ways loosely connected. Actions on the primary will have an effect on the standby. As a result, there is potential for negative interactions or conflicts between them. The easiest conflict to understand is performance: if a huge data load is taking place on the primary then this will generate a similar stream of WAL records on the standby, so standby queries may contend for system resources, such as I/O.

There are also additional types of conflict that can occur with hot standby. These conflicts are hard conflicts in the sense that queries might need to be canceled and, in some cases, sessions disconnected to resolve them. The user is provided with several ways to handle these conflicts. Conflict cases include:

Access Exclusive locks taken on the primary server, including both explicit LOCK commands and various DDL actions, conflict with table accesses in standby queries.

Dropping a tablespace on the primary conflicts with standby queries using that tablespace for temporary work files.

Dropping a database on the primary conflicts with sessions connected to that database on the standby.

Application of a vacuum cleanup record from WAL conflicts with standby transactions whose snapshots can still “see” any of the rows to be removed.

Application of a vacuum cleanup record from WAL conflicts with queries accessing the target page on the standby, whether or not the data to be removed is visible.

On the primary server, these cases simply result in waiting; and the user might choose to cancel either of the conflicting actions. However, on the standby there is no choice: the WAL-logged action already occurred on the primary so the standby must not fail to apply it. Furthermore, allowing WAL application to wait indefinitely may be very undesirable, because the standby's state will become increasingly far behind the primary's. Therefore, a mechanism is provided to forcibly cancel standby queries that conflict with to-be-applied WAL records.

An example of the problem situation is an administrator on the primary server running DROP TABLE on a table that is currently being queried on the standby server. Clearly the standby query cannot continue if the DROP TABLE is applied on the standby. If this situation occurred on the primary, the DROP TABLE would wait until the other query had finished. But when DROP TABLE is run on the primary, the primary doesn't have information about what queries are running on the standby, so it will not wait for any such standby queries. The WAL change records come through to the standby while the standby query is still running, causing a conflict. The standby server must either delay application of the WAL records (and everything after them, too) or else cancel the conflicting query so that the DROP TABLE can be applied.

When a conflicting query is short, it's typically desirable to allow it to complete by delaying WAL application for a little bit; but a long delay in WAL application is usually not desirable. So the cancel mechanism has parameters, max_standby_archive_delay and max_standby_streaming_delay, that define the maximum allowed delay in WAL application. Conflicting queries will be canceled once it has taken longer than the relevant delay setting to apply any newly-received WAL data. There are two parameters so that different delay values can be specified for the case of reading WAL data from an archive (i.e., initial recovery from a base backup or “catching up” a standby server that has fallen far behind) versus reading WAL data via streaming replication.

In a standby server that exists primarily for high availability, it's best to set the delay parameters relatively short, so that the server cannot fall far behind the primary due to delays caused by standby queries. However, if the standby server is meant for executing long-running queries, then a high or even infinite delay value may be preferable. Keep in mind however that a long-running query could cause other sessions on the standby server to not see recent changes on the primary, if it delays application of WAL records.

Once the delay specified by max_standby_archive_delay or max_standby_streaming_delay has been exceeded, conflicting queries will be canceled. This usually results just in a cancellation error, although in the case of replaying a DROP DATABASE the entire conflicting session will be terminated. Also, if the conflict is over a lock held by an idle transaction, the conflicting session is terminated (this behavior might change in the future).

Canceled queries may be retried immediately (after beginning a new transaction, of course). Since query cancellation depends on the nature of the WAL records being replayed, a query that was canceled may well succeed if it is executed again.

Keep in mind that the delay parameters are compared to the elapsed time since the WAL data was received by the standby server. Thus, the grace period allowed to any one query on the standby is never more than the delay parameter, and could be considerably less if the standby has already fallen behind as a result of waiting for previous queries to complete, or as a result of being unable to keep up with a heavy update load.

The most common reason for conflict between standby queries and WAL replay is “early cleanup”. Normally, PostgreSQL allows cleanup of old row versions when there are no transactions that need to see them to ensure correct visibility of data according to MVCC rules. However, this rule can only be applied for transactions executing on the primary. So it is possible that cleanup on the primary will remove row versions that are still visible to a transaction on the standby.

Row version cleanup isn't the only potential cause of conflicts with standby queries. All index-only scans (including those that run on standbys) must use an MVCC snapshot that “agrees” with the visibility map. Conflicts are therefore required whenever VACUUM sets a page as all-visible in the visibility map containing one or more rows not visible to all standby queries. So even running VACUUM against a table with no updated or deleted rows requiring cleanup might lead to conflicts.

Users should be clear that tables that are regularly and heavily updated on the primary server will quickly cause cancellation of longer running queries on the standby. In such cases the setting of a finite value for max_standby_archive_delay or max_standby_streaming_delay can be considered similar to setting statement_timeout.

Remedial possibilities exist if the number of standby-query cancellations is found to be unacceptable. The first option is to set the parameter hot_standby_feedback, which prevents VACUUM from removing recently-dead rows and so cleanup conflicts do not occur. If you do this, you should note that this will delay cleanup of dead rows on the primary, which may result in undesirable table bloat. However, the cleanup situation will be no worse than if the standby queries were running directly on the primary server, and you are still getting the benefit of off-loading execution onto the standby. If standby servers connect and disconnect frequently, you might want to make adjustments to handle the period when hot_standby_feedback feedback is not being provided. For example, consider increasing max_standby_archive_delay so that queries are not rapidly canceled by conflicts in WAL archive files during disconnected periods. You should also consider increasing max_standby_streaming_delay to avoid rapid cancellations by newly-arrived streaming WAL entries after reconnection.

The number of query cancels and the reason for them can be viewed using the pg_stat_database_conflicts system view on the standby server. The pg_stat_database system view also contains summary information.

Users can control whether a log message is produced when WAL replay is waiting longer than deadlock_timeout for conflicts. This is controlled by the log_recovery_conflict_waits parameter.

If hot_standby is on in postgresql.conf (the default value) and there is a standby.signal file present, the server will run in hot standby mode. However, it may take some time for hot standby connections to be allowed, because the server will not accept connections until it has completed sufficient recovery to provide a consistent state against which queries can run. During this period, clients that attempt to connect will be refused with an error message. To confirm the server has come up, either loop trying to connect from the application, or look for these messages in the server logs:

Consistency information is recorded once per checkpoint on the primary. It is not possible to enable hot standby when reading WAL written during a period when wal_level was not set to replica or logical on the primary. Even after reaching a consistent state, the recovery snapshot may not be ready for hot standby if both of the following conditions are met, delaying accepting read-only connections. To enable hot standby, long-lived write transactions with more than 64 subtransactions need to be closed on the primary.

A write transaction has more than 64 subtransactions

Very long-lived write transactions

If you are running file-based log shipping ("warm standby"), you might need to wait until the next WAL file arrives, which could be as long as the archive_timeout setting on the primary.

The settings of some parameters determine the size of shared memory for tracking transaction IDs, locks, and prepared transactions. These shared memory structures must be no smaller on a standby than on the primary in order to ensure that the standby does not run out of shared memory during recovery. For example, if the primary had used a prepared transaction but the standby had not allocated any shared memory for tracking prepared transactions, then recovery could not continue until the standby's configuration is changed. The parameters affected are:

max_prepared_transactions

max_locks_per_transaction

The easiest way to ensure this does not become a problem is to have these parameters set on the standbys to values equal to or greater than on the primary. Therefore, if you want to increase these values, you should do so on all standby servers first, before applying the changes to the primary server. Conversely, if you want to decrease these values, you should do so on the primary server first, before applying the changes to all standby servers. Keep in mind that when a standby is promoted, it becomes the new reference for the required parameter settings for the standbys that follow it. Therefore, to avoid this becoming a problem during a switchover or failover, it is recommended to keep these settings the same on all standby servers.

The WAL tracks changes to these parameters on the primary. If a hot standby processes WAL that indicates that the current value on the primary is higher than its own value, it will log a warning and pause recovery, for example:

At that point, the settings on the standby need to be updated and the instance restarted before recovery can continue. If the standby is not a hot standby, then when it encounters the incompatible parameter change, it will shut down immediately without pausing, since there is then no value in keeping it up.

It is important that the administrator select appropriate settings for max_standby_archive_delay and max_standby_streaming_delay. The best choices vary depending on business priorities. For example if the server is primarily tasked as a High Availability server, then you will want low delay settings, perhaps even zero, though that is a very aggressive setting. If the standby server is tasked as an additional server for decision support queries then it might be acceptable to set the maximum delay values to many hours, or even -1 which means wait forever for queries to complete.

Transaction status "hint bits" written on the primary are not WAL-logged, so data on the standby will likely re-write the hints again on the standby. Thus, the standby server will still perform disk writes even though all users are read-only; no changes occur to the data values themselves. Users will still write large sort temporary files and re-generate relcache info files, so no part of the database is truly read-only during hot standby mode. Note also that writes to remote databases using dblink module, and other operations outside the database using PL functions will still be possible, even though the transaction is read-only locally.

The following types of administration commands are not accepted during recovery mode:

Data Definition Language (DDL): e.g., CREATE INDEX

Privilege and Ownership: GRANT, REVOKE, REASSIGN

Maintenance commands: ANALYZE, VACUUM, CLUSTER, REINDEX

Again, note that some of these commands are actually allowed during "read only" mode transactions on the primary.

As a result, you cannot create additional indexes that exist solely on the standby, nor statistics that exist solely on the standby. If these administration commands are needed, they should be executed on the primary, and eventually those changes will propagate to the standby.

pg_cancel_backend() and pg_terminate_backend() will work on user backends, but not the startup process, which performs recovery. pg_stat_activity does not show recovering transactions as active. As a result, pg_prepared_xacts is always empty during recovery. If you wish to resolve in-doubt prepared transactions, view pg_prepared_xacts on the primary and issue commands to resolve transactions there or resolve them after the end of recovery.

pg_locks will show locks held by backends, as normal. pg_locks also shows a virtual transaction managed by the startup process that owns all AccessExclusiveLocks held by transactions being replayed by recovery. Note that the startup process does not acquire locks to make database changes, and thus locks other than AccessExclusiveLocks do not show in pg_locks for the Startup process; they are just presumed to exist.

The Nagios plugin check_pgsql will work, because the simple information it checks for exists. The check_postgres monitoring script will also work, though some reported values could give different or confusing results. For example, last vacuum time will not be maintained, since no vacuum occurs on the standby. Vacuums running on the primary do still send their changes to the standby.

WAL file control commands will not work during recovery, e.g., pg_backup_start, pg_switch_wal etc.

Dynamically loadable modules work, including pg_stat_statements.

Advisory locks work normally in recovery, including deadlock detection. Note that advisory locks are never WAL logged, so it is impossible for an advisory lock on either the primary or the standby to conflict with WAL replay. Nor is it possible to acquire an advisory lock on the primary and have it initiate a similar advisory lock on the standby. Advisory locks relate only to the server on which they are acquired.

Trigger-based replication systems such as Slony, Londiste and Bucardo won't run on the standby at all, though they will run happily on the primary server as long as the changes are not sent to standby servers to be applied. WAL replay is not trigger-based so you cannot relay from the standby to any system that requires additional database writes or relies on the use of triggers.

New OIDs cannot be assigned, though some UUID generators may still work as long as they do not rely on writing new status to the database.

Currently, temporary table creation is not allowed during read-only transactions, so in some cases existing scripts will not run correctly. This restriction might be relaxed in a later release. This is both an SQL standard compliance issue and a technical issue.

DROP TABLESPACE can only succeed if the tablespace is empty. Some standby users may be actively using the tablespace via their temp_tablespaces parameter. If there are temporary files in the tablespace, all active queries are canceled to ensure that temporary files are removed, so the tablespace can be removed and WAL replay can continue.

Running DROP DATABASE or ALTER DATABASE ... SET TABLESPACE on the primary will generate a WAL entry that will cause all users connected to that database on the standby to be forcibly disconnected. This action occurs immediately, whatever the setting of max_standby_streaming_delay. Note that ALTER DATABASE ... RENAME does not disconnect users, which in most cases will go unnoticed, though might in some cases cause a program confusion if it depends in some way upon database name.

In normal (non-recovery) mode, if you issue DROP USER or DROP ROLE for a role with login capability while that user is still connected then nothing happens to the connected user — they remain connected. The user cannot reconnect however. This behavior applies in recovery also, so a DROP USER on the primary does not disconnect that user on the standby.

The cumulative statistics system is active during recovery. All scans, reads, blocks, index usage, etc., will be recorded normally on the standby. However, WAL replay will not increment relation and database specific counters. I.e. replay will not increment pg_stat_all_tables columns (like n_tup_ins), nor will reads or writes performed by the startup process be tracked in the pgstatio views, nor will associated pg_stat_database columns be incremented.

Autovacuum is not active during recovery. It will start normally at the end of recovery.

The checkpointer process and the background writer process are active during recovery. The checkpointer process will perform restartpoints (similar to checkpoints on the primary) and the background writer process will perform normal block cleaning activities. This can include updates of the hint bit information stored on the standby server. The CHECKPOINT command is accepted during recovery, though it performs a restartpoint rather than a new checkpoint.

Various parameters have been mentioned above in Section 26.4.2 and Section 26.4.3.

On the primary, the wal_level parameter can be used. max_standby_archive_delay and max_standby_streaming_delay have no effect if set on the primary.

On the standby, parameters hot_standby, max_standby_archive_delay and max_standby_streaming_delay can be used.

There are several limitations of hot standby. These can and probably will be fixed in future releases:

Full knowledge of running transactions is required before snapshots can be taken. Transactions that use large numbers of subtransactions (currently greater than 64) will delay the start of read-only connections until the completion of the longest running write transaction. If this situation occurs, explanatory messages will be sent to the server log.

Valid starting points for standby queries are generated at each checkpoint on the primary. If the standby is shut down while the primary is in a shutdown state, it might not be possible to re-enter hot standby until the primary is started up, so that it generates further starting points in the WAL logs. This situation isn't a problem in the most common situations where it might happen. Generally, if the primary is shut down and not available anymore, that's likely due to a serious failure that requires the standby being converted to operate as the new primary anyway. And in situations where the primary is being intentionally taken down, coordinating to make sure the standby becomes the new primary smoothly is also standard procedure.

At the end of recovery, AccessExclusiveLocks held by prepared transactions will require twice the normal number of lock table entries. If you plan on running either a large number of concurrent prepared transactions that normally take AccessExclusiveLocks, or you plan on having one large transaction that takes many AccessExclusiveLocks, you are advised to select a larger value of max_locks_per_transaction, perhaps as much as twice the value of the parameter on the primary server. You need not consider this at all if your setting of max_prepared_transactions is 0.

The Serializable transaction isolation level is not yet available in hot standby. (See Section 13.2.3 and Section 13.4.1 for details.) An attempt to set a transaction to the serializable isolation level in hot standby mode will generate an error.

Examples:

Example 1 (unknown):

LOG:  entering standby mode

... then some time later ...

LOG:  consistent recovery state reached
LOG:  database system is ready to accept read-only connections

Example 2 (unknown):

WARNING:  hot standby is not possible because of insufficient parameter settings
DETAIL:  max_connections = 80 is a lower setting than on the primary server, where its value was 100.
LOG:  recovery has paused
DETAIL:  If recovery is unpaused, the server will shut down.
HINT:  You can then restart the server after making the necessary configuration changes.

---

PostgreSQL: Documentation: 18: 7.4. Combining Queries (UNION, INTERSECT, EXCEPT)

URL: https://www.postgresql.org/docs/current/queries-union.html

Contents:

• 7.4. Combining Queries (UNION, INTERSECT, EXCEPT) #

The results of two queries can be combined using the set operations union, intersection, and difference. The syntax is

where query1 and query2 are queries that can use any of the features discussed up to this point.

UNION effectively appends the result of query2 to the result of query1 (although there is no guarantee that this is the order in which the rows are actually returned). Furthermore, it eliminates duplicate rows from its result, in the same way as DISTINCT, unless UNION ALL is used.

INTERSECT returns all rows that are both in the result of query1 and in the result of query2. Duplicate rows are eliminated unless INTERSECT ALL is used.

EXCEPT returns all rows that are in the result of query1 but not in the result of query2. (This is sometimes called the difference between two queries.) Again, duplicates are eliminated unless EXCEPT ALL is used.

In order to calculate the union, intersection, or difference of two queries, the two queries must be “union compatible”, which means that they return the same number of columns and the corresponding columns have compatible data types, as described in Section 10.5.

Set operations can be combined, for example

which is equivalent to

As shown here, you can use parentheses to control the order of evaluation. Without parentheses, UNION and EXCEPT associate left-to-right, but INTERSECT binds more tightly than those two operators. Thus

You can also surround an individual query with parentheses. This is important if the query needs to use any of the clauses discussed in following sections, such as LIMIT. Without parentheses, you'll get a syntax error, or else the clause will be understood as applying to the output of the set operation rather than one of its inputs. For example,

is accepted, but it means

Examples:

Example 1 (unknown):

query1 UNION [ALL] query2
query1 INTERSECT [ALL] query2
query1 EXCEPT [ALL] query2

Example 2 (unknown):

query1 UNION query2 EXCEPT query3

Example 3 (unknown):

(query1 UNION query2) EXCEPT query3

Example 4 (unknown):

query1 UNION query2 INTERSECT query3

---

PostgreSQL: Documentation: 18: 33.2. Implementation Features

URL: https://www.postgresql.org/docs/current/lo-implementation.html

Contents:

• 33.2. Implementation Features #

The large object implementation breaks large objects up into “chunks” and stores the chunks in rows in the database. A B-tree index guarantees fast searches for the correct chunk number when doing random access reads and writes.

The chunks stored for a large object do not have to be contiguous. For example, if an application opens a new large object, seeks to offset 1000000, and writes a few bytes there, this does not result in allocation of 1000000 bytes worth of storage; only of chunks covering the range of data bytes actually written. A read operation will, however, read out zeroes for any unallocated locations preceding the last existing chunk. This corresponds to the common behavior of “sparsely allocated” files in Unix file systems.

As of PostgreSQL 9.0, large objects have an owner and a set of access permissions, which can be managed using GRANT and REVOKE. SELECT privileges are required to read a large object, and UPDATE privileges are required to write or truncate it. Only the large object's owner (or a database superuser) can delete, comment on, or change the owner of a large object. To adjust this behavior for compatibility with prior releases, see the lo_compat_privileges run-time parameter.

---

PostgreSQL: Documentation: 18: 34.10. Processing Embedded SQL Programs

URL: https://www.postgresql.org/docs/current/ecpg-process.html

Contents:

• 34.10. Processing Embedded SQL Programs #

Now that you have an idea how to form embedded SQL C programs, you probably want to know how to compile them. Before compiling you run the file through the embedded SQL C preprocessor, which converts the SQL statements you used to special function calls. After compiling, you must link with a special library that contains the needed functions. These functions fetch information from the arguments, perform the SQL command using the libpq interface, and put the result in the arguments specified for output.

The preprocessor program is called ecpg and is included in a normal PostgreSQL installation. Embedded SQL programs are typically named with an extension .pgc. If you have a program file called prog1.pgc, you can preprocess it by simply calling:

This will create a file called prog1.c. If your input files do not follow the suggested naming pattern, you can specify the output file explicitly using the -o option.

The preprocessed file can be compiled normally, for example:

The generated C source files include header files from the PostgreSQL installation, so if you installed PostgreSQL in a location that is not searched by default, you have to add an option such as -I/usr/local/pgsql/include to the compilation command line.

To link an embedded SQL program, you need to include the libecpg library, like so:

Again, you might have to add an option like -L/usr/local/pgsql/lib to that command line.

You can use pg_config or pkg-config with package name libecpg to get the paths for your installation.

If you manage the build process of a larger project using make, it might be convenient to include the following implicit rule to your makefiles:

The complete syntax of the ecpg command is detailed in ecpg.

The ecpg library is thread-safe by default. However, you might need to use some threading command-line options to compile your client code.

Examples:

Example 1 (unknown):

ecpg prog1.pgc

Example 2 (unknown):

cc -c prog1.c

Example 3 (unknown):

cc -o myprog prog1.o prog2.o ... -lecpg

Example 4 (unknown):

ECPG = ecpg

%.c: %.pgc
        $(ECPG) $<

---

PostgreSQL: Documentation: 18: 9.4. String Functions and Operators

URL: https://www.postgresql.org/docs/current/functions-string.html

Contents:

• 9.4. String Functions and Operators #
  • Note
  • 9.4.1. format #

This section describes functions and operators for examining and manipulating string values. Strings in this context include values of the types character, character varying, and text. Except where noted, these functions and operators are declared to accept and return type text. They will interchangeably accept character varying arguments. Values of type character will be converted to text before the function or operator is applied, resulting in stripping any trailing spaces in the character value.

SQL defines some string functions that use key words, rather than commas, to separate arguments. Details are in Table 9.9. PostgreSQL also provides versions of these functions that use the regular function invocation syntax (see Table 9.10).

The string concatenation operator (||) will accept non-string input, so long as at least one input is of string type, as shown in Table 9.9. For other cases, inserting an explicit coercion to text can be used to have non-string input accepted.

Table 9.9. SQL String Functions and Operators

Concatenates the two strings.

'Post' || 'greSQL' → PostgreSQL

text || anynonarray → text

anynonarray || text → text

Converts the non-string input to text, then concatenates the two strings. (The non-string input cannot be of an array type, because that would create ambiguity with the array || operators. If you want to concatenate an array's text equivalent, cast it to text explicitly.)

'Value: ' || 42 → Value: 42

btrim ( string text [, characters text ] ) → text

Removes the longest string containing only characters in characters (a space by default) from the start and end of string.

btrim('xyxtrimyyx', 'xyz') → trim

text IS [NOT] [form] NORMALIZED → boolean

Checks whether the string is in the specified Unicode normalization form. The optional form key word specifies the form: NFC (the default), NFD, NFKC, or NFKD. This expression can only be used when the server encoding is UTF8. Note that checking for normalization using this expression is often faster than normalizing possibly already normalized strings.

U&'\0061\0308bc' IS NFD NORMALIZED → t

bit_length ( text ) → integer

Returns number of bits in the string (8 times the octet_length).

bit_length('jose') → 32

char_length ( text ) → integer

character_length ( text ) → integer

Returns number of characters in the string.

char_length('josé') → 4

lower ( text ) → text

Converts the string to all lower case, according to the rules of the database's locale.

lpad ( string text, length integer [, fill text ] ) → text

Extends the string to length length by prepending the characters fill (a space by default). If the string is already longer than length then it is truncated (on the right).

lpad('hi', 5, 'xy') → xyxhi

ltrim ( string text [, characters text ] ) → text

Removes the longest string containing only characters in characters (a space by default) from the start of string.

ltrim('zzzytest', 'xyz') → test

normalize ( text [, form ] ) → text

Converts the string to the specified Unicode normalization form. The optional form key word specifies the form: NFC (the default), NFD, NFKC, or NFKD. This function can only be used when the server encoding is UTF8.

normalize(U&'\0061\0308bc', NFC) → U&'\00E4bc'

octet_length ( text ) → integer

Returns number of bytes in the string.

octet_length('josé') → 5 (if server encoding is UTF8)

octet_length ( character ) → integer

Returns number of bytes in the string. Since this version of the function accepts type character directly, it will not strip trailing spaces.

octet_length('abc '::character(4)) → 4

overlay ( string text PLACING newsubstring text FROM start integer [ FOR count integer ] ) → text

Replaces the substring of string that starts at the start'th character and extends for count characters with newsubstring. If count is omitted, it defaults to the length of newsubstring.

overlay('Txxxxas' placing 'hom' from 2 for 4) → Thomas

position ( substring text IN string text ) → integer

Returns first starting index of the specified substring within string, or zero if it's not present.

position('om' in 'Thomas') → 3

rpad ( string text, length integer [, fill text ] ) → text

Extends the string to length length by appending the characters fill (a space by default). If the string is already longer than length then it is truncated.

rpad('hi', 5, 'xy') → hixyx

rtrim ( string text [, characters text ] ) → text

Removes the longest string containing only characters in characters (a space by default) from the end of string.

rtrim('testxxzx', 'xyz') → test

substring ( string text [ FROM start integer ] [ FOR count integer ] ) → text

Extracts the substring of string starting at the start'th character if that is specified, and stopping after count characters if that is specified. Provide at least one of start and count.

substring('Thomas' from 2 for 3) → hom

substring('Thomas' from 3) → omas

substring('Thomas' for 2) → Th

substring ( string text FROM pattern text ) → text

Extracts the first substring matching POSIX regular expression; see Section 9.7.3.

substring('Thomas' from '...$') → mas

substring ( string text SIMILAR pattern text ESCAPE escape text ) → text

substring ( string text FROM pattern text FOR escape text ) → text

Extracts the first substring matching SQL regular expression; see Section 9.7.2. The first form has been specified since SQL:2003; the second form was only in SQL:1999 and should be considered obsolete.

substring('Thomas' similar '%#"oa#"' escape '#') → oma

trim ( [ LEADING | TRAILING | BOTH ] [ characters text ] FROM string text ) → text

Removes the longest string containing only characters in characters (a space by default) from the start, end, or both ends (BOTH is the default) of string.

trim(both 'xyz' from 'yxTomxx') → Tom

trim ( [ LEADING | TRAILING | BOTH ] [ FROM ] string text [, characters text ] ) → text

This is a non-standard syntax for trim().

trim(both from 'yxTomxx', 'xyz') → Tom

unicode_assigned ( text ) → boolean

Returns true if all characters in the string are assigned Unicode codepoints; false otherwise. This function can only be used when the server encoding is UTF8.

upper ( text ) → text

Converts the string to all upper case, according to the rules of the database's locale.

Additional string manipulation functions and operators are available and are listed in Table 9.10. (Some of these are used internally to implement the SQL-standard string functions listed in Table 9.9.) There are also pattern-matching operators, which are described in Section 9.7, and operators for full-text search, which are described in Chapter 12.

Table 9.10. Other String Functions and Operators

text ^@ text → boolean

Returns true if the first string starts with the second string (equivalent to the starts_with() function).

'alphabet' ^@ 'alph' → t

ascii ( text ) → integer

Returns the numeric code of the first character of the argument. In UTF8 encoding, returns the Unicode code point of the character. In other multibyte encodings, the argument must be an ASCII character.

chr ( integer ) → text

Returns the character with the given code. In UTF8 encoding the argument is treated as a Unicode code point. In other multibyte encodings the argument must designate an ASCII character. chr(0) is disallowed because text data types cannot store that character.

concat ( val1 "any" [, val2 "any" [, ...] ] ) → text

Concatenates the text representations of all the arguments. NULL arguments are ignored.

concat('abcde', 2, NULL, 22) → abcde222

concat_ws ( sep text, val1 "any" [, val2 "any" [, ...] ] ) → text

Concatenates all but the first argument, with separators. The first argument is used as the separator string, and should not be NULL. Other NULL arguments are ignored.

concat_ws(',', 'abcde', 2, NULL, 22) → abcde,2,22

format ( formatstr text [, formatarg "any" [, ...] ] ) → text

Formats arguments according to a format string; see Section 9.4.1. This function is similar to the C function sprintf.

format('Hello %s, %1$s', 'World') → Hello World, World

initcap ( text ) → text

Converts the first letter of each word to upper case and the rest to lower case. Words are sequences of alphanumeric characters separated by non-alphanumeric characters.

initcap('hi THOMAS') → Hi Thomas

casefold ( text ) → text

Performs case folding of the input string according to the collation. Case folding is similar to case conversion, but the purpose of case folding is to facilitate case-insensitive matching of strings, whereas the purpose of case conversion is to convert to a particular cased form. This function can only be used when the server encoding is UTF8.

Ordinarily, case folding simply converts to lowercase, but there may be exceptions depending on the collation. For instance, some characters have more than two lowercase variants, or fold to uppercase.

Case folding may change the length of the string. For instance, in the PG_UNICODE_FAST collation, ß (U+00DF) folds to ss.

casefold can be used for Unicode Default Caseless Matching. It does not always preserve the normalized form of the input string (see normalize).

The libc provider doesn't support case folding, so casefold is identical to lower.

left ( string text, n integer ) → text

Returns first n characters in the string, or when n is negative, returns all but last |n| characters.

left('abcde', 2) → ab

length ( text ) → integer

Returns the number of characters in the string.

Computes the MD5 hash of the argument, with the result written in hexadecimal.

md5('abc') → 900150983c​d6963f7d28

parse_ident ( qualified_identifier text [, strict_mode boolean DEFAULT true ] ) → text[]

Splits qualified_identifier into an array of identifiers, removing any quoting of individual identifiers. By default, extra characters after the last identifier are considered an error; but if the second parameter is false, then such extra characters are ignored. (This behavior is useful for parsing names for objects like functions.) Note that this function does not truncate over-length identifiers. If you want truncation you can cast the result to name[].

parse_ident('"SomeSchema".someTable') → {SomeSchema,sometable}

pg_client_encoding ( ) → name

Returns current client encoding name.

pg_client_encoding() → UTF8

quote_ident ( text ) → text

Returns the given string suitably quoted to be used as an identifier in an SQL statement string. Quotes are added only if necessary (i.e., if the string contains non-identifier characters or would be case-folded). Embedded quotes are properly doubled. See also Example 41.1.

quote_ident('Foo bar') → "Foo bar"

quote_literal ( text ) → text

Returns the given string suitably quoted to be used as a string literal in an SQL statement string. Embedded single-quotes and backslashes are properly doubled. Note that quote_literal returns null on null input; if the argument might be null, quote_nullable is often more suitable. See also Example 41.1.

quote_literal(E'O\'Reilly') → 'O''Reilly'

quote_literal ( anyelement ) → text

Converts the given value to text and then quotes it as a literal. Embedded single-quotes and backslashes are properly doubled.

quote_literal(42.5) → '42.5'

quote_nullable ( text ) → text

Returns the given string suitably quoted to be used as a string literal in an SQL statement string; or, if the argument is null, returns NULL. Embedded single-quotes and backslashes are properly doubled. See also Example 41.1.

quote_nullable(NULL) → NULL

quote_nullable ( anyelement ) → text

Converts the given value to text and then quotes it as a literal; or, if the argument is null, returns NULL. Embedded single-quotes and backslashes are properly doubled.

quote_nullable(42.5) → '42.5'

regexp_count ( string text, pattern text [, start integer [, flags text ] ] ) → integer

Returns the number of times the POSIX regular expression pattern matches in the string; see Section 9.7.3.

regexp_count('123456789012', '\d\d\d', 2) → 3

regexp_instr ( string text, pattern text [, start integer [, N integer [, endoption integer [, flags text [, subexpr integer ] ] ] ] ] ) → integer

Returns the position within string where the N'th match of the POSIX regular expression pattern occurs, or zero if there is no such match; see Section 9.7.3.

regexp_instr('ABCDEF', 'c(.)(..)', 1, 1, 0, 'i') → 3

regexp_instr('ABCDEF', 'c(.)(..)', 1, 1, 0, 'i', 2) → 5

regexp_like ( string text, pattern text [, flags text ] ) → boolean

Checks whether a match of the POSIX regular expression pattern occurs within string; see Section 9.7.3.

regexp_like('Hello World', 'world$', 'i') → t

regexp_match ( string text, pattern text [, flags text ] ) → text[]

Returns substrings within the first match of the POSIX regular expression pattern to the string; see Section 9.7.3.

regexp_match('foobarbequebaz', '(bar)(beque)') → {bar,beque}

regexp_matches ( string text, pattern text [, flags text ] ) → setof text[]

Returns substrings within the first match of the POSIX regular expression pattern to the string, or substrings within all such matches if the g flag is used; see Section 9.7.3.

regexp_matches('foobarbequebaz', 'ba.', 'g') →

regexp_replace ( string text, pattern text, replacement text [, flags text ] ) → text

Replaces the substring that is the first match to the POSIX regular expression pattern, or all such matches if the g flag is used; see Section 9.7.3.

regexp_replace('Thomas', '.[mN]a.', 'M') → ThM

regexp_replace ( string text, pattern text, replacement text, start integer [, N integer [, flags text ] ] ) → text

Replaces the substring that is the N'th match to the POSIX regular expression pattern, or all such matches if N is zero, with the search beginning at the start'th character of string. If N is omitted, it defaults to 1. See Section 9.7.3.

regexp_replace('Thomas', '.', 'X', 3, 2) → ThoXas

regexp_replace(string=>'hello world', pattern=>'l', replacement=>'XX', start=>1, "N"=>2) → helXXo world

regexp_split_to_array ( string text, pattern text [, flags text ] ) → text[]

Splits string using a POSIX regular expression as the delimiter, producing an array of results; see Section 9.7.3.

regexp_split_to_array('hello world', '\s+') → {hello,world}

regexp_split_to_table ( string text, pattern text [, flags text ] ) → setof text

Splits string using a POSIX regular expression as the delimiter, producing a set of results; see Section 9.7.3.

regexp_split_to_table('hello world', '\s+') →

regexp_substr ( string text, pattern text [, start integer [, N integer [, flags text [, subexpr integer ] ] ] ] ) → text

Returns the substring within string that matches the N'th occurrence of the POSIX regular expression pattern, or NULL if there is no such match; see Section 9.7.3.

regexp_substr('ABCDEF', 'c(.)(..)', 1, 1, 'i') → CDEF

regexp_substr('ABCDEF', 'c(.)(..)', 1, 1, 'i', 2) → EF

repeat ( string text, number integer ) → text

Repeats string the specified number of times.

repeat('Pg', 4) → PgPgPgPg

replace ( string text, from text, to text ) → text

Replaces all occurrences in string of substring from with substring to.

replace('abcdefabcd', 'cd', 'XX') → abXXefabXXef

reverse ( text ) → text

Reverses the order of the characters in the string.

reverse('abcde') → edcba

right ( string text, n integer ) → text

Returns last n characters in the string, or when n is negative, returns all but first |n| characters.

right('abcde', 2) → de

split_part ( string text, delimiter text, n integer ) → text

Splits string at occurrences of delimiter and returns the n'th field (counting from one), or when n is negative, returns the |n|'th-from-last field.

split_part('abc~@~def~@~ghi', '~@~', 2) → def

split_part('abc,def,ghi,jkl', ',', -2) → ghi

starts_with ( string text, prefix text ) → boolean

Returns true if string starts with prefix.

starts_with('alphabet', 'alph') → t

string_to_array ( string text, delimiter text [, null_string text ] ) → text[]

Splits the string at occurrences of delimiter and forms the resulting fields into a text array. If delimiter is NULL, each character in the string will become a separate element in the array. If delimiter is an empty string, then the string is treated as a single field. If null_string is supplied and is not NULL, fields matching that string are replaced by NULL. See also array_to_string.

string_to_array('xxyyzz', '~~', 'yy') → {xx,NULL,zz}

string_to_table ( string text, delimiter text [, null_string text ] ) → setof text

Splits the string at occurrences of delimiter and returns the resulting fields as a set of text rows. If delimiter is NULL, each character in the string will become a separate row of the result. If delimiter is an empty string, then the string is treated as a single field. If null_string is supplied and is not NULL, fields matching that string are replaced by NULL.

string_to_table('xx~^~yy~^~zz', '~^~', 'yy') →

strpos ( string text, substring text ) → integer

Returns first starting index of the specified substring within string, or zero if it's not present. (Same as position(substring in string), but note the reversed argument order.)

strpos('high', 'ig') → 2

substr ( string text, start integer [, count integer ] ) → text

Extracts the substring of string starting at the start'th character, and extending for count characters if that is specified. (Same as substring(string from start for count).)

substr('alphabet', 3) → phabet

substr('alphabet', 3, 2) → ph

to_ascii ( string text ) → text

to_ascii ( string text, encoding name ) → text

to_ascii ( string text, encoding integer ) → text

Converts string to ASCII from another encoding, which may be identified by name or number. If encoding is omitted the database encoding is assumed (which in practice is the only useful case). The conversion consists primarily of dropping accents. Conversion is only supported from LATIN1, LATIN2, LATIN9, and WIN1250 encodings. (See the unaccent module for another, more flexible solution.)

to_ascii('Karél') → Karel

to_bin ( integer ) → text

to_bin ( bigint ) → text

Converts the number to its equivalent two's complement binary representation.

to_bin(2147483647) → 1111111111111111111111111111111

to_bin(-1234) → 11111111111111111111101100101110

to_hex ( integer ) → text

to_hex ( bigint ) → text

Converts the number to its equivalent two's complement hexadecimal representation.

to_hex(2147483647) → 7fffffff

to_hex(-1234) → fffffb2e

to_oct ( integer ) → text

to_oct ( bigint ) → text

Converts the number to its equivalent two's complement octal representation.

to_oct(2147483647) → 17777777777

to_oct(-1234) → 37777775456

translate ( string text, from text, to text ) → text

Replaces each character in string that matches a character in the from set with the corresponding character in the to set. If from is longer than to, occurrences of the extra characters in from are deleted.

translate('12345', '143', 'ax') → a2x5

unistr ( text ) → text

Evaluate escaped Unicode characters in the argument. Unicode characters can be specified as \XXXX (4 hexadecimal digits), +XXXXXX (6 hexadecimal digits), \uXXXX (4 hexadecimal digits), or \UXXXXXXXX (8 hexadecimal digits). To specify a backslash, write two backslashes. All other characters are taken literally.

If the server encoding is not UTF-8, the Unicode code point identified by one of these escape sequences is converted to the actual server encoding; an error is reported if that's not possible.

This function provides a (non-standard) alternative to string constants with Unicode escapes (see Section 4.1.2.3).

unistr('d\0061t+000061') → data

unistr('d\u0061t\U00000061') → data

The concat, concat_ws and format functions are variadic, so it is possible to pass the values to be concatenated or formatted as an array marked with the VARIADIC keyword (see Section 36.5.6). The array's elements are treated as if they were separate ordinary arguments to the function. If the variadic array argument is NULL, concat and concat_ws return NULL, but format treats a NULL as a zero-element array.

See also the aggregate function string_agg in Section 9.21, and the functions for converting between strings and the bytea type in Table 9.13.

The function format produces output formatted according to a format string, in a style similar to the C function sprintf.

formatstr is a format string that specifies how the result should be formatted. Text in the format string is copied directly to the result, except where format specifiers are used. Format specifiers act as placeholders in the string, defining how subsequent function arguments should be formatted and inserted into the result. Each formatarg argument is converted to text according to the usual output rules for its data type, and then formatted and inserted into the result string according to the format specifier(s).

Format specifiers are introduced by a % character and have the form

where the component fields are:

A string of the form n$ where n is the index of the argument to print. Index 1 means the first argument after formatstr. If the position is omitted, the default is to use the next argument in sequence.

Additional options controlling how the format specifier's output is formatted. Currently the only supported flag is a minus sign (-) which will cause the format specifier's output to be left-justified. This has no effect unless the width field is also specified.

Specifies the minimum number of characters to use to display the format specifier's output. The output is padded on the left or right (depending on the - flag) with spaces as needed to fill the width. A too-small width does not cause truncation of the output, but is simply ignored. The width may be specified using any of the following: a positive integer; an asterisk (*) to use the next function argument as the width; or a string of the form *n$ to use the nth function argument as the width.

If the width comes from a function argument, that argument is consumed before the argument that is used for the format specifier's value. If the width argument is negative, the result is left aligned (as if the - flag had been specified) within a field of length abs(width).

The type of format conversion to use to produce the format specifier's output. The following types are supported:

s formats the argument value as a simple string. A null value is treated as an empty string.

I treats the argument value as an SQL identifier, double-quoting it if necessary. It is an error for the value to be null (equivalent to quote_ident).

L quotes the argument value as an SQL literal. A null value is displayed as the string NULL, without quotes (equivalent to quote_nullable).

In addition to the format specifiers described above, the special sequence %% may be used to output a literal % character.

Here are some examples of the basic format conversions:

Here are examples using width fields and the - flag:

These examples show use of position fields:

Unlike the standard C function sprintf, PostgreSQL's format function allows format specifiers with and without position fields to be mixed in the same format string. A format specifier without a position field always uses the next argument after the last argument consumed. In addition, the format function does not require all function arguments to be used in the format string. For example:

The %I and %L format specifiers are particularly useful for safely constructing dynamic SQL statements. See Example 41.1.

Examples:

Example 1 (unknown):

{bar}
 {baz}

Example 2 (unknown):

hello
 world

Example 3 (unknown):

xx
 NULL
 zz

Example 4 (unknown):

format(formatstr text [, formatarg "any" [, ...] ])

---

PostgreSQL: Documentation: 18: 26.1. Comparison of Different Solutions

URL: https://www.postgresql.org/docs/current/different-replication-solutions.html

Contents:

• 26.1. Comparison of Different Solutions #

Shared disk failover avoids synchronization overhead by having only one copy of the database. It uses a single disk array that is shared by multiple servers. If the main database server fails, the standby server is able to mount and start the database as though it were recovering from a database crash. This allows rapid failover with no data loss.

Shared hardware functionality is common in network storage devices. Using a network file system is also possible, though care must be taken that the file system has full POSIX behavior (see Section 18.2.2.1). One significant limitation of this method is that if the shared disk array fails or becomes corrupt, the primary and standby servers are both nonfunctional. Another issue is that the standby server should never access the shared storage while the primary server is running.

A modified version of shared hardware functionality is file system replication, where all changes to a file system are mirrored to a file system residing on another computer. The only restriction is that the mirroring must be done in a way that ensures the standby server has a consistent copy of the file system — specifically, writes to the standby must be done in the same order as those on the primary. DRBD is a popular file system replication solution for Linux.

Warm and hot standby servers can be kept current by reading a stream of write-ahead log (WAL) records. If the main server fails, the standby contains almost all of the data of the main server, and can be quickly made the new primary database server. This can be synchronous or asynchronous and can only be done for the entire database server.

A standby server can be implemented using file-based log shipping (Section 26.2) or streaming replication (see Section 26.2.5), or a combination of both. For information on hot standby, see Section 26.4.

Logical replication allows a database server to send a stream of data modifications to another server. PostgreSQL logical replication constructs a stream of logical data modifications from the WAL. Logical replication allows replication of data changes on a per-table basis. In addition, a server that is publishing its own changes can also subscribe to changes from another server, allowing data to flow in multiple directions. For more information on logical replication, see Chapter 29. Through the logical decoding interface (Chapter 47), third-party extensions can also provide similar functionality.

A trigger-based replication setup typically funnels data modification queries to a designated primary server. Operating on a per-table basis, the primary server sends data changes (typically) asynchronously to the standby servers. Standby servers can answer queries while the primary is running, and may allow some local data changes or write activity. This form of replication is often used for offloading large analytical or data warehouse queries.

Slony-I is an example of this type of replication, with per-table granularity, and support for multiple standby servers. Because it updates the standby server asynchronously (in batches), there is possible data loss during fail over.

With SQL-based replication middleware, a program intercepts every SQL query and sends it to one or all servers. Each server operates independently. Read-write queries must be sent to all servers, so that every server receives any changes. But read-only queries can be sent to just one server, allowing the read workload to be distributed among them.

If queries are simply broadcast unmodified, functions like random(), CURRENT_TIMESTAMP, and sequences can have different values on different servers. This is because each server operates independently, and because SQL queries are broadcast rather than actual data changes. If this is unacceptable, either the middleware or the application must determine such values from a single source and then use those values in write queries. Care must also be taken that all transactions either commit or abort on all servers, perhaps using two-phase commit (PREPARE TRANSACTION and COMMIT PREPARED). Pgpool-II and Continuent Tungsten are examples of this type of replication.

For servers that are not regularly connected or have slow communication links, like laptops or remote servers, keeping data consistent among servers is a challenge. Using asynchronous multimaster replication, each server works independently, and periodically communicates with the other servers to identify conflicting transactions. The conflicts can be resolved by users or conflict resolution rules. Bucardo is an example of this type of replication.

In synchronous multimaster replication, each server can accept write requests, and modified data is transmitted from the original server to every other server before each transaction commits. Heavy write activity can cause excessive locking and commit delays, leading to poor performance. Read requests can be sent to any server. Some implementations use shared disk to reduce the communication overhead. Synchronous multimaster replication is best for mostly read workloads, though its big advantage is that any server can accept write requests — there is no need to partition workloads between primary and standby servers, and because the data changes are sent from one server to another, there is no problem with non-deterministic functions like random().

PostgreSQL does not offer this type of replication, though PostgreSQL two-phase commit (PREPARE TRANSACTION and COMMIT PREPARED) can be used to implement this in application code or middleware.

Table 26.1 summarizes the capabilities of the various solutions listed above.

Table 26.1. High Availability, Load Balancing, and Replication Feature Matrix

There are a few solutions that do not fit into the above categories:

Data partitioning splits tables into data sets. Each set can be modified by only one server. For example, data can be partitioned by offices, e.g., London and Paris, with a server in each office. If queries combining London and Paris data are necessary, an application can query both servers, or primary/standby replication can be used to keep a read-only copy of the other office's data on each server.

Many of the above solutions allow multiple servers to handle multiple queries, but none allow a single query to use multiple servers to complete faster. This solution allows multiple servers to work concurrently on a single query. It is usually accomplished by splitting the data among servers and having each server execute its part of the query and return results to a central server where they are combined and returned to the user. This can be implemented using the PL/Proxy tool set.

It should also be noted that because PostgreSQL is open source and easily extended, a number of companies have taken PostgreSQL and created commercial closed-source solutions with unique failover, replication, and load balancing capabilities. These are not discussed here.

---

PostgreSQL: Documentation: 18: 27.1. Standard Unix Tools

URL: https://www.postgresql.org/docs/current/monitoring-ps.html

Contents:

• 27.1. Standard Unix Tools #
  • Tip

On most Unix platforms, PostgreSQL modifies its command title as reported by ps, so that individual server processes can readily be identified. A sample display is

(The appropriate invocation of ps varies across different platforms, as do the details of what is shown. This example is from a recent Linux system.) The first process listed here is the primary server process. The command arguments shown for it are the same ones used when it was launched. The next four processes are background worker processes automatically launched by the primary process. (The “autovacuum launcher” process will not be present if you have set the system not to run autovacuum.) Each of the remaining processes is a server process handling one client connection. Each such process sets its command line display in the form

The user, database, and (client) host items remain the same for the life of the client connection, but the activity indicator changes. The activity can be idle (i.e., waiting for a client command), idle in transaction (waiting for client inside a BEGIN block), or a command type name such as SELECT. Also, waiting is appended if the server process is presently waiting on a lock held by another session. In the above example we can infer that process 15606 is waiting for process 15610 to complete its transaction and thereby release some lock. (Process 15610 must be the blocker, because there is no other active session. In more complicated cases it would be necessary to look into the pg_locks system view to determine who is blocking whom.)

If cluster_name has been configured the cluster name will also be shown in ps output:

If you have turned off update_process_title then the activity indicator is not updated; the process title is set only once when a new process is launched. On some platforms this saves a measurable amount of per-command overhead; on others it's insignificant.

Solaris requires special handling. You must use /usr/ucb/ps, rather than /bin/ps. You also must use two w flags, not just one. In addition, your original invocation of the postgres command must have a shorter ps status display than that provided by each server process. If you fail to do all three things, the ps output for each server process will be the original postgres command line.

Examples:

Example 1 (unknown):

$ ps auxww | grep ^postgres
postgres  15551  0.0  0.1  57536  7132 pts/0    S    18:02   0:00 postgres -i
postgres  15554  0.0  0.0  57536  1184 ?        Ss   18:02   0:00 postgres: background writer
postgres  15555  0.0  0.0  57536   916 ?        Ss   18:02   0:00 postgres: checkpointer
postgres  15556  0.0  0.0  57536   916 ?        Ss   18:02   0:00 postgres: walwriter
postgres  15557  0.0  0.0  58504  2244 ?        Ss   18:02   0:00 postgres: autovacuum launcher
postgres  15582  0.0  0.0  58772  3080 ?        Ss   18:04   0:00 postgres: joe runbug 127.0.0.1 idle
postgres  15606  0.0  0.0  58772  3052 ?        Ss   18:07   0:00 postgres: tgl regression [local] SELECT waiting
postgres  15610  0.0  0.0  58772  3056 ?        Ss   18:07   0:00 postgres: tgl regression [local] idle in transaction

Example 2 (unknown):

postgres: user database host activity

Example 3 (unknown):

$ psql -c 'SHOW cluster_name'
 cluster_name
--------------
 server1
(1 row)

$ ps aux|grep server1
postgres   27093  0.0  0.0  30096  2752 ?        Ss   11:34   0:00 postgres: server1: background writer
...

---

PostgreSQL: Documentation: 18: 36.3. User-Defined Functions

URL: https://www.postgresql.org/docs/current/xfunc.html

Contents:

• 36.3. User-Defined Functions #

PostgreSQL provides four kinds of functions:

query language functions (functions written in SQL) (Section 36.5)

procedural language functions (functions written in, for example, PL/pgSQL or PL/Tcl) (Section 36.8)

internal functions (Section 36.9)

C-language functions (Section 36.10)

Every kind of function can take base types, composite types, or combinations of these as arguments (parameters). In addition, every kind of function can return a base type or a composite type. Functions can also be defined to return sets of base or composite values.

Many kinds of functions can take or return certain pseudo-types (such as polymorphic types), but the available facilities vary. Consult the description of each kind of function for more details.

It's easiest to define SQL functions, so we'll start by discussing those. Most of the concepts presented for SQL functions will carry over to the other types of functions.

Throughout this chapter, it can be useful to look at the reference page of the CREATE FUNCTION command to understand the examples better. Some examples from this chapter can be found in funcs.sql and funcs.c in the src/tutorial directory in the PostgreSQL source distribution.

---

PostgreSQL: Documentation: 18: SET AUTOCOMMIT

URL: https://www.postgresql.org/docs/current/ecpg-sql-set-autocommit.html

Contents:

• SET AUTOCOMMIT
• Synopsis
• Description
• Compatibility

SET AUTOCOMMIT — set the autocommit behavior of the current session

SET AUTOCOMMIT sets the autocommit behavior of the current database session. By default, embedded SQL programs are not in autocommit mode, so COMMIT needs to be issued explicitly when desired. This command can change the session to autocommit mode, where each individual statement is committed implicitly.

SET AUTOCOMMIT is an extension of PostgreSQL ECPG.

Examples:

Example 1 (unknown):

SET AUTOCOMMIT { = | TO } { ON | OFF }

---

PostgreSQL: Documentation: 18: 6.1. Inserting Data

URL: https://www.postgresql.org/docs/current/dml-insert.html

Contents:

• 6.1. Inserting Data #
  • Tip

When a table is created, it contains no data. The first thing to do before a database can be of much use is to insert data. Data is inserted one row at a time. You can also insert more than one row in a single command, but it is not possible to insert something that is not a complete row. Even if you know only some column values, a complete row must be created.

To create a new row, use the INSERT command. The command requires the table name and column values. For example, consider the products table from Chapter 5:

An example command to insert a row would be:

The data values are listed in the order in which the columns appear in the table, separated by commas. Usually, the data values will be literals (constants), but scalar expressions are also allowed.

The above syntax has the drawback that you need to know the order of the columns in the table. To avoid this you can also list the columns explicitly. For example, both of the following commands have the same effect as the one above:

Many users consider it good practice to always list the column names.

If you don't have values for all the columns, you can omit some of them. In that case, the columns will be filled with their default values. For example:

The second form is a PostgreSQL extension. It fills the columns from the left with as many values as are given, and the rest will be defaulted.

For clarity, you can also request default values explicitly, for individual columns or for the entire row:

You can insert multiple rows in a single command:

It is also possible to insert the result of a query (which might be no rows, one row, or many rows):

This provides the full power of the SQL query mechanism (Chapter 7) for computing the rows to be inserted.

When inserting a lot of data at the same time, consider using the COPY command. It is not as flexible as the INSERT command, but is more efficient. Refer to Section 14.4 for more information on improving bulk loading performance.

Examples:

Example 1 (unknown):

CREATE TABLE products (
    product_no integer,
    name text,
    price numeric
);

Example 2 (unknown):

INSERT INTO products VALUES (1, 'Cheese', 9.99);

Example 3 (unknown):

INSERT INTO products (product_no, name, price) VALUES (1, 'Cheese', 9.99);
INSERT INTO products (name, price, product_no) VALUES ('Cheese', 9.99, 1);

Example 4 (unknown):

INSERT INTO products (product_no, name) VALUES (1, 'Cheese');
INSERT INTO products VALUES (1, 'Cheese');

---

PostgreSQL: Documentation: 18: 32.18. LDAP Lookup of Connection Parameters

URL: https://www.postgresql.org/docs/current/libpq-ldap.html

Contents:

• 32.18. LDAP Lookup of Connection Parameters #

If libpq has been compiled with LDAP support (option --with-ldap for configure) it is possible to retrieve connection options like host or dbname via LDAP from a central server. The advantage is that if the connection parameters for a database change, the connection information doesn't have to be updated on all client machines.

LDAP connection parameter lookup uses the connection service file pg_service.conf (see Section 32.17). A line in a pg_service.conf stanza that starts with ldap:// will be recognized as an LDAP URL and an LDAP query will be performed. The result must be a list of keyword = value pairs which will be used to set connection options. The URL must conform to RFC 1959 and be of the form

where hostname defaults to localhost and port defaults to 389.

Processing of pg_service.conf is terminated after a successful LDAP lookup, but is continued if the LDAP server cannot be contacted. This is to provide a fallback with further LDAP URL lines that point to different LDAP servers, classical keyword = value pairs, or default connection options. If you would rather get an error message in this case, add a syntactically incorrect line after the LDAP URL.

A sample LDAP entry that has been created with the LDIF file

might be queried with the following LDAP URL:

You can also mix regular service file entries with LDAP lookups. A complete example for a stanza in pg_service.conf would be:

Examples:

Example 1 (unknown):

ldap://[hostname[:port]]/search_base?attribute?search_scope?filter

Example 2 (unknown):

version:1
dn:cn=mydatabase,dc=mycompany,dc=com
changetype:add
objectclass:top
objectclass:device
cn:mydatabase
description:host=dbserver.mycompany.com
description:port=5439
description:dbname=mydb
description:user=mydb_user
description:sslmode=require

Example 3 (unknown):

ldap://ldap.mycompany.com/dc=mycompany,dc=com?description?one?(cn=mydatabase)

Example 4 (unknown):

# only host and port are stored in LDAP, specify dbname and user explicitly
[customerdb]
dbname=customer
user=appuser
ldap://ldap.acme.com/cn=dbserver,cn=hosts?pgconnectinfo?base?(objectclass=*)

---

PostgreSQL: Documentation: 18: Legal Notice

URL: https://www.postgresql.org/docs/current/legalnotice.html

PostgreSQL Database Management System (also known as Postgres, formerly known as Postgres95)

Portions Copyright © 1996-2025, PostgreSQL Global Development Group

Portions Copyright © 1994, The Regents of the University of California

Permission to use, copy, modify, and distribute this software and its documentation for any purpose, without fee, and without a written agreement is hereby granted, provided that the above copyright notice and this paragraph and the following two paragraphs appear in all copies.

IN NO EVENT SHALL THE UNIVERSITY OF CALIFORNIA BE LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY OF CALIFORNIA HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

THE UNIVERSITY OF CALIFORNIA SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE PROVIDED HEREUNDER IS ON AN “AS-IS” BASIS, AND THE UNIVERSITY OF CALIFORNIA HAS NO OBLIGATIONS TO PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS.

---

PostgreSQL: Documentation: 18: SET CONNECTION

URL: https://www.postgresql.org/docs/current/ecpg-sql-set-connection.html

Contents:

• SET CONNECTION
• Synopsis
• Description
• Parameters
• Examples
• Compatibility
• See Also

SET CONNECTION — select a database connection

SET CONNECTION sets the “current” database connection, which is the one that all commands use unless overridden.

A database connection name established by the CONNECT command.

Set the connection to the current connection (thus, nothing happens).

SET CONNECTION is specified in the SQL standard.

Examples:

Example 1 (unknown):

SET CONNECTION [ TO | = ] connection_name

Example 2 (unknown):

EXEC SQL SET CONNECTION TO con2;
EXEC SQL SET CONNECTION = con1;

---

PostgreSQL: Documentation: 18: 36.12. User-Defined Aggregates

URL: https://www.postgresql.org/docs/current/xaggr.html

Contents:

• 36.12. User-Defined Aggregates #
  • Note
  • 36.12.1. Moving-Aggregate Mode #
  • 36.12.2. Polymorphic and Variadic Aggregates #
  • Note
  • 36.12.3. Ordered-Set Aggregates #
  • 36.12.4. Partial Aggregation #
  • 36.12.5. Support Functions for Aggregates #

Aggregate functions in PostgreSQL are defined in terms of state values and state transition functions. That is, an aggregate operates using a state value that is updated as each successive input row is processed. To define a new aggregate function, one selects a data type for the state value, an initial value for the state, and a state transition function. The state transition function takes the previous state value and the aggregate's input value(s) for the current row, and returns a new state value. A final function can also be specified, in case the desired result of the aggregate is different from the data that needs to be kept in the running state value. The final function takes the ending state value and returns whatever is wanted as the aggregate result. In principle, the transition and final functions are just ordinary functions that could also be used outside the context of the aggregate. (In practice, it's often helpful for performance reasons to create specialized transition functions that can only work when called as part of an aggregate.)

Thus, in addition to the argument and result data types seen by a user of the aggregate, there is an internal state-value data type that might be different from both the argument and result types.

If we define an aggregate that does not use a final function, we have an aggregate that computes a running function of the column values from each row. sum is an example of this kind of aggregate. sum starts at zero and always adds the current row's value to its running total. For example, if we want to make a sum aggregate to work on a data type for complex numbers, we only need the addition function for that data type. The aggregate definition would be:

which we might use like this:

(Notice that we are relying on function overloading: there is more than one aggregate named sum, but PostgreSQL can figure out which kind of sum applies to a column of type complex.)

The above definition of sum will return zero (the initial state value) if there are no nonnull input values. Perhaps we want to return null in that case instead — the SQL standard expects sum to behave that way. We can do this simply by omitting the initcond phrase, so that the initial state value is null. Ordinarily this would mean that the sfunc would need to check for a null state-value input. But for sum and some other simple aggregates like max and min, it is sufficient to insert the first nonnull input value into the state variable and then start applying the transition function at the second nonnull input value. PostgreSQL will do that automatically if the initial state value is null and the transition function is marked “strict” (i.e., not to be called for null inputs).

Another bit of default behavior for a “strict” transition function is that the previous state value is retained unchanged whenever a null input value is encountered. Thus, null values are ignored. If you need some other behavior for null inputs, do not declare your transition function as strict; instead code it to test for null inputs and do whatever is needed.

avg (average) is a more complex example of an aggregate. It requires two pieces of running state: the sum of the inputs and the count of the number of inputs. The final result is obtained by dividing these quantities. Average is typically implemented by using an array as the state value. For example, the built-in implementation of avg(float8) looks like:

float8_accum requires a three-element array, not just two elements, because it accumulates the sum of squares as well as the sum and count of the inputs. This is so that it can be used for some other aggregates as well as avg.

Aggregate function calls in SQL allow DISTINCT and ORDER BY options that control which rows are fed to the aggregate's transition function and in what order. These options are implemented behind the scenes and are not the concern of the aggregate's support functions.

For further details see the CREATE AGGREGATE command.

Aggregate functions can optionally support moving-aggregate mode, which allows substantially faster execution of aggregate functions within windows with moving frame starting points. (See Section 3.5 and Section 4.2.8 for information about use of aggregate functions as window functions.) The basic idea is that in addition to a normal “forward” transition function, the aggregate provides an inverse transition function, which allows rows to be removed from the aggregate's running state value when they exit the window frame. For example a sum aggregate, which uses addition as the forward transition function, would use subtraction as the inverse transition function. Without an inverse transition function, the window function mechanism must recalculate the aggregate from scratch each time the frame starting point moves, resulting in run time proportional to the number of input rows times the average frame length. With an inverse transition function, the run time is only proportional to the number of input rows.

The inverse transition function is passed the current state value and the aggregate input value(s) for the earliest row included in the current state. It must reconstruct what the state value would have been if the given input row had never been aggregated, but only the rows following it. This sometimes requires that the forward transition function keep more state than is needed for plain aggregation mode. Therefore, the moving-aggregate mode uses a completely separate implementation from the plain mode: it has its own state data type, its own forward transition function, and its own final function if needed. These can be the same as the plain mode's data type and functions, if there is no need for extra state.

As an example, we could extend the sum aggregate given above to support moving-aggregate mode like this:

The parameters whose names begin with m define the moving-aggregate implementation. Except for the inverse transition function minvfunc, they correspond to the plain-aggregate parameters without m.

The forward transition function for moving-aggregate mode is not allowed to return null as the new state value. If the inverse transition function returns null, this is taken as an indication that the inverse function cannot reverse the state calculation for this particular input, and so the aggregate calculation will be redone from scratch for the current frame starting position. This convention allows moving-aggregate mode to be used in situations where there are some infrequent cases that are impractical to reverse out of the running state value. The inverse transition function can “punt” on these cases, and yet still come out ahead so long as it can work for most cases. As an example, an aggregate working with floating-point numbers might choose to punt when a NaN (not a number) input has to be removed from the running state value.

When writing moving-aggregate support functions, it is important to be sure that the inverse transition function can reconstruct the correct state value exactly. Otherwise there might be user-visible differences in results depending on whether the moving-aggregate mode is used. An example of an aggregate for which adding an inverse transition function seems easy at first, yet where this requirement cannot be met is sum over float4 or float8 inputs. A naive declaration of sum(float8) could be

This aggregate, however, can give wildly different results than it would have without the inverse transition function. For example, consider

This query returns 0 as its second result, rather than the expected answer of 1. The cause is the limited precision of floating-point values: adding 1 to 1e20 results in 1e20 again, and so subtracting 1e20 from that yields 0, not 1. Note that this is a limitation of floating-point arithmetic in general, not a limitation of PostgreSQL.

Aggregate functions can use polymorphic state transition functions or final functions, so that the same functions can be used to implement multiple aggregates. See Section 36.2.5 for an explanation of polymorphic functions. Going a step further, the aggregate function itself can be specified with polymorphic input type(s) and state type, allowing a single aggregate definition to serve for multiple input data types. Here is an example of a polymorphic aggregate:

Here, the actual state type for any given aggregate call is the array type having the actual input type as elements. The behavior of the aggregate is to concatenate all the inputs into an array of that type. (Note: the built-in aggregate array_agg provides similar functionality, with better performance than this definition would have.)

Here's the output using two different actual data types as arguments:

Ordinarily, an aggregate function with a polymorphic result type has a polymorphic state type, as in the above example. This is necessary because otherwise the final function cannot be declared sensibly: it would need to have a polymorphic result type but no polymorphic argument type, which CREATE FUNCTION will reject on the grounds that the result type cannot be deduced from a call. But sometimes it is inconvenient to use a polymorphic state type. The most common case is where the aggregate support functions are to be written in C and the state type should be declared as internal because there is no SQL-level equivalent for it. To address this case, it is possible to declare the final function as taking extra “dummy” arguments that match the input arguments of the aggregate. Such dummy arguments are always passed as null values since no specific value is available when the final function is called. Their only use is to allow a polymorphic final function's result type to be connected to the aggregate's input type(s). For example, the definition of the built-in aggregate array_agg is equivalent to

Here, the finalfunc_extra option specifies that the final function receives, in addition to the state value, extra dummy argument(s) corresponding to the aggregate's input argument(s). The extra anynonarray argument allows the declaration of array_agg_finalfn to be valid.

An aggregate function can be made to accept a varying number of arguments by declaring its last argument as a VARIADIC array, in much the same fashion as for regular functions; see Section 36.5.6. The aggregate's transition function(s) must have the same array type as their last argument. The transition function(s) typically would also be marked VARIADIC, but this is not strictly required.

Variadic aggregates are easily misused in connection with the ORDER BY option (see Section 4.2.7), since the parser cannot tell whether the wrong number of actual arguments have been given in such a combination. Keep in mind that everything to the right of ORDER BY is a sort key, not an argument to the aggregate. For example, in

the parser will see this as a single aggregate function argument and three sort keys. However, the user might have intended

If myaggregate is variadic, both these calls could be perfectly valid.

For the same reason, it's wise to think twice before creating aggregate functions with the same names and different numbers of regular arguments.

The aggregates we have been describing so far are “normal” aggregates. PostgreSQL also supports ordered-set aggregates, which differ from normal aggregates in two key ways. First, in addition to ordinary aggregated arguments that are evaluated once per input row, an ordered-set aggregate can have “direct” arguments that are evaluated only once per aggregation operation. Second, the syntax for the ordinary aggregated arguments specifies a sort ordering for them explicitly. An ordered-set aggregate is usually used to implement a computation that depends on a specific row ordering, for instance rank or percentile, so that the sort ordering is a required aspect of any call. For example, the built-in definition of percentile_disc is equivalent to:

This aggregate takes a float8 direct argument (the percentile fraction) and an aggregated input that can be of any sortable data type. It could be used to obtain a median household income like this:

Here, 0.5 is a direct argument; it would make no sense for the percentile fraction to be a value varying across rows.

Unlike the case for normal aggregates, the sorting of input rows for an ordered-set aggregate is not done behind the scenes, but is the responsibility of the aggregate's support functions. The typical implementation approach is to keep a reference to a “tuplesort” object in the aggregate's state value, feed the incoming rows into that object, and then complete the sorting and read out the data in the final function. This design allows the final function to perform special operations such as injecting additional “hypothetical” rows into the data to be sorted. While normal aggregates can often be implemented with support functions written in PL/pgSQL or another PL language, ordered-set aggregates generally have to be written in C, since their state values aren't definable as any SQL data type. (In the above example, notice that the state value is declared as type internal — this is typical.) Also, because the final function performs the sort, it is not possible to continue adding input rows by executing the transition function again later. This means the final function is not READ_ONLY; it must be declared in CREATE AGGREGATE as READ_WRITE, or as SHAREABLE if it's possible for additional final-function calls to make use of the already-sorted state.

The state transition function for an ordered-set aggregate receives the current state value plus the aggregated input values for each row, and returns the updated state value. This is the same definition as for normal aggregates, but note that the direct arguments (if any) are not provided. The final function receives the last state value, the values of the direct arguments if any, and (if finalfunc_extra is specified) null values corresponding to the aggregated input(s). As with normal aggregates, finalfunc_extra is only really useful if the aggregate is polymorphic; then the extra dummy argument(s) are needed to connect the final function's result type to the aggregate's input type(s).

Currently, ordered-set aggregates cannot be used as window functions, and therefore there is no need for them to support moving-aggregate mode.

Optionally, an aggregate function can support partial aggregation. The idea of partial aggregation is to run the aggregate's state transition function over different subsets of the input data independently, and then to combine the state values resulting from those subsets to produce the same state value that would have resulted from scanning all the input in a single operation. This mode can be used for parallel aggregation by having different worker processes scan different portions of a table. Each worker produces a partial state value, and at the end those state values are combined to produce a final state value. (In the future this mode might also be used for purposes such as combining aggregations over local and remote tables; but that is not implemented yet.)

To support partial aggregation, the aggregate definition must provide a combine function, which takes two values of the aggregate's state type (representing the results of aggregating over two subsets of the input rows) and produces a new value of the state type, representing what the state would have been after aggregating over the combination of those sets of rows. It is unspecified what the relative order of the input rows from the two sets would have been. This means that it's usually impossible to define a useful combine function for aggregates that are sensitive to input row order.

As simple examples, MAX and MIN aggregates can be made to support partial aggregation by specifying the combine function as the same greater-of-two or lesser-of-two comparison function that is used as their transition function. SUM aggregates just need an addition function as combine function. (Again, this is the same as their transition function, unless the state value is wider than the input data type.)

The combine function is treated much like a transition function that happens to take a value of the state type, not of the underlying input type, as its second argument. In particular, the rules for dealing with null values and strict functions are similar. Also, if the aggregate definition specifies a non-null initcond, keep in mind that that will be used not only as the initial state for each partial aggregation run, but also as the initial state for the combine function, which will be called to combine each partial result into that state.

If the aggregate's state type is declared as internal, it is the combine function's responsibility that its result is allocated in the correct memory context for aggregate state values. This means in particular that when the first input is NULL it's invalid to simply return the second input, as that value will be in the wrong context and will not have sufficient lifespan.

When the aggregate's state type is declared as internal, it is usually also appropriate for the aggregate definition to provide a serialization function and a deserialization function, which allow such a state value to be copied from one process to another. Without these functions, parallel aggregation cannot be performed, and future applications such as local/remote aggregation will probably not work either.

A serialization function must take a single argument of type internal and return a result of type bytea, which represents the state value packaged up into a flat blob of bytes. Conversely, a deserialization function reverses that conversion. It must take two arguments of types bytea and internal, and return a result of type internal. (The second argument is unused and is always zero, but it is required for type-safety reasons.) The result of the deserialization function should simply be allocated in the current memory context, as unlike the combine function's result, it is not long-lived.

Worth noting also is that for an aggregate to be executed in parallel, the aggregate itself must be marked PARALLEL SAFE. The parallel-safety markings on its support functions are not consulted.

A function written in C can detect that it is being called as an aggregate support function by calling AggCheckCallContext, for example:

One reason for checking this is that when it is true, the first input must be a temporary state value and can therefore safely be modified in-place rather than allocating a new copy. See int8inc() for an example. (While aggregate transition functions are always allowed to modify the transition value in-place, aggregate final functions are generally discouraged from doing so; if they do so, the behavior must be declared when creating the aggregate. See CREATE AGGREGATE for more detail.)

The second argument of AggCheckCallContext can be used to retrieve the memory context in which aggregate state values are being kept. This is useful for transition functions that wish to use “expanded” objects (see Section 36.13.1) as their state values. On first call, the transition function should return an expanded object whose memory context is a child of the aggregate state context, and then keep returning the same expanded object on subsequent calls. See array_append() for an example. (array_append() is not the transition function of any built-in aggregate, but it is written to behave efficiently when used as transition function of a custom aggregate.)

Another support routine available to aggregate functions written in C is AggGetAggref, which returns the Aggref parse node that defines the aggregate call. This is mainly useful for ordered-set aggregates, which can inspect the substructure of the Aggref node to find out what sort ordering they are supposed to implement. Examples can be found in orderedsetaggs.c in the PostgreSQL source code.

Examples:

Example 1 (unknown):

CREATE AGGREGATE sum (complex)
(
    sfunc = complex_add,
    stype = complex,
    initcond = '(0,0)'
);

Example 2 (unknown):

SELECT sum(a) FROM test_complex;

   sum
-----------
 (34,53.9)

Example 3 (unknown):

CREATE AGGREGATE avg (float8)
(
    sfunc = float8_accum,
    stype = float8[],
    finalfunc = float8_avg,
    initcond = '{0,0,0}'
);

Example 4 (unknown):

CREATE AGGREGATE sum (complex)
(
    sfunc = complex_add,
    stype = complex,
    initcond = '(0,0)',
    msfunc = complex_add,
    minvfunc = complex_sub,
    mstype = complex,
    minitcond = '(0,0)'
);

---

PostgreSQL: Documentation: 18: EXPLAIN

URL: https://www.postgresql.org/docs/current/sql-explain.html

Contents:

• EXPLAIN
• Synopsis
• Description
  • Important
• Parameters
• Outputs
• Notes
• Examples
• Compatibility
• See Also

EXPLAIN — show the execution plan of a statement

This command displays the execution plan that the PostgreSQL planner generates for the supplied statement. The execution plan shows how the table(s) referenced by the statement will be scanned — by plain sequential scan, index scan, etc. — and if multiple tables are referenced, what join algorithms will be used to bring together the required rows from each input table.

The most critical part of the display is the estimated statement execution cost, which is the planner's guess at how long it will take to run the statement (measured in cost units that are arbitrary, but conventionally mean disk page fetches). Actually two numbers are shown: the start-up cost before the first row can be returned, and the total cost to return all the rows. For most queries the total cost is what matters, but in contexts such as a subquery in EXISTS, the planner will choose the smallest start-up cost instead of the smallest total cost (since the executor will stop after getting one row, anyway). Also, if you limit the number of rows to return with a LIMIT clause, the planner makes an appropriate interpolation between the endpoint costs to estimate which plan is really the cheapest.

The ANALYZE option causes the statement to be actually executed, not only planned. Then actual run time statistics are added to the display, including the total elapsed time expended within each plan node (in milliseconds) and the total number of rows it actually returned. This is useful for seeing whether the planner's estimates are close to reality.

Keep in mind that the statement is actually executed when the ANALYZE option is used. Although EXPLAIN will discard any output that a SELECT would return, other side effects of the statement will happen as usual. If you wish to use EXPLAIN ANALYZE on an INSERT, UPDATE, DELETE, MERGE, CREATE TABLE AS, or EXECUTE statement without letting the command affect your data, use this approach:

Carry out the command and show actual run times and other statistics. This parameter defaults to FALSE.

Display additional information regarding the plan. Specifically, include the output column list for each node in the plan tree, schema-qualify table and function names, always label variables in expressions with their range table alias, and always print the name of each trigger for which statistics are displayed. The query identifier will also be displayed if one has been computed, see compute_query_id for more details. This parameter defaults to FALSE.

Include information on the estimated startup and total cost of each plan node, as well as the estimated number of rows and the estimated width of each row. This parameter defaults to TRUE.

Include information on configuration parameters. Specifically, include options affecting query planning with value different from the built-in default value. This parameter defaults to FALSE.

Allow the statement to contain parameter placeholders like $1, and generate a generic plan that does not depend on the values of those parameters. See PREPARE for details about generic plans and the types of statement that support parameters. This parameter cannot be used together with ANALYZE. It defaults to FALSE.

Include information on buffer usage. Specifically, include the number of shared blocks hit, read, dirtied, and written, the number of local blocks hit, read, dirtied, and written, the number of temp blocks read and written, and the time spent reading and writing data file blocks, local blocks and temporary file blocks (in milliseconds) if track_io_timing is enabled. A hit means that a read was avoided because the block was found already in cache when needed. Shared blocks contain data from regular tables and indexes; local blocks contain data from temporary tables and indexes; while temporary blocks contain short-term working data used in sorts, hashes, Materialize plan nodes, and similar cases. The number of blocks dirtied indicates the number of previously unmodified blocks that were changed by this query; while the number of blocks written indicates the number of previously-dirtied blocks evicted from cache by this backend during query processing. The number of blocks shown for an upper-level node includes those used by all its child nodes. In text format, only non-zero values are printed. Buffers information is automatically included when ANALYZE is used.

Include information on the cost of serializing the query's output data, that is converting it to text or binary format to send to the client. This can be a significant part of the time required for regular execution of the query, if the datatype output functions are expensive or if TOASTed values must be fetched from out-of-line storage. EXPLAIN's default behavior, SERIALIZE NONE, does not perform these conversions. If SERIALIZE TEXT or SERIALIZE BINARY is specified, the appropriate conversions are performed, and the time spent doing so is measured (unless TIMING OFF is specified). If the BUFFERS option is also specified, then any buffer accesses involved in the conversions are counted too. In no case, however, will EXPLAIN actually send the resulting data to the client; hence network transmission costs cannot be investigated this way. Serialization may only be enabled when ANALYZE is also enabled. If SERIALIZE is written without an argument, TEXT is assumed.

Include information on WAL record generation. Specifically, include the number of records, number of full page images (fpi), the amount of WAL generated in bytes and the number of times the WAL buffers became full. In text format, only non-zero values are printed. This parameter may only be used when ANALYZE is also enabled. It defaults to FALSE.

Include actual startup time and time spent in each node in the output. The overhead of repeatedly reading the system clock can slow down the query significantly on some systems, so it may be useful to set this parameter to FALSE when only actual row counts, and not exact times, are needed. Run time of the entire statement is always measured, even when node-level timing is turned off with this option. This parameter may only be used when ANALYZE is also enabled. It defaults to TRUE.

Include summary information (e.g., totaled timing information) after the query plan. Summary information is included by default when ANALYZE is used but otherwise is not included by default, but can be enabled using this option. Planning time in EXPLAIN EXECUTE includes the time required to fetch the plan from the cache and the time required for re-planning, if necessary.

Include information on memory consumption by the query planning phase. Specifically, include the precise amount of storage used by planner in-memory structures, as well as total memory considering allocation overhead. This parameter defaults to FALSE.

Specify the output format, which can be TEXT, XML, JSON, or YAML. Non-text output contains the same information as the text output format, but is easier for programs to parse. This parameter defaults to TEXT.

Specifies whether the selected option should be turned on or off. You can write TRUE, ON, or 1 to enable the option, and FALSE, OFF, or 0 to disable it. The boolean value can also be omitted, in which case TRUE is assumed.

Any SELECT, INSERT, UPDATE, DELETE, MERGE, VALUES, EXECUTE, DECLARE, CREATE TABLE AS, or CREATE MATERIALIZED VIEW AS statement, whose execution plan you wish to see.

The command's result is a textual description of the plan selected for the statement, optionally annotated with execution statistics. Section 14.1 describes the information provided.

In order to allow the PostgreSQL query planner to make reasonably informed decisions when optimizing queries, the pg_statistic data should be up-to-date for all tables used in the query. Normally the autovacuum daemon will take care of that automatically. But if a table has recently had substantial changes in its contents, you might need to do a manual ANALYZE rather than wait for autovacuum to catch up with the changes.

In order to measure the run-time cost of each node in the execution plan, the current implementation of EXPLAIN ANALYZE adds profiling overhead to query execution. As a result, running EXPLAIN ANALYZE on a query can sometimes take significantly longer than executing the query normally. The amount of overhead depends on the nature of the query, as well as the platform being used. The worst case occurs for plan nodes that in themselves require very little time per execution, and on machines that have relatively slow operating system calls for obtaining the time of day.

To show the plan for a simple query on a table with a single integer column and 10000 rows:

Here is the same query, with JSON output formatting:

If there is an index and we use a query with an indexable WHERE condition, EXPLAIN might show a different plan:

Here is the same query, but in YAML format:

XML format is left as an exercise for the reader.

Here is the same plan with cost estimates suppressed:

Here is an example of a query plan for a query using an aggregate function:

Here is an example of using EXPLAIN EXECUTE to display the execution plan for a prepared query:

Of course, the specific numbers shown here depend on the actual contents of the tables involved. Also note that the numbers, and even the selected query strategy, might vary between PostgreSQL releases due to planner improvements. In addition, the ANALYZE command uses random sampling to estimate data statistics; therefore, it is possible for cost estimates to change after a fresh run of ANALYZE, even if the actual distribution of data in the table has not changed.

Notice that the previous example showed a “custom” plan for the specific parameter values given in EXECUTE. We might also wish to see the generic plan for a parameterized query, which can be done with GENERIC_PLAN:

In this case the parser correctly inferred that $1 and $2 should have the same data type as id, so the lack of parameter type information from PREPARE was not a problem. In other cases it might be necessary to explicitly specify types for the parameter symbols, which can be done by casting them, for example:

There is no EXPLAIN statement defined in the SQL standard.

The following syntax was used before PostgreSQL version 9.0 and is still supported:

Note that in this syntax, the options must be specified in exactly the order shown.

Examples:

Example 1 (unknown):

EXPLAIN [ ( option [, ...] ) ] statement

where option can be one of:

    ANALYZE [ boolean ]
    VERBOSE [ boolean ]
    COSTS [ boolean ]
    SETTINGS [ boolean ]
    GENERIC_PLAN [ boolean ]
    BUFFERS [ boolean ]
    SERIALIZE [ { NONE | TEXT | BINARY } ]
    WAL [ boolean ]
    TIMING [ boolean ]
    SUMMARY [ boolean ]
    MEMORY [ boolean ]
    FORMAT { TEXT | XML | JSON | YAML }

Example 2 (unknown):

BEGIN;
EXPLAIN ANALYZE ...;
ROLLBACK;

Example 3 (unknown):

EXPLAIN SELECT * FROM foo;

                       QUERY PLAN
---------------------------------------------------------
 Seq Scan on foo  (cost=0.00..155.00 rows=10000 width=4)
(1 row)

Example 4 (unknown):

EXPLAIN (FORMAT JSON) SELECT * FROM foo;
           QUERY PLAN
--------------------------------
 [                             +
   {                           +
     "Plan": {                 +
       "Node Type": "Seq Scan",+
       "Relation Name": "foo", +
       "Alias": "foo",         +
       "Startup Cost": 0.00,   +
       "Total Cost": 155.00,   +
       "Plan Rows": 10000,     +
       "Plan Width": 4         +
     }                         +
   }                           +
 ]
(1 row)

---

PostgreSQL: Documentation: 18: 35.5. applicable_roles

URL: https://www.postgresql.org/docs/current/infoschema-applicable-roles.html

Contents:

• 35.5. applicable_roles #

The view applicable_roles identifies all roles whose privileges the current user can use. This means there is some chain of role grants from the current user to the role in question. The current user itself is also an applicable role. The set of applicable roles is generally used for permission checking.

Table 35.3. applicable_roles Columns

grantee sql_identifier

Name of the role to which this role membership was granted (can be the current user, or a different role in case of nested role memberships)

role_name sql_identifier

is_grantable yes_or_no

YES if the grantee has the admin option on the role, NO if not

---

PostgreSQL: Documentation: 18: 21.3. Role Membership

URL: https://www.postgresql.org/docs/current/role-membership.html

Contents:

• 21.3. Role Membership #
  • Note
  • Note

It is frequently convenient to group users together to ease management of privileges: that way, privileges can be granted to, or revoked from, a group as a whole. In PostgreSQL this is done by creating a role that represents the group, and then granting membership in the group role to individual user roles.

To set up a group role, first create the role:

Typically a role being used as a group would not have the LOGIN attribute, though you can set it if you wish.

Once the group role exists, you can add and remove members using the GRANT and REVOKE commands:

You can grant membership to other group roles, too (since there isn't really any distinction between group roles and non-group roles). The database will not let you set up circular membership loops. Also, it is not permitted to grant membership in a role to PUBLIC.

The members of a group role can use the privileges of the role in two ways. First, member roles that have been granted membership with the SET option can do SET ROLE to temporarily “become” the group role. In this state, the database session has access to the privileges of the group role rather than the original login role, and any database objects created are considered owned by the group role, not the login role. Second, member roles that have been granted membership with the INHERIT option automatically have use of the privileges of those directly or indirectly a member of, though the chain stops at memberships lacking the inherit option. As an example, suppose we have done:

Immediately after connecting as role joe, a database session will have use of privileges granted directly to joe plus any privileges granted to admin and island, because joe “inherits” those privileges. However, privileges granted to wheel are not available, because even though joe is indirectly a member of wheel, the membership is via admin which was granted using WITH INHERIT FALSE. After:

the session would have use of only those privileges granted to admin, and not those granted to joe or island. After:

the session would have use of only those privileges granted to wheel, and not those granted to either joe or admin. The original privilege state can be restored with any of:

The SET ROLE command always allows selecting any role that the original login role is directly or indirectly a member of, provided that there is a chain of membership grants each of which has SET TRUE (which is the default). Thus, in the above example, it is not necessary to become admin before becoming wheel. On the other hand, it is not possible to become island at all; joe can only access those privileges via inheritance.

In the SQL standard, there is a clear distinction between users and roles, and users do not automatically inherit privileges while roles do. This behavior can be obtained in PostgreSQL by giving roles being used as SQL roles the INHERIT attribute, while giving roles being used as SQL users the NOINHERIT attribute. However, PostgreSQL defaults to giving all roles the INHERIT attribute, for backward compatibility with pre-8.1 releases in which users always had use of permissions granted to groups they were members of.

The role attributes LOGIN, SUPERUSER, CREATEDB, and CREATEROLE can be thought of as special privileges, but they are never inherited as ordinary privileges on database objects are. You must actually SET ROLE to a specific role having one of these attributes in order to make use of the attribute. Continuing the above example, we might choose to grant CREATEDB and CREATEROLE to the admin role. Then a session connecting as role joe would not have these privileges immediately, only after doing SET ROLE admin.

To destroy a group role, use DROP ROLE:

Any memberships in the group role are automatically revoked (but the member roles are not otherwise affected).

Examples:

Example 1 (unknown):

CREATE ROLE name;

Example 2 (unknown):

GRANT group_role TO role1, ... ;
REVOKE group_role FROM role1, ... ;

Example 3 (unknown):

CREATE ROLE joe LOGIN;
CREATE ROLE admin;
CREATE ROLE wheel;
CREATE ROLE island;
GRANT admin TO joe WITH INHERIT TRUE;
GRANT wheel TO admin WITH INHERIT FALSE;
GRANT island TO joe WITH INHERIT TRUE, SET FALSE;

Example 4 (unknown):

SET ROLE admin;

---

PostgreSQL: Documentation: 18: 18.10. Secure TCP/IP Connections with GSSAPI Encryption

URL: https://www.postgresql.org/docs/current/gssapi-enc.html

Contents:

• 18.10. Secure TCP/IP Connections with GSSAPI Encryption #
  • 18.10.1. Basic Setup #

PostgreSQL also has native support for using GSSAPI to encrypt client/server communications for increased security. Support requires that a GSSAPI implementation (such as MIT Kerberos) is installed on both client and server systems, and that support in PostgreSQL is enabled at build time (see Chapter 17).

The PostgreSQL server will listen for both normal and GSSAPI-encrypted connections on the same TCP port, and will negotiate with any connecting client whether to use GSSAPI for encryption (and for authentication). By default, this decision is up to the client (which means it can be downgraded by an attacker); see Section 20.1 about setting up the server to require the use of GSSAPI for some or all connections.

When using GSSAPI for encryption, it is common to use GSSAPI for authentication as well, since the underlying mechanism will determine both client and server identities (according to the GSSAPI implementation) in any case. But this is not required; another PostgreSQL authentication method can be chosen to perform additional verification.

Other than configuration of the negotiation behavior, GSSAPI encryption requires no setup beyond that which is necessary for GSSAPI authentication. (For more information on configuring that, see Section 20.6.)

---

PostgreSQL: Documentation: 18: 35.35. role_column_grants

URL: https://www.postgresql.org/docs/current/infoschema-role-column-grants.html

Contents:

• 35.35. role_column_grants #

The view role_column_grants identifies all privileges granted on columns where the grantor or grantee is a currently enabled role. Further information can be found under column_privileges. The only effective difference between this view and column_privileges is that this view omits columns that have been made accessible to the current user by way of a grant to PUBLIC.

Table 35.33. role_column_grants Columns

grantor sql_identifier

Name of the role that granted the privilege

grantee sql_identifier

Name of the role that the privilege was granted to

table_catalog sql_identifier

Name of the database that contains the table that contains the column (always the current database)

table_schema sql_identifier

Name of the schema that contains the table that contains the column

table_name sql_identifier

Name of the table that contains the column

column_name sql_identifier

privilege_type character_data

Type of the privilege: SELECT, INSERT, UPDATE, or REFERENCES

is_grantable yes_or_no

YES if the privilege is grantable, NO if not

---

PostgreSQL: Documentation: 18: 8.21. Pseudo-Types

URL: https://www.postgresql.org/docs/current/datatype-pseudo.html

Contents:

• 8.21. Pseudo-Types #

The PostgreSQL type system contains a number of special-purpose entries that are collectively called pseudo-types. A pseudo-type cannot be used as a column data type, but it can be used to declare a function's argument or result type. Each of the available pseudo-types is useful in situations where a function's behavior does not correspond to simply taking or returning a value of a specific SQL data type. Table 8.27 lists the existing pseudo-types.

Table 8.27. Pseudo-Types

Functions coded in C (whether built-in or dynamically loaded) can be declared to accept or return any of these pseudo-types. It is up to the function author to ensure that the function will behave safely when a pseudo-type is used as an argument type.

Functions coded in procedural languages can use pseudo-types only as allowed by their implementation languages. At present most procedural languages forbid use of a pseudo-type as an argument type, and allow only void and record as a result type (plus trigger or event_trigger when the function is used as a trigger or event trigger). Some also support polymorphic functions using the polymorphic pseudo-types, which are shown above and discussed in detail in Section 36.2.5.

The internal pseudo-type is used to declare functions that are meant only to be called internally by the database system, and not by direct invocation in an SQL query. If a function has at least one internal-type argument then it cannot be called from SQL. To preserve the type safety of this restriction it is important to follow this coding rule: do not create any function that is declared to return internal unless it has at least one internal argument.

---

PostgreSQL: Documentation: 18: Part VIII. Appendixes

URL: https://www.postgresql.org/docs/current/appendixes.html

Contents:

• Part VIII. Appendixes

---

PostgreSQL: Documentation: 18: 8.3. Character Types

URL: https://www.postgresql.org/docs/current/datatype-character.html

Contents:

• 8.3. Character Types #
  • Tip

Table 8.4. Character Types

Table 8.4 shows the general-purpose character types available in PostgreSQL.

SQL defines two primary character types: character varying(n) and character(n), where n is a positive integer. Both of these types can store strings up to n characters (not bytes) in length. An attempt to store a longer string into a column of these types will result in an error, unless the excess characters are all spaces, in which case the string will be truncated to the maximum length. (This somewhat bizarre exception is required by the SQL standard.) However, if one explicitly casts a value to character varying(n) or character(n), then an over-length value will be truncated to n characters without raising an error. (This too is required by the SQL standard.) If the string to be stored is shorter than the declared length, values of type character will be space-padded; values of type character varying will simply store the shorter string.

In addition, PostgreSQL provides the text type, which stores strings of any length. Although the text type is not in the SQL standard, several other SQL database management systems have it as well. text is PostgreSQL's native string data type, in that most built-in functions operating on strings are declared to take or return text not character varying. For many purposes, character varying acts as though it were a domain over text.

The type name varchar is an alias for character varying, while bpchar (with length specifier) and char are aliases for character. The varchar and char aliases are defined in the SQL standard; bpchar is a PostgreSQL extension.

If specified, the length n must be greater than zero and cannot exceed 10,485,760. If character varying (or varchar) is used without length specifier, the type accepts strings of any length. If bpchar lacks a length specifier, it also accepts strings of any length, but trailing spaces are semantically insignificant. If character (or char) lacks a specifier, it is equivalent to character(1).

Values of type character are physically padded with spaces to the specified width n, and are stored and displayed that way. However, trailing spaces are treated as semantically insignificant and disregarded when comparing two values of type character. In collations where whitespace is significant, this behavior can produce unexpected results; for example SELECT 'a '::CHAR(2) collate "C" < E'a\n'::CHAR(2) returns true, even though C locale would consider a space to be greater than a newline. Trailing spaces are removed when converting a character value to one of the other string types. Note that trailing spaces are semantically significant in character varying and text values, and when using pattern matching, that is LIKE and regular expressions.

The characters that can be stored in any of these data types are determined by the database character set, which is selected when the database is created. Regardless of the specific character set, the character with code zero (sometimes called NUL) cannot be stored. For more information refer to Section 23.3.

The storage requirement for a short string (up to 126 bytes) is 1 byte plus the actual string, which includes the space padding in the case of character. Longer strings have 4 bytes of overhead instead of 1. Long strings are compressed by the system automatically, so the physical requirement on disk might be less. Very long values are also stored in background tables so that they do not interfere with rapid access to shorter column values. In any case, the longest possible character string that can be stored is about 1 GB. (The maximum value that will be allowed for n in the data type declaration is less than that. It wouldn't be useful to change this because with multibyte character encodings the number of characters and bytes can be quite different. If you desire to store long strings with no specific upper limit, use text or character varying without a length specifier, rather than making up an arbitrary length limit.)

There is no performance difference among these three types, apart from increased storage space when using the blank-padded type, and a few extra CPU cycles to check the length when storing into a length-constrained column. While character(n) has performance advantages in some other database systems, there is no such advantage in PostgreSQL; in fact character(n) is usually the slowest of the three because of its additional storage costs. In most situations text or character varying should be used instead.

Refer to Section 4.1.2.1 for information about the syntax of string literals, and to Chapter 9 for information about available operators and functions.

Example 8.1. Using the Character Types

The char_length function is discussed in Section 9.4.

There are two other fixed-length character types in PostgreSQL, shown in Table 8.5. These are not intended for general-purpose use, only for use in the internal system catalogs. The name type is used to store identifiers. Its length is currently defined as 64 bytes (63 usable characters plus terminator) but should be referenced using the constant NAMEDATALEN in C source code. The length is set at compile time (and is therefore adjustable for special uses); the default maximum length might change in a future release. The type "char" (note the quotes) is different from char(1) in that it only uses one byte of storage, and therefore can store only a single ASCII character. It is used in the system catalogs as a simplistic enumeration type.

Table 8.5. Special Character Types

Examples:

Example 1 (unknown):

CREATE TABLE test1 (a character(4));
INSERT INTO test1 VALUES ('ok');
SELECT a, char_length(a) FROM test1; -- (1)

  a   | char_length
------+-------------
 ok   |           2


CREATE TABLE test2 (b varchar(5));
INSERT INTO test2 VALUES ('ok');
INSERT INTO test2 VALUES ('good      ');
INSERT INTO test2 VALUES ('too long');
ERROR:  value too long for type character varying(5)
INSERT INTO test2 VALUES ('too long'::varchar(5)); -- explicit truncation
SELECT b, char_length(b) FROM test2;

   b   | char_length
-------+-------------
 ok    |           2
 good  |           5
 too l |           5

---

PostgreSQL: Documentation: 18: 32.14. Event System

URL: https://www.postgresql.org/docs/current/libpq-events.html

Contents:

• 32.14. Event System #
  • 32.14.1. Event Types #
  • 32.14.2. Event Callback Procedure #
  • Caution
  • 32.14.3. Event Support Functions #
  • 32.14.4. Event Example #

libpq's event system is designed to notify registered event handlers about interesting libpq events, such as the creation or destruction of PGconn and PGresult objects. A principal use case is that this allows applications to associate their own data with a PGconn or PGresult and ensure that that data is freed at an appropriate time.

Each registered event handler is associated with two pieces of data, known to libpq only as opaque void * pointers. There is a pass-through pointer that is provided by the application when the event handler is registered with a PGconn. The pass-through pointer never changes for the life of the PGconn and all PGresults generated from it; so if used, it must point to long-lived data. In addition there is an instance data pointer, which starts out NULL in every PGconn and PGresult. This pointer can be manipulated using the PQinstanceData, PQsetInstanceData, PQresultInstanceData and PQresultSetInstanceData functions. Note that unlike the pass-through pointer, instance data of a PGconn is not automatically inherited by PGresults created from it. libpq does not know what pass-through and instance data pointers point to (if anything) and will never attempt to free them — that is the responsibility of the event handler.

The enum PGEventId names the types of events handled by the event system. All its values have names beginning with PGEVT. For each event type, there is a corresponding event info structure that carries the parameters passed to the event handlers. The event types are:

The register event occurs when PQregisterEventProc is called. It is the ideal time to initialize any instanceData an event procedure may need. Only one register event will be fired per event handler per connection. If the event procedure fails (returns zero), the registration is canceled.

When a PGEVT_REGISTER event is received, the evtInfo pointer should be cast to a PGEventRegister *. This structure contains a PGconn that should be in the CONNECTION_OK status; guaranteed if one calls PQregisterEventProc right after obtaining a good PGconn. When returning a failure code, all cleanup must be performed as no PGEVT_CONNDESTROY event will be sent.

The connection reset event is fired on completion of PQreset or PQresetPoll. In both cases, the event is only fired if the reset was successful. The return value of the event procedure is ignored in PostgreSQL v15 and later. With earlier versions, however, it's important to return success (nonzero) or the connection will be aborted.

When a PGEVT_CONNRESET event is received, the evtInfo pointer should be cast to a PGEventConnReset *. Although the contained PGconn was just reset, all event data remains unchanged. This event should be used to reset/reload/requery any associated instanceData. Note that even if the event procedure fails to process PGEVT_CONNRESET, it will still receive a PGEVT_CONNDESTROY event when the connection is closed.

The connection destroy event is fired in response to PQfinish. It is the event procedure's responsibility to properly clean up its event data as libpq has no ability to manage this memory. Failure to clean up will lead to memory leaks.

When a PGEVT_CONNDESTROY event is received, the evtInfo pointer should be cast to a PGEventConnDestroy *. This event is fired prior to PQfinish performing any other cleanup. The return value of the event procedure is ignored since there is no way of indicating a failure from PQfinish. Also, an event procedure failure should not abort the process of cleaning up unwanted memory.

The result creation event is fired in response to any query execution function that generates a result, including PQgetResult. This event will only be fired after the result has been created successfully.

When a PGEVT_RESULTCREATE event is received, the evtInfo pointer should be cast to a PGEventResultCreate *. The conn is the connection used to generate the result. This is the ideal place to initialize any instanceData that needs to be associated with the result. If an event procedure fails (returns zero), that event procedure will be ignored for the remaining lifetime of the result; that is, it will not receive PGEVT_RESULTCOPY or PGEVT_RESULTDESTROY events for this result or results copied from it.

The result copy event is fired in response to PQcopyResult. This event will only be fired after the copy is complete. Only event procedures that have successfully handled the PGEVT_RESULTCREATE or PGEVT_RESULTCOPY event for the source result will receive PGEVT_RESULTCOPY events.

When a PGEVT_RESULTCOPY event is received, the evtInfo pointer should be cast to a PGEventResultCopy *. The src result is what was copied while the dest result is the copy destination. This event can be used to provide a deep copy of instanceData, since PQcopyResult cannot do that. If an event procedure fails (returns zero), that event procedure will be ignored for the remaining lifetime of the new result; that is, it will not receive PGEVT_RESULTCOPY or PGEVT_RESULTDESTROY events for that result or results copied from it.

The result destroy event is fired in response to a PQclear. It is the event procedure's responsibility to properly clean up its event data as libpq has no ability to manage this memory. Failure to clean up will lead to memory leaks.

When a PGEVT_RESULTDESTROY event is received, the evtInfo pointer should be cast to a PGEventResultDestroy *. This event is fired prior to PQclear performing any other cleanup. The return value of the event procedure is ignored since there is no way of indicating a failure from PQclear. Also, an event procedure failure should not abort the process of cleaning up unwanted memory.

PGEventProc is a typedef for a pointer to an event procedure, that is, the user callback function that receives events from libpq. The signature of an event procedure must be

The evtId parameter indicates which PGEVT event occurred. The evtInfo pointer must be cast to the appropriate structure type to obtain further information about the event. The passThrough parameter is the pointer provided to PQregisterEventProc when the event procedure was registered. The function should return a non-zero value if it succeeds and zero if it fails.

A particular event procedure can be registered only once in any PGconn. This is because the address of the procedure is used as a lookup key to identify the associated instance data.

On Windows, functions can have two different addresses: one visible from outside a DLL and another visible from inside the DLL. One should be careful that only one of these addresses is used with libpq's event-procedure functions, else confusion will result. The simplest rule for writing code that will work is to ensure that event procedures are declared static. If the procedure's address must be available outside its own source file, expose a separate function to return the address.

Registers an event callback procedure with libpq.

An event procedure must be registered once on each PGconn you want to receive events about. There is no limit, other than memory, on the number of event procedures that can be registered with a connection. The function returns a non-zero value if it succeeds and zero if it fails.

The proc argument will be called when a libpq event is fired. Its memory address is also used to lookup instanceData. The name argument is used to refer to the event procedure in error messages. This value cannot be NULL or a zero-length string. The name string is copied into the PGconn, so what is passed need not be long-lived. The passThrough pointer is passed to the proc whenever an event occurs. This argument can be NULL.

Sets the connection conn's instanceData for procedure proc to data. This returns non-zero for success and zero for failure. (Failure is only possible if proc has not been properly registered in conn.)

Returns the connection conn's instanceData associated with procedure proc, or NULL if there is none.

Sets the result's instanceData for proc to data. This returns non-zero for success and zero for failure. (Failure is only possible if proc has not been properly registered in the result.)

Beware that any storage represented by data will not be accounted for by PQresultMemorySize, unless it is allocated using PQresultAlloc. (Doing so is recommendable because it eliminates the need to free such storage explicitly when the result is destroyed.)

Returns the result's instanceData associated with proc, or NULL if there is none.

Here is a skeleton example of managing private data associated with libpq connections and results.

Examples:

Example 1 (unknown):

typedef struct
{
    PGconn *conn;
} PGEventRegister;

Example 2 (unknown):

typedef struct
{
    PGconn *conn;
} PGEventConnReset;

Example 3 (unknown):

typedef struct
{
    PGconn *conn;
} PGEventConnDestroy;

Example 4 (unknown):

typedef struct
{
    PGconn *conn;
    PGresult *result;
} PGEventResultCreate;

---

PostgreSQL: Documentation: 18: 20.1. The pg_hba.conf File

URL: https://www.postgresql.org/docs/current/auth-pg-hba-conf.html

Contents:

• 20.1. The pg_hba.conf File #
  • Note
  • Note
  • Note
  • Warning
  • Tip

Client authentication is controlled by a configuration file, which traditionally is named pg_hba.conf and is stored in the database cluster's data directory. (HBA stands for host-based authentication.) A default pg_hba.conf file is installed when the data directory is initialized by initdb. It is possible to place the authentication configuration file elsewhere, however; see the hba_file configuration parameter.

The pg_hba.conf file is read on start-up and when the main server process receives a SIGHUP signal. If you edit the file on an active system, you will need to signal the postmaster (using pg_ctl reload, calling the SQL function pg_reload_conf(), or using kill -HUP) to make it re-read the file.

The preceding statement is not true on Microsoft Windows: there, any changes in the pg_hba.conf file are immediately applied by subsequent new connections.

The system view pg_hba_file_rules can be helpful for pre-testing changes to the pg_hba.conf file, or for diagnosing problems if loading of the file did not have the desired effects. Rows in the view with non-null error fields indicate problems in the corresponding lines of the file.

The general format of the pg_hba.conf file is a set of records, one per line. Blank lines are ignored, as is any text after the # comment character. A record can be continued onto the next line by ending the line with a backslash. (Backslashes are not special except at the end of a line.) A record is made up of a number of fields which are separated by spaces and/or tabs. Fields can contain white space if the field value is double-quoted. Quoting one of the keywords in a database, user, or address field (e.g., all or replication) makes the word lose its special meaning, and just match a database, user, or host with that name. Backslash line continuation applies even within quoted text or comments.

Each authentication record specifies a connection type, a client IP address range (if relevant for the connection type), a database name, a user name, and the authentication method to be used for connections matching these parameters. The first record with a matching connection type, client address, requested database, and user name is used to perform authentication. There is no “fall-through” or “backup”: if one record is chosen and the authentication fails, subsequent records are not considered. If no record matches, access is denied.

Each record can be an include directive or an authentication record. Include directives specify files that can be included, that contain additional records. The records will be inserted in place of the include directives. Include directives only contain two fields: include, include_if_exists or include_dir directive and the file or directory to be included. The file or directory can be a relative or absolute path, and can be double-quoted. For the include_dir form, all files not starting with a . and ending with .conf will be included. Multiple files within an include directory are processed in file name order (according to C locale rules, i.e., numbers before letters, and uppercase letters before lowercase ones).

A record can have several formats:

The meaning of the fields is as follows:

This record matches connection attempts using Unix-domain sockets. Without a record of this type, Unix-domain socket connections are disallowed.

This record matches connection attempts made using TCP/IP. host records match SSL or non-SSL connection attempts as well as GSSAPI encrypted or non-GSSAPI encrypted connection attempts.

Remote TCP/IP connections will not be possible unless the server is started with an appropriate value for the listen_addresses configuration parameter, since the default behavior is to listen for TCP/IP connections only on the local loopback address localhost.

This record matches connection attempts made using TCP/IP, but only when the connection is made with SSL encryption.

To make use of this option the server must be built with SSL support. Furthermore, SSL must be enabled by setting the ssl configuration parameter (see Section 18.9 for more information). Otherwise, the hostssl record is ignored except for logging a warning that it cannot match any connections.

This record type has the opposite behavior of hostssl; it only matches connection attempts made over TCP/IP that do not use SSL.

This record matches connection attempts made using TCP/IP, but only when the connection is made with GSSAPI encryption.

To make use of this option the server must be built with GSSAPI support. Otherwise, the hostgssenc record is ignored except for logging a warning that it cannot match any connections.

This record type has the opposite behavior of hostgssenc; it only matches connection attempts made over TCP/IP that do not use GSSAPI encryption.

Specifies which database name(s) this record matches. The value all specifies that it matches all databases. The value sameuser specifies that the record matches if the requested database has the same name as the requested user. The value samerole specifies that the requested user must be a member of the role with the same name as the requested database. (samegroup is an obsolete but still accepted spelling of samerole.) Superusers are not considered to be members of a role for the purposes of samerole unless they are explicitly members of the role, directly or indirectly, and not just by virtue of being a superuser. The value replication specifies that the record matches if a physical replication connection is requested, however, it doesn't match with logical replication connections. Note that physical replication connections do not specify any particular database whereas logical replication connections do specify it. Otherwise, this is the name of a specific PostgreSQL database or a regular expression. Multiple database names and/or regular expressions can be supplied by separating them with commas.

If the database name starts with a slash (/), the remainder of the name is treated as a regular expression. (See Section 9.7.3.1 for details of PostgreSQL's regular expression syntax.)

A separate file containing database names and/or regular expressions can be specified by preceding the file name with @.

Specifies which database user name(s) this record matches. The value all specifies that it matches all users. Otherwise, this is either the name of a specific database user, a regular expression (when starting with a slash (/), or a group name preceded by +. (Recall that there is no real distinction between users and groups in PostgreSQL; a + mark really means “match any of the roles that are directly or indirectly members of this role”, while a name without a + mark matches only that specific role.) For this purpose, a superuser is only considered to be a member of a role if they are explicitly a member of the role, directly or indirectly, and not just by virtue of being a superuser. Multiple user names and/or regular expressions can be supplied by separating them with commas.

If the user name starts with a slash (/), the remainder of the name is treated as a regular expression. (See Section 9.7.3.1 for details of PostgreSQL's regular expression syntax.)

A separate file containing user names and/or regular expressions can be specified by preceding the file name with @.

Specifies the client machine address(es) that this record matches. This field can contain either a host name, an IP address range, or one of the special key words mentioned below.

An IP address range is specified using standard numeric notation for the range's starting address, then a slash (/) and a CIDR mask length. The mask length indicates the number of high-order bits of the client IP address that must match. Bits to the right of this should be zero in the given IP address. There must not be any white space between the IP address, the /, and the CIDR mask length.

Typical examples of an IPv4 address range specified this way are 172.20.143.89/32 for a single host, or 172.20.143.0/24 for a small network, or 10.6.0.0/16 for a larger one. An IPv6 address range might look like ::1/128 for a single host (in this case the IPv6 loopback address) or fe80::7a31:c1ff:0000:0000/96 for a small network. 0.0.0.0/0 represents all IPv4 addresses, and ::0/0 represents all IPv6 addresses. To specify a single host, use a mask length of 32 for IPv4 or 128 for IPv6. In a network address, do not omit trailing zeroes.

An entry given in IPv4 format will match only IPv4 connections, and an entry given in IPv6 format will match only IPv6 connections, even if the represented address is in the IPv4-in-IPv6 range.

You can also write all to match any IP address, samehost to match any of the server's own IP addresses, or samenet to match any address in any subnet that the server is directly connected to.

If a host name is specified (anything that is not an IP address range or a special key word is treated as a host name), that name is compared with the result of a reverse name resolution of the client's IP address (e.g., reverse DNS lookup, if DNS is used). Host name comparisons are case insensitive. If there is a match, then a forward name resolution (e.g., forward DNS lookup) is performed on the host name to check whether any of the addresses it resolves to are equal to the client's IP address. If both directions match, then the entry is considered to match. (The host name that is used in pg_hba.conf should be the one that address-to-name resolution of the client's IP address returns, otherwise the line won't be matched. Some host name databases allow associating an IP address with multiple host names, but the operating system will only return one host name when asked to resolve an IP address.)

A host name specification that starts with a dot (.) matches a suffix of the actual host name. So .example.com would match foo.example.com (but not just example.com).

When host names are specified in pg_hba.conf, you should make sure that name resolution is reasonably fast. It can be of advantage to set up a local name resolution cache such as nscd. Also, you may wish to enable the configuration parameter log_hostname to see the client's host name instead of the IP address in the log.

These fields do not apply to local records.

Users sometimes wonder why host names are handled in this seemingly complicated way, with two name resolutions including a reverse lookup of the client's IP address. This complicates use of the feature in case the client's reverse DNS entry is not set up or yields some undesirable host name. It is done primarily for efficiency: this way, a connection attempt requires at most two resolver lookups, one reverse and one forward. If there is a resolver problem with some address, it becomes only that client's problem. A hypothetical alternative implementation that only did forward lookups would have to resolve every host name mentioned in pg_hba.conf during every connection attempt. That could be quite slow if many names are listed. And if there is a resolver problem with one of the host names, it becomes everyone's problem.

Also, a reverse lookup is necessary to implement the suffix matching feature, because the actual client host name needs to be known in order to match it against the pattern.

Note that this behavior is consistent with other popular implementations of host name-based access control, such as the Apache HTTP Server and TCP Wrappers.

These two fields can be used as an alternative to the IP-address/mask-length notation. Instead of specifying the mask length, the actual mask is specified in a separate column. For example, 255.0.0.0 represents an IPv4 CIDR mask length of 8, and 255.255.255.255 represents a CIDR mask length of 32.

These fields do not apply to local records.

Specifies the authentication method to use when a connection matches this record. The possible choices are summarized here; details are in Section 20.3. All the options are lower case and treated case sensitively, so even acronyms like ldap must be specified as lower case.

Allow the connection unconditionally. This method allows anyone that can connect to the PostgreSQL database server to login as any PostgreSQL user they wish, without the need for a password or any other authentication. See Section 20.4 for details.

Reject the connection unconditionally. This is useful for “filtering out” certain hosts from a group, for example a reject line could block a specific host from connecting, while a later line allows the remaining hosts in a specific network to connect.

Perform SCRAM-SHA-256 authentication to verify the user's password. See Section 20.5 for details.

Perform SCRAM-SHA-256 or MD5 authentication to verify the user's password. See Section 20.5 for details.

Support for MD5-encrypted passwords is deprecated and will be removed in a future release of PostgreSQL. Refer to Section 20.5 for details about migrating to another password type.

Require the client to supply an unencrypted password for authentication. Since the password is sent in clear text over the network, this should not be used on untrusted networks. See Section 20.5 for details.

Use GSSAPI to authenticate the user. This is only available for TCP/IP connections. See Section 20.6 for details. It can be used in conjunction with GSSAPI encryption.

Use SSPI to authenticate the user. This is only available on Windows. See Section 20.7 for details.

Obtain the operating system user name of the client by contacting the ident server on the client and check if it matches the requested database user name. Ident authentication can only be used on TCP/IP connections. When specified for local connections, peer authentication will be used instead. See Section 20.8 for details.

Obtain the client's operating system user name from the operating system and check if it matches the requested database user name. This is only available for local connections. See Section 20.9 for details.

Authenticate using an LDAP server. See Section 20.10 for details.

Authenticate using a RADIUS server. See Section 20.11 for details.

Authenticate using SSL client certificates. See Section 20.12 for details.

Authenticate using the Pluggable Authentication Modules (PAM) service provided by the operating system. See Section 20.13 for details.

Authenticate using the BSD Authentication service provided by the operating system. See Section 20.14 for details.

Authorize and optionally authenticate using a third-party OAuth 2.0 identity provider. See Section 20.15 for details.

After the auth-method field, there can be field(s) of the form name=value that specify options for the authentication method. Details about which options are available for which authentication methods appear below.

In addition to the method-specific options listed below, there is a method-independent authentication option clientcert, which can be specified in any hostssl record. This option can be set to verify-ca or verify-full. Both options require the client to present a valid (trusted) SSL certificate, while verify-full additionally enforces that the cn (Common Name) in the certificate matches the username or an applicable mapping. This behavior is similar to the cert authentication method (see Section 20.12) but enables pairing the verification of client certificates with any authentication method that supports hostssl entries.

On any record using client certificate authentication (i.e. one using the cert authentication method or one using the clientcert option), you can specify which part of the client certificate credentials to match using the clientname option. This option can have one of two values. If you specify clientname=CN, which is the default, the username is matched against the certificate's Common Name (CN). If instead you specify clientname=DN the username is matched against the entire Distinguished Name (DN) of the certificate. This option is probably best used in conjunction with a username map. The comparison is done with the DN in RFC 2253 format. To see the DN of a client certificate in this format, do

Care needs to be taken when using this option, especially when using regular expression matching against the DN.

This line will be replaced by the contents of the given file.

This line will be replaced by the content of the given file if the file exists. Otherwise, a message is logged to indicate that the file has been skipped.

This line will be replaced by the contents of all the files found in the directory, if they don't start with a . and end with .conf, processed in file name order (according to C locale rules, i.e., numbers before letters, and uppercase letters before lowercase ones).

Files included by @ constructs are read as lists of names, which can be separated by either whitespace or commas. Comments are introduced by #, just as in pg_hba.conf, and nested @ constructs are allowed. Unless the file name following @ is an absolute path, it is taken to be relative to the directory containing the referencing file.

Since the pg_hba.conf records are examined sequentially for each connection attempt, the order of the records is significant. Typically, earlier records will have tight connection match parameters and weaker authentication methods, while later records will have looser match parameters and stronger authentication methods. For example, one might wish to use trust authentication for local TCP/IP connections but require a password for remote TCP/IP connections. In this case a record specifying trust authentication for connections from 127.0.0.1 would appear before a record specifying password authentication for a wider range of allowed client IP addresses.

To connect to a particular database, a user must not only pass the pg_hba.conf checks, but must have the CONNECT privilege for the database. If you wish to restrict which users can connect to which databases, it's usually easier to control this by granting/revoking CONNECT privilege than to put the rules in pg_hba.conf entries.

Some examples of pg_hba.conf entries are shown in Example 20.1. See the next section for details on the different authentication methods.

Example 20.1. Example pg_hba.conf Entries

Examples:

Example 1 (unknown):

local               database  user  auth-method [auth-options]
host                database  user  address     auth-method  [auth-options]
hostssl             database  user  address     auth-method  [auth-options]
hostnossl           database  user  address     auth-method  [auth-options]
hostgssenc          database  user  address     auth-method  [auth-options]
hostnogssenc        database  user  address     auth-method  [auth-options]
host                database  user  IP-address  IP-mask      auth-method  [auth-options]
hostssl             database  user  IP-address  IP-mask      auth-method  [auth-options]
hostnossl           database  user  IP-address  IP-mask      auth-method  [auth-options]
hostgssenc          database  user  IP-address  IP-mask      auth-method  [auth-options]
hostnogssenc        database  user  IP-address  IP-mask      auth-method  [auth-options]
include             file
include_if_exists   file
include_dir         directory

Example 2 (unknown):

openssl x509 -in myclient.crt -noout -subject -nameopt RFC2253 | sed "s/^subject=//"

Example 3 (unknown):

# Allow any user on the local system to connect to any database with
# any database user name using Unix-domain sockets (the default for local
# connections).
#
# TYPE  DATABASE        USER            ADDRESS                 METHOD
local   all             all                                     trust

# The same using local loopback TCP/IP connections.
#
# TYPE  DATABASE        USER            ADDRESS                 METHOD
host    all             all             127.0.0.1/32            trust

# The same as the previous line, but using a separate netmask column
#
# TYPE  DATABASE        USER            IP-ADDRESS      IP-MASK             METHOD
host    all             all             127.0.0.1       255.255.255.255     trust

# The same over IPv6.
#
# TYPE  DATABASE        USER            ADDRESS                 METHOD
host    all             all             ::1/128                 trust

# The same using a host name (would typically cover both IPv4 and IPv6).
#
# TYPE  DATABASE        USER            ADDRESS                 METHOD
host    all             all             localhost               trust

# The same using a regular expression for DATABASE, that allows connection
# to any databases with a name beginning with "db" and finishing with a
# number using two to four digits (like "db1234" or "db12").
#
# TYPE  DATABASE        USER            ADDRESS                 METHOD
host    "/^db\d{2,4}$"  all             localhost               trust

# Allow any user from any host with IP address 192.168.93.x to connect
# to database "postgres" as the same user name that ident reports for
# the connection (typically the operating system user name).
#
# TYPE  DATABASE        USER            ADDRESS                 METHOD
host    postgres        all             192.168.93.0/24         ident

# Allow any user from host 192.168.12.10 to connect to database
# "postgres" if the user's password is correctly supplied.
#
# TYPE  DATABASE        USER            ADDRESS                 METHOD
host    postgres        all             192.168.12.10/32        scram-sha-256

# Allow any user from hosts in the example.com domain to connect to
# any database if the user's password is correctly supplied.
#
# Require SCRAM authentication for most users, but make an exception
# for user 'mike', who uses an older client that doesn't support SCRAM
# authentication.
#
# TYPE  DATABASE        USER            ADDRESS                 METHOD
host    all             mike            .example.com            md5
host    all             all             .example.com            scram-sha-256

# In the absence of preceding "host" lines, these three lines will
# reject all connections from 192.168.54.1 (since that entry will be
# matched first), but allow GSSAPI-encrypted connections from anywhere else
# on the Internet.  The zero mask causes no bits of the host IP address to
# be considered, so it matches any host.  Unencrypted GSSAPI connections
# (which "fall through" to the third line since "hostgssenc" only matches
# encrypted GSSAPI connections) are allowed, but only from 192.168.12.10.
#
# TYPE  DATABASE        USER            ADDRESS                 METHOD
host    all             all             192.168.54.1/32         reject
hostgssenc all          all             0.0.0.0/0               gss
host    all             all             192.168.12.10/32        gss

# Allow users from 192.168.x.x hosts to connect to any database, if
# they pass the ident check.  If, for example, ident says the user is
# "bryanh" and he requests to connect as PostgreSQL user "guest1", the
# connection is allowed if there is an entry in pg_ident.conf for map
# "omicron" that says "bryanh" is allowed to connect as "guest1".
#
# TYPE  DATABASE        USER            ADDRESS                 METHOD
host    all             all             192.168.0.0/16          ident map=omicron

# If these are the only four lines for local connections, they will
# allow local users to connect only to their own databases (databases
# with the same name as their database user name) except for users whose
# name end with "helpdesk", administrators and members of role "support",
# who can connect to all databases.  The file $PGDATA/admins contains a
# list of names of administrators.  Passwords are required in all cases.
#
# TYPE  DATABASE        USER            ADDRESS                 METHOD
local   sameuser        all                                     md5
local   all             /^.*helpdesk$                           md5
local   all             @admins                                 md5
local   all             +support                                md5

# The last two lines above can be combined into a single line:
local   all             @admins,+support                        md5

# The database column can also use lists and file names:
local   db1,db2,@demodbs  all                                   md5

---

PostgreSQL: Documentation: 18: 34.14. Embedded SQL Commands

URL: https://www.postgresql.org/docs/current/ecpg-sql-commands.html

Contents:

• 34.14. Embedded SQL Commands #

This section describes all SQL commands that are specific to embedded SQL. Also refer to the SQL commands listed in SQL Commands, which can also be used in embedded SQL, unless stated otherwise.

---

PostgreSQL: Documentation: 18: 35.6. attributes

URL: https://www.postgresql.org/docs/current/infoschema-attributes.html

Contents:

• 35.6. attributes #

The view attributes contains information about the attributes of composite data types defined in the database. (Note that the view does not give information about table columns, which are sometimes called attributes in PostgreSQL contexts.) Only those attributes are shown that the current user has access to (by way of being the owner of or having some privilege on the type).

Table 35.4. attributes Columns

udt_catalog sql_identifier

Name of the database containing the data type (always the current database)

udt_schema sql_identifier

Name of the schema containing the data type

udt_name sql_identifier

Name of the data type

attribute_name sql_identifier

Name of the attribute

ordinal_position cardinal_number

Ordinal position of the attribute within the data type (count starts at 1)

attribute_default character_data

Default expression of the attribute

is_nullable yes_or_no

YES if the attribute is possibly nullable, NO if it is known not nullable.

data_type character_data

Data type of the attribute, if it is a built-in type, or ARRAY if it is some array (in that case, see the view element_types), else USER-DEFINED (in that case, the type is identified in attribute_udt_name and associated columns).

character_maximum_length cardinal_number

If data_type identifies a character or bit string type, the declared maximum length; null for all other data types or if no maximum length was declared.

character_octet_length cardinal_number

If data_type identifies a character type, the maximum possible length in octets (bytes) of a datum; null for all other data types. The maximum octet length depends on the declared character maximum length (see above) and the server encoding.

character_set_catalog sql_identifier

Applies to a feature not available in PostgreSQL

character_set_schema sql_identifier

Applies to a feature not available in PostgreSQL

character_set_name sql_identifier

Applies to a feature not available in PostgreSQL

collation_catalog sql_identifier

Name of the database containing the collation of the attribute (always the current database), null if default or the data type of the attribute is not collatable

collation_schema sql_identifier

Name of the schema containing the collation of the attribute, null if default or the data type of the attribute is not collatable

collation_name sql_identifier

Name of the collation of the attribute, null if default or the data type of the attribute is not collatable

numeric_precision cardinal_number

If data_type identifies a numeric type, this column contains the (declared or implicit) precision of the type for this attribute. The precision indicates the number of significant digits. It can be expressed in decimal (base 10) or binary (base 2) terms, as specified in the column numeric_precision_radix. For all other data types, this column is null.

numeric_precision_radix cardinal_number

If data_type identifies a numeric type, this column indicates in which base the values in the columns numeric_precision and numeric_scale are expressed. The value is either 2 or 10. For all other data types, this column is null.

numeric_scale cardinal_number

If data_type identifies an exact numeric type, this column contains the (declared or implicit) scale of the type for this attribute. The scale indicates the number of significant digits to the right of the decimal point. It can be expressed in decimal (base 10) or binary (base 2) terms, as specified in the column numeric_precision_radix. For all other data types, this column is null.

datetime_precision cardinal_number

If data_type identifies a date, time, timestamp, or interval type, this column contains the (declared or implicit) fractional seconds precision of the type for this attribute, that is, the number of decimal digits maintained following the decimal point in the seconds value. For all other data types, this column is null.

interval_type character_data

If data_type identifies an interval type, this column contains the specification which fields the intervals include for this attribute, e.g., YEAR TO MONTH, DAY TO SECOND, etc. If no field restrictions were specified (that is, the interval accepts all fields), and for all other data types, this field is null.

interval_precision cardinal_number

Applies to a feature not available in PostgreSQL (see datetime_precision for the fractional seconds precision of interval type attributes)

attribute_udt_catalog sql_identifier

Name of the database that the attribute data type is defined in (always the current database)

attribute_udt_schema sql_identifier

Name of the schema that the attribute data type is defined in

attribute_udt_name sql_identifier

Name of the attribute data type

scope_catalog sql_identifier

Applies to a feature not available in PostgreSQL

scope_schema sql_identifier

Applies to a feature not available in PostgreSQL

scope_name sql_identifier

Applies to a feature not available in PostgreSQL

maximum_cardinality cardinal_number

Always null, because arrays always have unlimited maximum cardinality in PostgreSQL

dtd_identifier sql_identifier

An identifier of the data type descriptor of the attribute, unique among the data type descriptors pertaining to the composite type. This is mainly useful for joining with other instances of such identifiers. (The specific format of the identifier is not defined and not guaranteed to remain the same in future versions.)

is_derived_reference_attribute yes_or_no

Applies to a feature not available in PostgreSQL

See also under Section 35.17, a similarly structured view, for further information on some of the columns.

---

PostgreSQL: Documentation: 18: 19.9. Run-time Statistics

URL: https://www.postgresql.org/docs/current/runtime-config-statistics.html

Contents:

• 19.9. Run-time Statistics #
  • 19.9.1. Cumulative Query and Index Statistics #
  • Note
  • Note
  • 19.9.2. Statistics Monitoring #
  • Note

These parameters control the server-wide cumulative statistics system. When enabled, the data that is collected can be accessed via the pg_stat and pg_statio family of system views. Refer to Chapter 27 for more information.

Enables the collection of information on the currently executing command of each session, along with its identifier and the time when that command began execution. This parameter is on by default. Note that even when enabled, this information is only visible to superusers, roles with privileges of the pg_read_all_stats role and the user owning the sessions being reported on (including sessions belonging to a role they have the privileges of), so it should not represent a security risk. Only superusers and users with the appropriate SET privilege can change this setting.

Specifies the amount of memory reserved to store the text of the currently executing command for each active session, for the pg_stat_activity.query field. If this value is specified without units, it is taken as bytes. The default value is 1024 bytes. This parameter can only be set at server start.

Enables collection of statistics on database activity. This parameter is on by default, because the autovacuum daemon needs the collected information. Only superusers and users with the appropriate SET privilege can change this setting.

Enables timing of cost-based vacuum delay (see Section 19.10.2). This parameter is off by default, as it will repeatedly query the operating system for the current time, which may cause significant overhead on some platforms. You can use the pg_test_timing tool to measure the overhead of timing on your system. Cost-based vacuum delay timing information is displayed in pg_stat_progress_vacuum, pg_stat_progress_analyze, in the output of VACUUM and ANALYZE when the VERBOSE option is used, and by autovacuum for auto-vacuums and auto-analyzes when log_autovacuum_min_duration is set. Only superusers and users with the appropriate SET privilege can change this setting.

Enables timing of database I/O waits. This parameter is off by default, as it will repeatedly query the operating system for the current time, which may cause significant overhead on some platforms. You can use the pg_test_timing tool to measure the overhead of timing on your system. I/O timing information is displayed in pg_stat_database, pg_stat_io (if object is not wal), in the output of the pg_stat_get_backend_io() function (if object is not wal), in the output of EXPLAIN when the BUFFERS option is used, in the output of VACUUM when the VERBOSE option is used, by autovacuum for auto-vacuums and auto-analyzes, when log_autovacuum_min_duration is set and by pg_stat_statements. Only superusers and users with the appropriate SET privilege can change this setting.

Enables timing of WAL I/O waits. This parameter is off by default, as it will repeatedly query the operating system for the current time, which may cause significant overhead on some platforms. You can use the pg_test_timing tool to measure the overhead of timing on your system. I/O timing information is displayed in pg_stat_io for the object wal and in the output of the pg_stat_get_backend_io() function for the object wal. Only superusers and users with the appropriate SET privilege can change this setting.

Enables tracking of function call counts and time used. Specify pl to track only procedural-language functions, all to also track SQL and C language functions. The default is none, which disables function statistics tracking. Only superusers and users with the appropriate SET privilege can change this setting.

SQL-language functions that are simple enough to be “inlined” into the calling query will not be tracked, regardless of this setting.

Determines the behavior when cumulative statistics are accessed multiple times within a transaction. When set to none, each access re-fetches counters from shared memory. When set to cache, the first access to statistics for an object caches those statistics until the end of the transaction unless pg_stat_clear_snapshot() is called. When set to snapshot, the first statistics access caches all statistics accessible in the current database, until the end of the transaction unless pg_stat_clear_snapshot() is called. Changing this parameter in a transaction discards the statistics snapshot. The default is cache.

none is most suitable for monitoring systems. If values are only accessed once, it is the most efficient. cache ensures repeat accesses yield the same values, which is important for queries involving e.g. self-joins. snapshot can be useful when interactively inspecting statistics, but has higher overhead, particularly if many database objects exist.

Enables in-core computation of a query identifier. Query identifiers can be displayed in the pg_stat_activity view, using EXPLAIN, or emitted in the log if configured via the log_line_prefix parameter. The pg_stat_statements extension also requires a query identifier to be computed. Note that an external module can alternatively be used if the in-core query identifier computation method is not acceptable. In this case, in-core computation must be always disabled. Valid values are off (always disabled), on (always enabled), auto, which lets modules such as pg_stat_statements automatically enable it, and regress which has the same effect as auto, except that the query identifier is not shown in the EXPLAIN output in order to facilitate automated regression testing. The default is auto.

To ensure that only one query identifier is calculated and displayed, extensions that calculate query identifiers should throw an error if a query identifier has already been computed.

For each query, output performance statistics of the respective module to the server log. This is a crude profiling instrument, similar to the Unix getrusage() operating system facility. log_statement_stats reports total statement statistics, while the others report per-module statistics. log_statement_stats cannot be enabled together with any of the per-module options. All of these options are disabled by default. Only superusers and users with the appropriate SET privilege can change these settings.

---

PostgreSQL: Documentation: 18: 36.5. Query Language (SQL) Functions

URL: https://www.postgresql.org/docs/current/xfunc-sql.html

Contents:

• 36.5. Query Language (SQL) Functions #
  • 36.5.1. Arguments for SQL Functions #
  • Note
  • 36.5.2. SQL Functions on Base Types #
  • 36.5.3. SQL Functions on Composite Types #
  • 36.5.4. SQL Functions with Output Parameters #
  • 36.5.5. SQL Procedures with Output Parameters #
  • 36.5.6. SQL Functions with Variable Numbers of Arguments #
  • 36.5.7. SQL Functions with Default Values for Arguments #
  • 36.5.8. SQL Functions as Table Sources #

SQL functions execute an arbitrary list of SQL statements, returning the result of the last query in the list. In the simple (non-set) case, the first row of the last query's result will be returned. (Bear in mind that “the first row” of a multirow result is not well-defined unless you use ORDER BY.) If the last query happens to return no rows at all, the null value will be returned.

Alternatively, an SQL function can be declared to return a set (that is, multiple rows) by specifying the function's return type as SETOF sometype, or equivalently by declaring it as RETURNS TABLE(columns). In this case all rows of the last query's result are returned. Further details appear below.

The body of an SQL function must be a list of SQL statements separated by semicolons. A semicolon after the last statement is optional. Unless the function is declared to return void, the last statement must be a SELECT, or an INSERT, UPDATE, DELETE, or MERGE that has a RETURNING clause.

Any collection of commands in the SQL language can be packaged together and defined as a function. Besides SELECT queries, the commands can include data modification queries (INSERT, UPDATE, DELETE, and MERGE), as well as other SQL commands. (You cannot use transaction control commands, e.g., COMMIT, SAVEPOINT, and some utility commands, e.g., VACUUM, in SQL functions.) However, the final command must be a SELECT or have a RETURNING clause that returns whatever is specified as the function's return type. Alternatively, if you want to define an SQL function that performs actions but has no useful value to return, you can define it as returning void. For example, this function removes rows with negative salaries from the emp table:

You can also write this as a procedure, thus avoiding the issue of the return type. For example:

In simple cases like this, the difference between a function returning void and a procedure is mostly stylistic. However, procedures offer additional functionality such as transaction control that is not available in functions. Also, procedures are SQL standard whereas returning void is a PostgreSQL extension.

The syntax of the CREATE FUNCTION command requires the function body to be written as a string constant. It is usually most convenient to use dollar quoting (see Section 4.1.2.4) for the string constant. If you choose to use regular single-quoted string constant syntax, you must double single quote marks (') and backslashes () (assuming escape string syntax) in the body of the function (see Section 4.1.2.1).

Arguments of an SQL function can be referenced in the function body using either names or numbers. Examples of both methods appear below.

To use a name, declare the function argument as having a name, and then just write that name in the function body. If the argument name is the same as any column name in the current SQL command within the function, the column name will take precedence. To override this, qualify the argument name with the name of the function itself, that is function_name.argument_name. (If this would conflict with a qualified column name, again the column name wins. You can avoid the ambiguity by choosing a different alias for the table within the SQL command.)

In the older numeric approach, arguments are referenced using the syntax $n: $1 refers to the first input argument, $2 to the second, and so on. This will work whether or not the particular argument was declared with a name.

If an argument is of a composite type, then the dot notation, e.g., argname.fieldname or $1.fieldname, can be used to access attributes of the argument. Again, you might need to qualify the argument's name with the function name to make the form with an argument name unambiguous.

SQL function arguments can only be used as data values, not as identifiers. Thus for example this is reasonable:

but this will not work:

The ability to use names to reference SQL function arguments was added in PostgreSQL 9.2. Functions to be used in older servers must use the $n notation.

The simplest possible SQL function has no arguments and simply returns a base type, such as integer:

Notice that we defined a column alias within the function body for the result of the function (with the name result), but this column alias is not visible outside the function. Hence, the result is labeled one instead of result.

It is almost as easy to define SQL functions that take base types as arguments:

Alternatively, we could dispense with names for the arguments and use numbers:

Here is a more useful function, which might be used to debit a bank account:

A user could execute this function to debit account 17 by $100.00 as follows:

In this example, we chose the name accountno for the first argument, but this is the same as the name of a column in the bank table. Within the UPDATE command, accountno refers to the column bank.accountno, so tf1.accountno must be used to refer to the argument. We could of course avoid this by using a different name for the argument.

In practice one would probably like a more useful result from the function than a constant 1, so a more likely definition is:

which adjusts the balance and returns the new balance. The same thing could be done in one command using RETURNING:

If the final SELECT or RETURNING clause in an SQL function does not return exactly the function's declared result type, PostgreSQL will automatically cast the value to the required type, if that is possible with an implicit or assignment cast. Otherwise, you must write an explicit cast. For example, suppose we wanted the previous add_em function to return type float8 instead. It's sufficient to write

since the integer sum can be implicitly cast to float8. (See Chapter 10 or CREATE CAST for more about casts.)

When writing functions with arguments of composite types, we must not only specify which argument we want but also the desired attribute (field) of that argument. For example, suppose that emp is a table containing employee data, and therefore also the name of the composite type of each row of the table. Here is a function double_salary that computes what someone's salary would be if it were doubled:

Notice the use of the syntax $1.salary to select one field of the argument row value. Also notice how the calling SELECT command uses table_name.* to select the entire current row of a table as a composite value. The table row can alternatively be referenced using just the table name, like this:

but this usage is deprecated since it's easy to get confused. (See Section 8.16.5 for details about these two notations for the composite value of a table row.)

Sometimes it is handy to construct a composite argument value on-the-fly. This can be done with the ROW construct. For example, we could adjust the data being passed to the function:

It is also possible to build a function that returns a composite type. This is an example of a function that returns a single emp row:

In this example we have specified each of the attributes with a constant value, but any computation could have been substituted for these constants.

Note two important things about defining the function:

The select list order in the query must be exactly the same as that in which the columns appear in the composite type. (Naming the columns, as we did above, is irrelevant to the system.)

We must ensure each expression's type can be cast to that of the corresponding column of the composite type. Otherwise we'll get errors like this:

As with the base-type case, the system will not insert explicit casts automatically, only implicit or assignment casts.

A different way to define the same function is:

Here we wrote a SELECT that returns just a single column of the correct composite type. This isn't really better in this situation, but it is a handy alternative in some cases — for example, if we need to compute the result by calling another function that returns the desired composite value. Another example is that if we are trying to write a function that returns a domain over composite, rather than a plain composite type, it is always necessary to write it as returning a single column, since there is no way to cause a coercion of the whole row result.

We could call this function directly either by using it in a value expression:

or by calling it as a table function:

The second way is described more fully in Section 36.5.8.

When you use a function that returns a composite type, you might want only one field (attribute) from its result. You can do that with syntax like this:

The extra parentheses are needed to keep the parser from getting confused. If you try to do it without them, you get something like this:

Another option is to use functional notation for extracting an attribute:

As explained in Section 8.16.5, the field notation and functional notation are equivalent.

Another way to use a function returning a composite type is to pass the result to another function that accepts the correct row type as input:

An alternative way of describing a function's results is to define it with output parameters, as in this example:

This is not essentially different from the version of add_em shown in Section 36.5.2. The real value of output parameters is that they provide a convenient way of defining functions that return several columns. For example,

What has essentially happened here is that we have created an anonymous composite type for the result of the function. The above example has the same end result as

but not having to bother with the separate composite type definition is often handy. Notice that the names attached to the output parameters are not just decoration, but determine the column names of the anonymous composite type. (If you omit a name for an output parameter, the system will choose a name on its own.)

Notice that output parameters are not included in the calling argument list when invoking such a function from SQL. This is because PostgreSQL considers only the input parameters to define the function's calling signature. That means also that only the input parameters matter when referencing the function for purposes such as dropping it. We could drop the above function with either of

Parameters can be marked as IN (the default), OUT, INOUT, or VARIADIC. An INOUT parameter serves as both an input parameter (part of the calling argument list) and an output parameter (part of the result record type). VARIADIC parameters are input parameters, but are treated specially as described below.

Output parameters are also supported in procedures, but they work a bit differently from functions. In CALL commands, output parameters must be included in the argument list. For example, the bank account debiting routine from earlier could be written like this:

To call this procedure, an argument matching the OUT parameter must be included. It's customary to write NULL:

If you write something else, it must be an expression that is implicitly coercible to the declared type of the parameter, just as for input parameters. Note however that such an expression will not be evaluated.

When calling a procedure from PL/pgSQL, instead of writing NULL you must write a variable that will receive the procedure's output. See Section 41.6.3 for details.

SQL functions can be declared to accept variable numbers of arguments, so long as all the “optional” arguments are of the same data type. The optional arguments will be passed to the function as an array. The function is declared by marking the last parameter as VARIADIC; this parameter must be declared as being of an array type. For example:

Effectively, all the actual arguments at or beyond the VARIADIC position are gathered up into a one-dimensional array, as if you had written

You can't actually write that, though — or at least, it will not match this function definition. A parameter marked VARIADIC matches one or more occurrences of its element type, not of its own type.

Sometimes it is useful to be able to pass an already-constructed array to a variadic function; this is particularly handy when one variadic function wants to pass on its array parameter to another one. Also, this is the only secure way to call a variadic function found in a schema that permits untrusted users to create objects; see Section 10.3. You can do this by specifying VARIADIC in the call:

This prevents expansion of the function's variadic parameter into its element type, thereby allowing the array argument value to match normally. VARIADIC can only be attached to the last actual argument of a function call.

Specifying VARIADIC in the call is also the only way to pass an empty array to a variadic function, for example:

Simply writing SELECT mleast() does not work because a variadic parameter must match at least one actual argument. (You could define a second function also named mleast, with no parameters, if you wanted to allow such calls.)

The array element parameters generated from a variadic parameter are treated as not having any names of their own. This means it is not possible to call a variadic function using named arguments (Section 4.3), except when you specify VARIADIC. For example, this will work:

Functions can be declared with default values for some or all input arguments. The default values are inserted whenever the function is called with insufficiently many actual arguments. Since arguments can only be omitted from the end of the actual argument list, all parameters after a parameter with a default value have to have default values as well. (Although the use of named argument notation could allow this restriction to be relaxed, it's still enforced so that positional argument notation works sensibly.) Whether or not you use it, this capability creates a need for precautions when calling functions in databases where some users mistrust other users; see Section 10.3.

The = sign can also be used in place of the key word DEFAULT.

All SQL functions can be used in the FROM clause of a query, but it is particularly useful for functions returning composite types. If the function is defined to return a base type, the table function produces a one-column table. If the function is defined to return a composite type, the table function produces a column for each attribute of the composite type.

As the example shows, we can work with the columns of the function's result just the same as if they were columns of a regular table.

Note that we only got one row out of the function. This is because we did not use SETOF. That is described in the next section.

When an SQL function is declared as returning SETOF sometype, the function's final query is executed to completion, and each row it outputs is returned as an element of the result set.

This feature is normally used when calling the function in the FROM clause. In this case each row returned by the function becomes a row of the table seen by the query. For example, assume that table foo has the same contents as above, and we say:

It is also possible to return multiple rows with the columns defined by output parameters, like this:

The key point here is that you must write RETURNS SETOF record to indicate that the function returns multiple rows instead of just one. If there is only one output parameter, write that parameter's type instead of record.

It is frequently useful to construct a query's result by invoking a set-returning function multiple times, with the parameters for each invocation coming from successive rows of a table or subquery. The preferred way to do this is to use the LATERAL key word, which is described in Section 7.2.1.5. Here is an example using a set-returning function to enumerate elements of a tree structure:

This example does not do anything that we couldn't have done with a simple join, but in more complex calculations the option to put some of the work into a function can be quite convenient.

Functions returning sets can also be called in the select list of a query. For each row that the query generates by itself, the set-returning function is invoked, and an output row is generated for each element of the function's result set. The previous example could also be done with queries like these:

In the last SELECT, notice that no output row appears for Child2, Child3, etc. This happens because listchildren returns an empty set for those arguments, so no result rows are generated. This is the same behavior as we got from an inner join to the function result when using the LATERAL syntax.

PostgreSQL's behavior for a set-returning function in a query's select list is almost exactly the same as if the set-returning function had been written in a LATERAL FROM-clause item instead. For example,

is almost equivalent to

It would be exactly the same, except that in this specific example, the planner could choose to put g on the outside of the nested-loop join, since g has no actual lateral dependency on tab. That would result in a different output row order. Set-returning functions in the select list are always evaluated as though they are on the inside of a nested-loop join with the rest of the FROM clause, so that the function(s) are run to completion before the next row from the FROM clause is considered.

If there is more than one set-returning function in the query's select list, the behavior is similar to what you get from putting the functions into a single LATERAL ROWS FROM( ... ) FROM-clause item. For each row from the underlying query, there is an output row using the first result from each function, then an output row using the second result, and so on. If some of the set-returning functions produce fewer outputs than others, null values are substituted for the missing data, so that the total number of rows emitted for one underlying row is the same as for the set-returning function that produced the most outputs. Thus the set-returning functions run “in lockstep” until they are all exhausted, and then execution continues with the next underlying row.

Set-returning functions can be nested in a select list, although that is not allowed in FROM-clause items. In such cases, each level of nesting is treated separately, as though it were a separate LATERAL ROWS FROM( ... ) item. For example, in

the set-returning functions srf2, srf3, and srf5 would be run in lockstep for each row of tab, and then srf1 and srf4 would be applied in lockstep to each row produced by the lower functions.

Set-returning functions cannot be used within conditional-evaluation constructs, such as CASE or COALESCE. For example, consider

It might seem that this should produce five repetitions of input rows that have x > 0, and a single repetition of those that do not; but actually, because generate_series(1, 5) would be run in an implicit LATERAL FROM item before the CASE expression is ever evaluated, it would produce five repetitions of every input row. To reduce confusion, such cases produce a parse-time error instead.

If a function's last command is INSERT, UPDATE, DELETE, or MERGE with RETURNING, that command will always be executed to completion, even if the function is not declared with SETOF or the calling query does not fetch all the result rows. Any extra rows produced by the RETURNING clause are silently dropped, but the commanded table modifications still happen (and are all completed before returning from the function).

Before PostgreSQL 10, putting more than one set-returning function in the same select list did not behave very sensibly unless they always produced equal numbers of rows. Otherwise, what you got was a number of output rows equal to the least common multiple of the numbers of rows produced by the set-returning functions. Also, nested set-returning functions did not work as described above; instead, a set-returning function could have at most one set-returning argument, and each nest of set-returning functions was run independently. Also, conditional execution (set-returning functions inside CASE etc.) was previously allowed, complicating things even more. Use of the LATERAL syntax is recommended when writing queries that need to work in older PostgreSQL versions, because that will give consistent results across different versions. If you have a query that is relying on conditional execution of a set-returning function, you may be able to fix it by moving the conditional test into a custom set-returning function. For example,

This formulation will work the same in all versions of PostgreSQL.

There is another way to declare a function as returning a set, which is to use the syntax RETURNS TABLE(columns). This is equivalent to using one or more OUT parameters plus marking the function as returning SETOF record (or SETOF a single output parameter's type, as appropriate). This notation is specified in recent versions of the SQL standard, and thus may be more portable than using SETOF.

For example, the preceding sum-and-product example could also be done this way:

It is not allowed to use explicit OUT or INOUT parameters with the RETURNS TABLE notation — you must put all the output columns in the TABLE list.

SQL functions can be declared to accept and return the polymorphic types described in Section 36.2.5. Here is a polymorphic function make_array that builds up an array from two arbitrary data type elements:

Notice the use of the typecast 'a'::text to specify that the argument is of type text. This is required if the argument is just a string literal, since otherwise it would be treated as type unknown, and array of unknown is not a valid type. Without the typecast, you will get errors like this:

With make_array declared as above, you must provide two arguments that are of exactly the same data type; the system will not attempt to resolve any type differences. Thus for example this does not work:

An alternative approach is to use the “common” family of polymorphic types, which allows the system to try to identify a suitable common type:

Because the rules for common type resolution default to choosing type text when all inputs are of unknown types, this also works:

It is permitted to have polymorphic arguments with a fixed return type, but the converse is not. For example:

Polymorphism can be used with functions that have output arguments. For example:

Polymorphism can also be used with variadic functions. For example:

When an SQL function has one or more parameters of collatable data types, a collation is identified for each function call depending on the collations assigned to the actual arguments, as described in Section 23.2. If a collation is successfully identified (i.e., there are no conflicts of implicit collations among the arguments) then all the collatable parameters are treated as having that collation implicitly. This will affect the behavior of collation-sensitive operations within the function. For example, using the anyleast function described above, the result of

will depend on the database's default collation. In C locale the result will be ABC, but in many other locales it will be abc. The collation to use can be forced by adding a COLLATE clause to any of the arguments, for example

Alternatively, if you wish a function to operate with a particular collation regardless of what it is called with, insert COLLATE clauses as needed in the function definition. This version of anyleast would always use en_US locale to compare strings:

But note that this will throw an error if applied to a non-collatable data type.

If no common collation can be identified among the actual arguments, then an SQL function treats its parameters as having their data types' default collation (which is usually the database's default collation, but could be different for parameters of domain types).

The behavior of collatable parameters can be thought of as a limited form of polymorphism, applicable only to textual data types.

Examples:

Example 1 (unknown):

CREATE FUNCTION clean_emp() RETURNS void AS '
    DELETE FROM emp
        WHERE salary < 0;
' LANGUAGE SQL;

SELECT clean_emp();

 clean_emp
-----------

(1 row)

Example 2 (unknown):

CREATE PROCEDURE clean_emp() AS '
    DELETE FROM emp
        WHERE salary < 0;
' LANGUAGE SQL;

CALL clean_emp();

Example 3 (unknown):

INSERT INTO mytable VALUES ($1);

Example 4 (unknown):

INSERT INTO $1 VALUES (42);

---

PostgreSQL: Documentation: 18: 11.5. Combining Multiple Indexes

URL: https://www.postgresql.org/docs/current/indexes-bitmap-scans.html

Contents:

• 11.5. Combining Multiple Indexes #

A single index scan can only use query clauses that use the index's columns with operators of its operator class and are joined with AND. For example, given an index on (a, b) a query condition like WHERE a = 5 AND b = 6 could use the index, but a query like WHERE a = 5 OR b = 6 could not directly use the index.

Fortunately, PostgreSQL has the ability to combine multiple indexes (including multiple uses of the same index) to handle cases that cannot be implemented by single index scans. The system can form AND and OR conditions across several index scans. For example, a query like WHERE x = 42 OR x = 47 OR x = 53 OR x = 99 could be broken down into four separate scans of an index on x, each scan using one of the query clauses. The results of these scans are then ORed together to produce the result. Another example is that if we have separate indexes on x and y, one possible implementation of a query like WHERE x = 5 AND y = 6 is to use each index with the appropriate query clause and then AND together the index results to identify the result rows.

To combine multiple indexes, the system scans each needed index and prepares a bitmap in memory giving the locations of table rows that are reported as matching that index's conditions. The bitmaps are then ANDed and ORed together as needed by the query. Finally, the actual table rows are visited and returned. The table rows are visited in physical order, because that is how the bitmap is laid out; this means that any ordering of the original indexes is lost, and so a separate sort step will be needed if the query has an ORDER BY clause. For this reason, and because each additional index scan adds extra time, the planner will sometimes choose to use a simple index scan even though additional indexes are available that could have been used as well.

In all but the simplest applications, there are various combinations of indexes that might be useful, and the database developer must make trade-offs to decide which indexes to provide. Sometimes multicolumn indexes are best, but sometimes it's better to create separate indexes and rely on the index-combination feature. For example, if your workload includes a mix of queries that sometimes involve only column x, sometimes only column y, and sometimes both columns, you might choose to create two separate indexes on x and y, relying on index combination to process the queries that use both columns. You could also create a multicolumn index on (x, y). This index would typically be more efficient than index combination for queries involving both columns, but as discussed in Section 11.3, it would be less useful for queries involving only y. Just how useful will depend on how effective the B-tree index skip scan optimization is; if x has no more than several hundred distinct values, skip scan will make searches for specific y values execute reasonably efficiently. A combination of a multicolumn index on (x, y) and a separate index on y might also serve reasonably well. For queries involving only x, the multicolumn index could be used, though it would be larger and hence slower than an index on x alone. The last alternative is to create all three indexes, but this is probably only reasonable if the table is searched much more often than it is updated and all three types of query are common. If one of the types of query is much less common than the others, you'd probably settle for creating just the two indexes that best match the common types.

---

PostgreSQL: Documentation: 18: Chapter 18. Server Setup and Operation

URL: https://www.postgresql.org/docs/current/runtime.html

Contents:

• Chapter 18. Server Setup and Operation

This chapter discusses how to set up and run the database server, and its interactions with the operating system.

The directions in this chapter assume that you are working with plain PostgreSQL without any additional infrastructure, for example a copy that you built from source according to the directions in the preceding chapters. If you are working with a pre-packaged or vendor-supplied version of PostgreSQL, it is likely that the packager has made special provisions for installing and starting the database server according to your system's conventions. Consult the package-level documentation for details.

---

PostgreSQL: Documentation: 18: 27.4. Progress Reporting

URL: https://www.postgresql.org/docs/current/progress-reporting.html

Contents:

• 27.4. Progress Reporting #
  • 27.4.1. ANALYZE Progress Reporting #
  • Note
  • 27.4.2. CLUSTER Progress Reporting #
  • 27.4.3. COPY Progress Reporting #
  • 27.4.4. CREATE INDEX Progress Reporting #
  • 27.4.5. VACUUM Progress Reporting #
  • 27.4.6. Base Backup Progress Reporting #

PostgreSQL has the ability to report the progress of certain commands during command execution. Currently, the only commands which support progress reporting are ANALYZE, CLUSTER, CREATE INDEX, VACUUM, COPY, and BASE_BACKUP (i.e., replication command that pg_basebackup issues to take a base backup). This may be expanded in the future.

Whenever ANALYZE is running, the pg_stat_progress_analyze view will contain a row for each backend that is currently running that command. The tables below describe the information that will be reported and provide information about how to interpret it.

Table 27.38. pg_stat_progress_analyze View

Process ID of backend.

OID of the database to which this backend is connected.

Name of the database to which this backend is connected.

OID of the table being analyzed.

Current processing phase. See Table 27.39.

sample_blks_total bigint

Total number of heap blocks that will be sampled.

sample_blks_scanned bigint

Number of heap blocks scanned.

ext_stats_total bigint

Number of extended statistics.

ext_stats_computed bigint

Number of extended statistics computed. This counter only advances when the phase is computing extended statistics.

child_tables_total bigint

Number of child tables.

child_tables_done bigint

Number of child tables scanned. This counter only advances when the phase is acquiring inherited sample rows.

current_child_table_relid oid

OID of the child table currently being scanned. This field is only valid when the phase is acquiring inherited sample rows.

delay_time double precision

Total time spent sleeping due to cost-based delay (see Section 19.10.2, in milliseconds (if track_cost_delay_timing is enabled, otherwise zero).

Table 27.39. ANALYZE Phases

Note that when ANALYZE is run on a partitioned table without the ONLY keyword, all of its partitions are also recursively analyzed. In that case, ANALYZE progress is reported first for the parent table, whereby its inheritance statistics are collected, followed by that for each partition.

Whenever CLUSTER or VACUUM FULL is running, the pg_stat_progress_cluster view will contain a row for each backend that is currently running either command. The tables below describe the information that will be reported and provide information about how to interpret it.

Table 27.40. pg_stat_progress_cluster View

Process ID of backend.

OID of the database to which this backend is connected.

Name of the database to which this backend is connected.

OID of the table being clustered.

The command that is running. Either CLUSTER or VACUUM FULL.

Current processing phase. See Table 27.41.

cluster_index_relid oid

If the table is being scanned using an index, this is the OID of the index being used; otherwise, it is zero.

heap_tuples_scanned bigint

Number of heap tuples scanned. This counter only advances when the phase is seq scanning heap, index scanning heap or writing new heap.

heap_tuples_written bigint

Number of heap tuples written. This counter only advances when the phase is seq scanning heap, index scanning heap or writing new heap.

heap_blks_total bigint

Total number of heap blocks in the table. This number is reported as of the beginning of seq scanning heap.

heap_blks_scanned bigint

Number of heap blocks scanned. This counter only advances when the phase is seq scanning heap.

index_rebuild_count bigint

Number of indexes rebuilt. This counter only advances when the phase is rebuilding index.

Table 27.41. CLUSTER and VACUUM FULL Phases

Whenever COPY is running, the pg_stat_progress_copy view will contain one row for each backend that is currently running a COPY command. The table below describes the information that will be reported and provides information about how to interpret it.

Table 27.42. pg_stat_progress_copy View

Process ID of backend.

OID of the database to which this backend is connected.

Name of the database to which this backend is connected.

OID of the table on which the COPY command is executed. It is set to 0 if copying from a SELECT query.

The command that is running: COPY FROM, or COPY TO.

The I/O type that the data is read from or written to: FILE, PROGRAM, PIPE (for COPY FROM STDIN and COPY TO STDOUT), or CALLBACK (used for example during the initial table synchronization in logical replication).

bytes_processed bigint

Number of bytes already processed by COPY command.

Size of source file for COPY FROM command in bytes. It is set to 0 if not available.

tuples_processed bigint

Number of tuples already processed by COPY command.

tuples_excluded bigint

Number of tuples not processed because they were excluded by the WHERE clause of the COPY command.

tuples_skipped bigint

Number of tuples skipped because they contain malformed data. This counter only advances when a value other than stop is specified to the ON_ERROR option.

Whenever CREATE INDEX or REINDEX is running, the pg_stat_progress_create_index view will contain one row for each backend that is currently creating indexes. The tables below describe the information that will be reported and provide information about how to interpret it.

Table 27.43. pg_stat_progress_create_index View

Process ID of the backend creating indexes.

OID of the database to which this backend is connected.

Name of the database to which this backend is connected.

OID of the table on which the index is being created.

OID of the index being created or reindexed. During a non-concurrent CREATE INDEX, this is 0.

Specific command type: CREATE INDEX, CREATE INDEX CONCURRENTLY, REINDEX, or REINDEX CONCURRENTLY.

Current processing phase of index creation. See Table 27.44.

Total number of lockers to wait for, when applicable.

Number of lockers already waited for.

current_locker_pid bigint

Process ID of the locker currently being waited for.

Total number of blocks to be processed in the current phase.

Number of blocks already processed in the current phase.

Total number of tuples to be processed in the current phase.

Number of tuples already processed in the current phase.

partitions_total bigint

Total number of partitions on which the index is to be created or attached, including both direct and indirect partitions. 0 during a REINDEX, or when the index is not partitioned.

partitions_done bigint

Number of partitions on which the index has already been created or attached, including both direct and indirect partitions. 0 during a REINDEX, or when the index is not partitioned.

Table 27.44. CREATE INDEX Phases

Whenever VACUUM is running, the pg_stat_progress_vacuum view will contain one row for each backend (including autovacuum worker processes) that is currently vacuuming. The tables below describe the information that will be reported and provide information about how to interpret it. Progress for VACUUM FULL commands is reported via pg_stat_progress_cluster because both VACUUM FULL and CLUSTER rewrite the table, while regular VACUUM only modifies it in place. See Section 27.4.2.

Table 27.45. pg_stat_progress_vacuum View

Process ID of backend.

OID of the database to which this backend is connected.

Name of the database to which this backend is connected.

OID of the table being vacuumed.

Current processing phase of vacuum. See Table 27.46.

heap_blks_total bigint

Total number of heap blocks in the table. This number is reported as of the beginning of the scan; blocks added later will not be (and need not be) visited by this VACUUM.

heap_blks_scanned bigint

Number of heap blocks scanned. Because the visibility map is used to optimize scans, some blocks will be skipped without inspection; skipped blocks are included in this total, so that this number will eventually become equal to heap_blks_total when the vacuum is complete. This counter only advances when the phase is scanning heap.

heap_blks_vacuumed bigint

Number of heap blocks vacuumed. Unless the table has no indexes, this counter only advances when the phase is vacuuming heap. Blocks that contain no dead tuples are skipped, so the counter may sometimes skip forward in large increments.

index_vacuum_count bigint

Number of completed index vacuum cycles.

max_dead_tuple_bytes bigint

Amount of dead tuple data that we can store before needing to perform an index vacuum cycle, based on maintenance_work_mem.

dead_tuple_bytes bigint

Amount of dead tuple data collected since the last index vacuum cycle.

num_dead_item_ids bigint

Number of dead item identifiers collected since the last index vacuum cycle.

Total number of indexes that will be vacuumed or cleaned up. This number is reported at the beginning of the vacuuming indexes phase or the cleaning up indexes phase.

indexes_processed bigint

Number of indexes processed. This counter only advances when the phase is vacuuming indexes or cleaning up indexes.

delay_time double precision

Total time spent sleeping due to cost-based delay (see Section 19.10.2), in milliseconds (if track_cost_delay_timing is enabled, otherwise zero). This includes the time that any associated parallel workers have slept. However, parallel workers report their sleep time no more frequently than once per second, so the reported value may be slightly stale.

Table 27.46. VACUUM Phases

Whenever an application like pg_basebackup is taking a base backup, the pg_stat_progress_basebackup view will contain a row for each WAL sender process that is currently running the BASE_BACKUP replication command and streaming the backup. The tables below describe the information that will be reported and provide information about how to interpret it.

Table 27.47. pg_stat_progress_basebackup View

Process ID of a WAL sender process.

Current processing phase. See Table 27.48.

Total amount of data that will be streamed. This is estimated and reported as of the beginning of streaming database files phase. Note that this is only an approximation since the database may change during streaming database files phase and WAL log may be included in the backup later. This is always the same value as backup_streamed once the amount of data streamed exceeds the estimated total size. If the estimation is disabled in pg_basebackup (i.e., --no-estimate-size option is specified), this is NULL.

backup_streamed bigint

Amount of data streamed. This counter only advances when the phase is streaming database files or transferring wal files.

tablespaces_total bigint

Total number of tablespaces that will be streamed.

tablespaces_streamed bigint

Number of tablespaces streamed. This counter only advances when the phase is streaming database files.

Table 27.48. Base Backup Phases

---

PostgreSQL: Documentation: 18: 8.13. XML Type

URL: https://www.postgresql.org/docs/current/datatype-xml.html

Contents:

• 8.13. XML Type #
  • 8.13.1. Creating XML Values #
  • 8.13.2. Encoding Handling #
  • Caution
  • 8.13.3. Accessing XML Values #

The xml data type can be used to store XML data. Its advantage over storing XML data in a text field is that it checks the input values for well-formedness, and there are support functions to perform type-safe operations on it; see Section 9.15. Use of this data type requires the installation to have been built with configure --with-libxml.

The xml type can store well-formed “documents”, as defined by the XML standard, as well as “content” fragments, which are defined by reference to the more permissive “document node” of the XQuery and XPath data model. Roughly, this means that content fragments can have more than one top-level element or character node. The expression xmlvalue IS DOCUMENT can be used to evaluate whether a particular xml value is a full document or only a content fragment.

Limits and compatibility notes for the xml data type can be found in Section D.3.

To produce a value of type xml from character data, use the function xmlparse:

While this is the only way to convert character strings into XML values according to the SQL standard, the PostgreSQL-specific syntaxes:

The xml type does not validate input values against a document type declaration (DTD), even when the input value specifies a DTD. There is also currently no built-in support for validating against other XML schema languages such as XML Schema.

The inverse operation, producing a character string value from xml, uses the function xmlserialize:

type can be character, character varying, or text (or an alias for one of those). Again, according to the SQL standard, this is the only way to convert between type xml and character types, but PostgreSQL also allows you to simply cast the value.

The INDENT option causes the result to be pretty-printed, while NO INDENT (which is the default) just emits the original input string. Casting to a character type likewise produces the original string.

When a character string value is cast to or from type xml without going through XMLPARSE or XMLSERIALIZE, respectively, the choice of DOCUMENT versus CONTENT is determined by the “XML option” session configuration parameter, which can be set using the standard command:

or the more PostgreSQL-like syntax

The default is CONTENT, so all forms of XML data are allowed.

Care must be taken when dealing with multiple character encodings on the client, server, and in the XML data passed through them. When using the text mode to pass queries to the server and query results to the client (which is the normal mode), PostgreSQL converts all character data passed between the client and the server and vice versa to the character encoding of the respective end; see Section 23.3. This includes string representations of XML values, such as in the above examples. This would ordinarily mean that encoding declarations contained in XML data can become invalid as the character data is converted to other encodings while traveling between client and server, because the embedded encoding declaration is not changed. To cope with this behavior, encoding declarations contained in character strings presented for input to the xml type are ignored, and content is assumed to be in the current server encoding. Consequently, for correct processing, character strings of XML data must be sent from the client in the current client encoding. It is the responsibility of the client to either convert documents to the current client encoding before sending them to the server, or to adjust the client encoding appropriately. On output, values of type xml will not have an encoding declaration, and clients should assume all data is in the current client encoding.

When using binary mode to pass query parameters to the server and query results back to the client, no encoding conversion is performed, so the situation is different. In this case, an encoding declaration in the XML data will be observed, and if it is absent, the data will be assumed to be in UTF-8 (as required by the XML standard; note that PostgreSQL does not support UTF-16). On output, data will have an encoding declaration specifying the client encoding, unless the client encoding is UTF-8, in which case it will be omitted.

Needless to say, processing XML data with PostgreSQL will be less error-prone and more efficient if the XML data encoding, client encoding, and server encoding are the same. Since XML data is internally processed in UTF-8, computations will be most efficient if the server encoding is also UTF-8.

Some XML-related functions may not work at all on non-ASCII data when the server encoding is not UTF-8. This is known to be an issue for xmltable() and xpath() in particular.

The xml data type is unusual in that it does not provide any comparison operators. This is because there is no well-defined and universally useful comparison algorithm for XML data. One consequence of this is that you cannot retrieve rows by comparing an xml column against a search value. XML values should therefore typically be accompanied by a separate key field such as an ID. An alternative solution for comparing XML values is to convert them to character strings first, but note that character string comparison has little to do with a useful XML comparison method.

Since there are no comparison operators for the xml data type, it is not possible to create an index directly on a column of this type. If speedy searches in XML data are desired, possible workarounds include casting the expression to a character string type and indexing that, or indexing an XPath expression. Of course, the actual query would have to be adjusted to search by the indexed expression.

The text-search functionality in PostgreSQL can also be used to speed up full-document searches of XML data. The necessary preprocessing support is, however, not yet available in the PostgreSQL distribution.

Examples:

Example 1 (unknown):

XMLPARSE ( { DOCUMENT | CONTENT } value)

Example 2 (unknown):

XMLPARSE (DOCUMENT '<?xml version="1.0"?><book><title>Manual</title><chapter>...</chapter></book>')
XMLPARSE (CONTENT 'abc<foo>bar</foo><bar>foo</bar>')

Example 3 (unknown):

xml '<foo>bar</foo>'
'<foo>bar</foo>'::xml

Example 4 (unknown):

XMLSERIALIZE ( { DOCUMENT | CONTENT } value AS type [ [ NO ] INDENT ] )

---

PostgreSQL: Documentation: 18: Part IV. Client Interfaces

URL: https://www.postgresql.org/docs/current/client-interfaces.html

Contents:

• Part IV. Client Interfaces

This part describes the client programming interfaces distributed with PostgreSQL. Each of these chapters can be read independently. There are many external programming interfaces for client programs that are distributed separately. They contain their own documentation (Appendix H lists some of the more popular ones). Readers of this part should be familiar with using SQL to manipulate and query the database (see Part II) and of course with the programming language of their choice.

---

PostgreSQL: Documentation: 18: Appendix C. SQL Key Words

URL: https://www.postgresql.org/docs/current/sql-keywords-appendix.html

Contents:

• Appendix C. SQL Key Words

Table C.1 lists all tokens that are key words in the SQL standard and in PostgreSQL 18.0. Background information can be found in Section 4.1.1. (For space reasons, only the latest two versions of the SQL standard, and SQL-92 for historical comparison, are included. The differences between those and the other intermediate standard versions are small.)

SQL distinguishes between reserved and non-reserved key words. According to the standard, reserved key words are the only real key words; they are never allowed as identifiers. Non-reserved key words only have a special meaning in particular contexts and can be used as identifiers in other contexts. Most non-reserved key words are actually the names of built-in tables and functions specified by SQL. The concept of non-reserved key words essentially only exists to declare that some predefined meaning is attached to a word in some contexts.

In the PostgreSQL parser, life is a bit more complicated. There are several different classes of tokens ranging from those that can never be used as an identifier to those that have absolutely no special status in the parser, but are considered ordinary identifiers. (The latter is usually the case for functions specified by SQL.) Even reserved key words are not completely reserved in PostgreSQL, but can be used as column labels (for example, SELECT 55 AS CHECK, even though CHECK is a reserved key word).

In Table C.1 in the column for PostgreSQL we classify as “non-reserved” those key words that are explicitly known to the parser but are allowed as column or table names. Some key words that are otherwise non-reserved cannot be used as function or data type names and are marked accordingly. (Most of these words represent built-in functions or data types with special syntax. The function or type is still available but it cannot be redefined by the user.) Labeled “reserved” are those tokens that are not allowed as column or table names. Some reserved key words are allowable as names for functions or data types; this is also shown in the table. If not so marked, a reserved key word is only allowed as a column label. A blank entry in this column means that the word is treated as an ordinary identifier by PostgreSQL.

Furthermore, while most key words can be used as “bare” column labels without writing AS before them (as described in Section 7.3.2), there are a few that require a leading AS to avoid ambiguity. These are marked in the table as “requires AS”.

As a general rule, if you get spurious parser errors for commands that use any of the listed key words as an identifier, you should try quoting the identifier to see if the problem goes away.

It is important to understand before studying Table C.1 that the fact that a key word is not reserved in PostgreSQL does not mean that the feature related to the word is not implemented. Conversely, the presence of a key word does not indicate the existence of a feature.

Table C.1. SQL Key Words

---

PostgreSQL: Documentation: 18: 9.21. Aggregate Functions

URL: https://www.postgresql.org/docs/current/functions-aggregate.html

Contents:

• 9.21. Aggregate Functions #
  • Note
  • Note

Aggregate functions compute a single result from a set of input values. The built-in general-purpose aggregate functions are listed in Table 9.62 while statistical aggregates are in Table 9.63. The built-in within-group ordered-set aggregate functions are listed in Table 9.64 while the built-in within-group hypothetical-set ones are in Table 9.65. Grouping operations, which are closely related to aggregate functions, are listed in Table 9.66. The special syntax considerations for aggregate functions are explained in Section 4.2.7. Consult Section 2.7 for additional introductory information.

Aggregate functions that support Partial Mode are eligible to participate in various optimizations, such as parallel aggregation.

While all aggregates below accept an optional ORDER BY clause (as outlined in Section 4.2.7), the clause has only been added to aggregates whose output is affected by ordering.

Table 9.62. General-Purpose Aggregate Functions

any_value ( anyelement ) → same as input type

Returns an arbitrary value from the non-null input values.

array_agg ( anynonarray ORDER BY input_sort_columns ) → anyarray

Collects all the input values, including nulls, into an array.

array_agg ( anyarray ORDER BY input_sort_columns ) → anyarray

Concatenates all the input arrays into an array of one higher dimension. (The inputs must all have the same dimensionality, and cannot be empty or null.)

avg ( smallint ) → numeric

avg ( integer ) → numeric

avg ( bigint ) → numeric

avg ( numeric ) → numeric

avg ( real ) → double precision

avg ( double precision ) → double precision

avg ( interval ) → interval

Computes the average (arithmetic mean) of all the non-null input values.

bit_and ( smallint ) → smallint

bit_and ( integer ) → integer

bit_and ( bigint ) → bigint

bit_and ( bit ) → bit

Computes the bitwise AND of all non-null input values.

bit_or ( smallint ) → smallint

bit_or ( integer ) → integer

bit_or ( bigint ) → bigint

Computes the bitwise OR of all non-null input values.

bit_xor ( smallint ) → smallint

bit_xor ( integer ) → integer

bit_xor ( bigint ) → bigint

bit_xor ( bit ) → bit

Computes the bitwise exclusive OR of all non-null input values. Can be useful as a checksum for an unordered set of values.

bool_and ( boolean ) → boolean

Returns true if all non-null input values are true, otherwise false.

bool_or ( boolean ) → boolean

Returns true if any non-null input value is true, otherwise false.

Computes the number of input rows.

count ( "any" ) → bigint

Computes the number of input rows in which the input value is not null.

every ( boolean ) → boolean

This is the SQL standard's equivalent to bool_and.

json_agg ( anyelement ORDER BY input_sort_columns ) → json

jsonb_agg ( anyelement ORDER BY input_sort_columns ) → jsonb

Collects all the input values, including nulls, into a JSON array. Values are converted to JSON as per to_json or to_jsonb.

json_agg_strict ( anyelement ) → json

jsonb_agg_strict ( anyelement ) → jsonb

Collects all the input values, skipping nulls, into a JSON array. Values are converted to JSON as per to_json or to_jsonb.

json_arrayagg ( [ value_expression ] [ ORDER BY sort_expression ] [ { NULL | ABSENT } ON NULL ] [ RETURNING data_type [ FORMAT JSON [ ENCODING UTF8 ] ] ])

Behaves in the same way as json_array but as an aggregate function so it only takes one value_expression parameter. If ABSENT ON NULL is specified, any NULL values are omitted. If ORDER BY is specified, the elements will appear in the array in that order rather than in the input order.

SELECT json_arrayagg(v) FROM (VALUES(2),(1)) t(v) → [2, 1]

json_objectagg ( [ { key_expression { VALUE | ':' } value_expression } ] [ { NULL | ABSENT } ON NULL ] [ { WITH | WITHOUT } UNIQUE [ KEYS ] ] [ RETURNING data_type [ FORMAT JSON [ ENCODING UTF8 ] ] ])

Behaves like json_object, but as an aggregate function, so it only takes one key_expression and one value_expression parameter.

SELECT json_objectagg(k:v) FROM (VALUES ('a'::text,current_date),('b',current_date + 1)) AS t(k,v) → { "a" : "2022-05-10", "b" : "2022-05-11" }

json_object_agg ( key "any", value "any" ORDER BY input_sort_columns ) → json

jsonb_object_agg ( key "any", value "any" ORDER BY input_sort_columns ) → jsonb

Collects all the key/value pairs into a JSON object. Key arguments are coerced to text; value arguments are converted as per to_json or to_jsonb. Values can be null, but keys cannot.

json_object_agg_strict ( key "any", value "any" ) → json

jsonb_object_agg_strict ( key "any", value "any" ) → jsonb

Collects all the key/value pairs into a JSON object. Key arguments are coerced to text; value arguments are converted as per to_json or to_jsonb. The key can not be null. If the value is null then the entry is skipped,

json_object_agg_unique ( key "any", value "any" ) → json

jsonb_object_agg_unique ( key "any", value "any" ) → jsonb

Collects all the key/value pairs into a JSON object. Key arguments are coerced to text; value arguments are converted as per to_json or to_jsonb. Values can be null, but keys cannot. If there is a duplicate key an error is thrown.

json_object_agg_unique_strict ( key "any", value "any" ) → json

jsonb_object_agg_unique_strict ( key "any", value "any" ) → jsonb

Collects all the key/value pairs into a JSON object. Key arguments are coerced to text; value arguments are converted as per to_json or to_jsonb. The key can not be null. If the value is null then the entry is skipped. If there is a duplicate key an error is thrown.

max ( see text ) → same as input type

Computes the maximum of the non-null input values. Available for any numeric, string, date/time, or enum type, as well as bytea, inet, interval, money, oid, pg_lsn, tid, xid8, and also arrays and composite types containing sortable data types.

min ( see text ) → same as input type

Computes the minimum of the non-null input values. Available for any numeric, string, date/time, or enum type, as well as bytea, inet, interval, money, oid, pg_lsn, tid, xid8, and also arrays and composite types containing sortable data types.

range_agg ( value anyrange ) → anymultirange

range_agg ( value anymultirange ) → anymultirange

Computes the union of the non-null input values.

range_intersect_agg ( value anyrange ) → anyrange

range_intersect_agg ( value anymultirange ) → anymultirange

Computes the intersection of the non-null input values.

string_agg ( value text, delimiter text ) → text

string_agg ( value bytea, delimiter bytea ORDER BY input_sort_columns ) → bytea

Concatenates the non-null input values into a string. Each value after the first is preceded by the corresponding delimiter (if it's not null).

sum ( smallint ) → bigint

sum ( integer ) → bigint

sum ( bigint ) → numeric

sum ( numeric ) → numeric

sum ( double precision ) → double precision

sum ( interval ) → interval

sum ( money ) → money

Computes the sum of the non-null input values.

xmlagg ( xml ORDER BY input_sort_columns ) → xml

Concatenates the non-null XML input values (see Section 9.15.1.8).

It should be noted that except for count, these functions return a null value when no rows are selected. In particular, sum of no rows returns null, not zero as one might expect, and array_agg returns null rather than an empty array when there are no input rows. The coalesce function can be used to substitute zero or an empty array for null when necessary.

The aggregate functions array_agg, json_agg, jsonb_agg, json_agg_strict, jsonb_agg_strict, json_object_agg, jsonb_object_agg, json_object_agg_strict, jsonb_object_agg_strict, json_object_agg_unique, jsonb_object_agg_unique, json_object_agg_unique_strict, jsonb_object_agg_unique_strict, string_agg, and xmlagg, as well as similar user-defined aggregate functions, produce meaningfully different result values depending on the order of the input values. This ordering is unspecified by default, but can be controlled by writing an ORDER BY clause within the aggregate call, as shown in Section 4.2.7. Alternatively, supplying the input values from a sorted subquery will usually work. For example:

Beware that this approach can fail if the outer query level contains additional processing, such as a join, because that might cause the subquery's output to be reordered before the aggregate is computed.

The boolean aggregates bool_and and bool_or correspond to the standard SQL aggregates every and any or some. PostgreSQL supports every, but not any or some, because there is an ambiguity built into the standard syntax:

Here ANY can be considered either as introducing a subquery, or as being an aggregate function, if the subquery returns one row with a Boolean value. Thus the standard name cannot be given to these aggregates.

Users accustomed to working with other SQL database management systems might be disappointed by the performance of the count aggregate when it is applied to the entire table. A query like:

will require effort proportional to the size of the table: PostgreSQL will need to scan either the entire table or the entirety of an index that includes all rows in the table.

Table 9.63 shows aggregate functions typically used in statistical analysis. (These are separated out merely to avoid cluttering the listing of more-commonly-used aggregates.) Functions shown as accepting numeric_type are available for all the types smallint, integer, bigint, numeric, real, and double precision. Where the description mentions N, it means the number of input rows for which all the input expressions are non-null. In all cases, null is returned if the computation is meaningless, for example when N is zero.

Table 9.63. Aggregate Functions for Statistics

corr ( Y double precision, X double precision ) → double precision

Computes the correlation coefficient.

covar_pop ( Y double precision, X double precision ) → double precision

Computes the population covariance.

covar_samp ( Y double precision, X double precision ) → double precision

Computes the sample covariance.

regr_avgx ( Y double precision, X double precision ) → double precision

Computes the average of the independent variable, sum(X)/N.

regr_avgy ( Y double precision, X double precision ) → double precision

Computes the average of the dependent variable, sum(Y)/N.

regr_count ( Y double precision, X double precision ) → bigint

Computes the number of rows in which both inputs are non-null.

regr_intercept ( Y double precision, X double precision ) → double precision

Computes the y-intercept of the least-squares-fit linear equation determined by the (X, Y) pairs.

regr_r2 ( Y double precision, X double precision ) → double precision

Computes the square of the correlation coefficient.

regr_slope ( Y double precision, X double precision ) → double precision

Computes the slope of the least-squares-fit linear equation determined by the (X, Y) pairs.

regr_sxx ( Y double precision, X double precision ) → double precision

Computes the “sum of squares” of the independent variable, sum(X^2) - sum(X)^2/N.

regr_sxy ( Y double precision, X double precision ) → double precision

Computes the “sum of products” of independent times dependent variables, sum(X*Y) - sum(X) * sum(Y)/N.

regr_syy ( Y double precision, X double precision ) → double precision

Computes the “sum of squares” of the dependent variable, sum(Y^2) - sum(Y)^2/N.

stddev ( numeric_type ) → double precision for real or double precision, otherwise numeric

This is a historical alias for stddev_samp.

stddev_pop ( numeric_type ) → double precision for real or double precision, otherwise numeric

Computes the population standard deviation of the input values.

stddev_samp ( numeric_type ) → double precision for real or double precision, otherwise numeric

Computes the sample standard deviation of the input values.

variance ( numeric_type ) → double precision for real or double precision, otherwise numeric

This is a historical alias for var_samp.

var_pop ( numeric_type ) → double precision for real or double precision, otherwise numeric

Computes the population variance of the input values (square of the population standard deviation).

var_samp ( numeric_type ) → double precision for real or double precision, otherwise numeric

Computes the sample variance of the input values (square of the sample standard deviation).

Table 9.64 shows some aggregate functions that use the ordered-set aggregate syntax. These functions are sometimes referred to as “inverse distribution” functions. Their aggregated input is introduced by ORDER BY, and they may also take a direct argument that is not aggregated, but is computed only once. All these functions ignore null values in their aggregated input. For those that take a fraction parameter, the fraction value must be between 0 and 1; an error is thrown if not. However, a null fraction value simply produces a null result.

Table 9.64. Ordered-Set Aggregate Functions

mode () WITHIN GROUP ( ORDER BY anyelement ) → anyelement

Computes the mode, the most frequent value of the aggregated argument (arbitrarily choosing the first one if there are multiple equally-frequent values). The aggregated argument must be of a sortable type.

percentile_cont ( fraction double precision ) WITHIN GROUP ( ORDER BY double precision ) → double precision

percentile_cont ( fraction double precision ) WITHIN GROUP ( ORDER BY interval ) → interval

Computes the continuous percentile, a value corresponding to the specified fraction within the ordered set of aggregated argument values. This will interpolate between adjacent input items if needed.

percentile_cont ( fractions double precision[] ) WITHIN GROUP ( ORDER BY double precision ) → double precision[]

percentile_cont ( fractions double precision[] ) WITHIN GROUP ( ORDER BY interval ) → interval[]

Computes multiple continuous percentiles. The result is an array of the same dimensions as the fractions parameter, with each non-null element replaced by the (possibly interpolated) value corresponding to that percentile.

percentile_disc ( fraction double precision ) WITHIN GROUP ( ORDER BY anyelement ) → anyelement

Computes the discrete percentile, the first value within the ordered set of aggregated argument values whose position in the ordering equals or exceeds the specified fraction. The aggregated argument must be of a sortable type.

percentile_disc ( fractions double precision[] ) WITHIN GROUP ( ORDER BY anyelement ) → anyarray

Computes multiple discrete percentiles. The result is an array of the same dimensions as the fractions parameter, with each non-null element replaced by the input value corresponding to that percentile. The aggregated argument must be of a sortable type.

Each of the “hypothetical-set” aggregates listed in Table 9.65 is associated with a window function of the same name defined in Section 9.22. In each case, the aggregate's result is the value that the associated window function would have returned for the “hypothetical” row constructed from args, if such a row had been added to the sorted group of rows represented by the sorted_args. For each of these functions, the list of direct arguments given in args must match the number and types of the aggregated arguments given in sorted_args. Unlike most built-in aggregates, these aggregates are not strict, that is they do not drop input rows containing nulls. Null values sort according to the rule specified in the ORDER BY clause.

Table 9.65. Hypothetical-Set Aggregate Functions

rank ( args ) WITHIN GROUP ( ORDER BY sorted_args ) → bigint

Computes the rank of the hypothetical row, with gaps; that is, the row number of the first row in its peer group.

dense_rank ( args ) WITHIN GROUP ( ORDER BY sorted_args ) → bigint

Computes the rank of the hypothetical row, without gaps; this function effectively counts peer groups.

percent_rank ( args ) WITHIN GROUP ( ORDER BY sorted_args ) → double precision

Computes the relative rank of the hypothetical row, that is (rank - 1) / (total rows - 1). The value thus ranges from 0 to 1 inclusive.

cume_dist ( args ) WITHIN GROUP ( ORDER BY sorted_args ) → double precision

Computes the cumulative distribution, that is (number of rows preceding or peers with hypothetical row) / (total rows). The value thus ranges from 1/N to 1.

Table 9.66. Grouping Operations

GROUPING ( group_by_expression(s) ) → integer

Returns a bit mask indicating which GROUP BY expressions are not included in the current grouping set. Bits are assigned with the rightmost argument corresponding to the least-significant bit; each bit is 0 if the corresponding expression is included in the grouping criteria of the grouping set generating the current result row, and 1 if it is not included.

The grouping operations shown in Table 9.66 are used in conjunction with grouping sets (see Section 7.2.4) to distinguish result rows. The arguments to the GROUPING function are not actually evaluated, but they must exactly match expressions given in the GROUP BY clause of the associated query level. For example:

Here, the grouping value 0 in the first four rows shows that those have been grouped normally, over both the grouping columns. The value 1 indicates that model was not grouped by in the next-to-last two rows, and the value 3 indicates that neither make nor model was grouped by in the last row (which therefore is an aggregate over all the input rows).

Examples:

Example 1 (unknown):

SELECT xmlagg(x) FROM (SELECT x FROM test ORDER BY y DESC) AS tab;

Example 2 (unknown):

SELECT b1 = ANY((SELECT b2 FROM t2 ...)) FROM t1 ...;

Example 3 (unknown):

SELECT count(*) FROM sometable;

Example 4 (javascript):

=> SELECT * FROM items_sold;
 make  | model | sales
-------+-------+-------
 Foo   | GT    |  10
 Foo   | Tour  |  20
 Bar   | City  |  15
 Bar   | Sport |  5
(4 rows)

=> SELECT make, model, GROUPING(make,model), sum(sales) FROM items_sold GROUP BY ROLLUP(make,model);
 make  | model | grouping | sum
-------+-------+----------+-----
 Foo   | GT    |        0 | 10
 Foo   | Tour  |        0 | 20
 Bar   | City  |        0 | 15
 Bar   | Sport |        0 | 5
 Foo   |       |        1 | 30
 Bar   |       |        1 | 20
       |       |        3 | 50
(7 rows)

---

PostgreSQL: Documentation: 18: 36.8. Procedural Language Functions

URL: https://www.postgresql.org/docs/current/xfunc-pl.html

Contents:

• 36.8. Procedural Language Functions #

PostgreSQL allows user-defined functions to be written in other languages besides SQL and C. These other languages are generically called procedural languages (PLs). Procedural languages aren't built into the PostgreSQL server; they are offered by loadable modules. See Chapter 40 and following chapters for more information.

---

PostgreSQL: Documentation: 18: 35.51. sql_sizing

URL: https://www.postgresql.org/docs/current/infoschema-sql-sizing.html

Contents:

• 35.51. sql_sizing #

The table sql_sizing contains information about various size limits and maximum values in PostgreSQL. This information is primarily intended for use in the context of the ODBC interface; users of other interfaces will probably find this information to be of little use. For this reason, the individual sizing items are not described here; you will find them in the description of the ODBC interface.

Table 35.49. sql_sizing Columns

sizing_id cardinal_number

Identifier of the sizing item

sizing_name character_data

Descriptive name of the sizing item

supported_value cardinal_number

Value of the sizing item, or 0 if the size is unlimited or cannot be determined, or null if the features for which the sizing item is applicable are not supported

comments character_data

Possibly a comment pertaining to the sizing item

---

PostgreSQL: Documentation: 18: 27.6. Monitoring Disk Usage

URL: https://www.postgresql.org/docs/current/diskusage.html

Contents:

• 27.6. Monitoring Disk Usage #
  • 27.6.1. Determining Disk Usage #
  • 27.6.2. Disk Full Failure #
  • Tip

This section discusses how to monitor the disk usage of a PostgreSQL database system.

Each table has a primary heap disk file where most of the data is stored. If the table has any columns with potentially-wide values, there also might be a TOAST file associated with the table, which is used to store values too wide to fit comfortably in the main table (see Section 66.2). There will be one valid index on the TOAST table, if present. There also might be indexes associated with the base table. Each table and index is stored in a separate disk file — possibly more than one file, if the file would exceed one gigabyte. Naming conventions for these files are described in Section 66.1.

You can monitor disk space in three ways: using the SQL functions listed in Table 9.102, using the oid2name module, or using manual inspection of the system catalogs. The SQL functions are the easiest to use and are generally recommended. The remainder of this section shows how to do it by inspection of the system catalogs.

Using psql on a recently vacuumed or analyzed database, you can issue queries to see the disk usage of any table:

Each page is typically 8 kilobytes. (Remember, relpages is only updated by VACUUM, ANALYZE, and a few DDL commands such as CREATE INDEX.) The file path name is of interest if you want to examine the table's disk file directly.

To show the space used by TOAST tables, use a query like the following:

You can easily display index sizes, too:

It is easy to find your largest tables and indexes using this information:

The most important disk monitoring task of a database administrator is to make sure the disk doesn't become full. A filled data disk will not result in data corruption, but it might prevent useful activity from occurring. If the disk holding the WAL files grows full, database server panic and consequent shutdown might occur.

If you cannot free up additional space on the disk by deleting other things, you can move some of the database files to other file systems by making use of tablespaces. See Section 22.6 for more information about that.

Some file systems perform badly when they are almost full, so do not wait until the disk is completely full to take action.

If your system supports per-user disk quotas, then the database will naturally be subject to whatever quota is placed on the user the server runs as. Exceeding the quota will have the same bad effects as running out of disk space entirely.

Examples:

Example 1 (unknown):

SELECT pg_relation_filepath(oid), relpages FROM pg_class WHERE relname = 'customer';

 pg_relation_filepath | relpages
----------------------+----------
 base/16384/16806     |       60
(1 row)

Example 2 (unknown):

SELECT relname, relpages
FROM pg_class,
     (SELECT reltoastrelid
      FROM pg_class
      WHERE relname = 'customer') AS ss
WHERE oid = ss.reltoastrelid OR
      oid = (SELECT indexrelid
             FROM pg_index
             WHERE indrelid = ss.reltoastrelid)
ORDER BY relname;

       relname        | relpages
----------------------+----------
 pg_toast_16806       |        0
 pg_toast_16806_index |        1

Example 3 (unknown):

SELECT c2.relname, c2.relpages
FROM pg_class c, pg_class c2, pg_index i
WHERE c.relname = 'customer' AND
      c.oid = i.indrelid AND
      c2.oid = i.indexrelid
ORDER BY c2.relname;

      relname      | relpages
-------------------+----------
 customer_id_index |       26

Example 4 (unknown):

SELECT relname, relpages
FROM pg_class
ORDER BY relpages DESC;

       relname        | relpages
----------------------+----------
 bigtable             |     3290
 customer             |     3144

---

PostgreSQL: Documentation: 18: 11.12. Examining Index Usage

URL: https://www.postgresql.org/docs/current/indexes-examine.html

Contents:

• 11.12. Examining Index Usage #

Although indexes in PostgreSQL do not need maintenance or tuning, it is still important to check which indexes are actually used by the real-life query workload. Examining index usage for an individual query is done with the EXPLAIN command; its application for this purpose is illustrated in Section 14.1. It is also possible to gather overall statistics about index usage in a running server, as described in Section 27.2.

It is difficult to formulate a general procedure for determining which indexes to create. There are a number of typical cases that have been shown in the examples throughout the previous sections. A good deal of experimentation is often necessary. The rest of this section gives some tips for that:

Always run ANALYZE first. This command collects statistics about the distribution of the values in the table. This information is required to estimate the number of rows returned by a query, which is needed by the planner to assign realistic costs to each possible query plan. In absence of any real statistics, some default values are assumed, which are almost certain to be inaccurate. Examining an application's index usage without having run ANALYZE is therefore a lost cause. See Section 24.1.3 and Section 24.1.6 for more information.

Use real data for experimentation. Using test data for setting up indexes will tell you what indexes you need for the test data, but that is all.

It is especially fatal to use very small test data sets. While selecting 1000 out of 100000 rows could be a candidate for an index, selecting 1 out of 100 rows will hardly be, because the 100 rows probably fit within a single disk page, and there is no plan that can beat sequentially fetching 1 disk page.

Also be careful when making up test data, which is often unavoidable when the application is not yet in production. Values that are very similar, completely random, or inserted in sorted order will skew the statistics away from the distribution that real data would have.

When indexes are not used, it can be useful for testing to force their use. There are run-time parameters that can turn off various plan types (see Section 19.7.1). For instance, turning off sequential scans (enable_seqscan) and nested-loop joins (enable_nestloop), which are the most basic plans, will force the system to use a different plan. If the system still chooses a sequential scan or nested-loop join then there is probably a more fundamental reason why the index is not being used; for example, the query condition does not match the index. (What kind of query can use what kind of index is explained in the previous sections.)

If forcing index usage does use the index, then there are two possibilities: Either the system is right and using the index is indeed not appropriate, or the cost estimates of the query plans are not reflecting reality. So you should time your query with and without indexes. The EXPLAIN ANALYZE command can be useful here.

If it turns out that the cost estimates are wrong, there are, again, two possibilities. The total cost is computed from the per-row costs of each plan node times the selectivity estimate of the plan node. The costs estimated for the plan nodes can be adjusted via run-time parameters (described in Section 19.7.2). An inaccurate selectivity estimate is due to insufficient statistics. It might be possible to improve this by tuning the statistics-gathering parameters (see ALTER TABLE).

If you do not succeed in adjusting the costs to be more appropriate, then you might have to resort to forcing index usage explicitly. You might also want to contact the PostgreSQL developers to examine the issue.

---

PostgreSQL: Documentation: 18: 36.11. Function Optimization Information

URL: https://www.postgresql.org/docs/current/xfunc-optimization.html

Contents:

• 36.11. Function Optimization Information #

By default, a function is just a “black box” that the database system knows very little about the behavior of. However, that means that queries using the function may be executed much less efficiently than they could be. It is possible to supply additional knowledge that helps the planner optimize function calls.

Some basic facts can be supplied by declarative annotations provided in the CREATE FUNCTION command. Most important of these is the function's volatility category (IMMUTABLE, STABLE, or VOLATILE); one should always be careful to specify this correctly when defining a function. The parallel safety property (PARALLEL UNSAFE, PARALLEL RESTRICTED, or PARALLEL SAFE) must also be specified if you hope to use the function in parallelized queries. It can also be useful to specify the function's estimated execution cost, and/or the number of rows a set-returning function is estimated to return. However, the declarative way of specifying those two facts only allows specifying a constant value, which is often inadequate.

It is also possible to attach a planner support function to an SQL-callable function (called its target function), and thereby provide knowledge about the target function that is too complex to be represented declaratively. Planner support functions have to be written in C (although their target functions might not be), so this is an advanced feature that relatively few people will use.

A planner support function must have the SQL signature

It is attached to its target function by specifying the SUPPORT clause when creating the target function.

The details of the API for planner support functions can be found in file src/include/nodes/supportnodes.h in the PostgreSQL source code. Here we provide just an overview of what planner support functions can do. The set of possible requests to a support function is extensible, so more things might be possible in future versions.

Some function calls can be simplified during planning based on properties specific to the function. For example, int4mul(n, 1) could be simplified to just n. This type of transformation can be performed by a planner support function, by having it implement the SupportRequestSimplify request type. The support function will be called for each instance of its target function found in a query parse tree. If it finds that the particular call can be simplified into some other form, it can build and return a parse tree representing that expression. This will automatically work for operators based on the function, too — in the example just given, n * 1 would also be simplified to n. (But note that this is just an example; this particular optimization is not actually performed by standard PostgreSQL.) We make no guarantee that PostgreSQL will never call the target function in cases that the support function could simplify. Ensure rigorous equivalence between the simplified expression and an actual execution of the target function.

For target functions that return boolean, it is often useful to estimate the fraction of rows that will be selected by a WHERE clause using that function. This can be done by a support function that implements the SupportRequestSelectivity request type.

If the target function's run time is highly dependent on its inputs, it may be useful to provide a non-constant cost estimate for it. This can be done by a support function that implements the SupportRequestCost request type.

For target functions that return sets, it is often useful to provide a non-constant estimate for the number of rows that will be returned. This can be done by a support function that implements the SupportRequestRows request type.

For target functions that return boolean, it may be possible to convert a function call appearing in WHERE into an indexable operator clause or clauses. The converted clauses might be exactly equivalent to the function's condition, or they could be somewhat weaker (that is, they might accept some values that the function condition does not). In the latter case the index condition is said to be lossy; it can still be used to scan an index, but the function call will have to be executed for each row returned by the index to see if it really passes the WHERE condition or not. To create such conditions, the support function must implement the SupportRequestIndexCondition request type.

Examples:

Example 1 (unknown):

supportfn(internal) returns internal

---

PostgreSQL: Documentation: 18: Chapter 62. Table Access Method Interface Definition

URL: https://www.postgresql.org/docs/current/tableam.html

Contents:

• Chapter 62. Table Access Method Interface Definition

This chapter explains the interface between the core PostgreSQL system and table access methods, which manage the storage for tables. The core system knows little about these access methods beyond what is specified here, so it is possible to develop entirely new access method types by writing add-on code.

Each table access method is described by a row in the pg_am system catalog. The pg_am entry specifies a name and a handler function for the table access method. These entries can be created and deleted using the CREATE ACCESS METHOD and DROP ACCESS METHOD SQL commands.

A table access method handler function must be declared to accept a single argument of type internal and to return the pseudo-type table_am_handler. The argument is a dummy value that simply serves to prevent handler functions from being called directly from SQL commands.

Here is how an extension SQL script file might create a table access method handler:

The result of the function must be a pointer to a struct of type TableAmRoutine, which contains everything that the core code needs to know to make use of the table access method. The return value needs to be of server lifetime, which is typically achieved by defining it as a static const variable in global scope.

Here is how a source file with the table access method handler might look like:

The TableAmRoutine struct, also called the access method's API struct, defines the behavior of the access method using callbacks. These callbacks are pointers to plain C functions and are not visible or callable at the SQL level. All the callbacks and their behavior is defined in the TableAmRoutine structure (with comments inside the struct defining the requirements for callbacks). Most callbacks have wrapper functions, which are documented from the point of view of a user (rather than an implementor) of the table access method. For details, please refer to the src/include/access/tableam.h file.

To implement an access method, an implementor will typically need to implement an AM-specific type of tuple table slot (see src/include/executor/tuptable.h), which allows code outside the access method to hold references to tuples of the AM, and to access the columns of the tuple.

Currently, the way an AM actually stores data is fairly unconstrained. For example, it's possible, but not required, to use postgres' shared buffer cache. In case it is used, it likely makes sense to use PostgreSQL's standard page layout as described in Section 66.6.

One fairly large constraint of the table access method API is that, currently, if the AM wants to support modifications and/or indexes, it is necessary for each tuple to have a tuple identifier (TID) consisting of a block number and an item number (see also Section 66.6). It is not strictly necessary that the sub-parts of TIDs have the same meaning they e.g., have for heap, but if bitmap scan support is desired (it is optional), the block number needs to provide locality.

For crash safety, an AM can use postgres' WAL, or a custom implementation. If WAL is chosen, either Generic WAL Records can be used, or a Custom WAL Resource Manager can be implemented.

To implement transactional support in a manner that allows different table access methods be accessed within a single transaction, it likely is necessary to closely integrate with the machinery in src/backend/access/transam/xlog.c.

Any developer of a new table access method can refer to the existing heap implementation present in src/backend/access/heap/heapam_handler.c for details of its implementation.

Examples:

Example 1 (unknown):

CREATE OR REPLACE FUNCTION my_tableam_handler(internal)
  RETURNS table_am_handler AS 'my_extension', 'my_tableam_handler'
  LANGUAGE C STRICT;

CREATE ACCESS METHOD myam TYPE TABLE HANDLER my_tableam_handler;

Example 2 (javascript):

#include "postgres.h"

#include "access/tableam.h"
#include "fmgr.h"

PG_MODULE_MAGIC;

static const TableAmRoutine my_tableam_methods = {
    .type = T_TableAmRoutine,

    /* Methods of TableAmRoutine omitted from example, add them here. */
};

PG_FUNCTION_INFO_V1(my_tableam_handler);

Datum
my_tableam_handler(PG_FUNCTION_ARGS)
{
    PG_RETURN_POINTER(&my_tableam_methods);
}

---

PostgreSQL: Documentation: 18: 35.1. The Schema

URL: https://www.postgresql.org/docs/current/infoschema-schema.html

Contents:

• 35.1. The Schema #

The information schema itself is a schema named information_schema. This schema automatically exists in all databases. The owner of this schema is the initial database user in the cluster, and that user naturally has all the privileges on this schema, including the ability to drop it (but the space savings achieved by that are minuscule).

By default, the information schema is not in the schema search path, so you need to access all objects in it through qualified names. Since the names of some of the objects in the information schema are generic names that might occur in user applications, you should be careful if you want to put the information schema in the path.

---

PostgreSQL: Documentation: 18: 28.5. WAL Configuration

URL: https://www.postgresql.org/docs/current/wal-configuration.html

Contents:

• 28.5. WAL Configuration #

There are several WAL-related configuration parameters that affect database performance. This section explains their use. Consult Chapter 19 for general information about setting server configuration parameters.

Checkpoints are points in the sequence of transactions at which it is guaranteed that the heap and index data files have been updated with all information written before that checkpoint. At checkpoint time, all dirty data pages are flushed to disk and a special checkpoint record is written to the WAL file. (The change records were previously flushed to the WAL files.) In the event of a crash, the crash recovery procedure looks at the latest checkpoint record to determine the point in the WAL (known as the redo record) from which it should start the REDO operation. Any changes made to data files before that point are guaranteed to be already on disk. Hence, after a checkpoint, WAL segments preceding the one containing the redo record are no longer needed and can be recycled or removed. (When WAL archiving is being done, the WAL segments must be archived before being recycled or removed.)

The checkpoint requirement of flushing all dirty data pages to disk can cause a significant I/O load. For this reason, checkpoint activity is throttled so that I/O begins at checkpoint start and completes before the next checkpoint is due to start; this minimizes performance degradation during checkpoints.

The server's checkpointer process automatically performs a checkpoint every so often. A checkpoint is begun every checkpoint_timeout seconds, or if max_wal_size is about to be exceeded, whichever comes first. The default settings are 5 minutes and 1 GB, respectively. If no WAL has been written since the previous checkpoint, new checkpoints will be skipped even if checkpoint_timeout has passed. (If WAL archiving is being used and you want to put a lower limit on how often files are archived in order to bound potential data loss, you should adjust the archive_timeout parameter rather than the checkpoint parameters.) It is also possible to force a checkpoint by using the SQL command CHECKPOINT.

Reducing checkpoint_timeout and/or max_wal_size causes checkpoints to occur more often. This allows faster after-crash recovery, since less work will need to be redone. However, one must balance this against the increased cost of flushing dirty data pages more often. If full_page_writes is set (as is the default), there is another factor to consider. To ensure data page consistency, the first modification of a data page after each checkpoint results in logging the entire page content. In that case, a smaller checkpoint interval increases the volume of output to the WAL, partially negating the goal of using a smaller interval, and in any case causing more disk I/O.

Checkpoints are fairly expensive, first because they require writing out all currently dirty buffers, and second because they result in extra subsequent WAL traffic as discussed above. It is therefore wise to set the checkpointing parameters high enough so that checkpoints don't happen too often. As a simple sanity check on your checkpointing parameters, you can set the checkpoint_warning parameter. If checkpoints happen closer together than checkpoint_warning seconds, a message will be output to the server log recommending increasing max_wal_size. Occasional appearance of such a message is not cause for alarm, but if it appears often then the checkpoint control parameters should be increased. Bulk operations such as large COPY transfers might cause a number of such warnings to appear if you have not set max_wal_size high enough.

To avoid flooding the I/O system with a burst of page writes, writing dirty buffers during a checkpoint is spread over a period of time. That period is controlled by checkpoint_completion_target, which is given as a fraction of the checkpoint interval (configured by using checkpoint_timeout). The I/O rate is adjusted so that the checkpoint finishes when the given fraction of checkpoint_timeout seconds have elapsed, or before max_wal_size is exceeded, whichever is sooner. With the default value of 0.9, PostgreSQL can be expected to complete each checkpoint a bit before the next scheduled checkpoint (at around 90% of the last checkpoint's duration). This spreads out the I/O as much as possible so that the checkpoint I/O load is consistent throughout the checkpoint interval. The disadvantage of this is that prolonging checkpoints affects recovery time, because more WAL segments will need to be kept around for possible use in recovery. A user concerned about the amount of time required to recover might wish to reduce checkpoint_timeout so that checkpoints occur more frequently but still spread the I/O across the checkpoint interval. Alternatively, checkpoint_completion_target could be reduced, but this would result in times of more intense I/O (during the checkpoint) and times of less I/O (after the checkpoint completed but before the next scheduled checkpoint) and therefore is not recommended. Although checkpoint_completion_target could be set as high as 1.0, it is typically recommended to set it to no higher than 0.9 (the default) since checkpoints include some other activities besides writing dirty buffers. A setting of 1.0 is quite likely to result in checkpoints not being completed on time, which would result in performance loss due to unexpected variation in the number of WAL segments needed.

On Linux and POSIX platforms checkpoint_flush_after allows you to force OS pages written by the checkpoint to be flushed to disk after a configurable number of bytes. Otherwise, these pages may be kept in the OS's page cache, inducing a stall when fsync is issued at the end of a checkpoint. This setting will often help to reduce transaction latency, but it also can have an adverse effect on performance; particularly for workloads that are bigger than shared_buffers, but smaller than the OS's page cache.

The number of WAL segment files in pg_wal directory depends on min_wal_size, max_wal_size and the amount of WAL generated in previous checkpoint cycles. When old WAL segment files are no longer needed, they are removed or recycled (that is, renamed to become future segments in the numbered sequence). If, due to a short-term peak of WAL output rate, max_wal_size is exceeded, the unneeded segment files will be removed until the system gets back under this limit. Below that limit, the system recycles enough WAL files to cover the estimated need until the next checkpoint, and removes the rest. The estimate is based on a moving average of the number of WAL files used in previous checkpoint cycles. The moving average is increased immediately if the actual usage exceeds the estimate, so it accommodates peak usage rather than average usage to some extent. min_wal_size puts a minimum on the amount of WAL files recycled for future usage; that much WAL is always recycled for future use, even if the system is idle and the WAL usage estimate suggests that little WAL is needed.

Independently of max_wal_size, the most recent wal_keep_size megabytes of WAL files plus one additional WAL file are kept at all times. Also, if WAL archiving is used, old segments cannot be removed or recycled until they are archived. If WAL archiving cannot keep up with the pace that WAL is generated, or if archive_command or archive_library fails repeatedly, old WAL files will accumulate in pg_wal until the situation is resolved. A slow or failed standby server that uses a replication slot will have the same effect (see Section 26.2.6). Similarly, if WAL summarization is enabled, old segments are kept until they are summarized.

In archive recovery or standby mode, the server periodically performs restartpoints, which are similar to checkpoints in normal operation: the server forces all its state to disk, updates the pg_control file to indicate that the already-processed WAL data need not be scanned again, and then recycles any old WAL segment files in the pg_wal directory. Restartpoints can't be performed more frequently than checkpoints on the primary because restartpoints can only be performed at checkpoint records. A restartpoint can be demanded by a schedule or by an external request. The restartpoints_timed counter in the pg_stat_checkpointer view counts the first ones while the restartpoints_req the second. A restartpoint is triggered by schedule when a checkpoint record is reached if at least checkpoint_timeout seconds have passed since the last performed restartpoint or when the previous attempt to perform the restartpoint has failed. In the last case, the next restartpoint will be scheduled in 15 seconds. A restartpoint is triggered by request due to similar reasons like checkpoint but mostly if WAL size is about to exceed max_wal_size However, because of limitations on when a restartpoint can be performed, max_wal_size is often exceeded during recovery, by up to one checkpoint cycle's worth of WAL. (max_wal_size is never a hard limit anyway, so you should always leave plenty of headroom to avoid running out of disk space.) The restartpoints_done counter in the pg_stat_checkpointer view counts the restartpoints that have really been performed.

In some cases, when the WAL size on the primary increases quickly, for instance during massive INSERT, the restartpoints_req counter on the standby may demonstrate a peak growth. This occurs because requests to create a new restartpoint due to increased WAL consumption cannot be performed because the safe checkpoint record since the last restartpoint has not yet been replayed on the standby. This behavior is normal and does not lead to an increase in system resource consumption. Only the restartpoints_done counter among the restartpoint-related ones indicates that noticeable system resources have been spent.

There are two commonly used internal WAL functions: XLogInsertRecord and XLogFlush. XLogInsertRecord is used to place a new record into the WAL buffers in shared memory. If there is no space for the new record, XLogInsertRecord will have to write (move to kernel cache) a few filled WAL buffers. This is undesirable because XLogInsertRecord is used on every database low level modification (for example, row insertion) at a time when an exclusive lock is held on affected data pages, so the operation needs to be as fast as possible. What is worse, writing WAL buffers might also force the creation of a new WAL segment, which takes even more time. Normally, WAL buffers should be written and flushed by an XLogFlush request, which is made, for the most part, at transaction commit time to ensure that transaction records are flushed to permanent storage. On systems with high WAL output, XLogFlush requests might not occur often enough to prevent XLogInsertRecord from having to do writes. On such systems one should increase the number of WAL buffers by modifying the wal_buffers parameter. When full_page_writes is set and the system is very busy, setting wal_buffers higher will help smooth response times during the period immediately following each checkpoint.

The commit_delay parameter defines for how many microseconds a group commit leader process will sleep after acquiring a lock within XLogFlush, while group commit followers queue up behind the leader. This delay allows other server processes to add their commit records to the WAL buffers so that all of them will be flushed by the leader's eventual sync operation. No sleep will occur if fsync is not enabled, or if fewer than commit_siblings other sessions are currently in active transactions; this avoids sleeping when it's unlikely that any other session will commit soon. Note that on some platforms, the resolution of a sleep request is ten milliseconds, so that any nonzero commit_delay setting between 1 and 10000 microseconds would have the same effect. Note also that on some platforms, sleep operations may take slightly longer than requested by the parameter.

Since the purpose of commit_delay is to allow the cost of each flush operation to be amortized across concurrently committing transactions (potentially at the expense of transaction latency), it is necessary to quantify that cost before the setting can be chosen intelligently. The higher that cost is, the more effective commit_delay is expected to be in increasing transaction throughput, up to a point. The pg_test_fsync program can be used to measure the average time in microseconds that a single WAL flush operation takes. A value of half of the average time the program reports it takes to flush after a single 8kB write operation is often the most effective setting for commit_delay, so this value is recommended as the starting point to use when optimizing for a particular workload. While tuning commit_delay is particularly useful when the WAL is stored on high-latency rotating disks, benefits can be significant even on storage media with very fast sync times, such as solid-state drives or RAID arrays with a battery-backed write cache; but this should definitely be tested against a representative workload. Higher values of commit_siblings should be used in such cases, whereas smaller commit_siblings values are often helpful on higher latency media. Note that it is quite possible that a setting of commit_delay that is too high can increase transaction latency by so much that total transaction throughput suffers.

When commit_delay is set to zero (the default), it is still possible for a form of group commit to occur, but each group will consist only of sessions that reach the point where they need to flush their commit records during the window in which the previous flush operation (if any) is occurring. At higher client counts a “gangway effect” tends to occur, so that the effects of group commit become significant even when commit_delay is zero, and thus explicitly setting commit_delay tends to help less. Setting commit_delay can only help when (1) there are some concurrently committing transactions, and (2) throughput is limited to some degree by commit rate; but with high rotational latency this setting can be effective in increasing transaction throughput with as few as two clients (that is, a single committing client with one sibling transaction).

The wal_sync_method parameter determines how PostgreSQL will ask the kernel to force WAL updates out to disk. All the options should be the same in terms of reliability, with the exception of fsync_writethrough, which can sometimes force a flush of the disk cache even when other options do not do so. However, it's quite platform-specific which one will be the fastest. You can test the speeds of different options using the pg_test_fsync program. Note that this parameter is irrelevant if fsync has been turned off.

Enabling the wal_debug configuration parameter (provided that PostgreSQL has been compiled with support for it) will result in each XLogInsertRecord and XLogFlush WAL call being logged to the server log. This option might be replaced by a more general mechanism in the future.

There are two internal functions to write WAL data to disk: XLogWrite and issue_xlog_fsync. When track_wal_io_timing is enabled, the total amounts of time XLogWrite writes and issue_xlog_fsync syncs WAL data to disk are counted as write_time and fsync_time in pg_stat_io for the object wal, respectively. XLogWrite is normally called by XLogInsertRecord (when there is no space for the new record in WAL buffers), XLogFlush and the WAL writer, to write WAL buffers to disk and call issue_xlog_fsync. issue_xlog_fsync is normally called by XLogWrite to sync WAL files to disk. If wal_sync_method is either open_datasync or open_sync, a write operation in XLogWrite guarantees to sync written WAL data to disk and issue_xlog_fsync does nothing. If wal_sync_method is either fdatasync, fsync, or fsync_writethrough, the write operation moves WAL buffers to kernel cache and issue_xlog_fsync syncs them to disk. Regardless of the setting of track_wal_io_timing, the number of times XLogWrite writes and issue_xlog_fsync syncs WAL data to disk are also counted as writes and fsyncs in pg_stat_io for the object wal, respectively.

The recovery_prefetch parameter can be used to reduce I/O wait times during recovery by instructing the kernel to initiate reads of disk blocks that will soon be needed but are not currently in PostgreSQL's buffer pool. The maintenance_io_concurrency and wal_decode_buffer_size settings limit prefetching concurrency and distance, respectively. By default, it is set to try, which enables the feature on systems that support issuing read-ahead advice.

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PostgreSQL: Documentation: 18: 9.29. Trigger Functions

URL: https://www.postgresql.org/docs/current/functions-trigger.html

Contents:

• 9.29. Trigger Functions #

While many uses of triggers involve user-written trigger functions, PostgreSQL provides a few built-in trigger functions that can be used directly in user-defined triggers. These are summarized in Table 9.110. (Additional built-in trigger functions exist, which implement foreign key constraints and deferred index constraints. Those are not documented here since users need not use them directly.)

For more information about creating triggers, see CREATE TRIGGER.

Table 9.110. Built-In Trigger Functions

suppress_redundant_updates_trigger ( ) → trigger

Suppresses do-nothing update operations. See below for details.

CREATE TRIGGER ... suppress_redundant_updates_trigger()

tsvector_update_trigger ( ) → trigger

Automatically updates a tsvector column from associated plain-text document column(s). The text search configuration to use is specified by name as a trigger argument. See Section 12.4.3 for details.

CREATE TRIGGER ... tsvector_update_trigger(tsvcol, 'pg_catalog.swedish', title, body)

tsvector_update_trigger_column ( ) → trigger

Automatically updates a tsvector column from associated plain-text document column(s). The text search configuration to use is taken from a regconfig column of the table. See Section 12.4.3 for details.

CREATE TRIGGER ... tsvector_update_trigger_column(tsvcol, tsconfigcol, title, body)

The suppress_redundant_updates_trigger function, when applied as a row-level BEFORE UPDATE trigger, will prevent any update that does not actually change the data in the row from taking place. This overrides the normal behavior which always performs a physical row update regardless of whether or not the data has changed. (This normal behavior makes updates run faster, since no checking is required, and is also useful in certain cases.)

Ideally, you should avoid running updates that don't actually change the data in the record. Redundant updates can cost considerable unnecessary time, especially if there are lots of indexes to alter, and space in dead rows that will eventually have to be vacuumed. However, detecting such situations in client code is not always easy, or even possible, and writing expressions to detect them can be error-prone. An alternative is to use suppress_redundant_updates_trigger, which will skip updates that don't change the data. You should use this with care, however. The trigger takes a small but non-trivial time for each record, so if most of the records affected by updates do actually change, use of this trigger will make updates run slower on average.

The suppress_redundant_updates_trigger function can be added to a table like this:

In most cases, you need to fire this trigger last for each row, so that it does not override other triggers that might wish to alter the row. Bearing in mind that triggers fire in name order, you would therefore choose a trigger name that comes after the name of any other trigger you might have on the table. (Hence the “z” prefix in the example.)

Examples:

Example 1 (unknown):

CREATE TRIGGER z_min_update
BEFORE UPDATE ON tablename
FOR EACH ROW EXECUTE FUNCTION suppress_redundant_updates_trigger();

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PostgreSQL: Documentation: 18: 20.4. Trust Authentication

URL: https://www.postgresql.org/docs/current/auth-trust.html

Contents:

• 20.4. Trust Authentication #

When trust authentication is specified, PostgreSQL assumes that anyone who can connect to the server is authorized to access the database with whatever database user name they specify (even superuser names). Of course, restrictions made in the database and user columns still apply. This method should only be used when there is adequate operating-system-level protection on connections to the server.

trust authentication is appropriate and very convenient for local connections on a single-user workstation. It is usually not appropriate by itself on a multiuser machine. However, you might be able to use trust even on a multiuser machine, if you restrict access to the server's Unix-domain socket file using file-system permissions. To do this, set the unix_socket_permissions (and possibly unix_socket_group) configuration parameters as described in Section 19.3. Or you could set the unix_socket_directories configuration parameter to place the socket file in a suitably restricted directory.

Setting file-system permissions only helps for Unix-socket connections. Local TCP/IP connections are not restricted by file-system permissions. Therefore, if you want to use file-system permissions for local security, remove the host ... 127.0.0.1 ... line from pg_hba.conf, or change it to a non-trust authentication method.

trust authentication is only suitable for TCP/IP connections if you trust every user on every machine that is allowed to connect to the server by the pg_hba.conf lines that specify trust. It is seldom reasonable to use trust for any TCP/IP connections other than those from localhost (127.0.0.1).

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PostgreSQL: Documentation: 18: Chapter 38. Event Triggers

URL: https://www.postgresql.org/docs/current/event-triggers.html

Contents:

• Chapter 38. Event Triggers

To supplement the trigger mechanism discussed in Chapter 37, PostgreSQL also provides event triggers. Unlike regular triggers, which are attached to a single table and capture only DML events, event triggers are global to a particular database and are capable of capturing DDL events.

Like regular triggers, event triggers can be written in any procedural language that includes event trigger support, or in C, but not in plain SQL.

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PostgreSQL: Documentation: 18: 36.16. Interfacing Extensions to Indexes

URL: https://www.postgresql.org/docs/current/xindex.html

Contents:

• 36.16. Interfacing Extensions to Indexes #
  • 36.16.1. Index Methods and Operator Classes #
  • 36.16.2. Index Method Strategies #
  • 36.16.3. Index Method Support Routines #
  • 36.16.4. An Example #
  • 36.16.5. Operator Classes and Operator Families #
  • Note
  • 36.16.6. System Dependencies on Operator Classes #
  • Note
  • 36.16.7. Ordering Operators #

The procedures described thus far let you define new types, new functions, and new operators. However, we cannot yet define an index on a column of a new data type. To do this, we must define an operator class for the new data type. Later in this section, we will illustrate this concept in an example: a new operator class for the B-tree index method that stores and sorts complex numbers in ascending absolute value order.

Operator classes can be grouped into operator families to show the relationships between semantically compatible classes. When only a single data type is involved, an operator class is sufficient, so we'll focus on that case first and then return to operator families.

Operator classes are associated with an index access method, such as B-Tree or GIN. Custom index access method may be defined with CREATE ACCESS METHOD. See Chapter 63 for details.

The routines for an index method do not directly know anything about the data types that the index method will operate on. Instead, an operator class identifies the set of operations that the index method needs to use to work with a particular data type. Operator classes are so called because one thing they specify is the set of WHERE-clause operators that can be used with an index (i.e., can be converted into an index-scan qualification). An operator class can also specify some support function that are needed by the internal operations of the index method, but do not directly correspond to any WHERE-clause operator that can be used with the index.

It is possible to define multiple operator classes for the same data type and index method. By doing this, multiple sets of indexing semantics can be defined for a single data type. For example, a B-tree index requires a sort ordering to be defined for each data type it works on. It might be useful for a complex-number data type to have one B-tree operator class that sorts the data by complex absolute value, another that sorts by real part, and so on. Typically, one of the operator classes will be deemed most commonly useful and will be marked as the default operator class for that data type and index method.

The same operator class name can be used for several different index methods (for example, both B-tree and hash index methods have operator classes named int4_ops), but each such class is an independent entity and must be defined separately.

The operators associated with an operator class are identified by “strategy numbers”, which serve to identify the semantics of each operator within the context of its operator class. For example, B-trees impose a strict ordering on keys, lesser to greater, and so operators like “less than” and “greater than or equal to” are interesting with respect to a B-tree. Because PostgreSQL allows the user to define operators, PostgreSQL cannot look at the name of an operator (e.g., < or >=) and tell what kind of comparison it is. Instead, the index method defines a set of “strategies”, which can be thought of as generalized operators. Each operator class specifies which actual operator corresponds to each strategy for a particular data type and interpretation of the index semantics.

The B-tree index method defines five strategies, shown in Table 36.3.

Table 36.3. B-Tree Strategies

Hash indexes support only equality comparisons, and so they use only one strategy, shown in Table 36.4.

Table 36.4. Hash Strategies

GiST indexes are more flexible: they do not have a fixed set of strategies at all. Instead, the “consistency” support routine of each particular GiST operator class interprets the strategy numbers however it likes. As an example, several of the built-in GiST index operator classes index two-dimensional geometric objects, providing the “R-tree” strategies shown in Table 36.5. Four of these are true two-dimensional tests (overlaps, same, contains, contained by); four of them consider only the X direction; and the other four provide the same tests in the Y direction.

Table 36.5. GiST Two-Dimensional “R-tree” Strategies

SP-GiST indexes are similar to GiST indexes in flexibility: they don't have a fixed set of strategies. Instead the support routines of each operator class interpret the strategy numbers according to the operator class's definition. As an example, the strategy numbers used by the built-in operator classes for points are shown in Table 36.6.

Table 36.6. SP-GiST Point Strategies

GIN indexes are similar to GiST and SP-GiST indexes, in that they don't have a fixed set of strategies either. Instead the support routines of each operator class interpret the strategy numbers according to the operator class's definition. As an example, the strategy numbers used by the built-in operator class for arrays are shown in Table 36.7.

Table 36.7. GIN Array Strategies

BRIN indexes are similar to GiST, SP-GiST and GIN indexes in that they don't have a fixed set of strategies either. Instead the support routines of each operator class interpret the strategy numbers according to the operator class's definition. As an example, the strategy numbers used by the built-in Minmax operator classes are shown in Table 36.8.

Table 36.8. BRIN Minmax Strategies

Notice that all the operators listed above return Boolean values. In practice, all operators defined as index method search operators must return type boolean, since they must appear at the top level of a WHERE clause to be used with an index. (Some index access methods also support ordering operators, which typically don't return Boolean values; that feature is discussed in Section 36.16.7.)

Strategies aren't usually enough information for the system to figure out how to use an index. In practice, the index methods require additional support routines in order to work. For example, the B-tree index method must be able to compare two keys and determine whether one is greater than, equal to, or less than the other. Similarly, the hash index method must be able to compute hash codes for key values. These operations do not correspond to operators used in qualifications in SQL commands; they are administrative routines used by the index methods, internally.

Just as with strategies, the operator class identifies which specific functions should play each of these roles for a given data type and semantic interpretation. The index method defines the set of functions it needs, and the operator class identifies the correct functions to use by assigning them to the “support function numbers” specified by the index method.

Additionally, some opclasses allow users to specify parameters which control their behavior. Each builtin index access method has an optional options support function, which defines a set of opclass-specific parameters.

B-trees require a comparison support function, and allow four additional support functions to be supplied at the operator class author's option, as shown in Table 36.9. The requirements for these support functions are explained further in Section 65.1.3.

Table 36.9. B-Tree Support Functions

Hash indexes require one support function, and allow two additional ones to be supplied at the operator class author's option, as shown in Table 36.10.

Table 36.10. Hash Support Functions

GiST indexes have twelve support functions, seven of which are optional, as shown in Table 36.11. (For more information see Section 65.2.)

Table 36.11. GiST Support Functions

SP-GiST indexes have six support functions, one of which is optional, as shown in Table 36.12. (For more information see Section 65.3.)

Table 36.12. SP-GiST Support Functions

GIN indexes have seven support functions, four of which are optional, as shown in Table 36.13. (For more information see Section 65.4.)

Table 36.13. GIN Support Functions

BRIN indexes have five basic support functions, one of which is optional, as shown in Table 36.14. Some versions of the basic functions require additional support functions to be provided. (For more information see Section 65.5.3.)

Table 36.14. BRIN Support Functions

Unlike search operators, support functions return whichever data type the particular index method expects; for example in the case of the comparison function for B-trees, a signed integer. The number and types of the arguments to each support function are likewise dependent on the index method. For B-tree and hash the comparison and hashing support functions take the same input data types as do the operators included in the operator class, but this is not the case for most GiST, SP-GiST, GIN, and BRIN support functions.

Now that we have seen the ideas, here is the promised example of creating a new operator class. (You can find a working copy of this example in src/tutorial/complex.c and src/tutorial/complex.sql in the source distribution.) The operator class encapsulates operators that sort complex numbers in absolute value order, so we choose the name complex_abs_ops. First, we need a set of operators. The procedure for defining operators was discussed in Section 36.14. For an operator class on B-trees, the operators we require are:

The least error-prone way to define a related set of comparison operators is to write the B-tree comparison support function first, and then write the other functions as one-line wrappers around the support function. This reduces the odds of getting inconsistent results for corner cases. Following this approach, we first write:

Now the less-than function looks like:

The other four functions differ only in how they compare the internal function's result to zero.

Next we declare the functions and the operators based on the functions to SQL:

It is important to specify the correct commutator and negator operators, as well as suitable restriction and join selectivity functions, otherwise the optimizer will be unable to make effective use of the index.

Other things worth noting are happening here:

There can only be one operator named, say, = and taking type complex for both operands. In this case we don't have any other operator = for complex, but if we were building a practical data type we'd probably want = to be the ordinary equality operation for complex numbers (and not the equality of the absolute values). In that case, we'd need to use some other operator name for complex_abs_eq.

Although PostgreSQL can cope with functions having the same SQL name as long as they have different argument data types, C can only cope with one global function having a given name. So we shouldn't name the C function something simple like abs_eq. Usually it's a good practice to include the data type name in the C function name, so as not to conflict with functions for other data types.

We could have made the SQL name of the function abs_eq, relying on PostgreSQL to distinguish it by argument data types from any other SQL function of the same name. To keep the example simple, we make the function have the same names at the C level and SQL level.

The next step is the registration of the support routine required by B-trees. The example C code that implements this is in the same file that contains the operator functions. This is how we declare the function:

Now that we have the required operators and support routine, we can finally create the operator class:

And we're done! It should now be possible to create and use B-tree indexes on complex columns.

We could have written the operator entries more verbosely, as in:

but there is no need to do so when the operators take the same data type we are defining the operator class for.

The above example assumes that you want to make this new operator class the default B-tree operator class for the complex data type. If you don't, just leave out the word DEFAULT.

So far we have implicitly assumed that an operator class deals with only one data type. While there certainly can be only one data type in a particular index column, it is often useful to index operations that compare an indexed column to a value of a different data type. Also, if there is use for a cross-data-type operator in connection with an operator class, it is often the case that the other data type has a related operator class of its own. It is helpful to make the connections between related classes explicit, because this can aid the planner in optimizing SQL queries (particularly for B-tree operator classes, since the planner contains a great deal of knowledge about how to work with them).

To handle these needs, PostgreSQL uses the concept of an operator family. An operator family contains one or more operator classes, and can also contain indexable operators and corresponding support functions that belong to the family as a whole but not to any single class within the family. We say that such operators and functions are “loose” within the family, as opposed to being bound into a specific class. Typically each operator class contains single-data-type operators while cross-data-type operators are loose in the family.

All the operators and functions in an operator family must have compatible semantics, where the compatibility requirements are set by the index method. You might therefore wonder why bother to single out particular subsets of the family as operator classes; and indeed for many purposes the class divisions are irrelevant and the family is the only interesting grouping. The reason for defining operator classes is that they specify how much of the family is needed to support any particular index. If there is an index using an operator class, then that operator class cannot be dropped without dropping the index — but other parts of the operator family, namely other operator classes and loose operators, could be dropped. Thus, an operator class should be specified to contain the minimum set of operators and functions that are reasonably needed to work with an index on a specific data type, and then related but non-essential operators can be added as loose members of the operator family.

As an example, PostgreSQL has a built-in B-tree operator family integer_ops, which includes operator classes int8_ops, int4_ops, and int2_ops for indexes on bigint (int8), integer (int4), and smallint (int2) columns respectively. The family also contains cross-data-type comparison operators allowing any two of these types to be compared, so that an index on one of these types can be searched using a comparison value of another type. The family could be duplicated by these definitions:

Notice that this definition “overloads” the operator strategy and support function numbers: each number occurs multiple times within the family. This is allowed so long as each instance of a particular number has distinct input data types. The instances that have both input types equal to an operator class's input type are the primary operators and support functions for that operator class, and in most cases should be declared as part of the operator class rather than as loose members of the family.

In a B-tree operator family, all the operators in the family must sort compatibly, as is specified in detail in Section 65.1.2. For each operator in the family there must be a support function having the same two input data types as the operator. It is recommended that a family be complete, i.e., for each combination of data types, all operators are included. Each operator class should include just the non-cross-type operators and support function for its data type.

To build a multiple-data-type hash operator family, compatible hash support functions must be created for each data type supported by the family. Here compatibility means that the functions are guaranteed to return the same hash code for any two values that are considered equal by the family's equality operators, even when the values are of different types. This is usually difficult to accomplish when the types have different physical representations, but it can be done in some cases. Furthermore, casting a value from one data type represented in the operator family to another data type also represented in the operator family via an implicit or binary coercion cast must not change the computed hash value. Notice that there is only one support function per data type, not one per equality operator. It is recommended that a family be complete, i.e., provide an equality operator for each combination of data types. Each operator class should include just the non-cross-type equality operator and the support function for its data type.

GiST, SP-GiST, and GIN indexes do not have any explicit notion of cross-data-type operations. The set of operators supported is just whatever the primary support functions for a given operator class can handle.

In BRIN, the requirements depends on the framework that provides the operator classes. For operator classes based on minmax, the behavior required is the same as for B-tree operator families: all the operators in the family must sort compatibly, and casts must not change the associated sort ordering.

Prior to PostgreSQL 8.3, there was no concept of operator families, and so any cross-data-type operators intended to be used with an index had to be bound directly into the index's operator class. While this approach still works, it is deprecated because it makes an index's dependencies too broad, and because the planner can handle cross-data-type comparisons more effectively when both data types have operators in the same operator family.

PostgreSQL uses operator classes to infer the properties of operators in more ways than just whether they can be used with indexes. Therefore, you might want to create operator classes even if you have no intention of indexing any columns of your data type.

In particular, there are SQL features such as ORDER BY and DISTINCT that require comparison and sorting of values. To implement these features on a user-defined data type, PostgreSQL looks for the default B-tree operator class for the data type. The “equals” member of this operator class defines the system's notion of equality of values for GROUP BY and DISTINCT, and the sort ordering imposed by the operator class defines the default ORDER BY ordering.

If there is no default B-tree operator class for a data type, the system will look for a default hash operator class. But since that kind of operator class only provides equality, it is only able to support grouping not sorting.

When there is no default operator class for a data type, you will get errors like “could not identify an ordering operator” if you try to use these SQL features with the data type.

In PostgreSQL versions before 7.4, sorting and grouping operations would implicitly use operators named =, <, and >. The new behavior of relying on default operator classes avoids having to make any assumption about the behavior of operators with particular names.

Sorting by a non-default B-tree operator class is possible by specifying the class's less-than operator in a USING option, for example

Alternatively, specifying the class's greater-than operator in USING selects a descending-order sort.

Comparison of arrays of a user-defined type also relies on the semantics defined by the type's default B-tree operator class. If there is no default B-tree operator class, but there is a default hash operator class, then array equality is supported, but not ordering comparisons.

Another SQL feature that requires even more data-type-specific knowledge is the RANGE offset PRECEDING/FOLLOWING framing option for window functions (see Section 4.2.8). For a query such as

it is not sufficient to know how to order by x; the database must also understand how to “subtract 5” or “add 10” to the current row's value of x to identify the bounds of the current window frame. Comparing the resulting bounds to other rows' values of x is possible using the comparison operators provided by the B-tree operator class that defines the ORDER BY ordering — but addition and subtraction operators are not part of the operator class, so which ones should be used? Hard-wiring that choice would be undesirable, because different sort orders (different B-tree operator classes) might need different behavior. Therefore, a B-tree operator class can specify an in_range support function that encapsulates the addition and subtraction behaviors that make sense for its sort order. It can even provide more than one in_range support function, in case there is more than one data type that makes sense to use as the offset in RANGE clauses. If the B-tree operator class associated with the window's ORDER BY clause does not have a matching in_range support function, the RANGE offset PRECEDING/FOLLOWING option is not supported.

Another important point is that an equality operator that appears in a hash operator family is a candidate for hash joins, hash aggregation, and related optimizations. The hash operator family is essential here since it identifies the hash function(s) to use.

Some index access methods (currently, only GiST and SP-GiST) support the concept of ordering operators. What we have been discussing so far are search operators. A search operator is one for which the index can be searched to find all rows satisfying WHERE indexed_column operator constant. Note that nothing is promised about the order in which the matching rows will be returned. In contrast, an ordering operator does not restrict the set of rows that can be returned, but instead determines their order. An ordering operator is one for which the index can be scanned to return rows in the order represented by ORDER BY indexed_column operator constant. The reason for defining ordering operators that way is that it supports nearest-neighbor searches, if the operator is one that measures distance. For example, a query like

finds the ten places closest to a given target point. A GiST index on the location column can do this efficiently because <-> is an ordering operator.

While search operators have to return Boolean results, ordering operators usually return some other type, such as float or numeric for distances. This type is normally not the same as the data type being indexed. To avoid hard-wiring assumptions about the behavior of different data types, the definition of an ordering operator is required to name a B-tree operator family that specifies the sort ordering of the result data type. As was stated in the previous section, B-tree operator families define PostgreSQL's notion of ordering, so this is a natural representation. Since the point <-> operator returns float8, it could be specified in an operator class creation command like this:

where float_ops is the built-in operator family that includes operations on float8. This declaration states that the index is able to return rows in order of increasing values of the <-> operator.

There are two special features of operator classes that we have not discussed yet, mainly because they are not useful with the most commonly used index methods.

Normally, declaring an operator as a member of an operator class (or family) means that the index method can retrieve exactly the set of rows that satisfy a WHERE condition using the operator. For example:

can be satisfied exactly by a B-tree index on the integer column. But there are cases where an index is useful as an inexact guide to the matching rows. For example, if a GiST index stores only bounding boxes for geometric objects, then it cannot exactly satisfy a WHERE condition that tests overlap between nonrectangular objects such as polygons. Yet we could use the index to find objects whose bounding box overlaps the bounding box of the target object, and then do the exact overlap test only on the objects found by the index. If this scenario applies, the index is said to be “lossy” for the operator. Lossy index searches are implemented by having the index method return a recheck flag when a row might or might not really satisfy the query condition. The core system will then test the original query condition on the retrieved row to see whether it should be returned as a valid match. This approach works if the index is guaranteed to return all the required rows, plus perhaps some additional rows, which can be eliminated by performing the original operator invocation. The index methods that support lossy searches (currently, GiST, SP-GiST and GIN) allow the support functions of individual operator classes to set the recheck flag, and so this is essentially an operator-class feature.

Consider again the situation where we are storing in the index only the bounding box of a complex object such as a polygon. In this case there's not much value in storing the whole polygon in the index entry — we might as well store just a simpler object of type box. This situation is expressed by the STORAGE option in CREATE OPERATOR CLASS: we'd write something like:

At present, only the GiST, SP-GiST, GIN and BRIN index methods support a STORAGE type that's different from the column data type. The GiST compress and decompress support routines must deal with data-type conversion when STORAGE is used. SP-GiST likewise requires a compress support function to convert to the storage type, when that is different; if an SP-GiST opclass also supports retrieving data, the reverse conversion must be handled by the consistent function. In GIN, the STORAGE type identifies the type of the “key” values, which normally is different from the type of the indexed column — for example, an operator class for integer-array columns might have keys that are just integers. The GIN extractValue and extractQuery support routines are responsible for extracting keys from indexed values. BRIN is similar to GIN: the STORAGE type identifies the type of the stored summary values, and operator classes' support procedures are responsible for interpreting the summary values correctly.

Examples:

Example 1 (unknown):

#define Mag(c)  ((c)->x*(c)->x + (c)->y*(c)->y)

static int
complex_abs_cmp_internal(Complex *a, Complex *b)
{
    double      amag = Mag(a),
                bmag = Mag(b);

    if (amag < bmag)
        return -1;
    if (amag > bmag)
        return 1;
    return 0;
}

Example 2 (unknown):

PG_FUNCTION_INFO_V1(complex_abs_lt);

Datum
complex_abs_lt(PG_FUNCTION_ARGS)
{
    Complex    *a = (Complex *) PG_GETARG_POINTER(0);
    Complex    *b = (Complex *) PG_GETARG_POINTER(1);

    PG_RETURN_BOOL(complex_abs_cmp_internal(a, b) < 0);
}

Example 3 (unknown):

CREATE FUNCTION complex_abs_lt(complex, complex) RETURNS bool
    AS 'filename', 'complex_abs_lt'
    LANGUAGE C IMMUTABLE STRICT;

CREATE OPERATOR < (
   leftarg = complex, rightarg = complex, procedure = complex_abs_lt,
   commutator = > , negator = >= ,
   restrict = scalarltsel, join = scalarltjoinsel
);

Example 4 (unknown):

CREATE FUNCTION complex_abs_cmp(complex, complex)
    RETURNS integer
    AS 'filename'
    LANGUAGE C IMMUTABLE STRICT;

---

PostgreSQL: Documentation: 18: 9.11. Geometric Functions and Operators

URL: https://www.postgresql.org/docs/current/functions-geometry.html

Contents:

• 9.11. Geometric Functions and Operators #
  • Caution
  • Note

The geometric types point, box, lseg, line, path, polygon, and circle have a large set of native support functions and operators, shown in Table 9.36, Table 9.37, and Table 9.38.

Table 9.36. Geometric Operators

geometric_type + point → geometric_type

Adds the coordinates of the second point to those of each point of the first argument, thus performing translation. Available for point, box, path, circle.

box '(1,1),(0,0)' + point '(2,0)' → (3,1),(2,0)

Concatenates two open paths (returns NULL if either path is closed).

path '[(0,0),(1,1)]' + path '[(2,2),(3,3),(4,4)]' → [(0,0),(1,1),(2,2),(3,3),(4,4)]

geometric_type - point → geometric_type

Subtracts the coordinates of the second point from those of each point of the first argument, thus performing translation. Available for point, box, path, circle.

box '(1,1),(0,0)' - point '(2,0)' → (-1,1),(-2,0)

geometric_type * point → geometric_type

Multiplies each point of the first argument by the second point (treating a point as being a complex number represented by real and imaginary parts, and performing standard complex multiplication). If one interprets the second point as a vector, this is equivalent to scaling the object's size and distance from the origin by the length of the vector, and rotating it counterclockwise around the origin by the vector's angle from the x axis. Available for point, box,[a] path, circle.

path '((0,0),(1,0),(1,1))' * point '(3.0,0)' → ((0,0),(3,0),(3,3))

path '((0,0),(1,0),(1,1))' * point(cosd(45), sind(45)) → ((0,0),​(0.7071067811865475,0.7071067811865475),​(0,1.414213562373095))

geometric_type / point → geometric_type

Divides each point of the first argument by the second point (treating a point as being a complex number represented by real and imaginary parts, and performing standard complex division). If one interprets the second point as a vector, this is equivalent to scaling the object's size and distance from the origin down by the length of the vector, and rotating it clockwise around the origin by the vector's angle from the x axis. Available for point, box,[a] path, circle.

path '((0,0),(1,0),(1,1))' / point '(2.0,0)' → ((0,0),(0.5,0),(0.5,0.5))

path '((0,0),(1,0),(1,1))' / point(cosd(45), sind(45)) → ((0,0),​(0.7071067811865476,-0.7071067811865476),​(1.4142135623730951,0))

@-@ geometric_type → double precision

Computes the total length. Available for lseg, path.

@-@ path '[(0,0),(1,0),(1,1)]' → 2

@@ geometric_type → point

Computes the center point. Available for box, lseg, polygon, circle.

@@ box '(2,2),(0,0)' → (1,1)

geometric_type → integer

Returns the number of points. Available for path, polygon.

path '((1,0),(0,1),(-1,0))' → 3

geometric_type # geometric_type → point

Computes the point of intersection, or NULL if there is none. Available for lseg, line.

lseg '[(0,0),(1,1)]' # lseg '[(1,0),(0,1)]' → (0.5,0.5)

Computes the intersection of two boxes, or NULL if there is none.

box '(2,2),(-1,-1)' # box '(1,1),(-2,-2)' → (1,1),(-1,-1)

geometric_type ## geometric_type → point

Computes the closest point to the first object on the second object. Available for these pairs of types: (point, box), (point, lseg), (point, line), (lseg, box), (lseg, lseg), (line, lseg).

point '(0,0)' ## lseg '[(2,0),(0,2)]' → (1,1)

geometric_type <-> geometric_type → double precision

Computes the distance between the objects. Available for all seven geometric types, for all combinations of point with another geometric type, and for these additional pairs of types: (box, lseg), (lseg, line), (polygon, circle) (and the commutator cases).

circle '<(0,0),1>' <-> circle '<(5,0),1>' → 3

geometric_type @> geometric_type → boolean

Does first object contain second? Available for these pairs of types: (box, point), (box, box), (path, point), (polygon, point), (polygon, polygon), (circle, point), (circle, circle).

circle '<(0,0),2>' @> point '(1,1)' → t

geometric_type <@ geometric_type → boolean

Is first object contained in or on second? Available for these pairs of types: (point, box), (point, lseg), (point, line), (point, path), (point, polygon), (point, circle), (box, box), (lseg, box), (lseg, line), (polygon, polygon), (circle, circle).

point '(1,1)' <@ circle '<(0,0),2>' → t

geometric_type && geometric_type → boolean

Do these objects overlap? (One point in common makes this true.) Available for box, polygon, circle.

box '(1,1),(0,0)' && box '(2,2),(0,0)' → t

geometric_type << geometric_type → boolean

Is first object strictly left of second? Available for point, box, polygon, circle.

circle '<(0,0),1>' << circle '<(5,0),1>' → t

geometric_type >> geometric_type → boolean

Is first object strictly right of second? Available for point, box, polygon, circle.

circle '<(5,0),1>' >> circle '<(0,0),1>' → t

geometric_type &< geometric_type → boolean

Does first object not extend to the right of second? Available for box, polygon, circle.

box '(1,1),(0,0)' &< box '(2,2),(0,0)' → t

geometric_type &> geometric_type → boolean

Does first object not extend to the left of second? Available for box, polygon, circle.

box '(3,3),(0,0)' &> box '(2,2),(0,0)' → t

geometric_type <<| geometric_type → boolean

Is first object strictly below second? Available for point, box, polygon, circle.

box '(3,3),(0,0)' <<| box '(5,5),(3,4)' → t

geometric_type |>> geometric_type → boolean

Is first object strictly above second? Available for point, box, polygon, circle.

box '(5,5),(3,4)' |>> box '(3,3),(0,0)' → t

geometric_type &<| geometric_type → boolean

Does first object not extend above second? Available for box, polygon, circle.

box '(1,1),(0,0)' &<| box '(2,2),(0,0)' → t

geometric_type |&> geometric_type → boolean

Does first object not extend below second? Available for box, polygon, circle.

box '(3,3),(0,0)' |&> box '(2,2),(0,0)' → t

Is first object below second (allows edges to touch)?

box '((1,1),(0,0))' <^ box '((2,2),(1,1))' → t

Is first object above second (allows edges to touch)?

box '((2,2),(1,1))' >^ box '((1,1),(0,0))' → t

geometric_type ?# geometric_type → boolean

Do these objects intersect? Available for these pairs of types: (box, box), (lseg, box), (lseg, lseg), (lseg, line), (line, box), (line, line), (path, path).

lseg '[(-1,0),(1,0)]' ?# box '(2,2),(-2,-2)' → t

?- lseg '[(-1,0),(1,0)]' → t

point ?- point → boolean

Are points horizontally aligned (that is, have same y coordinate)?

point '(1,0)' ?- point '(0,0)' → t

?| lseg '[(-1,0),(1,0)]' → f

point ?| point → boolean

Are points vertically aligned (that is, have same x coordinate)?

point '(0,1)' ?| point '(0,0)' → t

line ?-| line → boolean

lseg ?-| lseg → boolean

Are lines perpendicular?

lseg '[(0,0),(0,1)]' ?-| lseg '[(0,0),(1,0)]' → t

line ?|| line → boolean

lseg ?|| lseg → boolean

lseg '[(-1,0),(1,0)]' ?|| lseg '[(-1,2),(1,2)]' → t

geometric_type ~= geometric_type → boolean

Are these objects the same? Available for point, box, polygon, circle.

polygon '((0,0),(1,1))' ~= polygon '((1,1),(0,0))' → t

[a] “Rotating” a box with these operators only moves its corner points: the box is still considered to have sides parallel to the axes. Hence the box's size is not preserved, as a true rotation would do.

Note that the “same as” operator, ~=, represents the usual notion of equality for the point, box, polygon, and circle types. Some of the geometric types also have an = operator, but = compares for equal areas only. The other scalar comparison operators (<= and so on), where available for these types, likewise compare areas.

Before PostgreSQL 14, the point is strictly below/above comparison operators point <<| point and point |>> point were respectively called <^ and >^. These names are still available, but are deprecated and will eventually be removed.

Table 9.37. Geometric Functions

area ( geometric_type ) → double precision

Computes area. Available for box, path, circle. A path input must be closed, else NULL is returned. Also, if the path is self-intersecting, the result may be meaningless.

area(box '(2,2),(0,0)') → 4

center ( geometric_type ) → point

Computes center point. Available for box, circle.

center(box '(1,2),(0,0)') → (0.5,1)

diagonal ( box ) → lseg

Extracts box's diagonal as a line segment (same as lseg(box)).

diagonal(box '(1,2),(0,0)') → [(1,2),(0,0)]

diameter ( circle ) → double precision

Computes diameter of circle.

diameter(circle '<(0,0),2>') → 4

height ( box ) → double precision

Computes vertical size of box.

height(box '(1,2),(0,0)') → 2

isclosed ( path ) → boolean

isclosed(path '((0,0),(1,1),(2,0))') → t

isopen ( path ) → boolean

isopen(path '[(0,0),(1,1),(2,0)]') → t

length ( geometric_type ) → double precision

Computes the total length. Available for lseg, path.

length(path '((-1,0),(1,0))') → 4

npoints ( geometric_type ) → integer

Returns the number of points. Available for path, polygon.

npoints(path '[(0,0),(1,1),(2,0)]') → 3

pclose ( path ) → path

Converts path to closed form.

pclose(path '[(0,0),(1,1),(2,0)]') → ((0,0),(1,1),(2,0))

popen ( path ) → path

Converts path to open form.

popen(path '((0,0),(1,1),(2,0))') → [(0,0),(1,1),(2,0)]

radius ( circle ) → double precision

Computes radius of circle.

radius(circle '<(0,0),2>') → 2

slope ( point, point ) → double precision

Computes slope of a line drawn through the two points.

slope(point '(0,0)', point '(2,1)') → 0.5

width ( box ) → double precision

Computes horizontal size of box.

width(box '(1,2),(0,0)') → 1

Table 9.38. Geometric Type Conversion Functions

Computes box inscribed within the circle.

box(circle '<(0,0),2>') → (1.414213562373095,1.414213562373095),​(-1.414213562373095,-1.414213562373095)

Converts point to empty box.

box(point '(1,0)') → (1,0),(1,0)

box ( point, point ) → box

Converts any two corner points to box.

box(point '(0,1)', point '(1,0)') → (1,1),(0,0)

box ( polygon ) → box

Computes bounding box of polygon.

box(polygon '((0,0),(1,1),(2,0))') → (2,1),(0,0)

bound_box ( box, box ) → box

Computes bounding box of two boxes.

bound_box(box '(1,1),(0,0)', box '(4,4),(3,3)') → (4,4),(0,0)

circle ( box ) → circle

Computes smallest circle enclosing box.

circle(box '(1,1),(0,0)') → <(0.5,0.5),0.7071067811865476>

circle ( point, double precision ) → circle

Constructs circle from center and radius.

circle(point '(0,0)', 2.0) → <(0,0),2>

circle ( polygon ) → circle

Converts polygon to circle. The circle's center is the mean of the positions of the polygon's points, and the radius is the average distance of the polygon's points from that center.

circle(polygon '((0,0),(1,3),(2,0))') → <(1,1),1.6094757082487299>

line ( point, point ) → line

Converts two points to the line through them.

line(point '(-1,0)', point '(1,0)') → {0,-1,0}

Extracts box's diagonal as a line segment.

lseg(box '(1,0),(-1,0)') → [(1,0),(-1,0)]

lseg ( point, point ) → lseg

Constructs line segment from two endpoints.

lseg(point '(-1,0)', point '(1,0)') → [(-1,0),(1,0)]

path ( polygon ) → path

Converts polygon to a closed path with the same list of points.

path(polygon '((0,0),(1,1),(2,0))') → ((0,0),(1,1),(2,0))

point ( double precision, double precision ) → point

Constructs point from its coordinates.

point(23.4, -44.5) → (23.4,-44.5)

point ( box ) → point

Computes center of box.

point(box '(1,0),(-1,0)') → (0,0)

point ( circle ) → point

Computes center of circle.

point(circle '<(0,0),2>') → (0,0)

point ( lseg ) → point

Computes center of line segment.

point(lseg '[(-1,0),(1,0)]') → (0,0)

point ( polygon ) → point

Computes center of polygon (the mean of the positions of the polygon's points).

point(polygon '((0,0),(1,1),(2,0))') → (1,0.3333333333333333)

polygon ( box ) → polygon

Converts box to a 4-point polygon.

polygon(box '(1,1),(0,0)') → ((0,0),(0,1),(1,1),(1,0))

polygon ( circle ) → polygon

Converts circle to a 12-point polygon.

polygon(circle '<(0,0),2>') → ((-2,0),​(-1.7320508075688774,0.9999999999999999),​(-1.0000000000000002,1.7320508075688772),​(-1.2246063538-16,2),​(0.9999999999999996,1.7320508075688774),​(1.732050807568877,1.0000000000000007),​(2,2.4492127076-16),​(1.7320508075688776,-0.9999999999999994),​(1.0000000000000009,-1.7320508075688767),​(3.6738190614-16,-2),​(-0.9999999999999987,-1.732050807568878),​(-1.7320508075688767,-1.0000000000000009))

polygon ( integer, circle ) → polygon

Converts circle to an n-point polygon.

polygon(4, circle '<(3,0),1>') → ((2,0),​(3,1),​(4,1.2246063538-16),​(3,-1))

polygon ( path ) → polygon

Converts closed path to a polygon with the same list of points.

polygon(path '((0,0),(1,1),(2,0))') → ((0,0),(1,1),(2,0))

It is possible to access the two component numbers of a point as though the point were an array with indexes 0 and 1. For example, if t.p is a point column then SELECT p[0] FROM t retrieves the X coordinate and UPDATE t SET p[1] = ... changes the Y coordinate. In the same way, a value of type box or lseg can be treated as an array of two point values.

---

PostgreSQL: Documentation: 18: 21.4. Dropping Roles

URL: https://www.postgresql.org/docs/current/role-removal.html

Contents:

• 21.4. Dropping Roles #

Because roles can own database objects and can hold privileges to access other objects, dropping a role is often not just a matter of a quick DROP ROLE. Any objects owned by the role must first be dropped or reassigned to other owners; and any permissions granted to the role must be revoked.

Ownership of objects can be transferred one at a time using ALTER commands, for example:

Alternatively, the REASSIGN OWNED command can be used to reassign ownership of all objects owned by the role-to-be-dropped to a single other role. Because REASSIGN OWNED cannot access objects in other databases, it is necessary to run it in each database that contains objects owned by the role. (Note that the first such REASSIGN OWNED will change the ownership of any shared-across-databases objects, that is databases or tablespaces, that are owned by the role-to-be-dropped.)

Once any valuable objects have been transferred to new owners, any remaining objects owned by the role-to-be-dropped can be dropped with the DROP OWNED command. Again, this command cannot access objects in other databases, so it is necessary to run it in each database that contains objects owned by the role. Also, DROP OWNED will not drop entire databases or tablespaces, so it is necessary to do that manually if the role owns any databases or tablespaces that have not been transferred to new owners.

DROP OWNED also takes care of removing any privileges granted to the target role for objects that do not belong to it. Because REASSIGN OWNED does not touch such objects, it's typically necessary to run both REASSIGN OWNED and DROP OWNED (in that order!) to fully remove the dependencies of a role to be dropped.

In short then, the most general recipe for removing a role that has been used to own objects is:

When not all owned objects are to be transferred to the same successor owner, it's best to handle the exceptions manually and then perform the above steps to mop up.

If DROP ROLE is attempted while dependent objects still remain, it will issue messages identifying which objects need to be reassigned or dropped.

Examples:

Example 1 (unknown):

ALTER TABLE bobs_table OWNER TO alice;

Example 2 (unknown):

REASSIGN OWNED BY doomed_role TO successor_role;
DROP OWNED BY doomed_role;
-- repeat the above commands in each database of the cluster
DROP ROLE doomed_role;

---

PostgreSQL: Documentation: 18: 31.5. Test Coverage Examination

URL: https://www.postgresql.org/docs/current/regress-coverage.html

Contents:

• 31.5. Test Coverage Examination #
  • 31.5.1. Coverage with Autoconf and Make #
  • 31.5.2. Coverage with Meson #

The PostgreSQL source code can be compiled with coverage testing instrumentation, so that it becomes possible to examine which parts of the code are covered by the regression tests or any other test suite that is run with the code. This is currently supported when compiling with GCC, and it requires the gcov and lcov packages.

A typical workflow looks like this:

Then point your HTML browser to coverage/index.html.

If you don't have lcov or prefer text output over an HTML report, you can run

instead of make coverage-html, which will produce .gcov output files for each source file relevant to the test. (make coverage and make coverage-html will overwrite each other's files, so mixing them might be confusing.)

You can run several different tests before making the coverage report; the execution counts will accumulate. If you want to reset the execution counts between test runs, run:

You can run the make coverage-html or make coverage command in a subdirectory if you want a coverage report for only a portion of the code tree.

Use make distclean to clean up when done.

A typical workflow looks like this:

Then point your HTML browser to ./meson-logs/coveragereport/index.html.

You can run several different tests before making the coverage report; the execution counts will accumulate.

Examples:

Example 1 (unknown):

./configure --enable-coverage ... OTHER OPTIONS ...
make
make check # or other test suite
make coverage-html

Example 2 (unknown):

make coverage

Example 3 (unknown):

make coverage-clean

Example 4 (unknown):

meson setup -Db_coverage=true ... OTHER OPTIONS ... builddir/
meson compile -C builddir/
meson test -C builddir/
cd builddir/
ninja coverage-html

---

PostgreSQL: Documentation: 18: 21.6. Function Security

URL: https://www.postgresql.org/docs/current/perm-functions.html

Contents:

• 21.6. Function Security #

Functions, triggers and row-level security policies allow users to insert code into the backend server that other users might execute unintentionally. Hence, these mechanisms permit users to “Trojan horse” others with relative ease. The strongest protection is tight control over who can define objects. Where that is infeasible, write queries referring only to objects having trusted owners. Remove from search_path any schemas that permit untrusted users to create objects.

Functions run inside the backend server process with the operating system permissions of the database server daemon. If the programming language used for the function allows unchecked memory accesses, it is possible to change the server's internal data structures. Hence, among many other things, such functions can circumvent any system access controls. Function languages that allow such access are considered “untrusted”, and PostgreSQL allows only superusers to create functions written in those languages.

---

PostgreSQL: Documentation: 18: 29.7. Conflicts

URL: https://www.postgresql.org/docs/current/logical-replication-conflicts.html

Contents:

• 29.7. Conflicts #

Logical replication behaves similarly to normal DML operations in that the data will be updated even if it was changed locally on the subscriber node. If incoming data violates any constraints the replication will stop. This is referred to as a conflict. When replicating UPDATE or DELETE operations, missing data is also considered as a conflict, but does not result in an error and such operations will simply be skipped.

Additional logging is triggered, and the conflict statistics are collected (displayed in the pg_stat_subscription_stats view) in the following conflict cases:

Inserting a row that violates a NOT DEFERRABLE unique constraint. Note that to log the origin and commit timestamp details of the conflicting key, track_commit_timestamp should be enabled on the subscriber. In this case, an error will be raised until the conflict is resolved manually.

Updating a row that was previously modified by another origin. Note that this conflict can only be detected when track_commit_timestamp is enabled on the subscriber. Currently, the update is always applied regardless of the origin of the local row.

The updated value of a row violates a NOT DEFERRABLE unique constraint. Note that to log the origin and commit timestamp details of the conflicting key, track_commit_timestamp should be enabled on the subscriber. In this case, an error will be raised until the conflict is resolved manually. Note that when updating a partitioned table, if the updated row value satisfies another partition constraint resulting in the row being inserted into a new partition, the insert_exists conflict may arise if the new row violates a NOT DEFERRABLE unique constraint.

The row to be updated was not found. The update will simply be skipped in this scenario.

Deleting a row that was previously modified by another origin. Note that this conflict can only be detected when track_commit_timestamp is enabled on the subscriber. Currently, the delete is always applied regardless of the origin of the local row.

The row to be deleted was not found. The delete will simply be skipped in this scenario.

Inserting or updating a row violates multiple NOT DEFERRABLE unique constraints. Note that to log the origin and commit timestamp details of conflicting keys, ensure that track_commit_timestamp is enabled on the subscriber. In this case, an error will be raised until the conflict is resolved manually.

Note that there are other conflict scenarios, such as exclusion constraint violations. Currently, we do not provide additional details for them in the log.

The log format for logical replication conflicts is as follows:

The log provides the following information:

schemaname.tablename identifies the local relation involved in the conflict.

conflict_type is the type of conflict that occurred (e.g., insert_exists, update_exists).

detailed_explanation includes the origin, transaction ID, and commit timestamp of the transaction that modified the existing local row, if available.

The Key section includes the key values of the local row that violated a unique constraint for insert_exists, update_exists or multiple_unique_conflicts conflicts.

The existing local row section includes the local row if its origin differs from the remote row for update_origin_differs or delete_origin_differs conflicts, or if the key value conflicts with the remote row for insert_exists, update_exists or multiple_unique_conflicts conflicts.

The remote row section includes the new row from the remote insert or update operation that caused the conflict. Note that for an update operation, the column value of the new row will be null if the value is unchanged and toasted.

The replica identity section includes the replica identity key values that were used to search for the existing local row to be updated or deleted. This may include the full row value if the local relation is marked with REPLICA IDENTITY FULL.

column_name is the column name. For existing local row, remote row, and replica identity full cases, column names are logged only if the user lacks the privilege to access all columns of the table. If column names are present, they appear in the same order as the corresponding column values.

column_value is the column value. The large column values are truncated to 64 bytes.

Note that in case of multiple_unique_conflicts conflict, multiple detailed_explanation and detail_values lines will be generated, each detailing the conflict information associated with distinct unique constraints.

Logical replication operations are performed with the privileges of the role which owns the subscription. Permissions failures on target tables will cause replication conflicts, as will enabled row-level security on target tables that the subscription owner is subject to, without regard to whether any policy would ordinarily reject the INSERT, UPDATE, DELETE or TRUNCATE which is being replicated. This restriction on row-level security may be lifted in a future version of PostgreSQL.

A conflict that produces an error will stop the replication; it must be resolved manually by the user. Details about the conflict can be found in the subscriber's server log.

The resolution can be done either by changing data or permissions on the subscriber so that it does not conflict with the incoming change or by skipping the transaction that conflicts with the existing data. When a conflict produces an error, the replication won't proceed, and the logical replication worker will emit the following kind of message to the subscriber's server log:

The LSN of the transaction that contains the change violating the constraint and the replication origin name can be found from the server log (LSN 0/14C0378 and replication origin pg_16395 in the above case). The transaction that produced the conflict can be skipped by using ALTER SUBSCRIPTION ... SKIP with the finish LSN (i.e., LSN 0/14C0378). The finish LSN could be an LSN at which the transaction is committed or prepared on the publisher. Alternatively, the transaction can also be skipped by calling the pg_replication_origin_advance() function. Before using this function, the subscription needs to be disabled temporarily either by ALTER SUBSCRIPTION ... DISABLE or, the subscription can be used with the disable_on_error option. Then, you can use pg_replication_origin_advance() function with the node_name (i.e., pg_16395) and the next LSN of the finish LSN (i.e., 0/14C0379). The current position of origins can be seen in the pg_replication_origin_status system view. Please note that skipping the whole transaction includes skipping changes that might not violate any constraint. This can easily make the subscriber inconsistent. The additional details regarding conflicting rows, such as their origin and commit timestamp can be seen in the DETAIL line of the log. But note that this information is only available when track_commit_timestamp is enabled on the subscriber. Users can use this information to decide whether to retain the local change or adopt the remote alteration. For instance, the DETAIL line in the above log indicates that the existing row was modified locally. Users can manually perform a remote-change-win.

When the streaming mode is parallel, the finish LSN of failed transactions may not be logged. In that case, it may be necessary to change the streaming mode to on or off and cause the same conflicts again so the finish LSN of the failed transaction will be written to the server log. For the usage of finish LSN, please refer to ALTER SUBSCRIPTION ... SKIP.

Examples:

Example 1 (unknown):

LOG:  conflict detected on relation "schemaname.tablename": conflict=conflict_type
DETAIL:  detailed_explanation.
{detail_values [; ... ]}.

where detail_values is one of:

    Key (column_name [, ...])=(column_value [, ...])
    existing local row [(column_name [, ...])=](column_value [, ...])
    remote row [(column_name [, ...])=](column_value [, ...])
    replica identity {(column_name [, ...])=(column_value [, ...]) | full [(column_name [, ...])=](column_value [, ...])}

Example 2 (unknown):

ERROR:  conflict detected on relation "public.test": conflict=insert_exists
DETAIL:  Key already exists in unique index "t_pkey", which was modified locally in transaction 740 at 2024-06-26 10:47:04.727375+08.
Key (c)=(1); existing local row (1, 'local'); remote row (1, 'remote').
CONTEXT:  processing remote data for replication origin "pg_16395" during "INSERT" for replication target relation "public.test" in transaction 725 finished at 0/14C0378

---

PostgreSQL: Documentation: 18: SQL Commands

URL: https://www.postgresql.org/docs/current/sql-commands.html

Contents:

• SQL Commands

This part contains reference information for the SQL commands supported by PostgreSQL. By “SQL” the language in general is meant; information about the standards conformance and compatibility of each command can be found on the respective reference page.

---

PostgreSQL: Documentation: 18: 4.2. Value Expressions

URL: https://www.postgresql.org/docs/current/sql-expressions.html

Contents:

• 4.2. Value Expressions #
  • 4.2.1. Column References #
  • 4.2.2. Positional Parameters #
  • 4.2.3. Subscripts #
  • 4.2.4. Field Selection #
  • 4.2.5. Operator Invocations #
  • 4.2.6. Function Calls #
  • Note
  • 4.2.7. Aggregate Expressions #
  • 4.2.8. Window Function Calls #

Value expressions are used in a variety of contexts, such as in the target list of the SELECT command, as new column values in INSERT or UPDATE, or in search conditions in a number of commands. The result of a value expression is sometimes called a scalar, to distinguish it from the result of a table expression (which is a table). Value expressions are therefore also called scalar expressions (or even simply expressions). The expression syntax allows the calculation of values from primitive parts using arithmetic, logical, set, and other operations.

A value expression is one of the following:

A constant or literal value

A positional parameter reference, in the body of a function definition or prepared statement

A subscripted expression

A field selection expression

An operator invocation

An aggregate expression

A window function call

A collation expression

Another value expression in parentheses (used to group subexpressions and override precedence)

In addition to this list, there are a number of constructs that can be classified as an expression but do not follow any general syntax rules. These generally have the semantics of a function or operator and are explained in the appropriate location in Chapter 9. An example is the IS NULL clause.

We have already discussed constants in Section 4.1.2. The following sections discuss the remaining options.

A column can be referenced in the form:

correlation is the name of a table (possibly qualified with a schema name), or an alias for a table defined by means of a FROM clause. The correlation name and separating dot can be omitted if the column name is unique across all the tables being used in the current query. (See also Chapter 7.)

A positional parameter reference is used to indicate a value that is supplied externally to an SQL statement. Parameters are used in SQL function definitions and in prepared queries. Some client libraries also support specifying data values separately from the SQL command string, in which case parameters are used to refer to the out-of-line data values. The form of a parameter reference is:

For example, consider the definition of a function, dept, as:

Here the $1 references the value of the first function argument whenever the function is invoked.

If an expression yields a value of an array type, then a specific element of the array value can be extracted by writing

or multiple adjacent elements (an “array slice”) can be extracted by writing

(Here, the brackets [ ] are meant to appear literally.) Each subscript is itself an expression, which will be rounded to the nearest integer value.

In general the array expression must be parenthesized, but the parentheses can be omitted when the expression to be subscripted is just a column reference or positional parameter. Also, multiple subscripts can be concatenated when the original array is multidimensional. For example:

The parentheses in the last example are required. See Section 8.15 for more about arrays.

If an expression yields a value of a composite type (row type), then a specific field of the row can be extracted by writing

In general the row expression must be parenthesized, but the parentheses can be omitted when the expression to be selected from is just a table reference or positional parameter. For example:

(Thus, a qualified column reference is actually just a special case of the field selection syntax.) An important special case is extracting a field from a table column that is of a composite type:

The parentheses are required here to show that compositecol is a column name not a table name, or that mytable is a table name not a schema name in the second case.

You can ask for all fields of a composite value by writing .*:

This notation behaves differently depending on context; see Section 8.16.5 for details.

There are two possible syntaxes for an operator invocation:

where the operator token follows the syntax rules of Section 4.1.3, or is one of the key words AND, OR, and NOT, or is a qualified operator name in the form:

Which particular operators exist and whether they are unary or binary depends on what operators have been defined by the system or the user. Chapter 9 describes the built-in operators.

The syntax for a function call is the name of a function (possibly qualified with a schema name), followed by its argument list enclosed in parentheses:

For example, the following computes the square root of 2:

The list of built-in functions is in Chapter 9. Other functions can be added by the user.

When issuing queries in a database where some users mistrust other users, observe security precautions from Section 10.3 when writing function calls.

The arguments can optionally have names attached. See Section 4.3 for details.

A function that takes a single argument of composite type can optionally be called using field-selection syntax, and conversely field selection can be written in functional style. That is, the notations col(table) and table.col are interchangeable. This behavior is not SQL-standard but is provided in PostgreSQL because it allows use of functions to emulate “computed fields”. For more information see Section 8.16.5.

An aggregate expression represents the application of an aggregate function across the rows selected by a query. An aggregate function reduces multiple inputs to a single output value, such as the sum or average of the inputs. The syntax of an aggregate expression is one of the following:

where aggregate_name is a previously defined aggregate (possibly qualified with a schema name) and expression is any value expression that does not itself contain an aggregate expression or a window function call. The optional order_by_clause and filter_clause are described below.

The first form of aggregate expression invokes the aggregate once for each input row. The second form is the same as the first, since ALL is the default. The third form invokes the aggregate once for each distinct value of the expression (or distinct set of values, for multiple expressions) found in the input rows. The fourth form invokes the aggregate once for each input row; since no particular input value is specified, it is generally only useful for the count(*) aggregate function. The last form is used with ordered-set aggregate functions, which are described below.

Most aggregate functions ignore null inputs, so that rows in which one or more of the expression(s) yield null are discarded. This can be assumed to be true, unless otherwise specified, for all built-in aggregates.

For example, count(*) yields the total number of input rows; count(f1) yields the number of input rows in which f1 is non-null, since count ignores nulls; and count(distinct f1) yields the number of distinct non-null values of f1.

Ordinarily, the input rows are fed to the aggregate function in an unspecified order. In many cases this does not matter; for example, min produces the same result no matter what order it receives the inputs in. However, some aggregate functions (such as array_agg and string_agg) produce results that depend on the ordering of the input rows. When using such an aggregate, the optional order_by_clause can be used to specify the desired ordering. The order_by_clause has the same syntax as for a query-level ORDER BY clause, as described in Section 7.5, except that its expressions are always just expressions and cannot be output-column names or numbers. For example:

Since jsonb only keeps the last matching key, ordering of its keys can be significant:

When dealing with multiple-argument aggregate functions, note that the ORDER BY clause goes after all the aggregate arguments. For example, write this:

The latter is syntactically valid, but it represents a call of a single-argument aggregate function with two ORDER BY keys (the second one being rather useless since it's a constant).

If DISTINCT is specified with an order_by_clause, ORDER BY expressions can only reference columns in the DISTINCT list. For example:

Placing ORDER BY within the aggregate's regular argument list, as described so far, is used when ordering the input rows for general-purpose and statistical aggregates, for which ordering is optional. There is a subclass of aggregate functions called ordered-set aggregates for which an order_by_clause is required, usually because the aggregate's computation is only sensible in terms of a specific ordering of its input rows. Typical examples of ordered-set aggregates include rank and percentile calculations. For an ordered-set aggregate, the order_by_clause is written inside WITHIN GROUP (...), as shown in the final syntax alternative above. The expressions in the order_by_clause are evaluated once per input row just like regular aggregate arguments, sorted as per the order_by_clause's requirements, and fed to the aggregate function as input arguments. (This is unlike the case for a non-WITHIN GROUP order_by_clause, which is not treated as argument(s) to the aggregate function.) The argument expressions preceding WITHIN GROUP, if any, are called direct arguments to distinguish them from the aggregated arguments listed in the order_by_clause. Unlike regular aggregate arguments, direct arguments are evaluated only once per aggregate call, not once per input row. This means that they can contain variables only if those variables are grouped by GROUP BY; this restriction is the same as if the direct arguments were not inside an aggregate expression at all. Direct arguments are typically used for things like percentile fractions, which only make sense as a single value per aggregation calculation. The direct argument list can be empty; in this case, write just () not (*). (PostgreSQL will actually accept either spelling, but only the first way conforms to the SQL standard.)

An example of an ordered-set aggregate call is:

which obtains the 50th percentile, or median, value of the income column from table households. Here, 0.5 is a direct argument; it would make no sense for the percentile fraction to be a value varying across rows.

If FILTER is specified, then only the input rows for which the filter_clause evaluates to true are fed to the aggregate function; other rows are discarded. For example:

The predefined aggregate functions are described in Section 9.21. Other aggregate functions can be added by the user.

An aggregate expression can only appear in the result list or HAVING clause of a SELECT command. It is forbidden in other clauses, such as WHERE, because those clauses are logically evaluated before the results of aggregates are formed.

When an aggregate expression appears in a subquery (see Section 4.2.11 and Section 9.24), the aggregate is normally evaluated over the rows of the subquery. But an exception occurs if the aggregate's arguments (and filter_clause if any) contain only outer-level variables: the aggregate then belongs to the nearest such outer level, and is evaluated over the rows of that query. The aggregate expression as a whole is then an outer reference for the subquery it appears in, and acts as a constant over any one evaluation of that subquery. The restriction about appearing only in the result list or HAVING clause applies with respect to the query level that the aggregate belongs to.

A window function call represents the application of an aggregate-like function over some portion of the rows selected by a query. Unlike non-window aggregate calls, this is not tied to grouping of the selected rows into a single output row — each row remains separate in the query output. However the window function has access to all the rows that would be part of the current row's group according to the grouping specification (PARTITION BY list) of the window function call. The syntax of a window function call is one of the following:

where window_definition has the syntax

The optional frame_clause can be one of

where frame_start and frame_end can be one of

and frame_exclusion can be one of

Here, expression represents any value expression that does not itself contain window function calls.

window_name is a reference to a named window specification defined in the query's WINDOW clause. Alternatively, a full window_definition can be given within parentheses, using the same syntax as for defining a named window in the WINDOW clause; see the SELECT reference page for details. It's worth pointing out that OVER wname is not exactly equivalent to OVER (wname ...); the latter implies copying and modifying the window definition, and will be rejected if the referenced window specification includes a frame clause.

The PARTITION BY clause groups the rows of the query into partitions, which are processed separately by the window function. PARTITION BY works similarly to a query-level GROUP BY clause, except that its expressions are always just expressions and cannot be output-column names or numbers. Without PARTITION BY, all rows produced by the query are treated as a single partition. The ORDER BY clause determines the order in which the rows of a partition are processed by the window function. It works similarly to a query-level ORDER BY clause, but likewise cannot use output-column names or numbers. Without ORDER BY, rows are processed in an unspecified order.

The frame_clause specifies the set of rows constituting the window frame, which is a subset of the current partition, for those window functions that act on the frame instead of the whole partition. The set of rows in the frame can vary depending on which row is the current row. The frame can be specified in RANGE, ROWS or GROUPS mode; in each case, it runs from the frame_start to the frame_end. If frame_end is omitted, the end defaults to CURRENT ROW.

A frame_start of UNBOUNDED PRECEDING means that the frame starts with the first row of the partition, and similarly a frame_end of UNBOUNDED FOLLOWING means that the frame ends with the last row of the partition.

In RANGE or GROUPS mode, a frame_start of CURRENT ROW means the frame starts with the current row's first peer row (a row that the window's ORDER BY clause sorts as equivalent to the current row), while a frame_end of CURRENT ROW means the frame ends with the current row's last peer row. In ROWS mode, CURRENT ROW simply means the current row.

In the offset PRECEDING and offset FOLLOWING frame options, the offset must be an expression not containing any variables, aggregate functions, or window functions. The meaning of the offset depends on the frame mode:

In ROWS mode, the offset must yield a non-null, non-negative integer, and the option means that the frame starts or ends the specified number of rows before or after the current row.

In GROUPS mode, the offset again must yield a non-null, non-negative integer, and the option means that the frame starts or ends the specified number of peer groups before or after the current row's peer group, where a peer group is a set of rows that are equivalent in the ORDER BY ordering. (There must be an ORDER BY clause in the window definition to use GROUPS mode.)

In RANGE mode, these options require that the ORDER BY clause specify exactly one column. The offset specifies the maximum difference between the value of that column in the current row and its value in preceding or following rows of the frame. The data type of the offset expression varies depending on the data type of the ordering column. For numeric ordering columns it is typically of the same type as the ordering column, but for datetime ordering columns it is an interval. For example, if the ordering column is of type date or timestamp, one could write RANGE BETWEEN '1 day' PRECEDING AND '10 days' FOLLOWING. The offset is still required to be non-null and non-negative, though the meaning of “non-negative” depends on its data type.

In any case, the distance to the end of the frame is limited by the distance to the end of the partition, so that for rows near the partition ends the frame might contain fewer rows than elsewhere.

Notice that in both ROWS and GROUPS mode, 0 PRECEDING and 0 FOLLOWING are equivalent to CURRENT ROW. This normally holds in RANGE mode as well, for an appropriate data-type-specific meaning of “zero”.

The frame_exclusion option allows rows around the current row to be excluded from the frame, even if they would be included according to the frame start and frame end options. EXCLUDE CURRENT ROW excludes the current row from the frame. EXCLUDE GROUP excludes the current row and its ordering peers from the frame. EXCLUDE TIES excludes any peers of the current row from the frame, but not the current row itself. EXCLUDE NO OTHERS simply specifies explicitly the default behavior of not excluding the current row or its peers.

The default framing option is RANGE UNBOUNDED PRECEDING, which is the same as RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW. With ORDER BY, this sets the frame to be all rows from the partition start up through the current row's last ORDER BY peer. Without ORDER BY, this means all rows of the partition are included in the window frame, since all rows become peers of the current row.

Restrictions are that frame_start cannot be UNBOUNDED FOLLOWING, frame_end cannot be UNBOUNDED PRECEDING, and the frame_end choice cannot appear earlier in the above list of frame_start and frame_end options than the frame_start choice does — for example RANGE BETWEEN CURRENT ROW AND offset PRECEDING is not allowed. But, for example, ROWS BETWEEN 7 PRECEDING AND 8 PRECEDING is allowed, even though it would never select any rows.

If FILTER is specified, then only the input rows for which the filter_clause evaluates to true are fed to the window function; other rows are discarded. Only window functions that are aggregates accept a FILTER clause.

The built-in window functions are described in Table 9.67. Other window functions can be added by the user. Also, any built-in or user-defined general-purpose or statistical aggregate can be used as a window function. (Ordered-set and hypothetical-set aggregates cannot presently be used as window functions.)

The syntaxes using * are used for calling parameter-less aggregate functions as window functions, for example count() OVER (PARTITION BY x ORDER BY y). The asterisk () is customarily not used for window-specific functions. Window-specific functions do not allow DISTINCT or ORDER BY to be used within the function argument list.

Window function calls are permitted only in the SELECT list and the ORDER BY clause of the query.

More information about window functions can be found in Section 3.5, Section 9.22, and Section 7.2.5.

A type cast specifies a conversion from one data type to another. PostgreSQL accepts two equivalent syntaxes for type casts:

The CAST syntax conforms to SQL; the syntax with :: is historical PostgreSQL usage.

When a cast is applied to a value expression of a known type, it represents a run-time type conversion. The cast will succeed only if a suitable type conversion operation has been defined. Notice that this is subtly different from the use of casts with constants, as shown in Section 4.1.2.7. A cast applied to an unadorned string literal represents the initial assignment of a type to a literal constant value, and so it will succeed for any type (if the contents of the string literal are acceptable input syntax for the data type).

An explicit type cast can usually be omitted if there is no ambiguity as to the type that a value expression must produce (for example, when it is assigned to a table column); the system will automatically apply a type cast in such cases. However, automatic casting is only done for casts that are marked “OK to apply implicitly” in the system catalogs. Other casts must be invoked with explicit casting syntax. This restriction is intended to prevent surprising conversions from being applied silently.

It is also possible to specify a type cast using a function-like syntax:

However, this only works for types whose names are also valid as function names. For example, double precision cannot be used this way, but the equivalent float8 can. Also, the names interval, time, and timestamp can only be used in this fashion if they are double-quoted, because of syntactic conflicts. Therefore, the use of the function-like cast syntax leads to inconsistencies and should probably be avoided.

The function-like syntax is in fact just a function call. When one of the two standard cast syntaxes is used to do a run-time conversion, it will internally invoke a registered function to perform the conversion. By convention, these conversion functions have the same name as their output type, and thus the “function-like syntax” is nothing more than a direct invocation of the underlying conversion function. Obviously, this is not something that a portable application should rely on. For further details see CREATE CAST.

The COLLATE clause overrides the collation of an expression. It is appended to the expression it applies to:

where collation is a possibly schema-qualified identifier. The COLLATE clause binds tighter than operators; parentheses can be used when necessary.

If no collation is explicitly specified, the database system either derives a collation from the columns involved in the expression, or it defaults to the default collation of the database if no column is involved in the expression.

The two common uses of the COLLATE clause are overriding the sort order in an ORDER BY clause, for example:

and overriding the collation of a function or operator call that has locale-sensitive results, for example:

Note that in the latter case the COLLATE clause is attached to an input argument of the operator we wish to affect. It doesn't matter which argument of the operator or function call the COLLATE clause is attached to, because the collation that is applied by the operator or function is derived by considering all arguments, and an explicit COLLATE clause will override the collations of all other arguments. (Attaching non-matching COLLATE clauses to more than one argument, however, is an error. For more details see Section 23.2.) Thus, this gives the same result as the previous example:

But this is an error:

because it attempts to apply a collation to the result of the > operator, which is of the non-collatable data type boolean.

A scalar subquery is an ordinary SELECT query in parentheses that returns exactly one row with one column. (See Chapter 7 for information about writing queries.) The SELECT query is executed and the single returned value is used in the surrounding value expression. It is an error to use a query that returns more than one row or more than one column as a scalar subquery. (But if, during a particular execution, the subquery returns no rows, there is no error; the scalar result is taken to be null.) The subquery can refer to variables from the surrounding query, which will act as constants during any one evaluation of the subquery. See also Section 9.24 for other expressions involving subqueries.

For example, the following finds the largest city population in each state:

An array constructor is an expression that builds an array value using values for its member elements. A simple array constructor consists of the key word ARRAY, a left square bracket [, a list of expressions (separated by commas) for the array element values, and finally a right square bracket ]. For example:

By default, the array element type is the common type of the member expressions, determined using the same rules as for UNION or CASE constructs (see Section 10.5). You can override this by explicitly casting the array constructor to the desired type, for example:

This has the same effect as casting each expression to the array element type individually. For more on casting, see Section 4.2.9.

Multidimensional array values can be built by nesting array constructors. In the inner constructors, the key word ARRAY can be omitted. For example, these produce the same result:

Since multidimensional arrays must be rectangular, inner constructors at the same level must produce sub-arrays of identical dimensions. Any cast applied to the outer ARRAY constructor propagates automatically to all the inner constructors.

Multidimensional array constructor elements can be anything yielding an array of the proper kind, not only a sub-ARRAY construct. For example:

You can construct an empty array, but since it's impossible to have an array with no type, you must explicitly cast your empty array to the desired type. For example:

It is also possible to construct an array from the results of a subquery. In this form, the array constructor is written with the key word ARRAY followed by a parenthesized (not bracketed) subquery. For example:

The subquery must return a single column. If the subquery's output column is of a non-array type, the resulting one-dimensional array will have an element for each row in the subquery result, with an element type matching that of the subquery's output column. If the subquery's output column is of an array type, the result will be an array of the same type but one higher dimension; in this case all the subquery rows must yield arrays of identical dimensionality, else the result would not be rectangular.

The subscripts of an array value built with ARRAY always begin with one. For more information about arrays, see Section 8.15.

A row constructor is an expression that builds a row value (also called a composite value) using values for its member fields. A row constructor consists of the key word ROW, a left parenthesis, zero or more expressions (separated by commas) for the row field values, and finally a right parenthesis. For example:

The key word ROW is optional when there is more than one expression in the list.

A row constructor can include the syntax rowvalue., which will be expanded to a list of the elements of the row value, just as occurs when the . syntax is used at the top level of a SELECT list (see Section 8.16.5). For example, if table t has columns f1 and f2, these are the same:

Before PostgreSQL 8.2, the .* syntax was not expanded in row constructors, so that writing ROW(t., 42) created a two-field row whose first field was another row value. The new behavior is usually more useful. If you need the old behavior of nested row values, write the inner row value without ., for instance ROW(t, 42).

By default, the value created by a ROW expression is of an anonymous record type. If necessary, it can be cast to a named composite type — either the row type of a table, or a composite type created with CREATE TYPE AS. An explicit cast might be needed to avoid ambiguity. For example:

Row constructors can be used to build composite values to be stored in a composite-type table column, or to be passed to a function that accepts a composite parameter. Also, it is possible to test rows using the standard comparison operators as described in Section 9.2, to compare one row against another as described in Section 9.25, and to use them in connection with subqueries, as discussed in Section 9.24.

The order of evaluation of subexpressions is not defined. In particular, the inputs of an operator or function are not necessarily evaluated left-to-right or in any other fixed order.

Furthermore, if the result of an expression can be determined by evaluating only some parts of it, then other subexpressions might not be evaluated at all. For instance, if one wrote:

then somefunc() would (probably) not be called at all. The same would be the case if one wrote:

Note that this is not the same as the left-to-right “short-circuiting” of Boolean operators that is found in some programming languages.

As a consequence, it is unwise to use functions with side effects as part of complex expressions. It is particularly dangerous to rely on side effects or evaluation order in WHERE and HAVING clauses, since those clauses are extensively reprocessed as part of developing an execution plan. Boolean expressions (AND/OR/NOT combinations) in those clauses can be reorganized in any manner allowed by the laws of Boolean algebra.

When it is essential to force evaluation order, a CASE construct (see Section 9.18) can be used. For example, this is an untrustworthy way of trying to avoid division by zero in a WHERE clause:

A CASE construct used in this fashion will defeat optimization attempts, so it should only be done when necessary. (In this particular example, it would be better to sidestep the problem by writing y > 1.5*x instead.)

CASE is not a cure-all for such issues, however. One limitation of the technique illustrated above is that it does not prevent early evaluation of constant subexpressions. As described in Section 36.7, functions and operators marked IMMUTABLE can be evaluated when the query is planned rather than when it is executed. Thus for example

is likely to result in a division-by-zero failure due to the planner trying to simplify the constant subexpression, even if every row in the table has x > 0 so that the ELSE arm would never be entered at run time.

While that particular example might seem silly, related cases that don't obviously involve constants can occur in queries executed within functions, since the values of function arguments and local variables can be inserted into queries as constants for planning purposes. Within PL/pgSQL functions, for example, using an IF-THEN-ELSE statement to protect a risky computation is much safer than just nesting it in a CASE expression.

Another limitation of the same kind is that a CASE cannot prevent evaluation of an aggregate expression contained within it, because aggregate expressions are computed before other expressions in a SELECT list or HAVING clause are considered. For example, the following query can cause a division-by-zero error despite seemingly having protected against it:

The min() and avg() aggregates are computed concurrently over all the input rows, so if any row has employees equal to zero, the division-by-zero error will occur before there is any opportunity to test the result of min(). Instead, use a WHERE or FILTER clause to prevent problematic input rows from reaching an aggregate function in the first place.

Examples:

Example 1 (unknown):

correlation.columnname

Example 2 (unknown):

CREATE FUNCTION dept(text) RETURNS dept
    AS $$ SELECT * FROM dept WHERE name = $1 $$
    LANGUAGE SQL;

Example 3 (unknown):

expression[subscript]

Example 4 (unknown):

expression[lower_subscript:upper_subscript]

---

PostgreSQL: Documentation: 18: 9.9. Date/Time Functions and Operators

URL: https://www.postgresql.org/docs/current/functions-datetime.html

Contents:

• 9.9. Date/Time Functions and Operators #
  • 9.9.1. EXTRACT, date_part #
  • Note
  • 9.9.2. date_trunc #
  • 9.9.3. date_bin #
  • 9.9.4. AT TIME ZONE and AT LOCAL #
  • 9.9.5. Current Date/Time #
  • Note
  • Tip
  • 9.9.6. Delaying Execution #

Table 9.33 shows the available functions for date/time value processing, with details appearing in the following subsections. Table 9.32 illustrates the behaviors of the basic arithmetic operators (+, *, etc.). For formatting functions, refer to Section 9.8. You should be familiar with the background information on date/time data types from Section 8.5.

In addition, the usual comparison operators shown in Table 9.1 are available for the date/time types. Dates and timestamps (with or without time zone) are all comparable, while times (with or without time zone) and intervals can only be compared to other values of the same data type. When comparing a timestamp without time zone to a timestamp with time zone, the former value is assumed to be given in the time zone specified by the TimeZone configuration parameter, and is rotated to UTC for comparison to the latter value (which is already in UTC internally). Similarly, a date value is assumed to represent midnight in the TimeZone zone when comparing it to a timestamp.

All the functions and operators described below that take time or timestamp inputs actually come in two variants: one that takes time with time zone or timestamp with time zone, and one that takes time without time zone or timestamp without time zone. For brevity, these variants are not shown separately. Also, the + and * operators come in commutative pairs (for example both date + integer and integer + date); we show only one of each such pair.

Table 9.32. Date/Time Operators

date + integer → date

Add a number of days to a date

date '2001-09-28' + 7 → 2001-10-05

date + interval → timestamp

Add an interval to a date

date '2001-09-28' + interval '1 hour' → 2001-09-28 01:00:00

date + time → timestamp

Add a time-of-day to a date

date '2001-09-28' + time '03:00' → 2001-09-28 03:00:00

interval + interval → interval

interval '1 day' + interval '1 hour' → 1 day 01:00:00

timestamp + interval → timestamp

Add an interval to a timestamp

timestamp '2001-09-28 01:00' + interval '23 hours' → 2001-09-29 00:00:00

time + interval → time

Add an interval to a time

time '01:00' + interval '3 hours' → 04:00:00

• interval → interval

• interval '23 hours' → -23:00:00

date - date → integer

Subtract dates, producing the number of days elapsed

date '2001-10-01' - date '2001-09-28' → 3

date - integer → date

Subtract a number of days from a date

date '2001-10-01' - 7 → 2001-09-24

date - interval → timestamp

Subtract an interval from a date

date '2001-09-28' - interval '1 hour' → 2001-09-27 23:00:00

time - time → interval

time '05:00' - time '03:00' → 02:00:00

time - interval → time

Subtract an interval from a time

time '05:00' - interval '2 hours' → 03:00:00

timestamp - interval → timestamp

Subtract an interval from a timestamp

timestamp '2001-09-28 23:00' - interval '23 hours' → 2001-09-28 00:00:00

interval - interval → interval

interval '1 day' - interval '1 hour' → 1 day -01:00:00

timestamp - timestamp → interval

Subtract timestamps (converting 24-hour intervals into days, similarly to justify_hours())

timestamp '2001-09-29 03:00' - timestamp '2001-07-27 12:00' → 63 days 15:00:00

interval * double precision → interval

Multiply an interval by a scalar

interval '1 second' * 900 → 00:15:00

interval '1 day' * 21 → 21 days

interval '1 hour' * 3.5 → 03:30:00

interval / double precision → interval

Divide an interval by a scalar

interval '1 hour' / 1.5 → 00:40:00

Table 9.33. Date/Time Functions

age ( timestamp, timestamp ) → interval

Subtract arguments, producing a “symbolic” result that uses years and months, rather than just days

age(timestamp '2001-04-10', timestamp '1957-06-13') → 43 years 9 mons 27 days

age ( timestamp ) → interval

Subtract argument from current_date (at midnight)

age(timestamp '1957-06-13') → 62 years 6 mons 10 days

clock_timestamp ( ) → timestamp with time zone

Current date and time (changes during statement execution); see Section 9.9.5

clock_timestamp() → 2019-12-23 14:39:53.662522-05

Current date; see Section 9.9.5

current_date → 2019-12-23

current_time → time with time zone

Current time of day; see Section 9.9.5

current_time → 14:39:53.662522-05

current_time ( integer ) → time with time zone

Current time of day, with limited precision; see Section 9.9.5

current_time(2) → 14:39:53.66-05

current_timestamp → timestamp with time zone

Current date and time (start of current transaction); see Section 9.9.5

current_timestamp → 2019-12-23 14:39:53.662522-05

current_timestamp ( integer ) → timestamp with time zone

Current date and time (start of current transaction), with limited precision; see Section 9.9.5

current_timestamp(0) → 2019-12-23 14:39:53-05

date_add ( timestamp with time zone, interval [, text ] ) → timestamp with time zone

Add an interval to a timestamp with time zone, computing times of day and daylight-savings adjustments according to the time zone named by the third argument, or the current TimeZone setting if that is omitted. The form with two arguments is equivalent to the timestamp with time zone + interval operator.

date_add('2021-10-31 00:00:00+02'::timestamptz, '1 day'::interval, 'Europe/Warsaw') → 2021-10-31 23:00:00+00

date_bin ( interval, timestamp, timestamp ) → timestamp

Bin input into specified interval aligned with specified origin; see Section 9.9.3

date_bin('15 minutes', timestamp '2001-02-16 20:38:40', timestamp '2001-02-16 20:05:00') → 2001-02-16 20:35:00

date_part ( text, timestamp ) → double precision

Get timestamp subfield (equivalent to extract); see Section 9.9.1

date_part('hour', timestamp '2001-02-16 20:38:40') → 20

date_part ( text, interval ) → double precision

Get interval subfield (equivalent to extract); see Section 9.9.1

date_part('month', interval '2 years 3 months') → 3

date_subtract ( timestamp with time zone, interval [, text ] ) → timestamp with time zone

Subtract an interval from a timestamp with time zone, computing times of day and daylight-savings adjustments according to the time zone named by the third argument, or the current TimeZone setting if that is omitted. The form with two arguments is equivalent to the timestamp with time zone - interval operator.

date_subtract('2021-11-01 00:00:00+01'::timestamptz, '1 day'::interval, 'Europe/Warsaw') → 2021-10-30 22:00:00+00

date_trunc ( text, timestamp ) → timestamp

Truncate to specified precision; see Section 9.9.2

date_trunc('hour', timestamp '2001-02-16 20:38:40') → 2001-02-16 20:00:00

date_trunc ( text, timestamp with time zone, text ) → timestamp with time zone

Truncate to specified precision in the specified time zone; see Section 9.9.2

date_trunc('day', timestamptz '2001-02-16 20:38:40+00', 'Australia/Sydney') → 2001-02-16 13:00:00+00

date_trunc ( text, interval ) → interval

Truncate to specified precision; see Section 9.9.2

date_trunc('hour', interval '2 days 3 hours 40 minutes') → 2 days 03:00:00

extract ( field from timestamp ) → numeric

Get timestamp subfield; see Section 9.9.1

extract(hour from timestamp '2001-02-16 20:38:40') → 20

extract ( field from interval ) → numeric

Get interval subfield; see Section 9.9.1

extract(month from interval '2 years 3 months') → 3

isfinite ( date ) → boolean

Test for finite date (not +/-infinity)

isfinite(date '2001-02-16') → true

isfinite ( timestamp ) → boolean

Test for finite timestamp (not +/-infinity)

isfinite(timestamp 'infinity') → false

isfinite ( interval ) → boolean

Test for finite interval (not +/-infinity)

isfinite(interval '4 hours') → true

justify_days ( interval ) → interval

Adjust interval, converting 30-day time periods to months

justify_days(interval '1 year 65 days') → 1 year 2 mons 5 days

justify_hours ( interval ) → interval

Adjust interval, converting 24-hour time periods to days

justify_hours(interval '50 hours 10 minutes') → 2 days 02:10:00

justify_interval ( interval ) → interval

Adjust interval using justify_days and justify_hours, with additional sign adjustments

justify_interval(interval '1 mon -1 hour') → 29 days 23:00:00

Current time of day; see Section 9.9.5

localtime → 14:39:53.662522

localtime ( integer ) → time

Current time of day, with limited precision; see Section 9.9.5

localtime(0) → 14:39:53

localtimestamp → timestamp

Current date and time (start of current transaction); see Section 9.9.5

localtimestamp → 2019-12-23 14:39:53.662522

localtimestamp ( integer ) → timestamp

Current date and time (start of current transaction), with limited precision; see Section 9.9.5

localtimestamp(2) → 2019-12-23 14:39:53.66

make_date ( year int, month int, day int ) → date

Create date from year, month and day fields (negative years signify BC)

make_date(2013, 7, 15) → 2013-07-15

make_interval ( [ years int [, months int [, weeks int [, days int [, hours int [, mins int [, secs double precision ]]]]]]] ) → interval

Create interval from years, months, weeks, days, hours, minutes and seconds fields, each of which can default to zero

make_interval(days => 10) → 10 days

make_time ( hour int, min int, sec double precision ) → time

Create time from hour, minute and seconds fields

make_time(8, 15, 23.5) → 08:15:23.5

make_timestamp ( year int, month int, day int, hour int, min int, sec double precision ) → timestamp

Create timestamp from year, month, day, hour, minute and seconds fields (negative years signify BC)

make_timestamp(2013, 7, 15, 8, 15, 23.5) → 2013-07-15 08:15:23.5

make_timestamptz ( year int, month int, day int, hour int, min int, sec double precision [, timezone text ] ) → timestamp with time zone

Create timestamp with time zone from year, month, day, hour, minute and seconds fields (negative years signify BC). If timezone is not specified, the current time zone is used; the examples assume the session time zone is Europe/London

make_timestamptz(2013, 7, 15, 8, 15, 23.5) → 2013-07-15 08:15:23.5+01

make_timestamptz(2013, 7, 15, 8, 15, 23.5, 'America/New_York') → 2013-07-15 13:15:23.5+01

now ( ) → timestamp with time zone

Current date and time (start of current transaction); see Section 9.9.5

now() → 2019-12-23 14:39:53.662522-05

statement_timestamp ( ) → timestamp with time zone

Current date and time (start of current statement); see Section 9.9.5

statement_timestamp() → 2019-12-23 14:39:53.662522-05

Current date and time (like clock_timestamp, but as a text string); see Section 9.9.5

timeofday() → Mon Dec 23 14:39:53.662522 2019 EST

transaction_timestamp ( ) → timestamp with time zone

Current date and time (start of current transaction); see Section 9.9.5

transaction_timestamp() → 2019-12-23 14:39:53.662522-05

to_timestamp ( double precision ) → timestamp with time zone

Convert Unix epoch (seconds since 1970-01-01 00:00:00+00) to timestamp with time zone

to_timestamp(1284352323) → 2010-09-13 04:32:03+00

In addition to these functions, the SQL OVERLAPS operator is supported:

This expression yields true when two time periods (defined by their endpoints) overlap, false when they do not overlap. The endpoints can be specified as pairs of dates, times, or time stamps; or as a date, time, or time stamp followed by an interval. When a pair of values is provided, either the start or the end can be written first; OVERLAPS automatically takes the earlier value of the pair as the start. Each time period is considered to represent the half-open interval start <= time < end, unless start and end are equal in which case it represents that single time instant. This means for instance that two time periods with only an endpoint in common do not overlap.

When adding an interval value to (or subtracting an interval value from) a timestamp or timestamp with time zone value, the months, days, and microseconds fields of the interval value are handled in turn. First, a nonzero months field advances or decrements the date of the timestamp by the indicated number of months, keeping the day of month the same unless it would be past the end of the new month, in which case the last day of that month is used. (For example, March 31 plus 1 month becomes April 30, but March 31 plus 2 months becomes May 31.) Then the days field advances or decrements the date of the timestamp by the indicated number of days. In both these steps the local time of day is kept the same. Finally, if there is a nonzero microseconds field, it is added or subtracted literally. When doing arithmetic on a timestamp with time zone value in a time zone that recognizes DST, this means that adding or subtracting (say) interval '1 day' does not necessarily have the same result as adding or subtracting interval '24 hours'. For example, with the session time zone set to America/Denver:

This happens because an hour was skipped due to a change in daylight saving time at 2005-04-03 02:00:00 in time zone America/Denver.

Note there can be ambiguity in the months field returned by age because different months have different numbers of days. PostgreSQL's approach uses the month from the earlier of the two dates when calculating partial months. For example, age('2004-06-01', '2004-04-30') uses April to yield 1 mon 1 day, while using May would yield 1 mon 2 days because May has 31 days, while April has only 30.

Subtraction of dates and timestamps can also be complex. One conceptually simple way to perform subtraction is to convert each value to a number of seconds using EXTRACT(EPOCH FROM ...), then subtract the results; this produces the number of seconds between the two values. This will adjust for the number of days in each month, timezone changes, and daylight saving time adjustments. Subtraction of date or timestamp values with the “-” operator returns the number of days (24-hours) and hours/minutes/seconds between the values, making the same adjustments. The age function returns years, months, days, and hours/minutes/seconds, performing field-by-field subtraction and then adjusting for negative field values. The following queries illustrate the differences in these approaches. The sample results were produced with timezone = 'US/Eastern'; there is a daylight saving time change between the two dates used:

The extract function retrieves subfields such as year or hour from date/time values. source must be a value expression of type timestamp, date, time, or interval. (Timestamps and times can be with or without time zone.) field is an identifier or string that selects what field to extract from the source value. Not all fields are valid for every input data type; for example, fields smaller than a day cannot be extracted from a date, while fields of a day or more cannot be extracted from a time. The extract function returns values of type numeric.

The following are valid field names:

The century; for interval values, the year field divided by 100

The day of the month (1–31); for interval values, the number of days

The year field divided by 10

The day of the week as Sunday (0) to Saturday (6)

Note that extract's day of the week numbering differs from that of the to_char(..., 'D') function.

The day of the year (1–365/366)

For timestamp with time zone values, the number of seconds since 1970-01-01 00:00:00 UTC (negative for timestamps before that); for date and timestamp values, the nominal number of seconds since 1970-01-01 00:00:00, without regard to timezone or daylight-savings rules; for interval values, the total number of seconds in the interval

You can convert an epoch value back to a timestamp with time zone with to_timestamp:

Beware that applying to_timestamp to an epoch extracted from a date or timestamp value could produce a misleading result: the result will effectively assume that the original value had been given in UTC, which might not be the case.

The hour field (0–23 in timestamps, unrestricted in intervals)

The day of the week as Monday (1) to Sunday (7)

This is identical to dow except for Sunday. This matches the ISO 8601 day of the week numbering.

The ISO 8601 week-numbering year that the date falls in

Each ISO 8601 week-numbering year begins with the Monday of the week containing the 4th of January, so in early January or late December the ISO year may be different from the Gregorian year. See the week field for more information.

The Julian Date corresponding to the date or timestamp. Timestamps that are not local midnight result in a fractional value. See Section B.7 for more information.

The seconds field, including fractional parts, multiplied by 1 000 000; note that this includes full seconds

The millennium; for interval values, the year field divided by 1000

Years in the 1900s are in the second millennium. The third millennium started January 1, 2001.

The seconds field, including fractional parts, multiplied by 1000. Note that this includes full seconds.

The minutes field (0–59)

The number of the month within the year (1–12); for interval values, the number of months modulo 12 (0–11)

The quarter of the year (1–4) that the date is in; for interval values, the month field divided by 3 plus 1

The seconds field, including any fractional seconds

The time zone offset from UTC, measured in seconds. Positive values correspond to time zones east of UTC, negative values to zones west of UTC. (Technically, PostgreSQL does not use UTC because leap seconds are not handled.)

The hour component of the time zone offset

The minute component of the time zone offset

The number of the ISO 8601 week-numbering week of the year. By definition, ISO weeks start on Mondays and the first week of a year contains January 4 of that year. In other words, the first Thursday of a year is in week 1 of that year.

In the ISO week-numbering system, it is possible for early-January dates to be part of the 52nd or 53rd week of the previous year, and for late-December dates to be part of the first week of the next year. For example, 2005-01-01 is part of the 53rd week of year 2004, and 2006-01-01 is part of the 52nd week of year 2005, while 2012-12-31 is part of the first week of 2013. It's recommended to use the isoyear field together with week to get consistent results.

For interval values, the week field is simply the number of integral days divided by 7.

The year field. Keep in mind there is no 0 AD, so subtracting BC years from AD years should be done with care.

When processing an interval value, the extract function produces field values that match the interpretation used by the interval output function. This can produce surprising results if one starts with a non-normalized interval representation, for example:

When the input value is +/-Infinity, extract returns +/-Infinity for monotonically-increasing fields (epoch, julian, year, isoyear, decade, century, and millennium for timestamp inputs; epoch, hour, day, year, decade, century, and millennium for interval inputs). For other fields, NULL is returned. PostgreSQL versions before 9.6 returned zero for all cases of infinite input.

The extract function is primarily intended for computational processing. For formatting date/time values for display, see Section 9.8.

The date_part function is modeled on the traditional Ingres equivalent to the SQL-standard function extract:

Note that here the field parameter needs to be a string value, not a name. The valid field names for date_part are the same as for extract. For historical reasons, the date_part function returns values of type double precision. This can result in a loss of precision in certain uses. Using extract is recommended instead.

The function date_trunc is conceptually similar to the trunc function for numbers.

source is a value expression of type timestamp, timestamp with time zone, or interval. (Values of type date and time are cast automatically to timestamp or interval, respectively.) field selects to which precision to truncate the input value. The return value is likewise of type timestamp, timestamp with time zone, or interval, and it has all fields that are less significant than the selected one set to zero (or one, for day and month).

Valid values for field are:

When the input value is of type timestamp with time zone, the truncation is performed with respect to a particular time zone; for example, truncation to day produces a value that is midnight in that zone. By default, truncation is done with respect to the current TimeZone setting, but the optional time_zone argument can be provided to specify a different time zone. The time zone name can be specified in any of the ways described in Section 8.5.3.

A time zone cannot be specified when processing timestamp without time zone or interval inputs. These are always taken at face value.

Examples (assuming the local time zone is America/New_York):

The function date_bin “bins” the input timestamp into the specified interval (the stride) aligned with a specified origin.

source is a value expression of type timestamp or timestamp with time zone. (Values of type date are cast automatically to timestamp.) stride is a value expression of type interval. The return value is likewise of type timestamp or timestamp with time zone, and it marks the beginning of the bin into which the source is placed.

In the case of full units (1 minute, 1 hour, etc.), it gives the same result as the analogous date_trunc call, but the difference is that date_bin can truncate to an arbitrary interval.

The stride interval must be greater than zero and cannot contain units of month or larger.

The AT TIME ZONE operator converts time stamp without time zone to/from time stamp with time zone, and time with time zone values to different time zones. Table 9.34 shows its variants.

Table 9.34. AT TIME ZONE and AT LOCAL Variants

timestamp without time zone AT TIME ZONE zone → timestamp with time zone

Converts given time stamp without time zone to time stamp with time zone, assuming the given value is in the named time zone.

timestamp '2001-02-16 20:38:40' at time zone 'America/Denver' → 2001-02-17 03:38:40+00

timestamp without time zone AT LOCAL → timestamp with time zone

Converts given time stamp without time zone to time stamp with the session's TimeZone value as time zone.

timestamp '2001-02-16 20:38:40' at local → 2001-02-17 03:38:40+00

timestamp with time zone AT TIME ZONE zone → timestamp without time zone

Converts given time stamp with time zone to time stamp without time zone, as the time would appear in that zone.

timestamp with time zone '2001-02-16 20:38:40-05' at time zone 'America/Denver' → 2001-02-16 18:38:40

timestamp with time zone AT LOCAL → timestamp without time zone

Converts given time stamp with time zone to time stamp without time zone, as the time would appear with the session's TimeZone value as time zone.

timestamp with time zone '2001-02-16 20:38:40-05' at local → 2001-02-16 18:38:40

time with time zone AT TIME ZONE zone → time with time zone

Converts given time with time zone to a new time zone. Since no date is supplied, this uses the currently active UTC offset for the named destination zone.

time with time zone '05:34:17-05' at time zone 'UTC' → 10:34:17+00

time with time zone AT LOCAL → time with time zone

Converts given time with time zone to a new time zone. Since no date is supplied, this uses the currently active UTC offset for the session's TimeZone value.

Assuming the session's TimeZone is set to UTC:

time with time zone '05:34:17-05' at local → 10:34:17+00

In these expressions, the desired time zone zone can be specified either as a text value (e.g., 'America/Los_Angeles') or as an interval (e.g., INTERVAL '-08:00'). In the text case, a time zone name can be specified in any of the ways described in Section 8.5.3. The interval case is only useful for zones that have fixed offsets from UTC, so it is not very common in practice.

The syntax AT LOCAL may be used as shorthand for AT TIME ZONE local, where local is the session's TimeZone value.

Examples (assuming the current TimeZone setting is America/Los_Angeles):

The first example adds a time zone to a value that lacks it, and displays the value using the current TimeZone setting. The second example shifts the time stamp with time zone value to the specified time zone, and returns the value without a time zone. This allows storage and display of values different from the current TimeZone setting. The third example converts Tokyo time to Chicago time. The fourth example shifts the time stamp with time zone value to the time zone currently specified by the TimeZone setting and returns the value without a time zone. The fifth example demonstrates that the sign in a POSIX-style time zone specification has the opposite meaning of the sign in an ISO-8601 datetime literal, as described in Section 8.5.3 and Appendix B.

The sixth example is a cautionary tale. Due to the fact that there is no date associated with the input value, the conversion is made using the current date of the session. Therefore, this static example may show a wrong result depending on the time of the year it is viewed because 'America/Los_Angeles' observes Daylight Savings Time.

The function timezone(zone, timestamp) is equivalent to the SQL-conforming construct timestamp AT TIME ZONE zone.

The function timezone(zone, time) is equivalent to the SQL-conforming construct time AT TIME ZONE zone.

The function timezone(timestamp) is equivalent to the SQL-conforming construct timestamp AT LOCAL.

The function timezone(time) is equivalent to the SQL-conforming construct time AT LOCAL.

PostgreSQL provides a number of functions that return values related to the current date and time. These SQL-standard functions all return values based on the start time of the current transaction:

CURRENT_TIME and CURRENT_TIMESTAMP deliver values with time zone; LOCALTIME and LOCALTIMESTAMP deliver values without time zone.

CURRENT_TIME, CURRENT_TIMESTAMP, LOCALTIME, and LOCALTIMESTAMP can optionally take a precision parameter, which causes the result to be rounded to that many fractional digits in the seconds field. Without a precision parameter, the result is given to the full available precision.

Since these functions return the start time of the current transaction, their values do not change during the transaction. This is considered a feature: the intent is to allow a single transaction to have a consistent notion of the “current” time, so that multiple modifications within the same transaction bear the same time stamp.

Other database systems might advance these values more frequently.

PostgreSQL also provides functions that return the start time of the current statement, as well as the actual current time at the instant the function is called. The complete list of non-SQL-standard time functions is:

transaction_timestamp() is equivalent to CURRENT_TIMESTAMP, but is named to clearly reflect what it returns. statement_timestamp() returns the start time of the current statement (more specifically, the time of receipt of the latest command message from the client). statement_timestamp() and transaction_timestamp() return the same value during the first statement of a transaction, but might differ during subsequent statements. clock_timestamp() returns the actual current time, and therefore its value changes even within a single SQL statement. timeofday() is a historical PostgreSQL function. Like clock_timestamp(), it returns the actual current time, but as a formatted text string rather than a timestamp with time zone value. now() is a traditional PostgreSQL equivalent to transaction_timestamp().

All the date/time data types also accept the special literal value now to specify the current date and time (again, interpreted as the transaction start time). Thus, the following three all return the same result:

Do not use the third form when specifying a value to be evaluated later, for example in a DEFAULT clause for a table column. The system will convert now to a timestamp as soon as the constant is parsed, so that when the default value is needed, the time of the table creation would be used! The first two forms will not be evaluated until the default value is used, because they are function calls. Thus they will give the desired behavior of defaulting to the time of row insertion. (See also Section 8.5.1.4.)

The following functions are available to delay execution of the server process:

pg_sleep makes the current session's process sleep until the given number of seconds have elapsed. Fractional-second delays can be specified. pg_sleep_for is a convenience function to allow the sleep time to be specified as an interval. pg_sleep_until is a convenience function for when a specific wake-up time is desired. For example:

The effective resolution of the sleep interval is platform-specific; 0.01 seconds is a common value. The sleep delay will be at least as long as specified. It might be longer depending on factors such as server load. In particular, pg_sleep_until is not guaranteed to wake up exactly at the specified time, but it will not wake up any earlier.

Make sure that your session does not hold more locks than necessary when calling pg_sleep or its variants. Otherwise other sessions might have to wait for your sleeping process, slowing down the entire system.

Examples:

Example 1 (unknown):

(start1, end1) OVERLAPS (start2, end2)
(start1, length1) OVERLAPS (start2, length2)

Example 2 (unknown):

SELECT (DATE '2001-02-16', DATE '2001-12-21') OVERLAPS
       (DATE '2001-10-30', DATE '2002-10-30');
Result: true
SELECT (DATE '2001-02-16', INTERVAL '100 days') OVERLAPS
       (DATE '2001-10-30', DATE '2002-10-30');
Result: false
SELECT (DATE '2001-10-29', DATE '2001-10-30') OVERLAPS
       (DATE '2001-10-30', DATE '2001-10-31');
Result: false
SELECT (DATE '2001-10-30', DATE '2001-10-30') OVERLAPS
       (DATE '2001-10-30', DATE '2001-10-31');
Result: true

Example 3 (unknown):

SELECT timestamp with time zone '2005-04-02 12:00:00-07' + interval '1 day';
Result: 2005-04-03 12:00:00-06
SELECT timestamp with time zone '2005-04-02 12:00:00-07' + interval '24 hours';
Result: 2005-04-03 13:00:00-06

Example 4 (unknown):

SELECT EXTRACT(EPOCH FROM timestamptz '2013-07-01 12:00:00') -
       EXTRACT(EPOCH FROM timestamptz '2013-03-01 12:00:00');
Result: 10537200.000000
SELECT (EXTRACT(EPOCH FROM timestamptz '2013-07-01 12:00:00') -
        EXTRACT(EPOCH FROM timestamptz '2013-03-01 12:00:00'))
        / 60 / 60 / 24;
Result: 121.9583333333333333
SELECT timestamptz '2013-07-01 12:00:00' - timestamptz '2013-03-01 12:00:00';
Result: 121 days 23:00:00
SELECT age(timestamptz '2013-07-01 12:00:00', timestamptz '2013-03-01 12:00:00');
Result: 4 mons

---

PostgreSQL: Documentation: 18: 28.6. WAL Internals

URL: https://www.postgresql.org/docs/current/wal-internals.html

Contents:

• 28.6. WAL Internals #

WAL is automatically enabled; no action is required from the administrator except ensuring that the disk-space requirements for the WAL files are met, and that any necessary tuning is done (see Section 28.5).

WAL records are appended to the WAL files as each new record is written. The insert position is described by a Log Sequence Number (LSN) that is a byte offset into the WAL, increasing monotonically with each new record. LSN values are returned as the datatype pg_lsn. Values can be compared to calculate the volume of WAL data that separates them, so they are used to measure the progress of replication and recovery.

WAL files are stored in the directory pg_wal under the data directory, as a set of segment files, normally each 16 MB in size (but the size can be changed by altering the --wal-segsize initdb option). Each segment is divided into pages, normally 8 kB each (this size can be changed via the --with-wal-blocksize configure option). The WAL record headers are described in access/xlogrecord.h; the record content is dependent on the type of event that is being logged. Segment files are given ever-increasing numbers as names, starting at 000000010000000000000001. The numbers do not wrap, but it will take a very, very long time to exhaust the available stock of numbers.

It is advantageous if the WAL is located on a different disk from the main database files. This can be achieved by moving the pg_wal directory to another location (while the server is shut down, of course) and creating a symbolic link from the original location in the main data directory to the new location.

The aim of WAL is to ensure that the log is written before database records are altered, but this can be subverted by disk drives that falsely report a successful write to the kernel, when in fact they have only cached the data and not yet stored it on the disk. A power failure in such a situation might lead to irrecoverable data corruption. Administrators should try to ensure that disks holding PostgreSQL's WAL files do not make such false reports. (See Section 28.1.)

After a checkpoint has been made and the WAL flushed, the checkpoint's position is saved in the file pg_control. Therefore, at the start of recovery, the server first reads pg_control and then the checkpoint record; then it performs the REDO operation by scanning forward from the WAL location indicated in the checkpoint record. Because the entire content of data pages is saved in the WAL on the first page modification after a checkpoint (assuming full_page_writes is not disabled), all pages changed since the checkpoint will be restored to a consistent state.

To deal with the case where pg_control is corrupt, we should support the possibility of scanning existing WAL segments in reverse order — newest to oldest — in order to find the latest checkpoint. This has not been implemented yet. pg_control is small enough (less than one disk page) that it is not subject to partial-write problems, and as of this writing there have been no reports of database failures due solely to the inability to read pg_control itself. So while it is theoretically a weak spot, pg_control does not seem to be a problem in practice.

---

PostgreSQL: Documentation: 18: 32.22. Building libpq Programs

URL: https://www.postgresql.org/docs/current/libpq-build.html

Contents:

• 32.22. Building libpq Programs #

To build (i.e., compile and link) a program using libpq you need to do all of the following things:

Include the libpq-fe.h header file:

If you failed to do that then you will normally get error messages from your compiler similar to:

Point your compiler to the directory where the PostgreSQL header files were installed, by supplying the -Idirectory option to your compiler. (In some cases the compiler will look into the directory in question by default, so you can omit this option.) For instance, your compile command line could look like:

If you are using makefiles then add the option to the CPPFLAGS variable:

If there is any chance that your program might be compiled by other users then you should not hardcode the directory location like that. Instead, you can run the utility pg_config to find out where the header files are on the local system:

If you have pkg-config installed, you can run instead:

Note that this will already include the -I in front of the path.

Failure to specify the correct option to the compiler will result in an error message such as:

When linking the final program, specify the option -lpq so that the libpq library gets pulled in, as well as the option -Ldirectory to point the compiler to the directory where the libpq library resides. (Again, the compiler will search some directories by default.) For maximum portability, put the -L option before the -lpq option. For example:

You can find out the library directory using pg_config as well:

Or again use pkg-config:

Note again that this prints the full options, not only the path.

Error messages that point to problems in this area could look like the following:

This means you forgot -lpq.

This means you forgot the -L option or did not specify the right directory.

Examples:

Example 1 (cpp):

#include <libpq-fe.h>

Example 2 (unknown):

foo.c: In function `main':
foo.c:34: `PGconn' undeclared (first use in this function)
foo.c:35: `PGresult' undeclared (first use in this function)
foo.c:54: `CONNECTION_BAD' undeclared (first use in this function)
foo.c:68: `PGRES_COMMAND_OK' undeclared (first use in this function)
foo.c:95: `PGRES_TUPLES_OK' undeclared (first use in this function)

Example 3 (unknown):

cc -c -I/usr/local/pgsql/include testprog.c

Example 4 (unknown):

CPPFLAGS += -I/usr/local/pgsql/include

---

PostgreSQL: Documentation: 18: 11.6. Unique Indexes

URL: https://www.postgresql.org/docs/current/indexes-unique.html

Contents:

• 11.6. Unique Indexes #
  • Note

Indexes can also be used to enforce uniqueness of a column's value, or the uniqueness of the combined values of more than one column.

Currently, only B-tree indexes can be declared unique.

When an index is declared unique, multiple table rows with equal indexed values are not allowed. By default, null values in a unique column are not considered equal, allowing multiple nulls in the column. The NULLS NOT DISTINCT option modifies this and causes the index to treat nulls as equal. A multicolumn unique index will only reject cases where all indexed columns are equal in multiple rows.

PostgreSQL automatically creates a unique index when a unique constraint or primary key is defined for a table. The index covers the columns that make up the primary key or unique constraint (a multicolumn index, if appropriate), and is the mechanism that enforces the constraint.

There's no need to manually create indexes on unique columns; doing so would just duplicate the automatically-created index.

Examples:

Example 1 (unknown):

CREATE UNIQUE INDEX name ON table (column [, ...]) [ NULLS [ NOT ] DISTINCT ];

---

PostgreSQL: Documentation: 18: 35.39. role_usage_grants

URL: https://www.postgresql.org/docs/current/infoschema-role-usage-grants.html

Contents:

• 35.39. role_usage_grants #

The view role_usage_grants identifies USAGE privileges granted on various kinds of objects where the grantor or grantee is a currently enabled role. Further information can be found under usage_privileges. The only effective difference between this view and usage_privileges is that this view omits objects that have been made accessible to the current user by way of a grant to PUBLIC.

Table 35.37. role_usage_grants Columns

grantor sql_identifier

The name of the role that granted the privilege

grantee sql_identifier

The name of the role that the privilege was granted to

object_catalog sql_identifier

Name of the database containing the object (always the current database)

object_schema sql_identifier

Name of the schema containing the object, if applicable, else an empty string

object_name sql_identifier

object_type character_data

COLLATION or DOMAIN or FOREIGN DATA WRAPPER or FOREIGN SERVER or SEQUENCE

privilege_type character_data

is_grantable yes_or_no

YES if the privilege is grantable, NO if not

---

PostgreSQL: Documentation: 18: 5.8. Privileges

URL: https://www.postgresql.org/docs/current/ddl-priv.html

Contents:

• 5.8. Privileges #

When an object is created, it is assigned an owner. The owner is normally the role that executed the creation statement. For most kinds of objects, the initial state is that only the owner (or a superuser) can do anything with the object. To allow other roles to use it, privileges must be granted.

There are different kinds of privileges: SELECT, INSERT, UPDATE, DELETE, TRUNCATE, REFERENCES, TRIGGER, CREATE, CONNECT, TEMPORARY, EXECUTE, USAGE, SET, ALTER SYSTEM, and MAINTAIN. The privileges applicable to a particular object vary depending on the object's type (table, function, etc.). More detail about the meanings of these privileges appears below. The following sections and chapters will also show you how these privileges are used.

The right to modify or destroy an object is inherent in being the object's owner, and cannot be granted or revoked in itself. (However, like all privileges, that right can be inherited by members of the owning role; see Section 21.3.)

An object can be assigned to a new owner with an ALTER command of the appropriate kind for the object, for example

Superusers can always do this; ordinary roles can only do it if they are both the current owner of the object (or inherit the privileges of the owning role) and able to SET ROLE to the new owning role.

To assign privileges, the GRANT command is used. For example, if joe is an existing role, and accounts is an existing table, the privilege to update the table can be granted with:

Writing ALL in place of a specific privilege grants all privileges that are relevant for the object type.

The special “role” name PUBLIC can be used to grant a privilege to every role on the system. Also, “group” roles can be set up to help manage privileges when there are many users of a database — for details see Chapter 21.

To revoke a previously-granted privilege, use the fittingly named REVOKE command:

Ordinarily, only the object's owner (or a superuser) can grant or revoke privileges on an object. However, it is possible to grant a privilege “with grant option”, which gives the recipient the right to grant it in turn to others. If the grant option is subsequently revoked then all who received the privilege from that recipient (directly or through a chain of grants) will lose the privilege. For details see the GRANT and REVOKE reference pages.

An object's owner can choose to revoke their own ordinary privileges, for example to make a table read-only for themselves as well as others. But owners are always treated as holding all grant options, so they can always re-grant their own privileges.

The available privileges are:

Allows SELECT from any column, or specific column(s), of a table, view, materialized view, or other table-like object. Also allows use of COPY TO. This privilege is also needed to reference existing column values in UPDATE, DELETE, or MERGE. For sequences, this privilege also allows use of the currval function. For large objects, this privilege allows the object to be read.

Allows INSERT of a new row into a table, view, etc. Can be granted on specific column(s), in which case only those columns may be assigned to in the INSERT command (other columns will therefore receive default values). Also allows use of COPY FROM.

Allows UPDATE of any column, or specific column(s), of a table, view, etc. (In practice, any nontrivial UPDATE command will require SELECT privilege as well, since it must reference table columns to determine which rows to update, and/or to compute new values for columns.) SELECT ... FOR UPDATE and SELECT ... FOR SHARE also require this privilege on at least one column, in addition to the SELECT privilege. For sequences, this privilege allows use of the nextval and setval functions. For large objects, this privilege allows writing or truncating the object.

Allows DELETE of a row from a table, view, etc. (In practice, any nontrivial DELETE command will require SELECT privilege as well, since it must reference table columns to determine which rows to delete.)

Allows TRUNCATE on a table.

Allows creation of a foreign key constraint referencing a table, or specific column(s) of a table.

Allows creation of a trigger on a table, view, etc.

For databases, allows new schemas and publications to be created within the database, and allows trusted extensions to be installed within the database.

For schemas, allows new objects to be created within the schema. To rename an existing object, you must own the object and have this privilege for the containing schema.

For tablespaces, allows tables, indexes, and temporary files to be created within the tablespace, and allows databases to be created that have the tablespace as their default tablespace.

Note that revoking this privilege will not alter the existence or location of existing objects.

Allows the grantee to connect to the database. This privilege is checked at connection startup (in addition to checking any restrictions imposed by pg_hba.conf).

Allows temporary tables to be created while using the database.

Allows calling a function or procedure, including use of any operators that are implemented on top of the function. This is the only type of privilege that is applicable to functions and procedures.

For procedural languages, allows use of the language for the creation of functions in that language. This is the only type of privilege that is applicable to procedural languages.

For schemas, allows access to objects contained in the schema (assuming that the objects' own privilege requirements are also met). Essentially this allows the grantee to “look up” objects within the schema. Without this permission, it is still possible to see the object names, e.g., by querying system catalogs. Also, after revoking this permission, existing sessions might have statements that have previously performed this lookup, so this is not a completely secure way to prevent object access.

For sequences, allows use of the currval and nextval functions.

For types and domains, allows use of the type or domain in the creation of tables, functions, and other schema objects. (Note that this privilege does not control all “usage” of the type, such as values of the type appearing in queries. It only prevents objects from being created that depend on the type. The main purpose of this privilege is controlling which users can create dependencies on a type, which could prevent the owner from changing the type later.)

For foreign-data wrappers, allows creation of new servers using the foreign-data wrapper.

For foreign servers, allows creation of foreign tables using the server. Grantees may also create, alter, or drop their own user mappings associated with that server.

Allows a server configuration parameter to be set to a new value within the current session. (While this privilege can be granted on any parameter, it is meaningless except for parameters that would normally require superuser privilege to set.)

Allows a server configuration parameter to be configured to a new value using the ALTER SYSTEM command.

Allows VACUUM, ANALYZE, CLUSTER, REFRESH MATERIALIZED VIEW, REINDEX, LOCK TABLE, and database object statistics manipulation functions (see Table 9.105) on a relation.

The privileges required by other commands are listed on the reference page of the respective command.

PostgreSQL grants privileges on some types of objects to PUBLIC by default when the objects are created. No privileges are granted to PUBLIC by default on tables, table columns, sequences, foreign data wrappers, foreign servers, large objects, schemas, tablespaces, or configuration parameters. For other types of objects, the default privileges granted to PUBLIC are as follows: CONNECT and TEMPORARY (create temporary tables) privileges for databases; EXECUTE privilege for functions and procedures; and USAGE privilege for languages and data types (including domains). The object owner can, of course, REVOKE both default and expressly granted privileges. (For maximum security, issue the REVOKE in the same transaction that creates the object; then there is no window in which another user can use the object.) Also, these default privilege settings can be overridden using the ALTER DEFAULT PRIVILEGES command.

Table 5.1 shows the one-letter abbreviations that are used for these privilege types in ACL values. You will see these letters in the output of the psql commands listed below, or when looking at ACL columns of system catalogs.

Table 5.1. ACL Privilege Abbreviations

Table 5.2 summarizes the privileges available for each type of SQL object, using the abbreviations shown above. It also shows the psql command that can be used to examine privilege settings for each object type.

Table 5.2. Summary of Access Privileges

The privileges that have been granted for a particular object are displayed as a list of aclitem entries, each having the format:

Each aclitem lists all the permissions of one grantee that have been granted by a particular grantor. Specific privileges are represented by one-letter abbreviations from Table 5.1, with * appended if the privilege was granted with grant option. For example, calvin=rw/hobbes specifies that the role calvin has the privilege SELECT (r) with grant option () as well as the non-grantable privilege UPDATE (w), both granted by the role hobbes. If calvin also has some privileges on the same object granted by a different grantor, those would appear as a separate aclitem entry. An empty grantee field in an aclitem stands for PUBLIC.

As an example, suppose that user miriam creates table mytable and does:

Then psql's \dp command would show:

If the “Access privileges” column is empty for a given object, it means the object has default privileges (that is, its privileges entry in the relevant system catalog is null). Default privileges always include all privileges for the owner, and can include some privileges for PUBLIC depending on the object type, as explained above. The first GRANT or REVOKE on an object will instantiate the default privileges (producing, for example, miriam=arwdDxt/miriam) and then modify them per the specified request. Similarly, entries are shown in “Column privileges” only for columns with nondefault privileges. (Note: for this purpose, “default privileges” always means the built-in default privileges for the object's type. An object whose privileges have been affected by an ALTER DEFAULT PRIVILEGES command will always be shown with an explicit privilege entry that includes the effects of the ALTER.)

Notice that the owner's implicit grant options are not marked in the access privileges display. A * will appear only when grant options have been explicitly granted to someone.

The “Access privileges” column shows (none) when the object's privileges entry is non-null but empty. This means that no privileges are granted at all, even to the object's owner — a rare situation. (The owner still has implicit grant options in this case, and so could re-grant her own privileges; but she has none at the moment.)

Examples:

Example 1 (unknown):

ALTER TABLE table_name OWNER TO new_owner;

Example 2 (unknown):

GRANT UPDATE ON accounts TO joe;

Example 3 (unknown):

REVOKE ALL ON accounts FROM PUBLIC;

Example 4 (unknown):

grantee=privilege-abbreviation[*].../grantor

---

PostgreSQL: Documentation: 18: Preface

URL: https://www.postgresql.org/docs/current/preface.html

Contents:

• Preface

This book is the official documentation of PostgreSQL. It has been written by the PostgreSQL developers and other volunteers in parallel to the development of the PostgreSQL software. It describes all the functionality that the current version of PostgreSQL officially supports.

To make the large amount of information about PostgreSQL manageable, this book has been organized in several parts. Each part is targeted at a different class of users, or at users in different stages of their PostgreSQL experience:

Part I is an informal introduction for new users.

Part II documents the SQL query language environment, including data types and functions, as well as user-level performance tuning. Every PostgreSQL user should read this.

Part III describes the installation and administration of the server. Everyone who runs a PostgreSQL server, be it for private use or for others, should read this part.

Part IV describes the programming interfaces for PostgreSQL client programs.

Part V contains information for advanced users about the extensibility capabilities of the server. Topics include user-defined data types and functions.

Part VI contains reference information about SQL commands, client and server programs. This part supports the other parts with structured information sorted by command or program.

Part VII contains assorted information that might be of use to PostgreSQL developers.

---

PostgreSQL: Documentation: 18: Chapter 23. Localization

URL: https://www.postgresql.org/docs/current/charset.html

Contents:

• Chapter 23. Localization

This chapter describes the available localization features from the point of view of the administrator. PostgreSQL supports two localization facilities:

Using the locale features of the operating system to provide locale-specific collation order, number formatting, translated messages, and other aspects. This is covered in Section 23.1 and Section 23.2.

Providing a number of different character sets to support storing text in all kinds of languages, and providing character set translation between client and server. This is covered in Section 23.3.

---

PostgreSQL: Documentation: 18: 32.20. OAuth Support

URL: https://www.postgresql.org/docs/current/libpq-oauth.html

Contents:

• 32.20. OAuth Support #
  • Note
  • 32.20.1. Authdata Hooks #
  • 32.20.1.1. Hook Types #
  • 32.20.2. Debugging and Developer Settings #
  • Warning

libpq implements support for the OAuth v2 Device Authorization client flow, documented in RFC 8628, as an optional module. See the installation documentation for information on how to enable support for Device Authorization as a builtin flow.

When support is enabled and the optional module installed, libpq will use the builtin flow by default if the server requests a bearer token during authentication. This flow can be utilized even if the system running the client application does not have a usable web browser, for example when running a client via SSH.

The builtin flow will, by default, print a URL to visit and a user code to enter there:

(This prompt may be customized.) The user will then log into their OAuth provider, which will ask whether to allow libpq and the server to perform actions on their behalf. It is always a good idea to carefully review the URL and permissions displayed, to ensure they match expectations, before continuing. Permissions should not be given to untrusted third parties.

Client applications may implement their own flows to customize interaction and integration with applications. See Section 32.20.1 for more information on how add a custom flow to libpq.

For an OAuth client flow to be usable, the connection string must at minimum contain oauth_issuer and oauth_client_id. (These settings are determined by your organization's OAuth provider.) The builtin flow additionally requires the OAuth authorization server to publish a device authorization endpoint.

The builtin Device Authorization flow is not currently supported on Windows. Custom client flows may still be implemented.

The behavior of the OAuth flow may be modified or replaced by a client using the following hook API:

Sets the PGauthDataHook, overriding libpq's handling of one or more aspects of its OAuth client flow.

If hook is NULL, the default handler will be reinstalled. Otherwise, the application passes a pointer to a callback function with the signature:

which libpq will call when an action is required of the application. type describes the request being made, conn is the connection handle being authenticated, and data points to request-specific metadata. The contents of this pointer are determined by type; see Section 32.20.1.1 for the supported list.

Hooks can be chained together to allow cooperative and/or fallback behavior. In general, a hook implementation should examine the incoming type (and, potentially, the request metadata and/or the settings for the particular conn in use) to decide whether or not to handle a specific piece of authdata. If not, it should delegate to the previous hook in the chain (retrievable via PQgetAuthDataHook).

Success is indicated by returning an integer greater than zero. Returning a negative integer signals an error condition and abandons the connection attempt. (A zero value is reserved for the default implementation.)

Retrieves the current value of PGauthDataHook.

At initialization time (before the first call to PQsetAuthDataHook), this function will return PQdefaultAuthDataHook.

The following PGauthData types and their corresponding data structures are defined:

Replaces the default user prompt during the builtin device authorization client flow. data points to an instance of PGpromptOAuthDevice:

The OAuth Device Authorization flow which can be included in libpq requires the end user to visit a URL with a browser, then enter a code which permits libpq to connect to the server on their behalf. The default prompt simply prints the verification_uri and user_code on standard error. Replacement implementations may display this information using any preferred method, for example with a GUI.

This callback is only invoked during the builtin device authorization flow. If the application installs a custom OAuth flow, or libpq was not built with support for the builtin flow, this authdata type will not be used.

If a non-NULL verification_uri_complete is provided, it may optionally be used for non-textual verification (for example, by displaying a QR code). The URL and user code should still be displayed to the end user in this case, because the code will be manually confirmed by the provider, and the URL lets users continue even if they can't use the non-textual method. For more information, see section 3.3.1 in RFC 8628.

Adds a custom implementation of a flow, replacing the builtin flow if it is installed. The hook should either directly return a Bearer token for the current user/issuer/scope combination, if one is available without blocking, or else set up an asynchronous callback to retrieve one.

data points to an instance of PGoauthBearerRequest, which should be filled in by the implementation:

Two pieces of information are provided to the hook by libpq: openid_configuration contains the URL of an OAuth discovery document describing the authorization server's supported flows, and scope contains a (possibly empty) space-separated list of OAuth scopes which are required to access the server. Either or both may be NULL to indicate that the information was not discoverable. (In this case, implementations may be able to establish the requirements using some other preconfigured knowledge, or they may choose to fail.)

The final output of the hook is token, which must point to a valid Bearer token for use on the connection. (This token should be issued by the oauth_issuer and hold the requested scopes, or the connection will be rejected by the server's validator module.) The allocated token string must remain valid until libpq is finished connecting; the hook should set a cleanup callback which will be called when libpq no longer requires it.

If an implementation cannot immediately produce a token during the initial call to the hook, it should set the async callback to handle nonblocking communication with the authorization server. [16] This will be called to begin the flow immediately upon return from the hook. When the callback cannot make further progress without blocking, it should return either PGRES_POLLING_READING or PGRES_POLLING_WRITING after setting *pgsocket to the file descriptor that will be marked ready to read/write when progress can be made again. (This descriptor is then provided to the top-level polling loop via PQsocket().) Return PGRES_POLLING_OK after setting token when the flow is complete, or PGRES_POLLING_FAILED to indicate failure.

Implementations may wish to store additional data for bookkeeping across calls to the async and cleanup callbacks. The user pointer is provided for this purpose; libpq will not touch its contents and the application may use it at its convenience. (Remember to free any allocations during token cleanup.)

A "dangerous debugging mode" may be enabled by setting the environment variable PGOAUTHDEBUG=UNSAFE. This functionality is provided for ease of local development and testing only. It does several things that you will not want a production system to do:

permits the use of unencrypted HTTP during the OAuth provider exchange

allows the system's trusted CA list to be completely replaced using the PGOAUTHCAFILE environment variable

prints HTTP traffic (containing several critical secrets) to standard error during the OAuth flow

permits the use of zero-second retry intervals, which can cause the client to busy-loop and pointlessly consume CPU

Do not share the output of the OAuth flow traffic with third parties. It contains secrets that can be used to attack your clients and servers.

[16] Performing blocking operations during the PQAUTHDATA_OAUTH_BEARER_TOKEN hook callback will interfere with nonblocking connection APIs such as PQconnectPoll and prevent concurrent connections from making progress. Applications which only ever use the synchronous connection primitives, such as PQconnectdb, may synchronously retrieve a token during the hook instead of implementing the async callback, but they will necessarily be limited to one connection at a time.

Examples:

Example 1 (unknown):

$ psql 'dbname=postgres oauth_issuer=https://example.com oauth_client_id=...'
Visit https://example.com/device and enter the code: ABCD-EFGH

Example 2 (unknown):

void PQsetAuthDataHook(PQauthDataHook_type hook);

Example 3 (unknown):

int hook_fn(PGauthData type, PGconn *conn, void *data);

Example 4 (unknown):

PQauthDataHook_type PQgetAuthDataHook(void);

---

PostgreSQL: Documentation: 18: 7.2. Table Expressions

URL: https://www.postgresql.org/docs/current/queries-table-expressions.html

Contents:

• 7.2. Table Expressions #
  • 7.2.1. The FROM Clause #
  • 7.2.1.1. Joined Tables #
  • Note
  • Note
  • 7.2.1.2. Table and Column Aliases #
  • 7.2.1.3. Subqueries #
  • 7.2.1.4. Table Functions #
  • 7.2.1.5. LATERAL Subqueries #
  • 7.2.2. The WHERE Clause #

A table expression computes a table. The table expression contains a FROM clause that is optionally followed by WHERE, GROUP BY, and HAVING clauses. Trivial table expressions simply refer to a table on disk, a so-called base table, but more complex expressions can be used to modify or combine base tables in various ways.

The optional WHERE, GROUP BY, and HAVING clauses in the table expression specify a pipeline of successive transformations performed on the table derived in the FROM clause. All these transformations produce a virtual table that provides the rows that are passed to the select list to compute the output rows of the query.

The FROM clause derives a table from one or more other tables given in a comma-separated table reference list.

A table reference can be a table name (possibly schema-qualified), or a derived table such as a subquery, a JOIN construct, or complex combinations of these. If more than one table reference is listed in the FROM clause, the tables are cross-joined (that is, the Cartesian product of their rows is formed; see below). The result of the FROM list is an intermediate virtual table that can then be subject to transformations by the WHERE, GROUP BY, and HAVING clauses and is finally the result of the overall table expression.

When a table reference names a table that is the parent of a table inheritance hierarchy, the table reference produces rows of not only that table but all of its descendant tables, unless the key word ONLY precedes the table name. However, the reference produces only the columns that appear in the named table — any columns added in subtables are ignored.

Instead of writing ONLY before the table name, you can write * after the table name to explicitly specify that descendant tables are included. There is no real reason to use this syntax any more, because searching descendant tables is now always the default behavior. However, it is supported for compatibility with older releases.

A joined table is a table derived from two other (real or derived) tables according to the rules of the particular join type. Inner, outer, and cross-joins are available. The general syntax of a joined table is

Joins of all types can be chained together, or nested: either or both T1 and T2 can be joined tables. Parentheses can be used around JOIN clauses to control the join order. In the absence of parentheses, JOIN clauses nest left-to-right.

For every possible combination of rows from T1 and T2 (i.e., a Cartesian product), the joined table will contain a row consisting of all columns in T1 followed by all columns in T2. If the tables have N and M rows respectively, the joined table will have N * M rows.

FROM T1 CROSS JOIN T2 is equivalent to FROM T1 INNER JOIN T2 ON TRUE (see below). It is also equivalent to FROM T1, T2.

This latter equivalence does not hold exactly when more than two tables appear, because JOIN binds more tightly than comma. For example FROM T1 CROSS JOIN T2 INNER JOIN T3 ON condition is not the same as FROM T1, T2 INNER JOIN T3 ON condition because the condition can reference T1 in the first case but not the second.

The words INNER and OUTER are optional in all forms. INNER is the default; LEFT, RIGHT, and FULL imply an outer join.

The join condition is specified in the ON or USING clause, or implicitly by the word NATURAL. The join condition determines which rows from the two source tables are considered to “match”, as explained in detail below.

The possible types of qualified join are:

For each row R1 of T1, the joined table has a row for each row in T2 that satisfies the join condition with R1.

First, an inner join is performed. Then, for each row in T1 that does not satisfy the join condition with any row in T2, a joined row is added with null values in columns of T2. Thus, the joined table always has at least one row for each row in T1.

First, an inner join is performed. Then, for each row in T2 that does not satisfy the join condition with any row in T1, a joined row is added with null values in columns of T1. This is the converse of a left join: the result table will always have a row for each row in T2.

First, an inner join is performed. Then, for each row in T1 that does not satisfy the join condition with any row in T2, a joined row is added with null values in columns of T2. Also, for each row of T2 that does not satisfy the join condition with any row in T1, a joined row with null values in the columns of T1 is added.

The ON clause is the most general kind of join condition: it takes a Boolean value expression of the same kind as is used in a WHERE clause. A pair of rows from T1 and T2 match if the ON expression evaluates to true.

The USING clause is a shorthand that allows you to take advantage of the specific situation where both sides of the join use the same name for the joining column(s). It takes a comma-separated list of the shared column names and forms a join condition that includes an equality comparison for each one. For example, joining T1 and T2 with USING (a, b) produces the join condition ON T1.a = T2.a AND T1.b = T2.b.

Furthermore, the output of JOIN USING suppresses redundant columns: there is no need to print both of the matched columns, since they must have equal values. While JOIN ON produces all columns from T1 followed by all columns from T2, JOIN USING produces one output column for each of the listed column pairs (in the listed order), followed by any remaining columns from T1, followed by any remaining columns from T2.

Finally, NATURAL is a shorthand form of USING: it forms a USING list consisting of all column names that appear in both input tables. As with USING, these columns appear only once in the output table. If there are no common column names, NATURAL JOIN behaves like CROSS JOIN.

USING is reasonably safe from column changes in the joined relations since only the listed columns are combined. NATURAL is considerably more risky since any schema changes to either relation that cause a new matching column name to be present will cause the join to combine that new column as well.

To put this together, assume we have tables t1:

then we get the following results for the various joins:

The join condition specified with ON can also contain conditions that do not relate directly to the join. This can prove useful for some queries but needs to be thought out carefully. For example:

Notice that placing the restriction in the WHERE clause produces a different result:

This is because a restriction placed in the ON clause is processed before the join, while a restriction placed in the WHERE clause is processed after the join. That does not matter with inner joins, but it matters a lot with outer joins.

A temporary name can be given to tables and complex table references to be used for references to the derived table in the rest of the query. This is called a table alias.

To create a table alias, write

The AS key word is optional noise. alias can be any identifier.

A typical application of table aliases is to assign short identifiers to long table names to keep the join clauses readable. For example:

The alias becomes the new name of the table reference so far as the current query is concerned — it is not allowed to refer to the table by the original name elsewhere in the query. Thus, this is not valid:

Table aliases are mainly for notational convenience, but it is necessary to use them when joining a table to itself, e.g.:

Parentheses are used to resolve ambiguities. In the following example, the first statement assigns the alias b to the second instance of my_table, but the second statement assigns the alias to the result of the join:

Another form of table aliasing gives temporary names to the columns of the table, as well as the table itself:

If fewer column aliases are specified than the actual table has columns, the remaining columns are not renamed. This syntax is especially useful for self-joins or subqueries.

When an alias is applied to the output of a JOIN clause, the alias hides the original name(s) within the JOIN. For example:

is not valid; the table alias a is not visible outside the alias c.

Subqueries specifying a derived table must be enclosed in parentheses. They may be assigned a table alias name, and optionally column alias names (as in Section 7.2.1.2). For example:

This example is equivalent to FROM table1 AS alias_name. More interesting cases, which cannot be reduced to a plain join, arise when the subquery involves grouping or aggregation.

A subquery can also be a VALUES list:

Again, a table alias is optional. Assigning alias names to the columns of the VALUES list is optional, but is good practice. For more information see Section 7.7.

According to the SQL standard, a table alias name must be supplied for a subquery. PostgreSQL allows AS and the alias to be omitted, but writing one is good practice in SQL code that might be ported to another system.

Table functions are functions that produce a set of rows, made up of either base data types (scalar types) or composite data types (table rows). They are used like a table, view, or subquery in the FROM clause of a query. Columns returned by table functions can be included in SELECT, JOIN, or WHERE clauses in the same manner as columns of a table, view, or subquery.

Table functions may also be combined using the ROWS FROM syntax, with the results returned in parallel columns; the number of result rows in this case is that of the largest function result, with smaller results padded with null values to match.

If the WITH ORDINALITY clause is specified, an additional column of type bigint will be added to the function result columns. This column numbers the rows of the function result set, starting from 1. (This is a generalization of the SQL-standard syntax for UNNEST ... WITH ORDINALITY.) By default, the ordinal column is called ordinality, but a different column name can be assigned to it using an AS clause.

The special table function UNNEST may be called with any number of array parameters, and it returns a corresponding number of columns, as if UNNEST (Section 9.19) had been called on each parameter separately and combined using the ROWS FROM construct.

If no table_alias is specified, the function name is used as the table name; in the case of a ROWS FROM() construct, the first function's name is used.

If column aliases are not supplied, then for a function returning a base data type, the column name is also the same as the function name. For a function returning a composite type, the result columns get the names of the individual attributes of the type.

In some cases it is useful to define table functions that can return different column sets depending on how they are invoked. To support this, the table function can be declared as returning the pseudo-type record with no OUT parameters. When such a function is used in a query, the expected row structure must be specified in the query itself, so that the system can know how to parse and plan the query. This syntax looks like:

When not using the ROWS FROM() syntax, the column_definition list replaces the column alias list that could otherwise be attached to the FROM item; the names in the column definitions serve as column aliases. When using the ROWS FROM() syntax, a column_definition list can be attached to each member function separately; or if there is only one member function and no WITH ORDINALITY clause, a column_definition list can be written in place of a column alias list following ROWS FROM().

Consider this example:

The dblink function (part of the dblink module) executes a remote query. It is declared to return record since it might be used for any kind of query. The actual column set must be specified in the calling query so that the parser knows, for example, what * should expand to.

This example uses ROWS FROM:

It joins two functions into a single FROM target. json_to_recordset() is instructed to return two columns, the first integer and the second text. The result of generate_series() is used directly. The ORDER BY clause sorts the column values as integers.

Subqueries appearing in FROM can be preceded by the key word LATERAL. This allows them to reference columns provided by preceding FROM items. (Without LATERAL, each subquery is evaluated independently and so cannot cross-reference any other FROM item.)

Table functions appearing in FROM can also be preceded by the key word LATERAL, but for functions the key word is optional; the function's arguments can contain references to columns provided by preceding FROM items in any case.

A LATERAL item can appear at the top level in the FROM list, or within a JOIN tree. In the latter case it can also refer to any items that are on the left-hand side of a JOIN that it is on the right-hand side of.

When a FROM item contains LATERAL cross-references, evaluation proceeds as follows: for each row of the FROM item providing the cross-referenced column(s), or set of rows of multiple FROM items providing the columns, the LATERAL item is evaluated using that row or row set's values of the columns. The resulting row(s) are joined as usual with the rows they were computed from. This is repeated for each row or set of rows from the column source table(s).

A trivial example of LATERAL is

This is not especially useful since it has exactly the same result as the more conventional

LATERAL is primarily useful when the cross-referenced column is necessary for computing the row(s) to be joined. A common application is providing an argument value for a set-returning function. For example, supposing that vertices(polygon) returns the set of vertices of a polygon, we could identify close-together vertices of polygons stored in a table with:

This query could also be written

or in several other equivalent formulations. (As already mentioned, the LATERAL key word is unnecessary in this example, but we use it for clarity.)

It is often particularly handy to LEFT JOIN to a LATERAL subquery, so that source rows will appear in the result even if the LATERAL subquery produces no rows for them. For example, if get_product_names() returns the names of products made by a manufacturer, but some manufacturers in our table currently produce no products, we could find out which ones those are like this:

The syntax of the WHERE clause is

where search_condition is any value expression (see Section 4.2) that returns a value of type boolean.

After the processing of the FROM clause is done, each row of the derived virtual table is checked against the search condition. If the result of the condition is true, the row is kept in the output table, otherwise (i.e., if the result is false or null) it is discarded. The search condition typically references at least one column of the table generated in the FROM clause; this is not required, but otherwise the WHERE clause will be fairly useless.

The join condition of an inner join can be written either in the WHERE clause or in the JOIN clause. For example, these table expressions are equivalent:

Which one of these you use is mainly a matter of style. The JOIN syntax in the FROM clause is probably not as portable to other SQL database management systems, even though it is in the SQL standard. For outer joins there is no choice: they must be done in the FROM clause. The ON or USING clause of an outer join is not equivalent to a WHERE condition, because it results in the addition of rows (for unmatched input rows) as well as the removal of rows in the final result.

Here are some examples of WHERE clauses:

fdt is the table derived in the FROM clause. Rows that do not meet the search condition of the WHERE clause are eliminated from fdt. Notice the use of scalar subqueries as value expressions. Just like any other query, the subqueries can employ complex table expressions. Notice also how fdt is referenced in the subqueries. Qualifying c1 as fdt.c1 is only necessary if c1 is also the name of a column in the derived input table of the subquery. But qualifying the column name adds clarity even when it is not needed. This example shows how the column naming scope of an outer query extends into its inner queries.

After passing the WHERE filter, the derived input table might be subject to grouping, using the GROUP BY clause, and elimination of group rows using the HAVING clause.

The GROUP BY clause is used to group together those rows in a table that have the same values in all the columns listed. The order in which the columns are listed does not matter. The effect is to combine each set of rows having common values into one group row that represents all rows in the group. This is done to eliminate redundancy in the output and/or compute aggregates that apply to these groups. For instance:

In the second query, we could not have written SELECT * FROM test1 GROUP BY x, because there is no single value for the column y that could be associated with each group. The grouped-by columns can be referenced in the select list since they have a single value in each group.

In general, if a table is grouped, columns that are not listed in GROUP BY cannot be referenced except in aggregate expressions. An example with aggregate expressions is:

Here sum is an aggregate function that computes a single value over the entire group. More information about the available aggregate functions can be found in Section 9.21.

Grouping without aggregate expressions effectively calculates the set of distinct values in a column. This can also be achieved using the DISTINCT clause (see Section 7.3.3).

Here is another example: it calculates the total sales for each product (rather than the total sales of all products):

In this example, the columns product_id, p.name, and p.price must be in the GROUP BY clause since they are referenced in the query select list (but see below). The column s.units does not have to be in the GROUP BY list since it is only used in an aggregate expression (sum(...)), which represents the sales of a product. For each product, the query returns a summary row about all sales of the product.

If the products table is set up so that, say, product_id is the primary key, then it would be enough to group by product_id in the above example, since name and price would be functionally dependent on the product ID, and so there would be no ambiguity about which name and price value to return for each product ID group.

In strict SQL, GROUP BY can only group by columns of the source table but PostgreSQL extends this to also allow GROUP BY to group by columns in the select list. Grouping by value expressions instead of simple column names is also allowed.

If a table has been grouped using GROUP BY, but only certain groups are of interest, the HAVING clause can be used, much like a WHERE clause, to eliminate groups from the result. The syntax is:

Expressions in the HAVING clause can refer both to grouped expressions and to ungrouped expressions (which necessarily involve an aggregate function).

Again, a more realistic example:

In the example above, the WHERE clause is selecting rows by a column that is not grouped (the expression is only true for sales during the last four weeks), while the HAVING clause restricts the output to groups with total gross sales over 5000. Note that the aggregate expressions do not necessarily need to be the same in all parts of the query.

If a query contains aggregate function calls, but no GROUP BY clause, grouping still occurs: the result is a single group row (or perhaps no rows at all, if the single row is then eliminated by HAVING). The same is true if it contains a HAVING clause, even without any aggregate function calls or GROUP BY clause.

More complex grouping operations than those described above are possible using the concept of grouping sets. The data selected by the FROM and WHERE clauses is grouped separately by each specified grouping set, aggregates computed for each group just as for simple GROUP BY clauses, and then the results returned. For example:

Each sublist of GROUPING SETS may specify zero or more columns or expressions and is interpreted the same way as though it were directly in the GROUP BY clause. An empty grouping set means that all rows are aggregated down to a single group (which is output even if no input rows were present), as described above for the case of aggregate functions with no GROUP BY clause.

References to the grouping columns or expressions are replaced by null values in result rows for grouping sets in which those columns do not appear. To distinguish which grouping a particular output row resulted from, see Table 9.66.

A shorthand notation is provided for specifying two common types of grouping set. A clause of the form

represents the given list of expressions and all prefixes of the list including the empty list; thus it is equivalent to

This is commonly used for analysis over hierarchical data; e.g., total salary by department, division, and company-wide total.

represents the given list and all of its possible subsets (i.e., the power set). Thus

The individual elements of a CUBE or ROLLUP clause may be either individual expressions, or sublists of elements in parentheses. In the latter case, the sublists are treated as single units for the purposes of generating the individual grouping sets. For example:

The CUBE and ROLLUP constructs can be used either directly in the GROUP BY clause, or nested inside a GROUPING SETS clause. If one GROUPING SETS clause is nested inside another, the effect is the same as if all the elements of the inner clause had been written directly in the outer clause.

If multiple grouping items are specified in a single GROUP BY clause, then the final list of grouping sets is the Cartesian product of the individual items. For example:

When specifying multiple grouping items together, the final set of grouping sets might contain duplicates. For example:

If these duplicates are undesirable, they can be removed using the DISTINCT clause directly on the GROUP BY. Therefore:

This is not the same as using SELECT DISTINCT because the output rows may still contain duplicates. If any of the ungrouped columns contains NULL, it will be indistinguishable from the NULL used when that same column is grouped.

The construct (a, b) is normally recognized in expressions as a row constructor. Within the GROUP BY clause, this does not apply at the top levels of expressions, and (a, b) is parsed as a list of expressions as described above. If for some reason you need a row constructor in a grouping expression, use ROW(a, b).

If the query contains any window functions (see Section 3.5, Section 9.22 and Section 4.2.8), these functions are evaluated after any grouping, aggregation, and HAVING filtering is performed. That is, if the query uses any aggregates, GROUP BY, or HAVING, then the rows seen by the window functions are the group rows instead of the original table rows from FROM/WHERE.

When multiple window functions are used, all the window functions having equivalent PARTITION BY and ORDER BY clauses in their window definitions are guaranteed to see the same ordering of the input rows, even if the ORDER BY does not uniquely determine the ordering. However, no guarantees are made about the evaluation of functions having different PARTITION BY or ORDER BY specifications. (In such cases a sort step is typically required between the passes of window function evaluations, and the sort is not guaranteed to preserve ordering of rows that its ORDER BY sees as equivalent.)

Currently, window functions always require presorted data, and so the query output will be ordered according to one or another of the window functions' PARTITION BY/ORDER BY clauses. It is not recommended to rely on this, however. Use an explicit top-level ORDER BY clause if you want to be sure the results are sorted in a particular way.

Examples:

Example 1 (unknown):

FROM table_reference [, table_reference [, ...]]

Example 2 (unknown):

T1 join_type T2 [ join_condition ]

Example 3 (unknown):

T1 CROSS JOIN T2

Example 4 (unknown):

T1 { [INNER] | { LEFT | RIGHT | FULL } [OUTER] } JOIN T2 ON boolean_expression
T1 { [INNER] | { LEFT | RIGHT | FULL } [OUTER] } JOIN T2 USING ( join column list )
T1 NATURAL { [INNER] | { LEFT | RIGHT | FULL } [OUTER] } JOIN T2

---

PostgreSQL: Documentation: 18: 34.2. Managing Database Connections

URL: https://www.postgresql.org/docs/current/ecpg-connect.html

Contents:

• 34.2. Managing Database Connections #
  • 34.2.1. Connecting to the Database Server #
  • 34.2.2. Choosing a Connection #
  • 34.2.3. Closing a Connection #

This section describes how to open, close, and switch database connections.

One connects to a database using the following statement:

The target can be specified in the following ways:

The connection target DEFAULT initiates a connection to the default database under the default user name. No separate user name or connection name can be specified in that case.

If you specify the connection target directly (that is, not as a string literal or variable reference), then the components of the target are passed through normal SQL parsing; this means that, for example, the hostname must look like one or more SQL identifiers separated by dots, and those identifiers will be case-folded unless double-quoted. Values of any options must be SQL identifiers, integers, or variable references. Of course, you can put nearly anything into an SQL identifier by double-quoting it. In practice, it is probably less error-prone to use a (single-quoted) string literal or a variable reference than to write the connection target directly.

There are also different ways to specify the user name:

As above, the parameters username and password can be an SQL identifier, an SQL string literal, or a reference to a character variable.

If the connection target includes any options, those consist of keyword=value specifications separated by ampersands (&). The allowed key words are the same ones recognized by libpq (see Section 32.1.2). Spaces are ignored before any keyword or value, though not within or after one. Note that there is no way to write & within a value.

Notice that when specifying a socket connection (with the unix: prefix), the host name must be exactly localhost. To select a non-default socket directory, write the directory's pathname as the value of a host option in the options part of the target.

The connection-name is used to handle multiple connections in one program. It can be omitted if a program uses only one connection. The most recently opened connection becomes the current connection, which is used by default when an SQL statement is to be executed (see later in this chapter).

Here are some examples of CONNECT statements:

The last example makes use of the feature referred to above as character variable references. You will see in later sections how C variables can be used in SQL statements when you prefix them with a colon.

Be advised that the format of the connection target is not specified in the SQL standard. So if you want to develop portable applications, you might want to use something based on the last example above to encapsulate the connection target string somewhere.

If untrusted users have access to a database that has not adopted a secure schema usage pattern, begin each session by removing publicly-writable schemas from search_path. For example, add options=-c search_path= to options, or issue EXEC SQL SELECT pg_catalog.set_config('search_path', '', false); after connecting. This consideration is not specific to ECPG; it applies to every interface for executing arbitrary SQL commands.

SQL statements in embedded SQL programs are by default executed on the current connection, that is, the most recently opened one. If an application needs to manage multiple connections, then there are three ways to handle this.

The first option is to explicitly choose a connection for each SQL statement, for example:

This option is particularly suitable if the application needs to use several connections in mixed order.

If your application uses multiple threads of execution, they cannot share a connection concurrently. You must either explicitly control access to the connection (using mutexes) or use a connection for each thread.

The second option is to execute a statement to switch the current connection. That statement is:

This option is particularly convenient if many statements are to be executed on the same connection.

Here is an example program managing multiple database connections:

This example would produce this output:

The third option is to declare an SQL identifier linked to the connection, for example:

Once you link an SQL identifier to a connection, you execute dynamic SQL without an AT clause. Note that this option behaves like preprocessor directives, therefore the link is enabled only in the file.

Here is an example program using this option:

This example would produce this output, even if the default connection is testdb:

To close a connection, use the following statement:

The connection can be specified in the following ways:

If no connection name is specified, the current connection is closed.

It is good style that an application always explicitly disconnect from every connection it opened.

Examples:

Example 1 (unknown):

EXEC SQL CONNECT TO target [AS connection-name] [USER user-name];

Example 2 (javascript):

EXEC SQL CONNECT TO mydb@sql.mydomain.com;

EXEC SQL CONNECT TO tcp:postgresql://sql.mydomain.com/mydb AS myconnection USER john;

EXEC SQL BEGIN DECLARE SECTION;
const char *target = "mydb@sql.mydomain.com";
const char *user = "john";
const char *passwd = "secret";
EXEC SQL END DECLARE SECTION;
 ...
EXEC SQL CONNECT TO :target USER :user USING :passwd;
/* or EXEC SQL CONNECT TO :target USER :user/:passwd; */

Example 3 (unknown):

EXEC SQL AT connection-name SELECT ...;

Example 4 (unknown):

EXEC SQL SET CONNECTION connection-name;

---

PostgreSQL: Documentation: 18: 13.2. Transaction Isolation

URL: https://www.postgresql.org/docs/current/transaction-iso.html

Contents:

• 13.2. Transaction Isolation #
  • Important
  • 13.2.1. Read Committed Isolation Level #
  • 13.2.2. Repeatable Read Isolation Level #
  • Note
  • 13.2.3. Serializable Isolation Level #

The SQL standard defines four levels of transaction isolation. The most strict is Serializable, which is defined by the standard in a paragraph which says that any concurrent execution of a set of Serializable transactions is guaranteed to produce the same effect as running them one at a time in some order. The other three levels are defined in terms of phenomena, resulting from interaction between concurrent transactions, which must not occur at each level. The standard notes that due to the definition of Serializable, none of these phenomena are possible at that level. (This is hardly surprising -- if the effect of the transactions must be consistent with having been run one at a time, how could you see any phenomena caused by interactions?)

The phenomena which are prohibited at various levels are:

A transaction reads data written by a concurrent uncommitted transaction.

A transaction re-reads data it has previously read and finds that data has been modified by another transaction (that committed since the initial read).

A transaction re-executes a query returning a set of rows that satisfy a search condition and finds that the set of rows satisfying the condition has changed due to another recently-committed transaction.

The result of successfully committing a group of transactions is inconsistent with all possible orderings of running those transactions one at a time.

The SQL standard and PostgreSQL-implemented transaction isolation levels are described in Table 13.1.

Table 13.1. Transaction Isolation Levels

In PostgreSQL, you can request any of the four standard transaction isolation levels, but internally only three distinct isolation levels are implemented, i.e., PostgreSQL's Read Uncommitted mode behaves like Read Committed. This is because it is the only sensible way to map the standard isolation levels to PostgreSQL's multiversion concurrency control architecture.

The table also shows that PostgreSQL's Repeatable Read implementation does not allow phantom reads. This is acceptable under the SQL standard because the standard specifies which anomalies must not occur at certain isolation levels; higher guarantees are acceptable. The behavior of the available isolation levels is detailed in the following subsections.

To set the transaction isolation level of a transaction, use the command SET TRANSACTION.

Some PostgreSQL data types and functions have special rules regarding transactional behavior. In particular, changes made to a sequence (and therefore the counter of a column declared using serial) are immediately visible to all other transactions and are not rolled back if the transaction that made the changes aborts. See Section 9.17 and Section 8.1.4.

Read Committed is the default isolation level in PostgreSQL. When a transaction uses this isolation level, a SELECT query (without a FOR UPDATE/SHARE clause) sees only data committed before the query began; it never sees either uncommitted data or changes committed by concurrent transactions during the query's execution. In effect, a SELECT query sees a snapshot of the database as of the instant the query begins to run. However, SELECT does see the effects of previous updates executed within its own transaction, even though they are not yet committed. Also note that two successive SELECT commands can see different data, even though they are within a single transaction, if other transactions commit changes after the first SELECT starts and before the second SELECT starts.

UPDATE, DELETE, SELECT FOR UPDATE, and SELECT FOR SHARE commands behave the same as SELECT in terms of searching for target rows: they will only find target rows that were committed as of the command start time. However, such a target row might have already been updated (or deleted or locked) by another concurrent transaction by the time it is found. In this case, the would-be updater will wait for the first updating transaction to commit or roll back (if it is still in progress). If the first updater rolls back, then its effects are negated and the second updater can proceed with updating the originally found row. If the first updater commits, the second updater will ignore the row if the first updater deleted it, otherwise it will attempt to apply its operation to the updated version of the row. The search condition of the command (the WHERE clause) is re-evaluated to see if the updated version of the row still matches the search condition. If so, the second updater proceeds with its operation using the updated version of the row. In the case of SELECT FOR UPDATE and SELECT FOR SHARE, this means it is the updated version of the row that is locked and returned to the client.

INSERT with an ON CONFLICT DO UPDATE clause behaves similarly. In Read Committed mode, each row proposed for insertion will either insert or update. Unless there are unrelated errors, one of those two outcomes is guaranteed. If a conflict originates in another transaction whose effects are not yet visible to the INSERT, the UPDATE clause will affect that row, even though possibly no version of that row is conventionally visible to the command.

INSERT with an ON CONFLICT DO NOTHING clause may have insertion not proceed for a row due to the outcome of another transaction whose effects are not visible to the INSERT snapshot. Again, this is only the case in Read Committed mode.

MERGE allows the user to specify various combinations of INSERT, UPDATE and DELETE subcommands. A MERGE command with both INSERT and UPDATE subcommands looks similar to INSERT with an ON CONFLICT DO UPDATE clause but does not guarantee that either INSERT or UPDATE will occur. If MERGE attempts an UPDATE or DELETE and the row is concurrently updated but the join condition still passes for the current target and the current source tuple, then MERGE will behave the same as the UPDATE or DELETE commands and perform its action on the updated version of the row. However, because MERGE can specify several actions and they can be conditional, the conditions for each action are re-evaluated on the updated version of the row, starting from the first action, even if the action that had originally matched appears later in the list of actions. On the other hand, if the row is concurrently updated so that the join condition fails, then MERGE will evaluate the command's NOT MATCHED BY SOURCE and NOT MATCHED [BY TARGET] actions next, and execute the first one of each kind that succeeds. If the row is concurrently deleted, then MERGE will evaluate the command's NOT MATCHED [BY TARGET] actions, and execute the first one that succeeds. If MERGE attempts an INSERT and a unique index is present and a duplicate row is concurrently inserted, then a uniqueness violation error is raised; MERGE does not attempt to avoid such errors by restarting evaluation of MATCHED conditions.

Because of the above rules, it is possible for an updating command to see an inconsistent snapshot: it can see the effects of concurrent updating commands on the same rows it is trying to update, but it does not see effects of those commands on other rows in the database. This behavior makes Read Committed mode unsuitable for commands that involve complex search conditions; however, it is just right for simpler cases. For example, consider transferring $100 from one account to another:

If another transaction concurrently tries to change the balance of account 7534, we clearly want the second statement to start with the updated version of the account's row. Because each command is affecting only a predetermined row, letting it see the updated version of the row does not create any troublesome inconsistency.

More complex usage can produce undesirable results in Read Committed mode. For example, consider a DELETE command operating on data that is being both added and removed from its restriction criteria by another command, e.g., assume website is a two-row table with website.hits equaling 9 and 10:

The DELETE will have no effect even though there is a website.hits = 10 row before and after the UPDATE. This occurs because the pre-update row value 9 is skipped, and when the UPDATE completes and DELETE obtains a lock, the new row value is no longer 10 but 11, which no longer matches the criteria.

Because Read Committed mode starts each command with a new snapshot that includes all transactions committed up to that instant, subsequent commands in the same transaction will see the effects of the committed concurrent transaction in any case. The point at issue above is whether or not a single command sees an absolutely consistent view of the database.

The partial transaction isolation provided by Read Committed mode is adequate for many applications, and this mode is fast and simple to use; however, it is not sufficient for all cases. Applications that do complex queries and updates might require a more rigorously consistent view of the database than Read Committed mode provides.

The Repeatable Read isolation level only sees data committed before the transaction began; it never sees either uncommitted data or changes committed by concurrent transactions during the transaction's execution. (However, each query does see the effects of previous updates executed within its own transaction, even though they are not yet committed.) This is a stronger guarantee than is required by the SQL standard for this isolation level, and prevents all of the phenomena described in Table 13.1 except for serialization anomalies. As mentioned above, this is specifically allowed by the standard, which only describes the minimum protections each isolation level must provide.

This level is different from Read Committed in that a query in a repeatable read transaction sees a snapshot as of the start of the first non-transaction-control statement in the transaction, not as of the start of the current statement within the transaction. Thus, successive SELECT commands within a single transaction see the same data, i.e., they do not see changes made by other transactions that committed after their own transaction started.

Applications using this level must be prepared to retry transactions due to serialization failures.

UPDATE, DELETE, MERGE, SELECT FOR UPDATE, and SELECT FOR SHARE commands behave the same as SELECT in terms of searching for target rows: they will only find target rows that were committed as of the transaction start time. However, such a target row might have already been updated (or deleted or locked) by another concurrent transaction by the time it is found. In this case, the repeatable read transaction will wait for the first updating transaction to commit or roll back (if it is still in progress). If the first updater rolls back, then its effects are negated and the repeatable read transaction can proceed with updating the originally found row. But if the first updater commits (and actually updated or deleted the row, not just locked it) then the repeatable read transaction will be rolled back with the message

because a repeatable read transaction cannot modify or lock rows changed by other transactions after the repeatable read transaction began.

When an application receives this error message, it should abort the current transaction and retry the whole transaction from the beginning. The second time through, the transaction will see the previously-committed change as part of its initial view of the database, so there is no logical conflict in using the new version of the row as the starting point for the new transaction's update.

Note that only updating transactions might need to be retried; read-only transactions will never have serialization conflicts.

The Repeatable Read mode provides a rigorous guarantee that each transaction sees a completely stable view of the database. However, this view will not necessarily always be consistent with some serial (one at a time) execution of concurrent transactions of the same level. For example, even a read-only transaction at this level may see a control record updated to show that a batch has been completed but not see one of the detail records which is logically part of the batch because it read an earlier revision of the control record. Attempts to enforce business rules by transactions running at this isolation level are not likely to work correctly without careful use of explicit locks to block conflicting transactions.

The Repeatable Read isolation level is implemented using a technique known in academic database literature and in some other database products as Snapshot Isolation. Differences in behavior and performance may be observed when compared with systems that use a traditional locking technique that reduces concurrency. Some other systems may even offer Repeatable Read and Snapshot Isolation as distinct isolation levels with different behavior. The permitted phenomena that distinguish the two techniques were not formalized by database researchers until after the SQL standard was developed, and are outside the scope of this manual. For a full treatment, please see [berenson95].

Prior to PostgreSQL version 9.1, a request for the Serializable transaction isolation level provided exactly the same behavior described here. To retain the legacy Serializable behavior, Repeatable Read should now be requested.

The Serializable isolation level provides the strictest transaction isolation. This level emulates serial transaction execution for all committed transactions; as if transactions had been executed one after another, serially, rather than concurrently. However, like the Repeatable Read level, applications using this level must be prepared to retry transactions due to serialization failures. In fact, this isolation level works exactly the same as Repeatable Read except that it also monitors for conditions which could make execution of a concurrent set of serializable transactions behave in a manner inconsistent with all possible serial (one at a time) executions of those transactions. This monitoring does not introduce any blocking beyond that present in repeatable read, but there is some overhead to the monitoring, and detection of the conditions which could cause a serialization anomaly will trigger a serialization failure.

As an example, consider a table mytab, initially containing:

Suppose that serializable transaction A computes:

and then inserts the result (30) as the value in a new row with class = 2. Concurrently, serializable transaction B computes:

and obtains the result 300, which it inserts in a new row with class = 1. Then both transactions try to commit. If either transaction were running at the Repeatable Read isolation level, both would be allowed to commit; but since there is no serial order of execution consistent with the result, using Serializable transactions will allow one transaction to commit and will roll the other back with this message:

This is because if A had executed before B, B would have computed the sum 330, not 300, and similarly the other order would have resulted in a different sum computed by A.

When relying on Serializable transactions to prevent anomalies, it is important that any data read from a permanent user table not be considered valid until the transaction which read it has successfully committed. This is true even for read-only transactions, except that data read within a deferrable read-only transaction is known to be valid as soon as it is read, because such a transaction waits until it can acquire a snapshot guaranteed to be free from such problems before starting to read any data. In all other cases applications must not depend on results read during a transaction that later aborted; instead, they should retry the transaction until it succeeds.

To guarantee true serializability PostgreSQL uses predicate locking, which means that it keeps locks which allow it to determine when a write would have had an impact on the result of a previous read from a concurrent transaction, had it run first. In PostgreSQL these locks do not cause any blocking and therefore can not play any part in causing a deadlock. They are used to identify and flag dependencies among concurrent Serializable transactions which in certain combinations can lead to serialization anomalies. In contrast, a Read Committed or Repeatable Read transaction which wants to ensure data consistency may need to take out a lock on an entire table, which could block other users attempting to use that table, or it may use SELECT FOR UPDATE or SELECT FOR SHARE which not only can block other transactions but cause disk access.

Predicate locks in PostgreSQL, like in most other database systems, are based on data actually accessed by a transaction. These will show up in the pg_locks system view with a mode of SIReadLock. The particular locks acquired during execution of a query will depend on the plan used by the query, and multiple finer-grained locks (e.g., tuple locks) may be combined into fewer coarser-grained locks (e.g., page locks) during the course of the transaction to prevent exhaustion of the memory used to track the locks. A READ ONLY transaction may be able to release its SIRead locks before completion, if it detects that no conflicts can still occur which could lead to a serialization anomaly. In fact, READ ONLY transactions will often be able to establish that fact at startup and avoid taking any predicate locks. If you explicitly request a SERIALIZABLE READ ONLY DEFERRABLE transaction, it will block until it can establish this fact. (This is the only case where Serializable transactions block but Repeatable Read transactions don't.) On the other hand, SIRead locks often need to be kept past transaction commit, until overlapping read write transactions complete.

Consistent use of Serializable transactions can simplify development. The guarantee that any set of successfully committed concurrent Serializable transactions will have the same effect as if they were run one at a time means that if you can demonstrate that a single transaction, as written, will do the right thing when run by itself, you can have confidence that it will do the right thing in any mix of Serializable transactions, even without any information about what those other transactions might do, or it will not successfully commit. It is important that an environment which uses this technique have a generalized way of handling serialization failures (which always return with an SQLSTATE value of '40001'), because it will be very hard to predict exactly which transactions might contribute to the read/write dependencies and need to be rolled back to prevent serialization anomalies. The monitoring of read/write dependencies has a cost, as does the restart of transactions which are terminated with a serialization failure, but balanced against the cost and blocking involved in use of explicit locks and SELECT FOR UPDATE or SELECT FOR SHARE, Serializable transactions are the best performance choice for some environments.

While PostgreSQL's Serializable transaction isolation level only allows concurrent transactions to commit if it can prove there is a serial order of execution that would produce the same effect, it doesn't always prevent errors from being raised that would not occur in true serial execution. In particular, it is possible to see unique constraint violations caused by conflicts with overlapping Serializable transactions even after explicitly checking that the key isn't present before attempting to insert it. This can be avoided by making sure that all Serializable transactions that insert potentially conflicting keys explicitly check if they can do so first. For example, imagine an application that asks the user for a new key and then checks that it doesn't exist already by trying to select it first, or generates a new key by selecting the maximum existing key and adding one. If some Serializable transactions insert new keys directly without following this protocol, unique constraints violations might be reported even in cases where they could not occur in a serial execution of the concurrent transactions.

For optimal performance when relying on Serializable transactions for concurrency control, these issues should be considered:

Declare transactions as READ ONLY when possible.

Control the number of active connections, using a connection pool if needed. This is always an important performance consideration, but it can be particularly important in a busy system using Serializable transactions.

Don't put more into a single transaction than needed for integrity purposes.

Don't leave connections dangling “idle in transaction” longer than necessary. The configuration parameter idle_in_transaction_session_timeout may be used to automatically disconnect lingering sessions.

Eliminate explicit locks, SELECT FOR UPDATE, and SELECT FOR SHARE where no longer needed due to the protections automatically provided by Serializable transactions.

When the system is forced to combine multiple page-level predicate locks into a single relation-level predicate lock because the predicate lock table is short of memory, an increase in the rate of serialization failures may occur. You can avoid this by increasing max_pred_locks_per_transaction, max_pred_locks_per_relation, and/or max_pred_locks_per_page.

A sequential scan will always necessitate a relation-level predicate lock. This can result in an increased rate of serialization failures. It may be helpful to encourage the use of index scans by reducing random_page_cost and/or increasing cpu_tuple_cost. Be sure to weigh any decrease in transaction rollbacks and restarts against any overall change in query execution time.

The Serializable isolation level is implemented using a technique known in academic database literature as Serializable Snapshot Isolation, which builds on Snapshot Isolation by adding checks for serialization anomalies. Some differences in behavior and performance may be observed when compared with other systems that use a traditional locking technique. Please see [ports12] for detailed information.

Examples:

Example 1 (unknown):

BEGIN;
UPDATE accounts SET balance = balance + 100.00 WHERE acctnum = 12345;
UPDATE accounts SET balance = balance - 100.00 WHERE acctnum = 7534;
COMMIT;

Example 2 (unknown):

BEGIN;
UPDATE website SET hits = hits + 1;
-- run from another session:  DELETE FROM website WHERE hits = 10;
COMMIT;

Example 3 (unknown):

ERROR:  could not serialize access due to concurrent update

Example 4 (unknown):

class | value
-------+-------
     1 |    10
     1 |    20
     2 |   100
     2 |   200

---

PostgreSQL: Documentation: 18: Chapter 63. Index Access Method Interface Definition

URL: https://www.postgresql.org/docs/current/indexam.html

Contents:

• Chapter 63. Index Access Method Interface Definition

This chapter defines the interface between the core PostgreSQL system and index access methods, which manage individual index types. The core system knows nothing about indexes beyond what is specified here, so it is possible to develop entirely new index types by writing add-on code.

All indexes in PostgreSQL are what are known technically as secondary indexes; that is, the index is physically separate from the table file that it describes. Each index is stored as its own physical relation and so is described by an entry in the pg_class catalog. The contents of an index are entirely under the control of its index access method. In practice, all index access methods divide indexes into standard-size pages so that they can use the regular storage manager and buffer manager to access the index contents. (All the existing index access methods furthermore use the standard page layout described in Section 66.6, and most use the same format for index tuple headers; but these decisions are not forced on an access method.)

An index is effectively a mapping from some data key values to tuple identifiers, or TIDs, of row versions (tuples) in the index's parent table. A TID consists of a block number and an item number within that block (see Section 66.6). This is sufficient information to fetch a particular row version from the table. Indexes are not directly aware that under MVCC, there might be multiple extant versions of the same logical row; to an index, each tuple is an independent object that needs its own index entry. Thus, an update of a row always creates all-new index entries for the row, even if the key values did not change. (HOT tuples are an exception to this statement; but indexes do not deal with those, either.) Index entries for dead tuples are reclaimed (by vacuuming) when the dead tuples themselves are reclaimed.

---

PostgreSQL: Documentation: 18: 19.17. Developer Options

URL: https://www.postgresql.org/docs/current/runtime-config-developer.html

Contents:

• 19.17. Developer Options #

The following parameters are intended for developer testing, and should never be used on a production database. However, some of them can be used to assist with the recovery of severely damaged databases. As such, they have been excluded from the sample postgresql.conf file. Note that many of these parameters require special source compilation flags to work at all.

Allows tablespaces to be created as directories inside pg_tblspc, when an empty location string is provided to the CREATE TABLESPACE command. This is intended to allow testing replication scenarios where primary and standby servers are running on the same machine. Such directories are likely to confuse backup tools that expect to find only symbolic links in that location. Only superusers and users with the appropriate SET privilege can change this setting.

Allows modification of the structure of system tables as well as certain other risky actions on system tables. This is otherwise not allowed even for superusers. Ill-advised use of this setting can cause irretrievable data loss or seriously corrupt the database system. Only superusers and users with the appropriate SET privilege can change this setting.

This parameter contains a comma-separated list of C function names. If an error is raised and the name of the internal C function where the error happens matches a value in the list, then a backtrace is written to the server log together with the error message. This can be used to debug specific areas of the source code.

Backtrace support is not available on all platforms, and the quality of the backtraces depends on compilation options.

Only superusers and users with the appropriate SET privilege can change this setting.

Enabling this forces all parse and plan trees to be passed through copyObject(), to facilitate catching errors and omissions in copyObject(). The default is off.

This parameter is only available when DEBUG_NODE_TESTS_ENABLED was defined at compile time (which happens automatically when using the configure option --enable-cassert).

When set to 1, each system catalog cache entry is invalidated at the first possible opportunity, whether or not anything that would render it invalid really occurred. Caching of system catalogs is effectively disabled as a result, so the server will run extremely slowly. Higher values run the cache invalidation recursively, which is even slower and only useful for testing the caching logic itself. The default value of 0 selects normal catalog caching behavior.

This parameter can be very helpful when trying to trigger hard-to-reproduce bugs involving concurrent catalog changes, but it is otherwise rarely needed. See the source code files inval.c and pg_config_manual.h for details.

This parameter is supported when DISCARD_CACHES_ENABLED was defined at compile time (which happens automatically when using the configure option --enable-cassert). In production builds, its value will always be 0 and attempts to set it to another value will raise an error.

Ask the kernel to minimize caching effects for relation data and WAL files using O_DIRECT (most Unix-like systems), F_NOCACHE (macOS) or FILE_FLAG_NO_BUFFERING (Windows).

May be set to an empty string (the default) to disable use of direct I/O, or a comma-separated list of operations that should use direct I/O. The valid options are data for main data files, wal for WAL files, and wal_init for WAL files when being initially allocated.

Some operating systems and file systems do not support direct I/O, so non-default settings may be rejected at startup or cause errors.

Currently this feature reduces performance, and is intended for developer testing only.

Allows the use of parallel queries for testing purposes even in cases where no performance benefit is expected. The allowed values of debug_parallel_query are off (use parallel mode only when it is expected to improve performance), on (force parallel query for all queries for which it is thought to be safe), and regress (like on, but with additional behavior changes as explained below).

More specifically, setting this value to on will add a Gather node to the top of any query plan for which this appears to be safe, so that the query runs inside of a parallel worker. Even when a parallel worker is not available or cannot be used, operations such as starting a subtransaction that would be prohibited in a parallel query context will be prohibited unless the planner believes that this will cause the query to fail. If failures or unexpected results occur when this option is set, some functions used by the query may need to be marked PARALLEL UNSAFE (or, possibly, PARALLEL RESTRICTED).

Setting this value to regress has all of the same effects as setting it to on plus some additional effects that are intended to facilitate automated regression testing. Normally, messages from a parallel worker include a context line indicating that, but a setting of regress suppresses this line so that the output is the same as in non-parallel execution. Also, the Gather nodes added to plans by this setting are hidden in EXPLAIN output so that the output matches what would be obtained if this setting were turned off.

Enabling this forces all raw parse trees for DML statements to be scanned by raw_expression_tree_walker(), to facilitate catching errors and omissions in that function. The default is off.

This parameter is only available when DEBUG_NODE_TESTS_ENABLED was defined at compile time (which happens automatically when using the configure option --enable-cassert).

Enabling this forces all parse and plan trees to be passed through outfuncs.c/readfuncs.c, to facilitate catching errors and omissions in those modules. The default is off.

This parameter is only available when DEBUG_NODE_TESTS_ENABLED was defined at compile time (which happens automatically when using the configure option --enable-cassert).

Ignore system indexes when reading system tables (but still update the indexes when modifying the tables). This is useful when recovering from damaged system indexes. This parameter cannot be changed after session start.

The amount of time to delay when a new server process is started, after it conducts the authentication procedure. This is intended to give developers an opportunity to attach to the server process with a debugger. If this value is specified without units, it is taken as seconds. A value of zero (the default) disables the delay. This parameter cannot be changed after session start.

The amount of time to delay just after a new server process is forked, before it conducts the authentication procedure. This is intended to give developers an opportunity to attach to the server process with a debugger to trace down misbehavior in authentication. If this value is specified without units, it is taken as seconds. A value of zero (the default) disables the delay. This parameter can only be set in the postgresql.conf file or on the server command line.

Generates a great amount of debugging output for the LISTEN and NOTIFY commands. client_min_messages or log_min_messages must be DEBUG1 or lower to send this output to the client or server logs, respectively.

If on, emit information about resource usage during sort operations.

If on, emit information about lock usage. Information dumped includes the type of lock operation, the type of lock and the unique identifier of the object being locked or unlocked. Also included are bit masks for the lock types already granted on this object as well as for the lock types awaited on this object. For each lock type a count of the number of granted locks and waiting locks is also dumped as well as the totals. An example of the log file output is shown here:

Details of the structure being dumped may be found in src/include/storage/lock.h.

This parameter is only available if the LOCK_DEBUG macro was defined when PostgreSQL was compiled.

If on, emit information about lightweight lock usage. Lightweight locks are intended primarily to provide mutual exclusion of access to shared-memory data structures.

This parameter is only available if the LOCK_DEBUG macro was defined when PostgreSQL was compiled.

If on, emit information about user lock usage. Output is the same as for trace_locks, only for advisory locks.

This parameter is only available if the LOCK_DEBUG macro was defined when PostgreSQL was compiled.

If set, do not trace locks for tables below this OID (used to avoid output on system tables).

This parameter is only available if the LOCK_DEBUG macro was defined when PostgreSQL was compiled.

Unconditionally trace locks on this table (OID).

This parameter is only available if the LOCK_DEBUG macro was defined when PostgreSQL was compiled.

If set, dumps information about all current locks when a deadlock timeout occurs.

This parameter is only available if the LOCK_DEBUG macro was defined when PostgreSQL was compiled.

If set, logs system resource usage statistics (memory and CPU) on various B-tree operations.

This parameter is only available if the BTREE_BUILD_STATS macro was defined when PostgreSQL was compiled.

This parameter is intended to be used to check for bugs in the WAL redo routines. When enabled, full-page images of any buffers modified in conjunction with the WAL record are added to the record. If the record is subsequently replayed, the system will first apply each record and then test whether the buffers modified by the record match the stored images. In certain cases (such as hint bits), minor variations are acceptable, and will be ignored. Any unexpected differences will result in a fatal error, terminating recovery.

The default value of this setting is the empty string, which disables the feature. It can be set to all to check all records, or to a comma-separated list of resource managers to check only records originating from those resource managers. Currently, the supported resource managers are heap, heap2, btree, hash, gin, gist, sequence, spgist, brin, and generic. Extensions may define additional resource managers. Only superusers and users with the appropriate SET privilege can change this setting.

If on, emit WAL-related debugging output. This parameter is only available if the WAL_DEBUG macro was defined when PostgreSQL was compiled.

Only has effect if -k are enabled.

Detection of a checksum failure during a read normally causes PostgreSQL to report an error, aborting the current transaction. Setting ignore_checksum_failure to on causes the system to ignore the failure (but still report a warning), and continue processing. This behavior may cause crashes, propagate or hide corruption, or other serious problems. However, it may allow you to get past the error and retrieve undamaged tuples that might still be present in the table if the block header is still sane. If the header is corrupt an error will be reported even if this option is enabled. The default setting is off. Only superusers and users with the appropriate SET privilege can change this setting.

Detection of a damaged page header normally causes PostgreSQL to report an error, aborting the current transaction. Setting zero_damaged_pages to on causes the system to instead report a warning, zero out the damaged page in memory, and continue processing. This behavior will destroy data, namely all the rows on the damaged page. However, it does allow you to get past the error and retrieve rows from any undamaged pages that might be present in the table. It is useful for recovering data if corruption has occurred due to a hardware or software error. You should generally not set this on until you have given up hope of recovering data from the damaged pages of a table. Zeroed-out pages are not forced to disk so it is recommended to recreate the table or the index before turning this parameter off again. The default setting is off. Only superusers and users with the appropriate SET privilege can change this setting.

If set to off (the default), detection of WAL records having references to invalid pages during recovery causes PostgreSQL to raise a PANIC-level error, aborting the recovery. Setting ignore_invalid_pages to on causes the system to ignore invalid page references in WAL records (but still report a warning), and continue the recovery. This behavior may cause crashes, data loss, propagate or hide corruption, or other serious problems. However, it may allow you to get past the PANIC-level error, to finish the recovery, and to cause the server to start up. The parameter can only be set at server start. It only has effect during recovery or in standby mode.

If LLVM has the required functionality, register generated functions with GDB. This makes debugging easier. The default setting is off. This parameter can only be set at server start.

Writes the generated LLVM IR out to the file system, inside data_directory. This is only useful for working on the internals of the JIT implementation. The default setting is off. Only superusers and users with the appropriate SET privilege can change this setting.

Determines whether expressions are JIT compiled, when JIT compilation is activated (see Section 30.2). The default is on.

If LLVM has the required functionality, emit the data needed to allow perf to profile functions generated by JIT. This writes out files to ~/.debug/jit/; the user is responsible for performing cleanup when desired. The default setting is off. This parameter can only be set at server start.

Determines whether tuple deforming is JIT compiled, when JIT compilation is activated (see Section 30.2). The default is on.

When set to on, which is the default, PostgreSQL will automatically remove temporary files after a backend crash. If disabled, the files will be retained and may be used for debugging, for example. Repeated crashes may however result in accumulation of useless files. This parameter can only be set in the postgresql.conf file or on the server command line.

By default, after a backend crash the postmaster will stop remaining child processes by sending them SIGQUIT signals, which permits them to exit more-or-less gracefully. When this option is set to on, SIGABRT is sent instead. That normally results in production of a core dump file for each such child process. This can be handy for investigating the states of other processes after a crash. It can also consume lots of disk space in the event of repeated crashes, so do not enable this on systems you are not monitoring carefully. Beware that no support exists for cleaning up the core file(s) automatically. This parameter can only be set in the postgresql.conf file or on the server command line.

By default, after attempting to stop a child process with SIGQUIT, the postmaster will wait five seconds and then send SIGKILL to force immediate termination. When this option is set to on, SIGABRT is sent instead of SIGKILL. That normally results in production of a core dump file for each such child process. This can be handy for investigating the states of “stuck” child processes. It can also consume lots of disk space in the event of repeated crashes, so do not enable this on systems you are not monitoring carefully. Beware that no support exists for cleaning up the core file(s) automatically. This parameter can only be set in the postgresql.conf file or on the server command line.

The allowed values are buffered and immediate. The default is buffered. This parameter is intended to be used to test logical decoding and replication of large transactions. The effect of debug_logical_replication_streaming is different for the publisher and subscriber:

On the publisher side, debug_logical_replication_streaming allows streaming or serializing changes immediately in logical decoding. When set to immediate, stream each change if the streaming option of CREATE SUBSCRIPTION is enabled, otherwise, serialize each change. When set to buffered, the decoding will stream or serialize changes when logical_decoding_work_mem is reached.

On the subscriber side, if the streaming option is set to parallel, debug_logical_replication_streaming can be used to direct the leader apply worker to send changes to the shared memory queue or to serialize all changes to the file. When set to buffered, the leader sends changes to parallel apply workers via a shared memory queue. When set to immediate, the leader serializes all changes to files and notifies the parallel apply workers to read and apply them at the end of the transaction.

Examples:

Example 1 (unknown):

LOG:  LockAcquire: new: lock(0xb7acd844) id(24688,24696,0,0,0,1)
      grantMask(0) req(0,0,0,0,0,0,0)=0 grant(0,0,0,0,0,0,0)=0
      wait(0) type(AccessShareLock)
LOG:  GrantLock: lock(0xb7acd844) id(24688,24696,0,0,0,1)
      grantMask(2) req(1,0,0,0,0,0,0)=1 grant(1,0,0,0,0,0,0)=1
      wait(0) type(AccessShareLock)
LOG:  UnGrantLock: updated: lock(0xb7acd844) id(24688,24696,0,0,0,1)
      grantMask(0) req(0,0,0,0,0,0,0)=0 grant(0,0,0,0,0,0,0)=0
      wait(0) type(AccessShareLock)
LOG:  CleanUpLock: deleting: lock(0xb7acd844) id(24688,24696,0,0,0,1)
      grantMask(0) req(0,0,0,0,0,0,0)=0 grant(0,0,0,0,0,0,0)=0
      wait(0) type(INVALID)

---

PostgreSQL: Documentation: 18: 20.11. RADIUS Authentication

URL: https://www.postgresql.org/docs/current/auth-radius.html

Contents:

• 20.11. RADIUS Authentication #
  • Note

This authentication method operates similarly to password except that it uses RADIUS as the password verification method. RADIUS is used only to validate the user name/password pairs. Therefore the user must already exist in the database before RADIUS can be used for authentication.

When using RADIUS authentication, an Access Request message will be sent to the configured RADIUS server. This request will be of type Authenticate Only, and include parameters for user name, password (encrypted) and NAS Identifier. The request will be encrypted using a secret shared with the server. The RADIUS server will respond to this request with either Access Accept or Access Reject. There is no support for RADIUS accounting.

Multiple RADIUS servers can be specified, in which case they will be tried sequentially. If a negative response is received from a server, the authentication will fail. If no response is received, the next server in the list will be tried. To specify multiple servers, separate the server names with commas and surround the list with double quotes. If multiple servers are specified, the other RADIUS options can also be given as comma-separated lists, to provide individual values for each server. They can also be specified as a single value, in which case that value will apply to all servers.

The following configuration options are supported for RADIUS:

The DNS names or IP addresses of the RADIUS servers to connect to. This parameter is required.

The shared secrets used when talking securely to the RADIUS servers. This must have exactly the same value on the PostgreSQL and RADIUS servers. It is recommended that this be a string of at least 16 characters. This parameter is required.

The encryption vector used will only be cryptographically strong if PostgreSQL is built with support for OpenSSL. In other cases, the transmission to the RADIUS server should only be considered obfuscated, not secured, and external security measures should be applied if necessary.

The port numbers to connect to on the RADIUS servers. If no port is specified, the default RADIUS port (1812) will be used.

The strings to be used as NAS Identifier in the RADIUS requests. This parameter can be used, for example, to identify which database cluster the user is attempting to connect to, which can be useful for policy matching on the RADIUS server. If no identifier is specified, the default postgresql will be used.

If it is necessary to have a comma or whitespace in a RADIUS parameter value, that can be done by putting double quotes around the value, but it is tedious because two layers of double-quoting are now required. An example of putting whitespace into RADIUS secret strings is:

Examples:

Example 1 (unknown):

host ... radius radiusservers="server1,server2" radiussecrets="""secret one"",""secret two"""

---

PostgreSQL: Documentation: 18: 30.4. Extensibility

URL: https://www.postgresql.org/docs/current/jit-extensibility.html

Contents:

• 30.4. Extensibility #
  • 30.4.1. Inlining Support for Extensions #
  • Note
  • 30.4.2. Pluggable JIT Providers #
  • 30.4.2.1. JIT Provider Interface #

PostgreSQL's JIT implementation can inline the bodies of functions of types C and internal, as well as operators based on such functions. To do so for functions in extensions, the definitions of those functions need to be made available. When using PGXS to build an extension against a server that has been compiled with LLVM JIT support, the relevant files will be built and installed automatically.

The relevant files have to be installed into $pkglibdir/bitcode/$extension/ and a summary of them into $pkglibdir/bitcode/$extension.index.bc, where $pkglibdir is the directory returned by pg_config --pkglibdir and $extension is the base name of the extension's shared library.

For functions built into PostgreSQL itself, the bitcode is installed into $pkglibdir/bitcode/postgres.

PostgreSQL provides a JIT implementation based on LLVM. The interface to the JIT provider is pluggable and the provider can be changed without recompiling (although currently, the build process only provides inlining support data for LLVM). The active provider is chosen via the setting jit_provider.

A JIT provider is loaded by dynamically loading the named shared library. The normal library search path is used to locate the library. To provide the required JIT provider callbacks and to indicate that the library is actually a JIT provider, it needs to provide a C function named _PG_jit_provider_init. This function is passed a struct that needs to be filled with the callback function pointers for individual actions:

Examples:

Example 1 (unknown):

struct JitProviderCallbacks
{
    JitProviderResetAfterErrorCB reset_after_error;
    JitProviderReleaseContextCB release_context;
    JitProviderCompileExprCB compile_expr;
};

extern void _PG_jit_provider_init(JitProviderCallbacks *cb);

---

PostgreSQL: Documentation: 18: 29.4. Row Filters

URL: https://www.postgresql.org/docs/current/logical-replication-row-filter.html

Contents:

• 29.4. Row Filters #
  • 29.4.1. Row Filter Rules #
  • 29.4.2. Expression Restrictions #
  • 29.4.3. UPDATE Transformations #
  • 29.4.4. Partitioned Tables #
  • 29.4.5. Initial Data Synchronization #
  • Warning
  • Note
  • 29.4.6. Combining Multiple Row Filters #
  • 29.4.7. Examples #

By default, all data from all published tables will be replicated to the appropriate subscribers. The replicated data can be reduced by using a row filter. A user might choose to use row filters for behavioral, security or performance reasons. If a published table sets a row filter, a row is replicated only if its data satisfies the row filter expression. This allows a set of tables to be partially replicated. The row filter is defined per table. Use a WHERE clause after the table name for each published table that requires data to be filtered out. The WHERE clause must be enclosed by parentheses. See CREATE PUBLICATION for details.

Row filters are applied before publishing the changes. If the row filter evaluates to false or NULL then the row is not replicated. The WHERE clause expression is evaluated with the same role used for the replication connection (i.e. the role specified in the CONNECTION clause of the CREATE SUBSCRIPTION). Row filters have no effect for TRUNCATE command.

The WHERE clause allows only simple expressions. It cannot contain user-defined functions, operators, types, and collations, system column references or non-immutable built-in functions.

If a publication publishes UPDATE or DELETE operations, the row filter WHERE clause must contain only columns that are covered by the replica identity (see REPLICA IDENTITY). If a publication publishes only INSERT operations, the row filter WHERE clause can use any column.

Whenever an UPDATE is processed, the row filter expression is evaluated for both the old and new row (i.e. using the data before and after the update). If both evaluations are true, it replicates the UPDATE change. If both evaluations are false, it doesn't replicate the change. If only one of the old/new rows matches the row filter expression, the UPDATE is transformed to INSERT or DELETE, to avoid any data inconsistency. The row on the subscriber should reflect what is defined by the row filter expression on the publisher.

If the old row satisfies the row filter expression (it was sent to the subscriber) but the new row doesn't, then, from a data consistency perspective the old row should be removed from the subscriber. So the UPDATE is transformed into a DELETE.

If the old row doesn't satisfy the row filter expression (it wasn't sent to the subscriber) but the new row does, then, from a data consistency perspective the new row should be added to the subscriber. So the UPDATE is transformed into an INSERT.

Table 29.1 summarizes the applied transformations.

Table 29.1. UPDATE Transformation Summary

If the publication contains a partitioned table, the publication parameter publish_via_partition_root determines which row filter is used. If publish_via_partition_root is true, the root partitioned table's row filter is used. Otherwise, if publish_via_partition_root is false (default), each partition's row filter is used.

If the subscription requires copying pre-existing table data and a publication contains WHERE clauses, only data that satisfies the row filter expressions is copied to the subscriber.

If the subscription has several publications in which a table has been published with different WHERE clauses, rows that satisfy any of the expressions will be copied. See Section 29.4.6 for details.

Because initial data synchronization does not take into account the publish parameter when copying existing table data, some rows may be copied that would not be replicated using DML. Refer to Section 29.9.1, and see Section 29.2.2 for examples.

If the subscriber is in a release prior to 15, copy pre-existing data doesn't use row filters even if they are defined in the publication. This is because old releases can only copy the entire table data.

If the subscription has several publications in which the same table has been published with different row filters (for the same publish operation), those expressions get ORed together, so that rows satisfying any of the expressions will be replicated. This means all the other row filters for the same table become redundant if:

One of the publications has no row filter.

One of the publications was created using FOR ALL TABLES. This clause does not allow row filters.

One of the publications was created using FOR TABLES IN SCHEMA and the table belongs to the referred schema. This clause does not allow row filters.

Create some tables to be used in the following examples.

Create some publications. Publication p1 has one table (t1) and that table has a row filter. Publication p2 has two tables. Table t1 has no row filter, and table t2 has a row filter. Publication p3 has two tables, and both of them have a row filter.

psql can be used to show the row filter expressions (if defined) for each publication.

psql can be used to show the row filter expressions (if defined) for each table. See that table t1 is a member of two publications, but has a row filter only in p1. See that table t2 is a member of two publications, and has a different row filter in each of them.

On the subscriber node, create a table t1 with the same definition as the one on the publisher, and also create the subscription s1 that subscribes to the publication p1.

Insert some rows. Only the rows satisfying the t1 WHERE clause of publication p1 are replicated.

Update some data, where the old and new row values both satisfy the t1 WHERE clause of publication p1. The UPDATE replicates the change as normal.

Update some data, where the old row values did not satisfy the t1 WHERE clause of publication p1, but the new row values do satisfy it. The UPDATE is transformed into an INSERT and the change is replicated. See the new row on the subscriber.

Update some data, where the old row values satisfied the t1 WHERE clause of publication p1, but the new row values do not satisfy it. The UPDATE is transformed into a DELETE and the change is replicated. See that the row is removed from the subscriber.

The following examples show how the publication parameter publish_via_partition_root determines whether the row filter of the parent or child table will be used in the case of partitioned tables.

Create a partitioned table on the publisher.

Create the same tables on the subscriber.

Create a publication p4, and then subscribe to it. The publication parameter publish_via_partition_root is set as true. There are row filters defined on both the partitioned table (parent), and on the partition (child).

Insert some values directly into the parent and child tables. They replicate using the row filter of parent (because publish_via_partition_root is true).

Repeat the same test, but with a different value for publish_via_partition_root. The publication parameter publish_via_partition_root is set as false. A row filter is defined on the partition (child).

Do the inserts on the publisher same as before. They replicate using the row filter of child (because publish_via_partition_root is false).

Examples:

Example 1 (unknown):

/* pub # */ CREATE TABLE t1(a int, b int, c text, PRIMARY KEY(a,c));
/* pub # */ CREATE TABLE t2(d int, e int, f int, PRIMARY KEY(d));
/* pub # */ CREATE TABLE t3(g int, h int, i int, PRIMARY KEY(g));

Example 2 (unknown):

/* pub # */ CREATE PUBLICATION p1 FOR TABLE t1 WHERE (a > 5 AND c = 'NSW');
/* pub # */ CREATE PUBLICATION p2 FOR TABLE t1, t2 WHERE (e = 99);
/* pub # */ CREATE PUBLICATION p3 FOR TABLE t2 WHERE (d = 10), t3 WHERE (g = 10);

Example 3 (unknown):

/* pub # */ \dRp+
                                         Publication p1
  Owner   | All tables | Inserts | Updates | Deletes | Truncates | Generated columns | Via root
----------+------------+---------+---------+---------+-----------+-------------------+----------
 postgres | f          | t       | t       | t       | t         | none              | f
Tables:
    "public.t1" WHERE ((a > 5) AND (c = 'NSW'::text))

                                         Publication p2
  Owner   | All tables | Inserts | Updates | Deletes | Truncates | Generated columns | Via root
----------+------------+---------+---------+---------+-----------+-------------------+----------
 postgres | f          | t       | t       | t       | t         | none              | f
Tables:
    "public.t1"
    "public.t2" WHERE (e = 99)

                                         Publication p3
  Owner   | All tables | Inserts | Updates | Deletes | Truncates | Generated columns | Via root
----------+------------+---------+---------+---------+-----------+-------------------+----------
 postgres | f          | t       | t       | t       | t         | none              | f
Tables:
    "public.t2" WHERE (d = 10)
    "public.t3" WHERE (g = 10)

Example 4 (unknown):

/* pub # */ \d t1
                 Table "public.t1"
 Column |  Type   | Collation | Nullable | Default
--------+---------+-----------+----------+---------
 a      | integer |           | not null |
 b      | integer |           |          |
 c      | text    |           | not null |
Indexes:
    "t1_pkey" PRIMARY KEY, btree (a, c)
Publications:
    "p1" WHERE ((a > 5) AND (c = 'NSW'::text))
    "p2"

/* pub # */ \d t2
                 Table "public.t2"
 Column |  Type   | Collation | Nullable | Default
--------+---------+-----------+----------+---------
 d      | integer |           | not null |
 e      | integer |           |          |
 f      | integer |           |          |
Indexes:
    "t2_pkey" PRIMARY KEY, btree (d)
Publications:
    "p2" WHERE (e = 99)
    "p3" WHERE (d = 10)

/* pub # */ \d t3
                 Table "public.t3"
 Column |  Type   | Collation | Nullable | Default
--------+---------+-----------+----------+---------
 g      | integer |           | not null |
 h      | integer |           |          |
 i      | integer |           |          |
Indexes:
    "t3_pkey" PRIMARY KEY, btree (g)
Publications:
    "p3" WHERE (g = 10)

---

PostgreSQL: Documentation: 18: Chapter 66. Database Physical Storage

URL: https://www.postgresql.org/docs/current/storage.html

Contents:

• Chapter 66. Database Physical Storage

This chapter provides an overview of the physical storage format used by PostgreSQL databases.

---

PostgreSQL: Documentation: 18: 21.2. Role Attributes

URL: https://www.postgresql.org/docs/current/role-attributes.html

Contents:

• 21.2. Role Attributes #

A database role can have a number of attributes that define its privileges and interact with the client authentication system.

Only roles that have the LOGIN attribute can be used as the initial role name for a database connection. A role with the LOGIN attribute can be considered the same as a “database user”. To create a role with login privilege, use either:

(CREATE USER is equivalent to CREATE ROLE except that CREATE USER includes LOGIN by default, while CREATE ROLE does not.)

A database superuser bypasses all permission checks, except the right to log in. This is a dangerous privilege and should not be used carelessly; it is best to do most of your work as a role that is not a superuser. To create a new database superuser, use CREATE ROLE name SUPERUSER. You must do this as a role that is already a superuser.

A role must be explicitly given permission to create databases (except for superusers, since those bypass all permission checks). To create such a role, use CREATE ROLE name CREATEDB.

A role must be explicitly given permission to create more roles (except for superusers, since those bypass all permission checks). To create such a role, use CREATE ROLE name CREATEROLE. A role with CREATEROLE privilege can alter and drop roles which have been granted to the CREATEROLE user with the ADMIN option. Such a grant occurs automatically when a CREATEROLE user that is not a superuser creates a new role, so that by default, a CREATEROLE user can alter and drop the roles which they have created. Altering a role includes most changes that can be made using ALTER ROLE, including, for example, changing passwords. It also includes modifications to a role that can be made using the COMMENT and SECURITY LABEL commands.

However, CREATEROLE does not convey the ability to create SUPERUSER roles, nor does it convey any power over SUPERUSER roles that already exist. Furthermore, CREATEROLE does not convey the power to create REPLICATION users, nor the ability to grant or revoke the REPLICATION privilege, nor the ability to modify the role properties of such users. However, it does allow ALTER ROLE ... SET and ALTER ROLE ... RENAME to be used on REPLICATION roles, as well as the use of COMMENT ON ROLE, SECURITY LABEL ON ROLE, and DROP ROLE. Finally, CREATEROLE does not confer the ability to grant or revoke the BYPASSRLS privilege.

A role must explicitly be given permission to initiate streaming replication (except for superusers, since those bypass all permission checks). A role used for streaming replication must have LOGIN permission as well. To create such a role, use CREATE ROLE name REPLICATION LOGIN.

A password is only significant if the client authentication method requires the user to supply a password when connecting to the database. The password and md5 authentication methods make use of passwords. Database passwords are separate from operating system passwords. Specify a password upon role creation with CREATE ROLE name PASSWORD 'string'.

A role inherits the privileges of roles it is a member of, by default. However, to create a role which does not inherit privileges by default, use CREATE ROLE name NOINHERIT. Alternatively, inheritance can be overridden for individual grants by using WITH INHERIT TRUE or WITH INHERIT FALSE.

A role must be explicitly given permission to bypass every row-level security (RLS) policy (except for superusers, since those bypass all permission checks). To create such a role, use CREATE ROLE name BYPASSRLS as a superuser.

Connection limit can specify how many concurrent connections a role can make. -1 (the default) means no limit. Specify connection limit upon role creation with CREATE ROLE name CONNECTION LIMIT 'integer'.

A role's attributes can be modified after creation with ALTER ROLE. See the reference pages for the CREATE ROLE and ALTER ROLE commands for details.

A role can also have role-specific defaults for many of the run-time configuration settings described in Chapter 19. For example, if for some reason you want to disable index scans (hint: not a good idea) anytime you connect, you can use:

This will save the setting (but not set it immediately). In subsequent connections by this role it will appear as though SET enable_indexscan TO off had been executed just before the session started. You can still alter this setting during the session; it will only be the default. To remove a role-specific default setting, use ALTER ROLE rolename RESET varname. Note that role-specific defaults attached to roles without LOGIN privilege are fairly useless, since they will never be invoked.

When a non-superuser creates a role using the CREATEROLE privilege, the created role is automatically granted back to the creating user, just as if the bootstrap superuser had executed the command GRANT created_user TO creating_user WITH ADMIN TRUE, SET FALSE, INHERIT FALSE. Since a CREATEROLE user can only exercise special privileges with regard to an existing role if they have ADMIN OPTION on it, this grant is just sufficient to allow a CREATEROLE user to administer the roles they created. However, because it is created with INHERIT FALSE, SET FALSE, the CREATEROLE user doesn't inherit the privileges of the created role, nor can it access the privileges of that role using SET ROLE. However, since any user who has ADMIN OPTION on a role can grant membership in that role to any other user, the CREATEROLE user can gain access to the created role by simply granting that role back to themselves with the INHERIT and/or SET options. Thus, the fact that privileges are not inherited by default nor is SET ROLE granted by default is a safeguard against accidents, not a security feature. Also note that, because this automatic grant is granted by the bootstrap superuser, it cannot be removed or changed by the CREATEROLE user; however, any superuser could revoke it, modify it, and/or issue additional such grants to other CREATEROLE users. Whichever CREATEROLE users have ADMIN OPTION on a role at any given time can administer it.

Examples:

Example 1 (unknown):

CREATE ROLE name LOGIN;
CREATE USER name;

Example 2 (unknown):

ALTER ROLE myname SET enable_indexscan TO off;

---

PostgreSQL: Documentation: 18: 8.8. Geometric Types

URL: https://www.postgresql.org/docs/current/datatype-geometric.html

Contents:

• 8.8. Geometric Types #
  • 8.8.1. Points #
  • 8.8.2. Lines #
  • 8.8.3. Line Segments #
  • 8.8.4. Boxes #
  • 8.8.5. Paths #
  • 8.8.6. Polygons #
  • 8.8.7. Circles #

Geometric data types represent two-dimensional spatial objects. Table 8.20 shows the geometric types available in PostgreSQL.

Table 8.20. Geometric Types

In all these types, the individual coordinates are stored as double precision (float8) numbers.

A rich set of functions and operators is available to perform various geometric operations such as scaling, translation, rotation, and determining intersections. They are explained in Section 9.11.

Points are the fundamental two-dimensional building block for geometric types. Values of type point are specified using either of the following syntaxes:

where x and y are the respective coordinates, as floating-point numbers.

Points are output using the first syntax.

Lines are represented by the linear equation Ax + By + C = 0, where A and B are not both zero. Values of type line are input and output in the following form:

Alternatively, any of the following forms can be used for input:

where (x1,y1) and (x2,y2) are two different points on the line.

Line segments are represented by pairs of points that are the endpoints of the segment. Values of type lseg are specified using any of the following syntaxes:

where (x1,y1) and (x2,y2) are the end points of the line segment.

Line segments are output using the first syntax.

Boxes are represented by pairs of points that are opposite corners of the box. Values of type box are specified using any of the following syntaxes:

where (x1,y1) and (x2,y2) are any two opposite corners of the box.

Boxes are output using the second syntax.

Any two opposite corners can be supplied on input, but the values will be reordered as needed to store the upper right and lower left corners, in that order.

Paths are represented by lists of connected points. Paths can be open, where the first and last points in the list are considered not connected, or closed, where the first and last points are considered connected.

Values of type path are specified using any of the following syntaxes:

where the points are the end points of the line segments comprising the path. Square brackets ([]) indicate an open path, while parentheses (()) indicate a closed path. When the outermost parentheses are omitted, as in the third through fifth syntaxes, a closed path is assumed.

Paths are output using the first or second syntax, as appropriate.

Polygons are represented by lists of points (the vertices of the polygon). Polygons are very similar to closed paths; the essential semantic difference is that a polygon is considered to include the area within it, while a path is not.

An important implementation difference between polygons and paths is that the stored representation of a polygon includes its smallest bounding box. This speeds up certain search operations, although computing the bounding box adds overhead while constructing new polygons.

Values of type polygon are specified using any of the following syntaxes:

where the points are the end points of the line segments comprising the boundary of the polygon.

Polygons are output using the first syntax.

Circles are represented by a center point and radius. Values of type circle are specified using any of the following syntaxes:

where (x,y) is the center point and r is the radius of the circle.

Circles are output using the first syntax.

Examples:

Example 1 (unknown):

( x , y )
  x , y

Example 2 (unknown):

{ A, B, C }

Example 3 (unknown):

[ ( x1 , y1 ) , ( x2 , y2 ) ]
( ( x1 , y1 ) , ( x2 , y2 ) )
  ( x1 , y1 ) , ( x2 , y2 )
    x1 , y1   ,   x2 , y2

Example 4 (unknown):

[ ( x1 , y1 ) , ( x2 , y2 ) ]
( ( x1 , y1 ) , ( x2 , y2 ) )
  ( x1 , y1 ) , ( x2 , y2 )
    x1 , y1   ,   x2 , y2

---

PostgreSQL: Documentation: 18: 3. Conventions

URL: https://www.postgresql.org/docs/current/notation.html

Contents:

• 3. Conventions #

The following conventions are used in the synopsis of a command: brackets ([ and ]) indicate optional parts. Braces ({ and }) and vertical lines (|) indicate that you must choose one alternative. Dots (...) mean that the preceding element can be repeated. All other symbols, including parentheses, should be taken literally.

Where it enhances the clarity, SQL commands are preceded by the prompt =>, and shell commands are preceded by the prompt $. Normally, prompts are not shown, though.

An administrator is generally a person who is in charge of installing and running the server. A user could be anyone who is using, or wants to use, any part of the PostgreSQL system. These terms should not be interpreted too narrowly; this book does not have fixed presumptions about system administration procedures.

---

PostgreSQL: Documentation: 18: DECLARE STATEMENT

URL: https://www.postgresql.org/docs/current/ecpg-sql-declare-statement.html

Contents:

• DECLARE STATEMENT
• Synopsis
• Description
• Parameters
• Notes
• Examples
• Compatibility
• See Also

DECLARE STATEMENT — declare SQL statement identifier

DECLARE STATEMENT declares an SQL statement identifier. SQL statement identifier can be associated with the connection. When the identifier is used by dynamic SQL statements, the statements are executed using the associated connection. The namespace of the declaration is the precompile unit, and multiple declarations to the same SQL statement identifier are not allowed. Note that if the precompiler runs in Informix compatibility mode and some SQL statement is declared, "database" can not be used as a cursor name.

A database connection name established by the CONNECT command.

AT clause can be omitted, but such statement has no meaning.

The name of an SQL statement identifier, either as an SQL identifier or a host variable.

This association is valid only if the declaration is physically placed on top of a dynamic statement.

DECLARE STATEMENT is an extension of the SQL standard, but can be used in famous DBMSs.

Examples:

Example 1 (unknown):

EXEC SQL [ AT connection_name ] DECLARE statement_name STATEMENT

Example 2 (unknown):

EXEC SQL CONNECT TO postgres AS con1;
EXEC SQL AT con1 DECLARE sql_stmt STATEMENT;
EXEC SQL DECLARE cursor_name CURSOR FOR sql_stmt;
EXEC SQL PREPARE sql_stmt FROM :dyn_string;
EXEC SQL OPEN cursor_name;
EXEC SQL FETCH cursor_name INTO :column1;
EXEC SQL CLOSE cursor_name;

---

PostgreSQL: Documentation: 18: 29.5. Column Lists

URL: https://www.postgresql.org/docs/current/logical-replication-col-lists.html

Contents:

• 29.5. Column Lists #
  • Warning: Combining Column Lists from Multiple Publications
  • 29.5.1. Examples #

Each publication can optionally specify which columns of each table are replicated to subscribers. The table on the subscriber side must have at least all the columns that are published. If no column list is specified, then all columns on the publisher are replicated. See CREATE PUBLICATION for details on the syntax.

The choice of columns can be based on behavioral or performance reasons. However, do not rely on this feature for security: a malicious subscriber is able to obtain data from columns that are not specifically published. If security is a consideration, protections can be applied at the publisher side.

If no column list is specified, any columns added to the table later are automatically replicated. This means that having a column list which names all columns is not the same as having no column list at all.

A column list can contain only simple column references. The order of columns in the list is not preserved.

Generated columns can also be specified in a column list. This allows generated columns to be published, regardless of the publication parameter publish_generated_columns. See Section 29.6 for details.

Specifying a column list when the publication also publishes FOR TABLES IN SCHEMA is not supported.

For partitioned tables, the publication parameter publish_via_partition_root determines which column list is used. If publish_via_partition_root is true, the root partitioned table's column list is used. Otherwise, if publish_via_partition_root is false (the default), each partition's column list is used.

If a publication publishes UPDATE or DELETE operations, any column list must include the table's replica identity columns (see REPLICA IDENTITY). If a publication publishes only INSERT operations, then the column list may omit replica identity columns.

Column lists have no effect for the TRUNCATE command.

During initial data synchronization, only the published columns are copied. However, if the subscriber is from a release prior to 15, then all the columns in the table are copied during initial data synchronization, ignoring any column lists. If the subscriber is from a release prior to 18, then initial table synchronization won't copy generated columns even if they are defined in the publisher.

There's currently no support for subscriptions comprising several publications where the same table has been published with different column lists. CREATE SUBSCRIPTION disallows creating such subscriptions, but it is still possible to get into that situation by adding or altering column lists on the publication side after a subscription has been created.

This means changing the column lists of tables on publications that are already subscribed could lead to errors being thrown on the subscriber side.

If a subscription is affected by this problem, the only way to resume replication is to adjust one of the column lists on the publication side so that they all match; and then either recreate the subscription, or use ALTER SUBSCRIPTION ... DROP PUBLICATION to remove one of the offending publications and add it again.

Create a table t1 to be used in the following example.

Create a publication p1. A column list is defined for table t1 to reduce the number of columns that will be replicated. Notice that the order of column names in the column list does not matter.

psql can be used to show the column lists (if defined) for each publication.

psql can be used to show the column lists (if defined) for each table.

On the subscriber node, create a table t1 which now only needs a subset of the columns that were on the publisher table t1, and also create the subscription s1 that subscribes to the publication p1.

On the publisher node, insert some rows to table t1.

Only data from the column list of publication p1 is replicated.

Examples:

Example 1 (unknown):

/* pub # */ CREATE TABLE t1(id int, a text, b text, c text, d text, e text, PRIMARY KEY(id));

Example 2 (unknown):

/* pub # */ CREATE PUBLICATION p1 FOR TABLE t1 (id, b, a, d);

Example 3 (unknown):

/* pub # */ \dRp+
                                         Publication p1
  Owner   | All tables | Inserts | Updates | Deletes | Truncates | Generated columns | Via root
----------+------------+---------+---------+---------+-----------+-------------------+----------
 postgres | f          | t       | t       | t       | t         | none              | f
Tables:
    "public.t1" (id, a, b, d)

Example 4 (unknown):

/* pub # */ \d t1
                 Table "public.t1"
 Column |  Type   | Collation | Nullable | Default
--------+---------+-----------+----------+---------
 id     | integer |           | not null |
 a      | text    |           |          |
 b      | text    |           |          |
 c      | text    |           |          |
 d      | text    |           |          |
 e      | text    |           |          |
Indexes:
    "t1_pkey" PRIMARY KEY, btree (id)
Publications:
    "p1" (id, a, b, d)

---

PostgreSQL: Documentation: 18: Chapter 33. Large Objects

URL: https://www.postgresql.org/docs/current/largeobjects.html

Contents:

• Chapter 33. Large Objects

PostgreSQL has a large object facility, which provides stream-style access to user data that is stored in a special large-object structure. Streaming access is useful when working with data values that are too large to manipulate conveniently as a whole.

This chapter describes the implementation and the programming and query language interfaces to PostgreSQL large object data. We use the libpq C library for the examples in this chapter, but most programming interfaces native to PostgreSQL support equivalent functionality. Other interfaces might use the large object interface internally to provide generic support for large values. This is not described here.

---

PostgreSQL: Documentation: 18: 10.1. Overview

URL: https://www.postgresql.org/docs/current/typeconv-overview.html

Contents:

• 10.1. Overview #

SQL is a strongly typed language. That is, every data item has an associated data type which determines its behavior and allowed usage. PostgreSQL has an extensible type system that is more general and flexible than other SQL implementations. Hence, most type conversion behavior in PostgreSQL is governed by general rules rather than by ad hoc heuristics. This allows the use of mixed-type expressions even with user-defined types.

The PostgreSQL scanner/parser divides lexical elements into five fundamental categories: integers, non-integer numbers, strings, identifiers, and key words. Constants of most non-numeric types are first classified as strings. The SQL language definition allows specifying type names with strings, and this mechanism can be used in PostgreSQL to start the parser down the correct path. For example, the query:

has two literal constants, of type text and point. If a type is not specified for a string literal, then the placeholder type unknown is assigned initially, to be resolved in later stages as described below.

There are four fundamental SQL constructs requiring distinct type conversion rules in the PostgreSQL parser:

Much of the PostgreSQL type system is built around a rich set of functions. Functions can have one or more arguments. Since PostgreSQL permits function overloading, the function name alone does not uniquely identify the function to be called; the parser must select the right function based on the data types of the supplied arguments.

PostgreSQL allows expressions with prefix (one-argument) operators, as well as infix (two-argument) operators. Like functions, operators can be overloaded, so the same problem of selecting the right operator exists.

SQL INSERT and UPDATE statements place the results of expressions into a table. The expressions in the statement must be matched up with, and perhaps converted to, the types of the target columns.

Since all query results from a unionized SELECT statement must appear in a single set of columns, the types of the results of each SELECT clause must be matched up and converted to a uniform set. Similarly, the result expressions of a CASE construct must be converted to a common type so that the CASE expression as a whole has a known output type. Some other constructs, such as ARRAY[] and the GREATEST and LEAST functions, likewise require determination of a common type for several subexpressions.

The system catalogs store information about which conversions, or casts, exist between which data types, and how to perform those conversions. Additional casts can be added by the user with the CREATE CAST command. (This is usually done in conjunction with defining new data types. The set of casts between built-in types has been carefully crafted and is best not altered.)

An additional heuristic provided by the parser allows improved determination of the proper casting behavior among groups of types that have implicit casts. Data types are divided into several basic type categories, including boolean, numeric, string, bitstring, datetime, timespan, geometric, network, and user-defined. (For a list see Table 52.65; but note it is also possible to create custom type categories.) Within each category there can be one or more preferred types, which are preferred when there is a choice of possible types. With careful selection of preferred types and available implicit casts, it is possible to ensure that ambiguous expressions (those with multiple candidate parsing solutions) can be resolved in a useful way.

All type conversion rules are designed with several principles in mind:

Implicit conversions should never have surprising or unpredictable outcomes.

There should be no extra overhead in the parser or executor if a query does not need implicit type conversion. That is, if a query is well-formed and the types already match, then the query should execute without spending extra time in the parser and without introducing unnecessary implicit conversion calls in the query.

Additionally, if a query usually requires an implicit conversion for a function, and if then the user defines a new function with the correct argument types, the parser should use this new function and no longer do implicit conversion to use the old function.

Examples:

Example 1 (unknown):

SELECT text 'Origin' AS "label", point '(0,0)' AS "value";

 label  | value
--------+-------
 Origin | (0,0)
(1 row)

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PostgreSQL: Documentation: 18: 15.2. When Can Parallel Query Be Used?

URL: https://www.postgresql.org/docs/current/when-can-parallel-query-be-used.html

Contents:

• 15.2. When Can Parallel Query Be Used? #

There are several settings that can cause the query planner not to generate a parallel query plan under any circumstances. In order for any parallel query plans whatsoever to be generated, the following settings must be configured as indicated.

max_parallel_workers_per_gather must be set to a value that is greater than zero. This is a special case of the more general principle that no more workers should be used than the number configured via max_parallel_workers_per_gather.

In addition, the system must not be running in single-user mode. Since the entire database system is running as a single process in this situation, no background workers will be available.

Even when it is in general possible for parallel query plans to be generated, the planner will not generate them for a given query if any of the following are true:

The query writes any data or locks any database rows. If a query contains a data-modifying operation either at the top level or within a CTE, no parallel plans for that query will be generated. As an exception, the following commands, which create a new table and populate it, can use a parallel plan for the underlying SELECT part of the query:

CREATE MATERIALIZED VIEW

REFRESH MATERIALIZED VIEW

The query might be suspended during execution. In any situation in which the system thinks that partial or incremental execution might occur, no parallel plan is generated. For example, a cursor created using DECLARE CURSOR will never use a parallel plan. Similarly, a PL/pgSQL loop of the form FOR x IN query LOOP .. END LOOP will never use a parallel plan, because the parallel query system is unable to verify that the code in the loop is safe to execute while parallel query is active.

The query uses any function marked PARALLEL UNSAFE. Most system-defined functions are PARALLEL SAFE, but user-defined functions are marked PARALLEL UNSAFE by default. See the discussion of Section 15.4.

The query is running inside of another query that is already parallel. For example, if a function called by a parallel query issues an SQL query itself, that query will never use a parallel plan. This is a limitation of the current implementation, but it may not be desirable to remove this limitation, since it could result in a single query using a very large number of processes.

Even when a parallel query plan is generated for a particular query, there are several circumstances under which it will be impossible to execute that plan in parallel at execution time. If this occurs, the leader will execute the portion of the plan below the Gather node entirely by itself, almost as if the Gather node were not present. This will happen if any of the following conditions are met:

No background workers can be obtained because of the limitation that the total number of background workers cannot exceed max_worker_processes.

No background workers can be obtained because of the limitation that the total number of background workers launched for purposes of parallel query cannot exceed max_parallel_workers.

The client sends an Execute message with a non-zero fetch count. See the discussion of the extended query protocol. Since libpq currently provides no way to send such a message, this can only occur when using a client that does not rely on libpq. If this is a frequent occurrence, it may be a good idea to set max_parallel_workers_per_gather to zero in sessions where it is likely, so as to avoid generating query plans that may be suboptimal when run serially.

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PostgreSQL: Documentation: 18: 31.4. TAP Tests

URL: https://www.postgresql.org/docs/current/regress-tap.html

Contents:

• 31.4. TAP Tests #
  • 31.4.1. Environment Variables #

Various tests, particularly the client program tests under src/bin, use the Perl TAP tools and are run using the Perl testing program prove. You can pass command-line options to prove by setting the make variable PROVE_FLAGS, for example:

See the manual page of prove for more information.

The make variable PROVE_TESTS can be used to define a whitespace-separated list of paths relative to the Makefile invoking prove to run the specified subset of tests instead of the default t/*.pl. For example:

The TAP tests require the Perl module IPC::Run. This module is available from CPAN or an operating system package. They also require PostgreSQL to be configured with the option --enable-tap-tests.

Generically speaking, the TAP tests will test the executables in a previously-installed installation tree if you say make installcheck, or will build a new local installation tree from current sources if you say make check. In either case they will initialize a local instance (data directory) and transiently run a server in it. Some of these tests run more than one server. Thus, these tests can be fairly resource-intensive.

It's important to realize that the TAP tests will start test server(s) even when you say make installcheck; this is unlike the traditional non-TAP testing infrastructure, which expects to use an already-running test server in that case. Some PostgreSQL subdirectories contain both traditional-style and TAP-style tests, meaning that make installcheck will produce a mix of results from temporary servers and the already-running test server.

Data directories are named according to the test filename, and will be retained if a test fails. If the environment variable PG_TEST_NOCLEAN is set, data directories will be retained regardless of test status. For example, retaining the data directory regardless of test results when running the pg_dump tests:

This environment variable also prevents the test's temporary directories from being removed.

Many operations in the test suites use a 180-second timeout, which on slow hosts may lead to load-induced timeouts. Setting the environment variable PG_TEST_TIMEOUT_DEFAULT to a higher number will change the default to avoid this.

Examples:

Example 1 (unknown):

make -C src/bin check PROVE_FLAGS='--timer'

Example 2 (unknown):

make check PROVE_TESTS='t/001_test1.pl t/003_test3.pl'

Example 3 (unknown):

PG_TEST_NOCLEAN=1 make -C src/bin/pg_dump check

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