llms.md 31 KB
Tiger DataDocumentation
Over 3 million Tiger Datadatabases power customer-facing applications. Speed without sacrifice for real-time analytics, time series, and vector workloads. Creators of TimescaleDB.
Tiger Cloud is the modern Postgres data platform for all your applications. It enhances Postgres to handle time series, events, real-time analytics, and vector search—all in a single database alongside transactional workloads.
You get one system that handles live data ingestion, late and out-of-order updates, and low latency queries, with the performance, reliability, and scalability your app needs. Ideal for IoT, crypto, finance, SaaS, and a myriad other domains, Tiger Cloud allows you to build data-heavy, mission-critical apps while retaining the familiarity and reliability of PostgreSQL.
This repository contains the complete documentation for Tiger Dataproducts available at https://docs.tigerdata.com/.
Getting Started
- Get started overview: Introduction to Tiger Dataproducts and services
- Create a Tiger Cloud service: Learn about Tiger Cloud capabilities and create your first service
- Run queries from Tiger Cloud Console: Use the SQL editor and SQL Assistant in Tiger Cloud
- Try key Tiger Datafeatures: Explore hypertables, time buckets, compression, and continuous aggregates
- Start coding with TigerData: Connect and code with your preferred programming language
Core Features and Functionality
Hypertables
- About hypertables: Core concept for time-series optimization
- Create and manage hypertables: CRUD operations on hypertables
- Improve query performance: Performance optimization techniques
- Unique indexes on hypertables: Handling unique constraints
Hypercore (Columnar Storage)
- Hypercore overview: Advanced columnar storage for real-time analytics
- Real-time analytics in Hypercore: High-performance analytics capabilities
- Compression methods: Advanced compression techniques
- Secondary indexes: Indexing strategies for columnar data
- Modify data in Hypercore: Data modification operations
Continuous Aggregates
- About continuous aggregates: Materialized views for time-series data
- Create continuous aggregates: Implementation guide
- Real-time aggregates: Real-time query capabilities
- Hierarchical continuous aggregates: Multi-level aggregation
- Refresh policies: Automated refresh management
- Compression on continuous aggregates: Storage optimization
Hyperfunctions
- About hyperfunctions: Advanced analytical functions
- Function pipelines: Chaining analytical operations
- Statistical aggregates: Statistical analysis functions
- Time-weighted averages: Time-series averaging
- Gapfilling and interpolation: Handle missing data
- Counter aggregation: Monitor counter metrics
- Approximate count distincts: Efficient distinct counting
- Percentile approximation: Statistical percentile calculations
Data Operations
Writing Data
- About writing data: Overview of data ingestion
- Insert data: Insert operations and best practices
- Update data: Update existing records
- Upsert data: Insert or update patterns
- Delete data: Data deletion strategies
Querying Data
- About querying data: Query fundamentals
- SELECT queries: Basic and advanced SELECT operations
- Advanced analytic queries: Complex analytical queries
- SkipScan for DISTINCT: Optimized DISTINCT operations
Time Buckets
- About time buckets: Time-based data grouping
- Use time buckets: Implementation examples
Data Ingestion
- Import CSV data: CSV data import
- Import from MySQL: MySQL migration
- Import Parquet files: Parquet data ingestion
- Ingest from Kafka: Apache Kafka integration
- Ingest with Telegraf: Telegraf data collection
Data Management
Compression
- About compression: Storage optimization overview
- Compression design: Design considerations
- Manual compression: Manual compression operations
- Compression policies: Automated compression
- Modify compressed data: Working with compressed data
- Modify schemas: Schema changes on compressed tables
Data Retention
- About data retention: Automated data lifecycle management
- Create retention policies: Policy creation and management
- Data retention with continuous aggregates: Retention for aggregated data
- Manually drop chunks: Manual data removal
Data Tiering
- About data tiering: Multi-tier storage strategy
- Enable data tiering: Setup and configuration
- Query tiered data: Working with tiered storage
- Tiered data with replicas and forks: Advanced tiering scenarios
Jobs and Automation
- Create and manage jobs: Background job management
- Downsample and compress example: Automated data processing
- Generic retention example: Custom retention policies
- Tiered storage example: Automated tiering
Infrastructure and Operations
Tiger Cloud Services
- Service overview: Tiger Cloud service architecture
- Service management: Lifecycle management
- Service explorer: Service monitoring and insights
- Change resources: Scale compute and storage
- Connection pooling: Manage database connections
Configuration
- About configuration: Configuration overview
- Customize configuration: Custom settings
- Advanced parameters: Advanced tuning options
High Availability
- High availability overview: HA architecture and setup
- Read scaling: Read replica configuration
Backup and Restore
- Backup and restore overview: Cloud backup strategies
- Point-in-time recovery: PITR capabilities
Security
- Security overview: Security architecture
- Member management: User and role management
- Multi-factor authentication: MFA setup
- SAML authentication: SSO integration
- Client credentials: Application authentication
- Read-only role: Restricted access roles
- Strict SSL: SSL configuration
- VPC peering: Private network connectivity
- Transit Gateway: Multi-cloud connectivity
- IP allow list: Network access control
Schema Management
- About schemas: Schema design principles
- About indexing: Index strategies
- About constraints: Constraint management
- About tablespaces: Storage management
- Alter operations: Schema modifications
- Indexing: Index creation and management
- JSON support: Working with JSON data
- Triggers: Database triggers
- Foreign data wrappers: External data integration
Extensions
- pgvector: Vector similarity search
- PostGIS: Geospatial data support
- pgcrypto: Cryptographic functions
Monitoring and Metrics
- Monitoring overview: System monitoring
- AWS CloudWatch: CloudWatch integration
- Datadog: Datadog monitoring
- Prometheus metrics: Prometheus integration
Integrate AI with Tiger Data
- AI overview: Integrate AI with your Tiger Data products
- Integrate Tiger Cloud with your AI Assistant: Manage your services and optimize your schema and queries with your AI Assistant
- Aggregate organizational data with AI agents: Unify company knowledge with slack-native AI agents
- Integrate a slack-native AI agent: Configure a Slack-native AI agent to do what you want
- Key vector database concepts: Key concepts for working with pgvector data in Postgres
- SQL interface for pgvector: SQL interface for pgai, pgvector and pgvectorscale in Postgres
Tutorials and Examples
- Tutorials overview: Hands-on tutorials and examples
- Community cookbook: Code examples and recipes
- Real-time analytics for energy consumption: Energy data analysis
- Real-time analytics for transport: Transportation data analysis
- Simulate IoT sensor data: IoT data simulation
- Ingest real-time websocket data: WebSocket data streaming
Dataset Tutorials
- Bitcoin blockchain analysis: Analyze blockchain transactions with Hypercore
- Financial tick data analysis: High-frequency financial data
- Financial real-time ingestion: Real-time financial data streaming
- NYC taxi data analysis: Time-series analysis with NYC taxi data
- NYC taxi geospatial analysis: Geospatial data visualization
- Energy consumption analysis: Energy usage patterns and optimization
Integrations
Cloud Platforms
- AWS integrations: Amazon Web Services integration
- AWS Lambda: Serverless functions
- Amazon SageMaker: Machine learning platform
- Google Cloud: Google Cloud Platform integration
- Microsoft Azure: Microsoft Azure integration
Data Integration
- Apache Kafka: Kafka streaming integration
- Apache Airflow: Workflow orchestration
- Debezium: Change data capture
- Decodable: Real-time stream processing
- Fivetran: Data pipeline automation
- PostgreSQL: PostgreSQL compatibility
Visualization and Analytics
- Grafana: Monitoring and visualization
- Tableau: Business intelligence
- Power BI: Microsoft business analytics
Development Tools
- psql: PostgreSQL command line
- pgAdmin: PostgreSQL administration
- DBeaver: Database management tool
- Azure Data Studio: Microsoft database tool
- qStudio: SQL analytics platform
Monitoring and Observability
- Prometheus: Monitoring and alerting
- Datadog: Infrastructure monitoring
- CloudWatch: AWS monitoring service
Infrastructure
- Kubernetes: Container orchestration
- Terraform: Infrastructure as code
- Supabase: Backend-as-a-service
- Corporate Data Center: On-premises connectivity
Connection Details
- Find connection details: Service connection information
- Troubleshooting: Integration troubleshooting guide
Migration and Sync
- Migration overview: Migration strategies and tools
- pg_dump and restore: Traditional PostgreSQL migration
- Live migration: Low-downtime migration for large databases
- Live sync for PostgreSQL: Real-time sync from PostgreSQL
- Live sync for S3: Sync data from S3 storage
- Dual-write and backfill: Migration with zero downtime
- Migration troubleshooting: Common migration issues
Self-hosted TimescaleDB
Installation
- Self-hosted overview: Installation options
- Docker installation: Docker-based deployment
- Kubernetes installation: Kubernetes deployment
- Linux installation: Linux package installation
- macOS installation: macOS Homebrew/MacPorts
- Windows installation: Windows installation
- Source installation: Build from source
Configuration and Management
- Configuration overview: Configuration fundamentals
- TimescaleDB configuration: TimescaleDB-specific settings
- PostgreSQL configuration: PostgreSQL tuning
- Docker configuration: Docker-specific configuration
- timescaledb-tune: Automated tuning tool
- Telemetry: Usage telemetry configuration
Backup and Restore
- Backup overview: Self-hosted backup strategies
- Logical backups: pg_dump/pg_restore
- Physical backups: WAL-E and pgBackRest
- Docker and WAL-E: Container backup solutions
High Availability and Replication
- About high availability: HA architecture
- Configure replication: Replication setup
Migration
- Entire database migration: Full database migration
- Schema then data migration: Phased migration approach
- Same database migration: In-place migration
- Migrate from InfluxDB: InfluxDB migration
Upgrades and Maintenance
- About upgrades: Upgrade strategies
- Major upgrades: Major version upgrades
- Minor upgrades: Minor version upgrades
- Docker upgrades: Container upgrades
- PostgreSQL upgrades: PostgreSQL version upgrades
- Downgrade: Version rollback
Tooling
- About timescaledb-tune: Performance tuning tool
- Install toolkit: TimescaleDB toolkit installation
Storage Management
- Manage storage: Storage and tablespace management
Uninstallation
- Uninstall TimescaleDB: Clean removal
Managed Service for TimescaleDB (MST)
Getting Started
- About MST: Managed service overview
- Install MST: Service setup and configuration
- User management: User roles and permissions
- Billing: Pricing and billing information
Data Operations
- Ingest data: Data ingestion patterns
- Migrate to MST: Migration to managed service
Infrastructure and Networking
- Connection pools: Connection management
- PostgreSQL read replicas: Read scaling
- VPC peering overview: Private network connectivity
- AWS VPC peering: Amazon VPC integration
- AWS Transit Gateway: Multi-VPC connectivity
- Azure VPC peering: Microsoft Azure networking
- GCP VPC peering: Google Cloud networking
Operations and Monitoring
- Extensions: Available PostgreSQL extensions
- Security: Security configuration
- Maintenance: Maintenance windows and updates
- Failover: High availability failover
- Manage backups: Backup management
- View service logs: Log access and analysis
Tools and APIs
- Aiven Client: Command-line management tool
- REST API: Programmatic service management
- Identify index issues: Performance optimization
Integrations
- MST integrations overview: Integration options
- Grafana integration: Visualization
- Prometheus integration: Monitoring
- Datadog metrics: Infrastructure monitoring
- Logging integration: Log management
API Reference
Core APIs
- API overview: Complete API reference
- Hypertable management: Hypertable creation and management
- Hypercore APIs: Columnar storage operations
- Continuous aggregates: Materialized view management
- Compression APIs: Data compression functions
- Data retention: Retention policy management
- Jobs and automation: Background job management
Hyperfunctions
- Hyperfunctions overview: Advanced analytical functions
- Statistical aggregates: Statistical analysis
- Frequency analysis: Frequency and histogram functions
- Time-weighted averages: Time-series averaging
- Gapfilling and interpolation: Missing data handling
- Counter aggregates: Counter metrics
- Gauge aggregates: Gauge metrics
- State aggregates: State tracking
Configuration and Administration
- Configuration APIs: Database configuration
- Administration functions: Administrative operations
- Informational views: System information views
About TigerData
- About overview: Company and product information
- Pricing and account management: Pricing plans and billing
- TimescaleDB editions: Product tiers and features
- Changelog: Latest product updates
- Release notes: Version release information
- Whitepaper: Technical architecture paper
- Contribute to TigerData: Community contribution guide
Contributing
To contribute to this documentation:
- Fork or clone the repository
- Create a branch from
latest - Make your changes following the style guide in CONTRIBUTING.md
- Submit a pull request back to
latest - Sign the Contributor License Agreement (CLA) if this is your first contribution
The documentation is built using Gatsby and automatically generates preview links for pull requests.