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SQL Snippets

This is my personal collection of SQL examples and proof-of-concept files, specifically focused on TimescaleDB features and capabilities. The repository features a diverse range of SQL files demonstrating time-series data handling, hypertables, continuous aggregates, and other TimescaleDB-specific functionality.

Quick Start

  1. Start TimescaleDB: Use the Docker setup to run TimescaleDB locally:

    ./run_timescale_docker.sh

  2. Load environment: Source the environment variables:

    source .env

  3. Run any SQL snippet: Use the helper script to execute snippets:

    ./psql_cmd.sh <filename.sql>

    Example:

    ./psql_cmd.sh sensors.sql

Environment Setup

  • TimescaleDB Docker: Uses timescale/timescaledb-ha:pg17.4-ts2.21.3-all
  • Connection: postgres://postgres:password@localhost:5432/postgres
  • No pager: All psql commands are configured to disable paging for better command-line experience

Learning Categories

TimescaleDB Core Features: Files like hypertable_model.sql, caggs.sql, compression.sql, and retention.sql demonstrate the core TimescaleDB functionality including hypertables, continuous aggregates, compression policies, and data retention.

Time-Series Analytics: Files such as bollinger_bands.sql, correlation_matrix.sql, ohlcv.sql, and frequency.sql showcase advanced time-series analysis techniques using TimescaleDB's analytical functions.

Background Jobs & Automation: Examples like job.sql, jobs.sql, and various caggs files demonstrate TimescaleDB's background job system for automated data processing and maintenance.

Performance & Scale: Files like massive_distributed_inserts.sql, chunk_skipping.sql, and skip_scan_example.sql provide insights into TimescaleDB's performance optimization features.

File Conventions

  • All SQL files are self-contained and can be executed independently
  • Each file includes necessary setup and demonstrates specific TimescaleDB features
  • Use #file:run_timescale_docker.sh reference for database connection in any new snippets
  • Focus on practical, real-world TimescaleDB use cases