TimescaleDB

About TimescaleDB

A time-series SQL database built as a PostgreSQL extension, combining the reliability of PostgreSQL with optimizations for time-series workloads. TimescaleDB offers automatic partitioning, compression, and continuous aggregates for efficient time-series analysis.

Key Features

• 1PostgreSQL extension that adds automatic time-series table partitioning (hypertables)
• 2Continuous aggregates compute and store time-bucketed metrics incrementally
• 3Compression reduces storage by 90%+ for older time-series data
• 4Full SQL compatibility — all PostgreSQL tools and drivers work unchanged
• 5Real-time aggregates query both compressed and uncompressed data transparently

How Python Data Engineers Use TimescaleDB

Python data engineers use TimescaleDB as a drop-in upgrade for PostgreSQL when storing time-series data. The `psycopg2` and `asyncpg` drivers work without changes — engineers create a hypertable on the timestamp column and TimescaleDB automatically partitions data by time, enabling fast range queries on billions of rows without manual partition management.