Amazon Timestream
Serverless time-series database for IoT, metrics, and operational telemetry
Open-Source Projects
Discover top open-source software, updated regularly with real-world adoption signals.
Discover top open-source software, updated regularly with real-world adoption signals.
PostgreSQL extension delivering high-performance real-time analytics on time-series data
TimescaleDB adds hypertables, columnar storage and compression to PostgreSQL, enabling fast queries, low storage costs, and analytics on time-series data.
TimescaleDB extends PostgreSQL with a purpose‑built engine for time‑series workloads. It targets developers, data engineers, and analysts who need to ingest large volumes of event data while preserving the familiar SQL interface and transactional guarantees of PostgreSQL.
The extension introduces hypertables that automatically partition data by time, a hybrid row‑columnar store that compresses historic rows by up to 90 %, and continuous aggregates that refresh incrementally in the background. These features together provide sub‑second query latency on massive datasets, reduce storage footprints, and simplify real‑time dashboarding without custom ETL pipelines.
TimescaleDB can be run locally via Docker, installed on any supported PostgreSQL version (12‑17), or consumed as a managed service through Tiger Cloud. The Docker image offers a quick start for evaluation, while the managed offering adds automated backups, high availability, and scaling for production workloads.
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It uses a hybrid row‑columnar engine: recent rows stay in a rowstore for fast writes, while older data is compressed in a columnstore, reducing size by up to 90 %.
No. You create hypertables with standard CREATE TABLE syntax and then query them with ordinary SQL; TimescaleDB handles the optimization transparently.
TimescaleDB provides extensions for PostgreSQL 12 through 17; the Docker image in the README runs PostgreSQL 17.
Yes. Tiger Cloud offers a fully managed PostgreSQL instance with TimescaleDB pre‑installed, handling backups, HA, and scaling.
Continuous aggregates refresh incrementally in the background, recomputing only changed rows, whereas a standard materialized view must be rebuilt entirely on each refresh.
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