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timescale/timescaledb

A time-series database for high-performance real-time analytics packaged as a Postgres extension observed · 2026-08-28

github.com/timescale/timescaledb · homepage · C · NOASSERTION (other) observed · 2026-08-28

Health v2 · maintenance only

99/100

  • Activity 99
  • Release rhythm 98
  • Longevity 100

Flags: no_license

How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.

  • gap_med: 13
  • age_days: 3466
  • days_rel: 15
  • days_push: 7
  • n_releases_24m: 42

Full methodology

Adoption not part of the score

23423 stars · 1140 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

TimescaleDB is an open-source PostgreSQL extension that turns Postgres into a high-performance time-series database for real-time analytics on time-series and event data. It provides hypertables with automatic time-based partitioning, hybrid row-columnar storage with compression, continuous aggregates, tiered storage, and 200+ SQL time-series functions while remaining fully Postgres-compatible.

Use cases

  • store and query IoT sensor data at scale in Postgres
  • run real-time analytics dashboards on time-series metrics
  • ingest high-volume event or monitoring data with fast writes
  • compress historical time-series data to cut storage costs
  • build continuous aggregates for pre-computed rollups
  • analyze financial tick data with SQL time-series functions
  • tier old data to cheap object storage automatically
  • add time-series capabilities to an existing PostgreSQL application

When to choose

  • you already use PostgreSQL and want time-series scale without migrating to a new database
  • you need fast ingestion plus analytical queries on live and historical data in one system
  • you want SQL-native time-series features like time buckets, gap filling, and continuous aggregates
  • you need hybrid row-columnar storage with high compression ratios
  • you want to keep relational data (assets, customers) alongside time-series readings in one database

When to avoid

  • you need a fully managed database and don't want to self-host or use the vendor's cloud
  • your workload is purely relational OLTP with no time-series component
  • you require a non-Postgres ecosystem or NoSQL-style schema flexibility
  • you need strict open-source licensing - the project's license is not a standard OSI-approved license
  • you need multi-node horizontal scaling, which is no longer part of the open-source offering

Facets

plugin · maturity active

database analytics search-engine caching compression monitoring databases analytics time-series iot big-data self-hosted windows self-hosted cloud postgresql-extension time-series-database hypertables columnar-storage real-time-analytics continuous-aggregates data-tiering sql linux macos docker kubernetes

10 sources

Member repositories

RepositoryRoleHealth v2
timescale/timescaledbmain99

For agents

markdown · JSON · MCP: product_card(name="timescale/timescaledb")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem