# pipelinedb/pipelinedb

High-performance time-series aggregation for PostgreSQL

Repository: https://github.com/pipelinedb/pipelinedb
Canonical: https://ross.abutalabs.com/products/pipelinedb
Homepage: https://www.pipelinedb.com
Language: C
License: Apache-2.0
License Family: permissive
Topics: realtime, stream-processing, analytics, sql, push, postgresql, pipelinedb, time-series, aggregation
Last push: 2022-02-20T10:24:23+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 4664, "days_push": 1655, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2662, forks 243 (observed 2026-08-28T04:07:08.727532+00:00)

## What it is
PipelineDB is a PostgreSQL extension for high-performance time-series aggregation, letting you define continuous SQL queries that incrementally aggregate streaming data into queryable tables. It stores only aggregate output rather than raw events, acting like high-throughput, auto-refreshing materialized views.

## Use cases
- run continuous SQL aggregations over streaming event data in PostgreSQL
- build realtime reporting dashboards without a separate stream processor
- compute rolling time-series metrics like counts and averages at high throughput
- chain continuous queries into pipelines of incremental SQL transforms
- avoid storing raw event data while keeping aggregates queryable

## When to choose
- you already run PostgreSQL and want stream aggregation without extra infrastructure
- you need high-throughput incremental aggregation of time-series data
- you want SQL-based continuous views instead of learning a new stream-processing system
- you only need aggregate outputs, not raw event storage

## When to avoid
- you need a project with active development and new releases (it is in maintenance mode after joining Confluent)
- you need PostgreSQL 12 or newer (only PG 10 and 11 are supported)
- you need full stream processing features like exactly-once semantics across distributed sources
- you want to retain raw event data long-term

## Facets
- artifact type: library
- maturity: maintenance
- function: database, streaming, analytics, etl
- domain: databases, analytics, big-data, time-series
- platform: self-hosted
- tags: postgresql-extension, continuous-queries, time-series-aggregation, materialized-views, stream-processing, sql, real-time, linux, macos, web-server

## Member repositories
- pipelinedb/pipelinedb (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:08.727532+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:18:22.032193+00:00, confidence not recorded.
  - readme: https://github.com/pipelinedb/pipelinedb (fetched 2026-08-28T04:07:08.727532+00:00, sha b08f1f132194)
  - homepage: https://www.pipelinedb.com (fetched 2026-08-29T10:01:09.224789+00:00, sha 50c92f0f4ac2)
- Data as of 2026-08-30T08:39:29.467469+00:00.