# StarRocks/starrocks

The world's fastest open query engine for sub-second analytics both on and off the data lakehouse. With the flexibility to support nearly any scenario, StarRocks provides best-in-class performance for multi-dimensional analytics, real-time analytics, and ad-hoc queries. A Linux Foundation project.

Repository: https://github.com/StarRocks/starrocks
Canonical: https://ross.abutalabs.com/products/starrocks
Homepage: https://starrocks.io
Language: Java
License: Apache-2.0
License Family: permissive
Topics: database, olap, sql, analytics, big-data, realtime-database, vectorized, distributed-database, real-time-analytics, mpp, join, star-schema, real-time-updates, delta-lake, hudi, iceberg, lakehouse, datalake, lakehouse-platform, cloudnative
Last push: 2026-08-26T13:02:17+00:00

## Health v2 (maintenance only)
Score: 95/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 99, release rhythm 87, longevity 100
- inputs: {"age_days": 1825, "days_push": 7, "days_rel": 7, "gap_med": 7, "n_releases_24m": 74}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 12043, forks 2544 (observed 2026-08-28T04:10:51.466481+00:00)

## What it is
StarRocks is a high-performance distributed OLAP database and query engine built on an MPP architecture with a fully vectorized execution engine and columnar storage. It supports sub-second analytics on internal tables and directly on data lakehouse formats like Apache Iceberg, Delta Lake, and Hudi, with MySQL protocol compatibility.

## Use cases
- run sub-second ad-hoc SQL queries on large datasets
- query Apache Iceberg, Delta Lake, or Hudi tables without data migration
- power real-time dashboards and BI tools with high concurrency
- ingest streaming CDC data from Kafka and Flink for fresh analytics
- serve low-latency analytics to AI agents
- perform multi-dimensional and multi-table join analytics at scale

## When to choose
- you need sub-second query latency for complex analytical SQL at high concurrency
- you want to analyze data lakehouse tables directly without ETL or denormalization
- you need real-time updates and deletes with fast query performance
- you want MySQL protocol compatibility with existing BI tools

## When to avoid
- you need a simple transactional (OLTP) database for application CRUD workloads
- you cannot operate a distributed cluster and only need single-node embedded analytics
- your workload is primarily document or key-value storage rather than analytical SQL

## Facets
- artifact type: service
- maturity: active
- function: database, search-engine, analytics, streaming, etl
- domain: databases, big-data, analytics
- platform: cloud, self-hosted
- tags: olap, mpp, sql, lakehouse, data-warehouse, iceberg, delta-lake, hudi, vectorized-execution, mysql-protocol, columnar-storage, real-time-analytics, data-engineering, real-time, linux, docker, kubernetes

## Member repositories
- StarRocks/starrocks (main) score 95

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:51.466481+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 2026-08-29T17:14:59.059993+00:00, confidence not recorded.
  - readme: https://github.com/StarRocks/starrocks (fetched 2026-08-28T04:10:51.466481+00:00, sha 1f4d3deeb856)
  - homepage: https://starrocks.io (fetched 2026-08-29T08:12:39.673677+00:00, sha f4e279a5ed85)
  - site_page: https://docs.starrocks.io (fetched 2026-08-29T08:12:39.684184+00:00, sha 44136fa355b3)
  - site_page: https://docs.starrocks.io/docs/quick_start (fetched 2026-08-29T08:12:39.687970+00:00, sha ba5a12ff46b4)
  - site_page: https://docs.starrocks.io/docs/introduction/StarRocks_intro (fetched 2026-08-29T08:12:39.689560+00:00, sha 79a1ffbffbba)
  - site_page: https://www.starrocks.io/feature/index.html (fetched 2026-08-29T08:12:39.686114+00:00, sha 72ae8d8296e0)
- Data as of 2026-08-30T08:39:29.467469+00:00.