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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.
Sub-second ad-hoc analytics across data lakes and warehouses
StarRocks delivers sub‑second, ad‑hoc analytics on‑premise or directly on data lakehouse formats like Hive, Iceberg, Delta Lake and Hudi, with a vectorized SQL engine that’s 3× faster than alternatives.
StarRocks is a high‑performance, vectorized SQL engine designed for sub‑second, ad‑hoc analytics on both traditional data warehouses and modern lakehouse storage. It supports full ANSI‑SQL, the MySQL protocol, and a cost‑based optimizer that automatically generates efficient execution plans for complex multi‑dimensional queries. Real‑time upserts and intelligent materialized views keep data fresh while delivering fast query responses.
The system consists of Frontend and Backend nodes that scale horizontally without single points of failure. Starting with version 3.0, a shared‑data architecture further reduces cost and improves scalability. Deployment is straightforward on Linux environments using Docker or native binaries, and the engine can directly query external tables in Hive, Iceberg, Delta Lake, or Hudi without data movement. Resource management features enable tenant isolation and quota enforcement, making StarRocks suitable for cloud‑native, multi‑tenant analytics platforms.
When teams consider StarRocks, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Business intelligence dashboards with sub‑second refresh
Analysts receive instant query results across multi‑dimensional data, enabling real‑time decision making.
Lakehouse analytics without data movement
Teams query Hive/Iceberg tables directly, eliminating ETL latency and storage duplication.
Real‑time inventory tracking
Upserts on primary‑key tables keep stock levels current while supporting concurrent analytical queries.
Automated reporting via materialized views
Materialized views refresh on data load, delivering pre‑aggregated results with no manual maintenance.
No. It can query data directly from external lakehouse formats such as Hive, Iceberg, Delta Lake, and Hudi.
StarRocks implements ANSI‑SQL and is compatible with the MySQL protocol, so most standard queries work out of the box.
It uses a native vectorized execution engine and a cost‑based optimizer that fully exploits CPU parallelism.
Yes. Built‑in resource management lets you isolate workloads and enforce quotas per tenant.
StarRocks is released under the Apache License 2.0.
Project at a glance
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