Fast Data
Keep your analytics fresh to the second on mutable data. StarRocks resolves changes at ingest, so updates are immediately queryable without impacting query performance.
Primary Key table
StarRocks' primary key index enables data changes to be efficiently resolved during data ingestion, optimizing read performance while supporting sub-ten-second data freshness with mutable data.
Streaming & CDC ingestion
Stream inserts and updates directly from Flink and Kafka; StarRocks applies changes in real time, so you can analyze up-to-date records—no batch jobs, no stale results.
Fast Queries
Deliver sub-second latency at scale with an engine purpose-built for modern CPUs and complex SQL.
SIMD-optimized fully vectorized execution engine
Columnar storage and vectorized operators maximize modern CPUs for fast scans and aggregations. Built in C++ to take full advantage of SIMD instruction sets.
Cost-based optimizer
Uses table and column statistics to pick join order, pruning, and pushdown. Delivering stable plans for complex queries without hand-tuning.
Massively parallel joins & aggregations
Run large fact/fact joins and high-cardinality aggregations at scale with an MPP engine. Query normalized schemas directly, without pre-aggregation or denormalization.
Predictable p95/p99 under load
Resource-group isolation and skew-aware data layouts spread hotspots and cut wasted scans, stabilizing p95/p99 latency in multi-tenant workloads.
Fast Delivery
Ship features faster with no denormalization, no heavy ETL, and no lock-in. StarRocks runs directly on open table formats, so you keep governance intact while cutting complexity and cost.
Query open tables formats directly
Run sub-second analytics directly on Apache Iceberg, Delta Lake, and Apache Hudi without ingest pipelines and data copies, so governance stays intact.
Shared-data architecture
Persist data on object storage like S3 and scale compute separately, gaining elasticity while lowering long-term storage cost.
SQL compatibility
ANSI SQL syntax, MySQL protocol, and Trino/Presto dialect support. StarRocks is compatible with a wide range of clients and BI tools.
Asynchronous materialized view (AMV)
Build on demand to speed up heavy joins and aggregations, with automatic query rewrite so your SQL stays unchanged.
Fast for Agents
Built to serve LLMs and agents at scale—low latency, high concurrency, no manual tuning.
Extreme concurrency with consistent latency
Designed to handle high-volume, bursty queries from agents, delivering predictable sub-second responses.
Unified, real‑time access across all data
Agents get immediate context across operational, analytical, and historical data in open formats. No pipelines, no data silo.
LLM with context
StarRocks MCP server provides table and column metadata directly to your LLM agents, improving query accuracy and reducing hallucinations.
Built-in vector index
Support for approximate similarity search and embedding lookup gives agents memory and context for more intelligent, natural interactions.
