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Version: 2.5

StarRocks

StarRocks is a next-gen, high-performance analytical data warehouse that enables real-time, multi-dimensional, and highly concurrent data analysis. StarRocks has an MPP architecture and is equipped with a fully vectorized execution engine, a columnar storage engine that supports real-time updates, and is powered by a rich set of features including a fully-customized cost-based optimizer (CBO), intelligent materialized view and more. StarRocks supports real-time and batch data ingestion from a variety of data sources. It also allows you to directly analyze data stored in data lakes with zero data migration.

StarRocks is also compatible with MySQL protocols and can be easily connected using MySQL clients and popular BI tools. StarRocks is highly scalable, available, and easy to maintain. It is widely adopted in the industry, powering a variety of OLAP scenarios, such as real-time analytics, ad-hoc queries, data lake analytics and more.

StarRocks is licensed under Apache 2.0, available at the StarRocks GitHub repository (see the StarRocks license). StarRocks (i) links to or calls functions from third party software libraries, the licenses of which are available in the folder licenses-binary; and (ii) incorporates third party software code, the licenses of which are available in the folder licenses.

Join our web forum for asking general questions. Join our Slack channel for chat. For community news, read the StarRocks.io Blog. You can also follow us on LinkedIn to get first-hand updates on new features, events, and sharing.


Introduction

OLAP, features, architecture

Quick Start

Get up and running quickly.

Data Loading

Clean, transform, and load

Table Design

Tables, indexing, acceleration

Data Lakes

Iceberg, Hive, Delta Lake, …

Work with semi-structured data

JSON, map, struct, array

Integrations

BI tools, IDEs, Cloud authentication, …

Administration

Scale, backups, roles and privileges, …

Reference

SQL, functions, error codes, …

FAQs

Frequently asked questions.

Benchmarks

DB performance comparison benchmarks.