StarRocks is the world’s fastest open query engine for sub-second, ad-hoc analytics both on and off the data lakehouse. With average query performance 3x faster than other popular alternatives, StarRocks is a query engine that eliminates the need for denormalization and adapts to your use cases, without having to move your data or rewrite SQL. A Linux Foundation project.
Last Releases
- 3.3.15[BugFix] Revert Catch cancel status from be to retry (#59642) (#59988) Source: https://github.com/StarRocks/starrocks/releases/tag/3.3.15
- 3.5.0: [BugFix] Revert PR #59009 (backport #59815) (#59827)Signed-off-by: sevev qiangzh95@gmail.com Co-authored-by: zhangqiang qiangzh95@gmail.com Source: https://github.com/StarRocks/starrocks/releases/tag/3.5.0
- 3.4.4[BugFix] Fix invalid database id when recycling partition (backport #… Source: https://github.com/StarRocks/starrocks/releases/tag/3.4.4
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How to Install StarRocks Locally: A Comprehensive Guide for Developers and Data Engineers
Introduction Installing data tools locally is crucial for testing, developing, or experimenting with their functionality in a controlled environment. StarRocks, an open-source distributed SQL engine optimized for real-time analytics, is no exception. A full local installation involves deploying all its components: the Frontend (FE), Backend (BE), and optionally the Broker (for external file system support).…
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Troubleshooting Common Issues with Local StarRocks Installation
Introduction Setting up a data tool like StarRocks locally is invaluable for data engineers, as it enables rapid testing, sandbox development, and an opportunity to understand a system’s internal mechanics. However, the installation process often presents challenges that can be time-consuming if not addressed with accurate information. This guide focuses on common installation issues for…
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Exploring StarRocks: A High-Performance Analytics Database for Real-Time Data Insights
Introduction to StarRocks StarRocks is a high-performance analytics database designed for handling large volumes of data with low-latency query performance. Originally developed by ex-Baidu engineers, it addresses the challenges faced in high-throughput analytical workloads, especially where near real-time insights are critical. This tool has been gaining traction among data professionals due to its speed, efficiency,…