oceanbase open source analysis
The Fastest Distributed Database for Transactional, Analytical, and AI Workloads.
Project overview
⭐ 9867 · C++ · Last activity on GitHub: 2026-01-06
Why it matters for engineering teams
OceanBase addresses the challenge of managing large-scale, distributed databases that support both transactional and analytical workloads within a single system. It is particularly suited for machine learning and AI engineering teams who require a production ready solution that can handle complex queries and high concurrency with MySQL compatibility. The project has matured into a reliable open source tool for engineering teams operating in cloud-native environments, offering strong consistency and scalability. However, it may not be the right choice for teams prioritising simplicity over distributed architecture or those with minimal experience in managing distributed databases, as operational complexity can increase significantly.
When to use this project
Choose OceanBase when your application demands a scalable, distributed database capable of handling both OLTP and OLAP workloads in production. Consider alternatives if your workload is primarily simple transactional processing or if you prefer a managed database service with less operational overhead.
Team fit and typical use cases
Machine learning and AI engineers benefit most from OceanBase as a self hosted option for managing large-scale data with full-text and vector search capabilities. It is commonly used in products requiring real-time analytics, hybrid transactional and analytical processing, and cloud-native database solutions, supporting teams that need robust, scalable infrastructure for complex data workloads.
Best suited for
Topics and ecosystem
Activity and freshness
Latest commit on GitHub: 2026-01-06. Activity data is based on repeated RepoPi snapshots of the GitHub repository. It gives a quick, factual view of how alive the project is.