An observability database aims to ingest, analyze and store Metrics, Tracing and Logging data.

Clone this repo:
  1. 8709266 Support archive deleted schema rows when lifecycle row replay data from measure/stream (#1180) by mrproliu · 6 hours ago main
  2. 68e5a74 fix: remove mcp from release signing process (#1179) by Huang Youliang · 20 hours ago
  3. 4421b30 fix(lifecycle): resolve sender identity and add lifecycle migration dashboard (#1178) by Gao Hongtao · 2 days ago
  4. 6bd0863 Add vectorized pull-mode trace query pipeline (#1177) by Gao Hongtao · 2 days ago
  5. 6dc1b19 chore(deps): bump esbuild, @vitejs/plugin-vue and vite in /ui (#1176) by dependabot[bot] · 2 days ago

BanyanDB

Continuous Integration Go Report Card GitHub release GitHub release date GoDoc

BanyanDB, as an observability database, aims to ingest, analyze and store Metrics, Tracing and Logging data. It's designed to handle observability data generated by observability platform and APM system, like Apache SkyWalking etc.

Introduction

BanyanDB, as an observability database, aims to ingest, analyze and store Metrics, Tracing, and Logging data. It's designed to handle observability data generated by Apache SkyWalking. Before BanyanDB emerges, the Databases that SkyWalking adopted are not ideal for the APM data model, especially for saving tracing and logging data. Consequently, There’s room to improve the performance and resource usage based on the nature of SkyWalking data patterns.

The database research community usually uses RUM Conjecture to describe how a database access data. BanyanDB combines several access methods to build a comprehensive APM database to balance read cost, update cost, and memory overhead.

Contact us

Documentation

Contributing

For developers who want to contribute to this project, see the Contribution Guide.

License

Apache 2.0 License.