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

Clone this repo:
  1. 67a743f feat(trace): package sampler plugins into mountable images and add a plugin dev toolkit (#1214) by Gao Hongtao · 2 days ago main
  2. 7bb5998 Introduce parameter binding (`?` placeholders) into BydbQL (#1209) by mrproliu · 4 days ago
  3. eae4ef2 chore(deps): bump vitest to ^3.2.6 and echarts to ^6.1.0 to fix Dependabot alerts (#1212) by Gao Hongtao · 4 days ago
  4. 6fb3a1b feat(trace): finalization sampling (PIPELINE_EVENT_FINALIZE) (#1210) by Gao Hongtao · 4 days ago
  5. 59b33b5 Support multiple types for the same tag in a single measure or stream part (#1126) by Huang Youliang · 4 days ago

BanyanDB

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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.

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Documentation

Contributing

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

License

Apache 2.0 License.