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.
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.
- Submit an issue by selecting the BanyanDB component.
- Mail list: [email protected]. Mail to [email protected], follow the reply to subscribe the mail list.
- Send
Request to join SkyWalking slackmail to the mail list([email protected]), we will invite you in. - For Chinese speaker, send
[CN] Request to join SkyWalking slackmail to the mail list([email protected]), we will invite you in. - X (Twitter): @BanyanDB and @ASFSkyWalking
- gRPC server
- HTTP server
- Sharding
- Load balance
- Distributed query optimizer
- Data queue
- Schema management
- Time-series abstract layer
- Stream data processor
- Measure data processor
- Property data processor
- TopNAggregation processor
- Index processor
- TTL
- Cold data processor
- WAL
- Stream query processor
- Measure query processor
- Index reader
- Streaming pipeline processor(OR and nested querying)
- Parallel executor
- Cost-based optimizer
- Compaction
- Merge data files
- Sparse index
- Command-line
- Webapp
For developers who want to contribute to this project, see the Contribution Guide.