| commit | 395bbf2ff70c4c94b60ea610e9a5176545c4ae5b | [log] [tgz] |
|---|---|---|
| author | Gao Hongtao <hanahmily@gmail.com> | Thu Apr 25 22:10:49 2024 +0800 |
| committer | GitHub <noreply@github.com> | Thu Apr 25 22:10:49 2024 +0800 |
| tree | 52ad6d3626902954ceb9f8adc970b67d0987afc4 | |
| parent | bf6c09966ea2d62b1032ac26a86b307d11a479af [diff] |
Support querying multi-groups in a single request (#438)
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.
Request to join SkyWalking slack mail to the mail list(dev@skywalking.apache.org), we will invite you in.[CN] Request to join SkyWalking slack mail to the mail list(dev@skywalking.apache.org), we will invite you in.For developers who want to contribute to this project, see the Contribution Guide.