| tag | 00929a4c765473928acddfeb43e690e88d2912ff | |
|---|---|---|
| tagger | Gao Hongtao <hanahmily@gmail.com> | Thu Jun 02 12:56:49 2022 +0000 |
| object | 73946814b596bba0b979e7b3cad3166b86c54794 |
Release Apache SkyWalking BanyanDB 0.1.0
| commit | 73946814b596bba0b979e7b3cad3166b86c54794 | [log] [tgz] |
|---|---|---|
| author | Gao Hongtao <hanahmily@gmail.com> | Thu Jun 02 20:55:12 2022 +0800 |
| committer | GitHub <noreply@github.com> | Thu Jun 02 20:55:12 2022 +0800 |
| tree | 02bac9a779a2811e971f98791b9409cc9918bc8c | |
| parent | 313a2f40e61ec2045b90bfca63b67be615a1cb9b [diff] |
Add NOTICE to binary package and fix some typo (#128) * Add NOTICE to binary package and fix some typo * Update CONTRIBUTING.md Signed-off-by: Gao Hongtao <hanahmily@gmail.com>
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. In this talk, I will share the details about BanyanDB based on this conjecture, explaining why BanyanDB is more effective and reliable than other storage layers.
For developers who want to contribute to this project, see Contribution Guide