openGemini/openGemini
CNCF sandbox project, an open source distributed time-series database with high concurrency, high performance, and high scalability observed · 2026-09-03
Health v2 · maintenance only
84/100
- Activity 100
- Release rhythm 53
- Longevity 100
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: 67.0
- age_days: 1528
- days_rel: 233
- days_push: 0
- n_releases_24m: 7
Adoption not part of the score
1172 stars · 176 forks observed · 2026-09-03
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
openGemini is a cloud-native distributed time-series database written in Go, designed for storing and analyzing massive telemetry data such as metrics, logs, and traces. It is a CNCF sandbox project that emphasizes high performance, high scalability, and high cardinality support for observability workloads.
Use cases
- store and query massive telemetry data for observability
- handle high cardinality time-series data without excessive index memory usage
- deploy a distributed time-series database cluster that scales horizontally
- replace InfluxDB with better write performance and compression
- collect and analyze metrics, logs, and traces for system troubleshooting
- run a self-hosted time-series database for IoT sensor data
When to choose
- you need a distributed, horizontally scalable time-series database for observability or IoT workloads
- your data has high cardinality that overwhelms traditional time-series databases
- you want strong data compression (15:1 or higher) to reduce storage costs
- you prefer a cloud-native, CNCF-backed project with no third-party dependencies
- you need faster write performance than InfluxDB for time-series ingestion
When to avoid
- you need a simple single-node embedded time-series store for a small application
- your workload is relational with complex joins rather than time-series data
- you require a fully managed cloud database service rather than self-hosted deployment
- your team depends on a mature ecosystem with extensive third-party tooling beyond observability
Facets
service · maturity active
database monitoring analytics streaming databases monitoring iot big-data time-series cloud self-hosted go time-series-database observability distributed-database cncf-sandbox high-cardinality influxdb-compatible lsm-storage mpp-architecture telemetry-data columnar-storage devops linux docker kubernetes
2 sources
- readme: https://github.com/openGemini/openGemini · fetched 2026-09-03 · 60236ae0318d
- homepage: https://www.opengemini.org · fetched 2026-08-29 · 2c0f2ee93cc4
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| openGemini/openGemini | main | 84 |
For agents
markdown · JSON · MCP: product_card(name="openGemini/openGemini")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem