Gnocchi vs openGemini
Gnocchi
gnocchi.osci.io
openGemini
opengemini.orgSide by side
| Figure | Gnocchi | openGemini |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | — | — |
| Open source | — | — |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 0 | 1 |
| Spec score · Time-Series Databases | 7.7 (#21) | 8.5 (#8) |
| Architecture | Its API and metric processing services are stateless and can be scaled horizontally by running additional daemons or API endpoints. | It uses an MPP architecture with ts-sql, ts-meta, and ts-store components, and says components can be scaled independently. |
| Deployment | Gnocchi can be installed with pip, from source, using Docker, or using OpenShift. | The installation guide describes Linux, macOS, and Windows support; it recommends macOS and Windows for development and debugging. |
| Purpose | Gnocchi is an open-source time series database for storing and indexing time series data and resources at large scale. | openGemini is an open-source, cloud-native distributed time-series database for storing and analyzing large volumes of time-series data. |
| Storage | Documented storage options include file, Ceph, OpenStack Swift, Amazon S3, and Redis; Ceph is described as the recommended driver. | It uses column-based storage and describes data compression, automatic partitioning, and LSM-based storage techniques. |
Plans
In their own words
openGemini
openGemini is a free, open-source distributed database for storing and analyzing time-series data, including metrics, logs, and traces. Its stated application areas include monitoring and observability, IoT, and operations analysis. The system uses an MPP architecture with ts-sql, ts-meta, and ts-store components that can be scaled independently. Column-based storage, compression, automatic partitioning, and LSM-based techniques are part of its storage design. A built-in AI analysis platform provides real-time anomaly detection. Query options include InfluxQL, PromQL, and GeminiQL; ingestion methods include InfluxDB Line Protocol, Apache Arrow Flight, and Prometheus remote_write. It also provides HTTP APIs for expression and metadata queries and remote reads and writes. Deployment is supported on Linux, macOS, and Windows, as well as through Docker and Kubernetes. Authentication is disabled by default and must be enabled in configuration. PromQL compatibility covers 90% of Prometheus functions and operators, with some unsupported.