BTrDB vs openGemini
BTrDB
btrdb.readthedocs.ioopenGemini
opengemini.orgSide by side
| Figure | BTrDB | openGemini |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 1 |
| Spec score · Time-Series Databases | 8.8 (#3) | 8.5 (#8) |
| Security | The documentation describes TLS access on port 4411 and API-key authentication, which requires the TLS port. | Username and password authentication is supported but disabled by default in the configuration; HTTPS also requires configuration to enable. |
Plans
In their own words
BTrDB
BTrDB is a free, MIT-licensed, self-hosted time-series database for dense, high-precision univariate data. It partitions data by time, supports clustered deployments, and records stream changes as new versions that queries can target. Streams have unique UUIDs and can be grouped into collections with tags and annotations. The Python btrdb package supports nanosecond-timestamp queries and insertion through Stream.insert, PyArrow tables, or pandas DataFrames. Statistical queries can return minimum, maximum, mean, count, and standard deviation without reading every underlying point. StreamSets can be filtered and converted to pandas DataFrames or CSV files. Documentation shows Dash and Plotly visualization, Ray-worker serialization, TLS on port 4411, and API-key authentication requiring that TLS port. Arrow-based tabular methods are documented for commercial customers with Arrow-enabled servers. BTrDB reports measured cluster throughput and compression figures, and describes smart-grid aggregation at city or country scale as one target use case.
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.