BTrDB vs SiriDB
BTrDB
btrdb.readthedocs.io
SiriDB
siridb.comSide by side
| Figure | BTrDB | SiriDB |
|---|---|---|
| Price from | Free | €150/mo |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | — | — |
| macOS | — | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 3 |
| Spec score · Time-Series Databases | 8.8 (#3) | 7.5 (#27) |
| Integrations | The Python documentation includes examples using Dash and Plotly to plot data queried from BTrDB. | The maker lists a Grafana data source plugin and native connectors for C/C++, Python, Go, and Node.js. |
| License | The BTrDB server repository identifies its license as MIT. | The multi-node clustering version is available as open source under the MIT license. |
| Product | BTrDB is a time-partitioned, version-annotated clustered time-series database for high-density univariate data. | SiriDB is an open-source time-series database for analyzing and aggregating time-series data from sources including IoT and financial transactions. |
| Security | The documentation describes TLS access on port 4411 and API-key authentication, which requires the TLS port. | The HTTP API documentation describes basic authentication, while the SiriDB HTTP connector page says it supports SSL. |
| Support | The documentation invites users to submit pull requests or open a GitHub issue for additions or corrections. | Community support is available through Slack and GitHub, and commercial support packages include remote monitoring and health checks. |
Plans
SiriDB
- Basic support€150 a month
- Standard support€400 a month
- Premium support€800 a month
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.