BTrDB vs Gnocchi
BTrDB
btrdb.readthedocs.io
Gnocchi
gnocchi.osci.ioSide by side
| Figure | BTrDB | Gnocchi |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | — | — |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 0 |
| Spec score · Time-Series Databases | 8.8 (#3) | 7.7 (#21) |
| Installation | The maker recommends installing btrdb-python with pip and offers data-science and all-optional-dependency extras. | Gnocchi can be installed using pip, from source, or using Docker; the documentation also describes an OpenShift installation option. |
| Integrations | The Python documentation includes examples using Dash and Plotly to plot data queried from BTrDB. | The documentation lists support for Grafana, Prometheus Remote Write, Nagios/Icinga, StatsD, Collectd, InfluxDB line protocol, and AMQP 1.0. |
| Product | BTrDB is a time-partitioned, version-annotated clustered time-series database for high-density univariate data. | Gnocchi is an open-source time series database for storing and indexing time series data and resources at large scale. |
| Support | The documentation invites users to submit pull requests or open a GitHub issue for additions or corrections. | The project documentation points users to its source code, GitHub bug tracker, and the #gnocchi IRC channel on OFTC. |
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.