BTrDB vs TimeBase
BTrDB
btrdb.readthedocs.io
TimeBase
timebase.infoSide by side
| Figure | BTrDB | TimeBase |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | — | Yes |
| Windows | — | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 2 |
| Spec score · Time-Series Databases | 8.8 (#3) | 7.5 (#28) |
| Integrations | The Python documentation includes examples using Dash and Plotly to plot data queried from BTrDB. | The maker lists connectors for ClickHouse, Timescale, S3, Kafka, and Grafana. |
| Performance | The documentation reports 53 million inserted values per second and 119 million queried values per second on a measured four-node cluster of large EC2 nodes. | The maker states throughput of 2 million events per core per second and predictable latencies of 100us in standalone and cluster modes. |
| Product | BTrDB is a time-partitioned, version-annotated clustered time-series database for high-density univariate data. | TimeBase is an event-oriented, high-performance time-series database and messaging middleware with roots in financial markets. |
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.