BTrDB vs LinDB
BTrDB
btrdb.readthedocs.io
LinDB
lindb.ioSide by side
| Figure | BTrDB | LinDB |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | — | Yes |
| 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) | 8.7 (#5) |
| Integrations | The Python documentation includes examples using Dash and Plotly to plot data queried from BTrDB. | The documentation says data ingestion currently supports FlatBuffers, Protocol Buffers, and InfluxDB protocols. |
| 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 says a single server could support more than one million write TPS. |
| Product | BTrDB is a time-partitioned, version-annotated clustered time-series database for high-density univariate data. | LinDB is an open-source, cloud-native, horizontally scalable distributed time-series database. |
| Security | The documentation describes TLS access on port 4411 and API-key authentication, which requires the TLS port. | The introduction lists self-governance and defensive capability against malicious users as features. |
| Support | The documentation invites users to submit pull requests or open a GitHub issue for additions or corrections. | The product page links to GitHub channels for feature requests, bug reports, and questions. |
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.
LinDB
LinDB is an open-source distributed time-series database for IoT and IT operations monitoring. It stores and analyzes metrics across IaaS, PaaS, SaaS and application layers, using a schema-free model of metrics, tags and fields. LinQL provides SQL-like syntax for queries and cluster or database operations; data can be inserted and queried through an HTTP REST API, client connections and an admin console. Ingestion supports FlatBuffers, Protocol Buffers and InfluxDB protocols, with Go and Java client libraries listed. The system separates computation from storage and identifies ETCD as its only external dependency. It supports replicas, horizontal scaling, operation across multiple IDCs, and automatic minute, hour and day rollups. Deployment documentation covers standalone and cluster modes, with packages for Linux, Windows and macOS and a Docker guide for Linux. LinDB is free under Apache 2.0. Its data scope is limited to numeric time series with accuracy up to the second; it is not suited to text records or finer time precision.