FiloDB vs TimechoDB
FiloDB
github.com
TimechoDB
timecho-global.comSide by side
| Figure | FiloDB | TimechoDB |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | Yes |
| Open source | — | — |
| Windows | — | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 1 |
| Spec score · Time-Series Databases | 8.1 (#12) | 7.5 (#29) |
| Deployment | FiloDB is deployed as standalone processes in a cluster and requires Java, Cassandra, Kafka, Rust, and a C compiler to build; SBT is also listed as a build prerequisite. | The product supports embedded, edge, and cloud deployments, as well as standalone and distributed modes. |
| Integrations | The README describes using FiloDB as a Grafana data source through its Prometheus-compatible HTTP API and identifies Cassandra as its persistence store. | The ecosystem guide lists integrations including Kafka, Flink, Spark, Grafana, DataEase, DBeaver, ThingsBoard, Ignition, and Telegraf. |
| Product | FiloDB is an open-source distributed, real-time, in-memory time-series, event, and operational database with Prometheus query support. | TimechoDB is an enterprise-grade product based on Apache IoTDB with enhanced performance, expanded features, and customization. |
| Scale | It is designed to ingest and query many millions of time series, with data sharded across processes and distributed querying built in. | The maker says it has managed billions of time series, handled tens of millions of data points per second, and achieved a 10:1 lossless compression ratio. |
| Support | The repository invites contributions; it does not state a paid support plan on the README page. | The product page describes professional support with service-level agreements and lists expert support and usage training in its feature comparison. |
Plans
In their own words
FiloDB
FiloDB is a free, Apache-2.0 open-source database for distributed, real-time time-series, event, and operational data. It is designed to ingest and query many millions of time series, sharding data across processes with distributed querying. Columnar compression and off-heap memory management support substantial in-memory storage while reducing garbage collection. PromQL is available through an HTTP API and command-line interface; the README describes support as about 60%. Histograms are a first-class column type, with the project reporting possible compression and query-speed improvements of up to two orders of magnitude. Typical ingestion uses Apache Kafka, while the Gateway accepts Influx Line Protocol over TCP. Grafana can connect through the Prometheus-compatible API, and Cassandra is identified as the persistence store. Deployment uses clustered standalone processes and requires Java, Cassandra, Kafka, Rust, a C compiler, and SBT to build.
TimechoDB
TimechoDB has no write-up on Specifiction yet; its figures are above.