TimescaleDB co-founder here. TimescaleDB is a more versatile time-series database. It supports a variety of datatypes (text, ints, floats, arrays, json), allow

TimescaleDB co-founder here. TimescaleDB is a more versatile time-series database. It supports a variety of datatypes (text, ints, floats, arrays, json), allows for out-of-order writes and backfilling of old data, supports full SQL, JOINs between tables (eg for metadata), flexible continuous aggregates, native compression, and is backed by the reliability of Postgres. [0] M3DB seems much more limited in scope [1]: "Current Limitations Due to the nature of the requirements for the project, which are primarily to reduce the cost of ingesting and storing billions of timeseries and providing fast scalable reads, there are a few limitations currently that make M3DB not suitable for use as a general purpose time series database. The project has aimed to avoid compactions when at all possible, currently the only compactions M3DB performs are in-memory for the mutable compressed time series window (default configured at 2 hours). As such out of order writes are limited to the size of a single compressed time series window. Consequently backfilling large amounts of data is not currently possible. The project has also optimized the storage and retrieval of float64 values, as such there is no way to use it as a general time series database of arbitrary data structures just yet." [0] https://www.timescale.com/ https://www.timescale.com/ [1] https://m3db.github.io/m3/m3db/#current-limitations https://m3db.github.io/m3/m3db/#current-limitations