> TimescaleDB does not even attempt to compete with the performance of purpose-built time series DBs Not sure what you mean by this. TimescaleDB outperforms In

> TimescaleDB does not even attempt to compete with the performance of purpose-built time series DBs Not sure what you mean by this. TimescaleDB outperforms InfluxDB, another purpose-built time-series DB, on most workloads [0], especially on ones with high-cardinality [1]. In particular our key insight, which some may still find heretical and hard-to-believe, is that it is quite possible to produce best-in-class performance characteristics for time-series using a relational database. In particular, we have been able to add columnar compression to our row-oriented format resulting in 96%+ compression rates [2], and multi-node scale-out resulting in 10M+ inserts per second [3]. TimescaleDB is not designed for workloads where most queries touch all data points (ie full table scans). But that's not what time-series workloads look like. [0] https://blog.timescale.com/blog/timescaledb-vs-influxdb-for-time-series-data-timescale-influx-sql-nosql-36489299877/ https://blog.timescale.com/blog/timescaledb-vs-influxdb-for-... [1] https://blog.timescale.com/blog/what-is-high-cardinality-how-do-time-series-databases-influxdb-timescaledb-compare/ https://blog.timescale.com/blog/what-is-high-cardinality-how... [2] https://blog.timescale.com/blog/building-columnar-compression-in-a-row-oriented-database/ https://blog.timescale.com/blog/building-columnar-compressio... [3] https://blog.timescale.com/blog/building-a-distributed-time-series-database-on-postgresql/ https://blog.timescale.com/blog/building-a-distributed-time-...