[tbragin]

However if you really want to optimize data currently residing in Postgres for analytical workloads, as the original comment suggests - consider moving to a dedicated OLAP DB like ClickHouse.

See results from Gitlab benchmarking ClickHouse vs TimescaleDB: https://gitlab.com/gitlab-org/incubation-engineering/apm/apm...

Key findings:

* ClickHouse has a much smaller data volume footprint in all cases by almost a factor of 10.

* There are very few ClickHouse queries that have >1s latency at q95. TimescaleDB has multiple >1s latencies, including a few in the range of 15-25s.

Disclaimer: I work at ClickHouse

[osigurdson]

What we ended up doing is maintain meta-data in Postgres but time series data is stored in ClickHouse. Thanks for making / working on ClickHouse. I appreciate it very much.

[mfreed]

That PoC benchmark didn't turn on Timescale's columnar compression, which every real deployment uses. So misleading at best.

(Timescaler)

[tbragin]

Compression helped, but not enough in our experiments https://benchmark.clickhouse.com/#eyJzeXN0ZW0iOnsiQWxsb3lEQi...