[mplanchard]

I haven’t tried timescale, but I have found postgres with time-based partitions works very well for timeseries data. Unless you’ve got really heavy indexes, the insert speed is phenomenal, like you said, and you’ve got the freedom to split your partitions up into whatever size buckets makes the most sense for your ingestion and query patterns.

A really nice pattern has been to use change data capture and kafka to ship data off to clickhouse for long-term storage and analytics, which allows us to simply drop old partitions in postgres after some time.

[abraxas]

I think timescale will compress them heavily on your schedule so if that's acceptable to your use case you might be able to do away with clickhouse. Hard to say of course, without knowing details around your insertion and query patterns, retention requirements and aggregations you need. But timescale can do a lot of that with pretty straightforward syntax.