Very well written article! I remember the time when I was tuning Postgresql for achieving the maximum possible ingestion speed for ad analytics data. I end up w

Very well written article! I remember the time when I was tuning Postgresql for achieving the maximum possible ingestion speed for ad analytics data. I end up with the following recommendations - https://gist.github.com/valyala/ae3cbfa4104f1a022a2af9b8656b1131 https://gist.github.com/valyala/ae3cbfa4104f1a022a2af9b8656b... . After that I discovered ClickHouse and forgot about all this performance tuning, since ClickHouse could ingest the same as analytics data at 10x higher speed on the same hardware with default settings. On top of this, it could perform analytical queries by up to 1000 times faster than Postgresql on the same hardware. And, as a bonus, disk space usage dropped by more than 10 times compared to Postgresql. My prediction: if the ingestion of 750 billion rows takes 20 days on highly tuned TimescaleDB and Postgresql, it will take less than a day on Clickhouse with standard settings on the same hardware. P.S. I was conducting a similar benchmark on a single-node VictoriaMetrics a few years ago - it was capable ingesting 500 billion of samples in ~2 hours - https://valyala.medium.com/billy-how-victoriametrics-deals-with-more-than-500-billion-rows-e82ff8f725da https://valyala.medium.com/billy-how-victoriametrics-deals-w... . Every sample contains a single measurement, while every row in the original article contains 5 measurements. So, if extrapolating, it would take `(2h * 5)/500*750 = 15h` to ingest 750 billion of rows with weather measurements into a single-node VictoriaMetrics.