▲ | petergeoghegan 4 days ago | ||||||||||||||||||||||||||||||||||||||||
Can you provide more detail/a reference? I've done extensive work on improving the Postgres B-Tree code, over quite a number of releases. I'm not aware of any problems with high-insert workloads in particular. I have personally fixed a number of subtle issues that could lead to lower space utilization with such workloads [1][2] in the past, though. if there's a remaining problem in this area, then I'd very much like to know about it. [1] https://www.youtube.com/watch?v=p5RaATILoiE [2] https://speakerdeck.com/peterg/nbtree-arch-pgcon | |||||||||||||||||||||||||||||||||||||||||
▲ | dangoodmanUT 3 days ago | parent [-] | ||||||||||||||||||||||||||||||||||||||||
In a previous use case, when using postgres as a WAL-like append only store, I noticed that indexes would get massive. Then, after a while, they'd magically shrink. I had eventually switched to an API on top of Badger (golang KV), which afforded me an order of magnitude lower latency at ~30% of the resources IIRC. I'm sure there might have been some tuning I could have done to improve it. I've also heard similar behaviors exhibited from other folks who had similar high-write workloads on postgres. Sorry, I don't have anything super tangible to provide off the top of my head, or metrics/code I can share to recreate! It was also a project that required a lot of data to recreate the setup for. | |||||||||||||||||||||||||||||||||||||||||
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