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Comment by RedShift1 | Hacker News Reader

Maybe just vacuum much more aggressively? Also there have been a lot of changes to the vacuuming and auto vacuuming process these last few years, you can pretty much forget about it.

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Not in our experience; for our cases it is still a resource hog. We discussed it even less than a year ago with core devs and with a large postgres consultancy place; they said postgres doesn't fit our use case which was already our conclusion, no matter how much we want it to be. Mysql is smooth as butter. I have nothing to win from picking mysql just that it works; I rather use postgres as features / not oracle but...

Edit; also, as can be seen here in responses, and elsewhere on the web when discussing this, the fans say it's no problem, but many less religious users feel it's a massive design flaw (perfectly logical at the time, not so logical now) that sometimes will stop users from using it, which is a shame

What is your use case?

We have 100000s tables per database (1000s of those) (think sensor/iot data with some magic sauce that 0 of our competitors offer) that are heavy on the changes. And yes, maybe it's the wrong tool (is it though if it works without hickups?) for the job (but migrating would be severe so we would only attempt that if we are 100% sure it will work and if the endresult would be cheaper; remember; we are talking decades here, not a startup), but mysql has been taking this without any issues for decades with us (including the rapid growth of the past decade) now while far smaller setups with postgres have been really painful and all because of vacuum. We were postgres in 1999 when we ran many millions of records through it, but that was when we could do a full vacuum at night without anyone noticing. The internet grew a little bit, so that's not possible anymore. Vacuum improved too like everyone says here, and i'm not spreading the gospel or whatever; just fans (... what other word is there) blindly stating it can do loads 'now' they never considered is, well weird.

I'd generally call this amount of tables an antipattern - doing this basically implies that there's information stored in the table names that should be in rows instead, like IDs etc. -- But I'll admit that sensor related use cases have a tendency to stress the system in unusual ways, which may have forced this design.

Especially back when we started. Now we would've done it differently, but still think postgres wouldn't really work. Guess we will never now as even far smaller data sets do not work in the way we need them.

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