Re: default statistics target testing (was: Simple postgresql.conf wizard)
От | Robert Haas |
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Тема | Re: default statistics target testing (was: Simple postgresql.conf wizard) |
Дата | |
Msg-id | 603c8f070812052052p283a70f1x7b7b80ffeb821c5@mail.gmail.com обсуждение исходный текст |
Ответ на | Re: default statistics target testing (was: Simple postgresql.conf wizard) (Robert Treat <xzilla@users.sourceforge.net>) |
Список | pgsql-hackers |
>> > That is interesting. It would also be interesting to total up the time it >> > takes to run EXPLAIN (without ANALYZE) for a large number of queries. > I wonder if we'd see anything dramatically different using PREPARE... Well... the point here is to measure planning time. I would think that EXPLAIN would be the best way to get that information without confounding factors. >> OK, I did this. I actually tried 10 .. 100 in increments of 10 and >> then 100 ... 1000 in increments of 50, for 7 different queries of >> varying complexity (but all generally similar, including all of them >> having LIMIT 100 as is typical for this database). I planned each >> query 100 times with each default_statistics_target. The results were >> somewhat underwhelming. > The one thing this test seems to overlook is at what point do we see > diminshing returns from increasing dst. I think the way to do this would be > to plot dst setting vs. query time; Robert, do you think you could modify > your test to measure prepare time and then execute time over a series of > runs? I did some previous testing on query #1 where I determined that it runs just as fast with default_statistics_target=1 (no, that's not a typo) as default_statistics_target=1000. The plan is stable down to values in the 5-7 range; below that it changes but not appreciably for the worse. I could test the other queries but I suspect the results are similar because the tables are small and should be well-modelled even when the MCV and histogram sizes are small. The point here is to figure out how much we're paying in additional planning time in the worst-case scenario where the statistics aren't helping. ...Robert
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