Re: Incorrect estimates on correlated filters - Mailing list pgsql-performance

On Aug 12, 2008, at 4:59 PM, Chris Kratz wrote:

Ran into a re-occuring performance problem with some report queries again today.  In a nutshell, we have filters on either multiple joined tables, or multiple columns on a single table that are highly correlated.  So, the estimates come out grossly incorrect (the planner has no way to know they are correlated).  2000:1 for one I'm looking at right now.  Generally this doesn't matter, except in complex reporting queries like these when this is the first join of 40 other joins.  Because the estimate is wrong at the lowest level, it snowballs up through the rest of the joins causing the query to run very, very slowly.   In many of these cases, forcing nested loops off for the duration of the query fixes the problem.  But I have a couple that still are painfully slow and shouldn't be.

I've been reading through the archives with others having similar problems (including myself a year ago).  Am I right in assuming that at this point there is still little we can do in postgres to speed up this kind of query?  Right now the planner has no way to know the correlation between different columns in the same table, let alone columns in different tables.  So, it just assumes no correlation and returns incorrectly low estimates in cases like these.

The only solution I've come up with so far is to materialize portions of the larger query into subqueries with these correlated filters which are indexed and analyzed before joining into the larger query.  This would keep the incorrect estimates from snowballing up through the chain of joins.

Are there any other solutions to this problem?

Well... you could try and convince certain members of the community that we actually do need some kind of a query hint mechanism... ;)

I did make a suggestion a few months ago that involved sorting a table on different columns and recording the correlation of other columns. The scheme isn't perfect, but it would help detect cases like a field populated by a sequence and another field that's insert timestamp; those two fields would correlate highly, and you should even be able to correlate the two histograms; that would allow you to infer that most of the insert times for _id's between 100 and 200 will be between 2008-01-01 00:10 and 2008-01-01 00:20, for example.
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Decibel!, aka Jim C. Nasby, Database Architect  decibel@decibel.org
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