Re: [HACKERS] PATCH: multivariate histograms and MCV lists - Mailing list pgsql-hackers
From | Dean Rasheed |
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Subject | Re: [HACKERS] PATCH: multivariate histograms and MCV lists |
Date | |
Msg-id | CAEZATCVqG5gBkn0RM8rSU1ZKz1boVYshz_XM-=s+TQovqX4kdw@mail.gmail.com Whole thread Raw |
In response to | Re: [HACKERS] PATCH: multivariate histograms and MCV lists (Tomas Vondra <tomas.vondra@2ndquadrant.com>) |
Responses |
Re: [HACKERS] PATCH: multivariate histograms and MCV lists
|
List | pgsql-hackers |
On 17 July 2018 at 14:03, Tomas Vondra <tomas.vondra@2ndquadrant.com> wrote: > For equalities it's going to be hard. The only thing I can think of at the > moment is checking if there are any matching buckets at all, and using that > to decide whether to extrapolate the MCV selectivity to the non-MCV part or > not (or perhaps to what part of the non-MCV part). > So I decided to play a little more with this, experimenting with a much simpler approach -- this is for MCV's only at the moment, see the attached (very much WIP) patch (no doc or test updates, and lots of areas for improvement). The basic idea when building the MCV stats is to not just record the frequency of each combination of values, but also what I'm calling the "base frequency" -- that is the frequency that that combination of values would have if the columns were independent (i.e., the product of each value's individual frequency). The reasoning then, is that if we find an MCV entry matching the query clauses, the difference (frequency - base_frequency) can be viewed as a correction to be applied to the selectivity returned by clauselist_selectivity_simple(). If all possible values were covered by matching MCV entries, the sum of the base frequencies of the matching MCV entries would approximately cancel out with the simple selectivity, and only the MCV frequencies would be left (ignoring second order effects arising from the fact that clauselist_selectivity_simple() doesn't just sum up disjoint possibilities). For partial matches, it will use what multivariate stats are available to improve upon the simple selectivity. I wondered about just storing the difference (frequency - base_frequency) in the stats, but it's actually useful to have both values, because then the total of all the MCV frequencies can be used to set an upper bound on the non-MCV part. The advantage of this approach is that it is very simple, and in theory ought to be reasonably applicable to arbitrary combinations of clauses. Also, it naturally falls back to the univariate-based estimate when there are no matching MCV entries. In fact, even when there are no matching MCV entries, it can still improve upon the univariate estimate by capping it to 1-total_mcv_sel. I tested it with the same data posted previously and a few simple queries, and the initial results are quite encouraging. Where the previous patch sometimes gave noticeable over- or under-estimates, this patch generally did better: Query Actual rows Est (HEAD) Est (24 Jun patch) Est (new patch) Q1 50000 12625 48631 49308 Q2 40000 9375 40739 38710 Q3 90000 21644 172688 88018 Q4 140000 52048 267528 138228 Q5 140000 52978 267528 138228 Q6 140000 52050 267528 138228 Q7 829942 777806 149886 822788 Q8 749942 748302 692686 747922 Q9 15000 40989 27595 14131 Q10 15997 49853 27595 23121 Q1: a=1 and b=1 Q2: a=1 and b=2 Q3: a=1 and (b=1 or b=2) Q4: (a=1 or a=2) and (b=1 or b=2) Q5: (a=1 or a=2) and (b<=2) Q6: (a=1 or a=2 or a=4) and (b=1 or b=2) Q7: (a=1 or a=2) and not (b=2) Q8: (a=1 or a=2) and not (b=1 or b=2) Q9: a=3 and b>0 and b<3 Q10: a=3 and b>0 and b<1000 I've not tried anything with histograms. Possibly the histograms could be used as-is, to replace the non-MCV part (other_sel). Or, a similar approach could be used, recording the base frequency of each histogram bucket, and then using that to refine the other_sel estimate. Either way, I think it would be necessary to exclude equality clauses from the histograms, otherwise MCVs might end up being double-counted. Regards, Dean
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