Re: [patch] BUG #15005: ANALYZE can make pg_class.reltuples inaccurate.

On Mon, 12 Mar 2018 12:21:34 -0400
Tom Lane <tgl(at)sss(dot)pgh(dot)pa(dot)us> wrote:

> I wrote:
> > Maybe this type of situation is an argument for trusting an ANALYZE-based
> > estimate more than the VACUUM-based estimate. I remain uncomfortable with
> > that in cases where VACUUM looked at much more of the table than ANALYZE
> > did, though. Maybe we need some heuristic based on the number of pages
> > actually visited by each pass?
>
> I looked into doing something like that. It's possible, but it's fairly
> invasive; there's no clean way to compare those page counts without
> altering the API of acquire_sample_rows() to pass back the number of pages
> it visited. That would mean a change in FDW-visible APIs. We could do
> that, but I don't want to insist on it when there's nothing backing it up
> except a fear that *maybe* ANALYZE's estimate will be wrong often enough
> to worry about.
>
> So at this point I'm prepared to go forward with your patch, though not
> to risk back-patching it. Field experience will tell us if we need to
> do more. I propose the attached cosmetic refactoring, though.

I like the re-factor. Making vac_estimate_reltuples() specific to the
special case of vacuum and having the normal analyze case just in analyze
seems like an improvement overall.

It it helps I have been experimenting with your thought experiment (update
first 20% of rows, then delete 50% of those) to try to trick analyze. I
built test scripts and generate data and found the the current system
after vacuum is usually about 8% to 10% off on reltuples. Analyze moves it
very slowly closer to the true count. With the patch analyze nails it
immediately.

To see how this was affected by the relationship of table size and sample
I created another test setup just to iterate analyze runs and compare to
the true count. fter a couple thousand analyzes with various table mutations
and table sizes up to 100 M rows, (1.8 M pages) and
default_statistics_targets ranging from 1 to 1000 I am pretty confident that
we can get 2% accuracy for any size table even with the old statistics
target. The table size does not really matter much, the error drops and
clusters more tightly as sample size increases but past 10000 or so it's well
into diminishing returns.

Summary of 100 analyze runs for each line below.
Errors and sample fraction are in percent for easy reading.
/ ----------- percent ---------------/
testcase | Mrows | stats | pages | sample | fraction | maxerr | avgerr | stddev
-----------+-------+-------+---------+--------+----------+--------+--------+---------
first-last | 10 | 1 | 163935 | 300 | 0.001830 | 6.6663 | 2.3491 | 2.9310
first-last | 10 | 3 | 163935 | 900 | 0.005490 | 3.8886 | 1.2451 | 1.5960
first-last | 10 | 10 | 163935 | 3000 | 0.018300 | 2.8337 | 0.7539 | 0.9657
first-last | 10 | 33 | 163935 | 9900 | 0.060390 | 1.4903 | 0.3723 | 0.4653
first-last | 10 | 100 | 163935 | 30000 | 0.182999 | 0.6580 | 0.2221 | 0.2707
first-last | 10 | 333 | 163935 | 99900 | 0.609388 | 0.1960 | 0.0758 | 0.0897
first-last | 100 | 1 | 1639345 | 300 | 0.000183 | 8.7500 | 2.2292 | 2.8685
first-last | 100 | 3 | 1639345 | 900 | 0.000549 | 5.4166 | 1.1695 | 1.5431
first-last | 100 | 10 | 1639345 | 3000 | 0.001830 | 1.7916 | 0.6258 | 0.7593
first-last | 100 | 33 | 1639345 | 9900 | 0.006039 | 1.8182 | 0.4141 | 0.5433
first-last | 100 | 100 | 1639345 | 30000 | 0.018300 | 0.9417 | 0.2464 | 0.3098
first-last | 100 | 333 | 1639345 | 99900 | 0.060939 | 0.4642 | 0.1206 | 0.1542
first-last | 100 | 1000 | 1639345 | 300000 | 0.183000 | 0.2192 | 0.0626 | 0.0776
un-updated | 10 | 1 | 180328 | 300 | 0.001664 | 7.9259 | 2.2845 | 2.7806
un-updated | 10 | 3 | 180328 | 900 | 0.004991 | 4.2964 | 1.2923 | 1.5990
un-updated | 10 | 10 | 180328 | 3000 | 0.016636 | 2.2593 | 0.6734 | 0.8271
un-updated | 10 | 33 | 180328 | 9900 | 0.054900 | 0.9260 | 0.3305 | 0.3997
un-updated | 10 | 100 | 180328 | 30000 | 0.166364 | 1.0162 | 0.2024 | 0.2691
un-updated | 10 | 333 | 180328 | 99900 | 0.553991 | 0.2058 | 0.0683 | 0.0868
un-updated | 100 | 1 | 1803279 | 300 | 0.000166 | 7.1111 | 1.8793 | 2.3820
un-updated | 100 | 3 | 1803279 | 900 | 0.000499 | 3.8889 | 1.0586 | 1.3265
un-updated | 100 | 10 | 1803279 | 3000 | 0.001664 | 2.1407 | 0.6710 | 0.8376
un-updated | 100 | 33 | 1803279 | 9900 | 0.005490 | 1.1728 | 0.3779 | 0.4596
un-updated | 100 | 100 | 1803279 | 30000 | 0.016636 | 0.6256 | 0.1983 | 0.2551
un-updated | 100 | 333 | 1803279 | 99900 | 0.055399 | 0.3454 | 0.1181 | 0.1407
un-updated | 100 | 1000 | 1803279 | 300000 | 0.166364 | 0.1738 | 0.0593 | 0.0724

I also thought about the theory and am confident that there really is no way
to trick it. Basically if there are enough pages that are different to affect
the overall density, say 10% empty or so, there is no way a random sample
larger than a few hundred probes can miss them no matter how big the table is.
If there are few enough pages to "hide" from the sample, then they are so few
they don't matter anyway.

After all this my vote is for back patching too. I don't see any case where
the patched analyze is or could be worse than what we are doing. I'm happy to
provide my test cases if anyone is interested.

Thanks

-dg

--
David Gould daveg(at)sonic(dot)net
If simplicity worked, the world would be overrun with insects.