show default_statistics_target; --> 500 ALTER TABLE public.bigtable ALTER COLUMN instrumentid_ref SET STATISTICS 5000;
Here is the output of the "ANALYZE VERBOSE bigtable;" INFO: analyzing "public.bigtables" inheritance tree INFO: "bigtable_y2018": scanned 622250 of 10661013 pages, containing 11994670 live rows and 5091 dead rows; 622250 rows in sample, 205504753 estimated total rows INFO: "bigtable_y2019": scanned 520159 of 8911886 pages, containing 10017582 live rows and 6148 dead rows; 520159 rows in sample, 171631268 estimated total rows INFO: "bigtable_y2020": scanned 357591 of 6126616 pages, containing 7031238 live rows and 1534 dead rows; 357591 rows in sample, 120466385 estimated total rows INFO: analyzing "public.bigtable_y2018" INFO: "bigtable_y2018": scanned 1500000 of 10661013 pages, containing 28915115 live rows and 12589 dead rows; 1500000 rows in sample, 205509611 estimated total rows INFO: analyzing "public.bigtable_y2019" INFO: "bigtable_y2019": scanned 1500000 of 8911886 pages, containing 28888514 live rows and 17778 dead rows; 1500000 rows in sample, 171634096 estimated total rows INFO: analyzing "public.bigtable_y2020" INFO: "bigtable_y2020": scanned 1500000 of 6126616 pages, containing 29488967 live rows and 6330 dead rows; 1500000 rows in sample, 120445051 estimated total rows INFO: analyzing "public.bigtable_y2021" INFO: "bigtable_y2021": scanned 1 of 1 pages, containing 8 live rows and 0 dead rows; 8 rows in sample, 8 estimated total rows ANALYZE On the comment from Adrian: SELECT ( SELECT SUM (x) FROM UNNEST (most_common_freqs) x ) frac_MCV, n_distinct, array_length(most_common_vals,1) n_mcv, array_length(histogram_bounds,1) n_hist, tablename, FROM pg_stats WHERE schemaname = 'public' AND attname like 'instrumentid_ref' frac_MCV;n_distinct; n_mcv; n_hist;tablename 0.9205394 122160 2140 5001 "bigtable" 0.9203018 124312 1736 5001 "bigtable_y2018" 0.9258158 113846 2107 5001 "bigtable_y2020" 0.875 -0.375 2 "bigtable_y2021" 0.92304045 118267 2204 5001 "bigtable_y2019" select count(distinct instrumentid_ref) from bigtable --> 33 385 922 Bigtables instrumentid_ref is underestimated by 300X even when statistics target of the column is 5000; Pretty weird. K ‐‐‐‐‐‐‐ Original Message ‐‐‐‐‐‐‐ On Tuesday, June 23, 2020 3:07 PM, Klaudie Willis <klaudie.wil...@protonmail.com> wrote: > I didn't run it with "verbose" but otherwise, yes, several times. I can do it > again with verbose if you are interested in the output. Just give me some > time. 500M rows 50 columns, is no small job :) > > K > > ‐‐‐‐‐‐‐ Original Message ‐‐‐‐‐‐‐ > On Tuesday, June 23, 2020 2:51 PM, Ron <ronljohnso...@gmail.com> wrote: > >> Maybe I missed it, but did you run "ANALYZE VERBOSE bigtable;"? >> >> On 6/23/20 7:42 AM, Klaudie Willis wrote: >> >>> Friends, >>> >>> I run Postgresql 12.3, on Windows. I have just discovered a pretty >>> significant problem with Postgresql and my data. I have a large table, 500M >>> rows, 50 columns. It is split in 3 partitions by Year. In addition to the >>> primary key, one of the columns is indexed, and I do lookups on this. >>> >>> Select * from bigtable b where b.instrument_ref in (x,y,z,...) >>> limit 1000 >>> >>> It responded well with sub-second response, and it uses the index of the >>> column. However, when I changed it to: >>> >>> Select * from bigtable b where b.instrument_ref in (x,y,z,) >>> limit 10000 -- (notice 10K now) >>> >>> The planner decided to do a full table scan on the entire 500M row table! >>> And that did not work very well. First I had no clue as to why it did so, >>> and when I disabled sequential scan the query immediately returned. But I >>> should not have to do so. >>> >>> I got my first hint of why this problem occurs when I looked at the >>> statistics. For the column in question, "instrument_ref" the statistics >>> claimed it to be: >>> >>> The default_statistics_target=500, and analyze has been run. >>> select * from pg_stats where attname like 'instr%_ref'; -- Result: 40.000 >>> select count(distinct instrumentid_ref) from bigtable -- Result: 33 385 922 >>> (!!) >>> >>> That is an astonishing difference of almost a 1000X. >>> >>> When the planner only thinks there are 40K different values, then it makes >>> sense to switch to table scan in order to fill the limit=10.000. But it is >>> wrong, very wrong, an the query returns in 100s of seconds instead of a few. >>> >>> I have tried to increase the statistics target to 5000, and it helps, but >>> it reduces the error to 100X. Still crazy high. >>> >>> I understand that this is a known problem. I have read previous posts about >>> it, still I have never seen anyone reach such a high difference factor. >>> >>> I have considered these fixes: >>> - hardcode the statistics to a particular ratio of the total number of rows >>> - randomize the rows more, so that it does not suffer from page clustering. >>> However, this has probably other implications >>> >>> Feel free to comment :) >>> >>> K >> >> -- >> Angular momentum makes the world go 'round.
