Re: n_distinct off by a factor of 1000

[Klaudie Willis <Klaudie.Willis@xxxxxxxxxxxxxx>]

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.Willis@xxxxxxxxxxxxxx> 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 <ronljohnsonjr@xxxxxxxxx> 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

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