Thanks Tom for the clear explanation.
Unfortunately I don't get actual improvements. I use PG 11 and I run the following commands:
ALTER TABLE subscriptions ALTER tags SET STATISTICS 1000;
ANALYZE subscriptions;
However the bias remains pretty much the same (slightly worse after). Any idea?
On Sun, Feb 2, 2020 at 6:11 PM Tom Lane <tgl@xxxxxxxxxxxxx> wrote:
Marco Colli <collimarco91@xxxxxxxxx> writes:
> Let's say that you have a simple query like the following on a large table
> (for a multi-tenant application):
> SELECT "subscribers".* FROM "subscribers" WHERE "subscribers"."project_id"
> = 123 AND (tags @> ARRAY['de']::varchar[]);
> If you run EXPLAIN ANALYZE you can see that stats are completely wrong.
> For example I get an expected count of 3,500 rows whereas the actual
> result is 20 rows. This also results in bad query plans...
> In a previous discussion someone said that this wrong estimate is because
> @> uses a fixed selectivity of 0.001, **regardless of actual data**!!
> Is that true?
Hasn't been true since 9.2.
You might get some insight from looking into the most_common_elems,
most_common_elem_freqs, and elem_count_histogram fields of the pg_stats
view.
It seems likely to me that increasing the statistics target for this array
column would help. IIRC, estimates for values that don't show up in
most_common_elems are going to depend on the lowest frequency that *does*
show up there ... so if you want better resolution for non-common values,
you need more entries.
regards, tom lane
