Hey everyone,
I'm working on improving gathered statistics on several large tables (2TB / 500M records);
I've created extended stats on correlated columns, ran analyze, compared the pre and post explains, and it seems as though the extended statistics are ignored -
the estimation doesn't change much, and I can accurately derive the est. rows from multiplying the single-column freqs from pg_stats with the supplied values.
Context:
- pg 12.8
- relation w/ three columns of interest - user (text), row_type (text), deleted_at (timestamp).
- different users have different distributions for row_type
- deleted_at is set if said record is marked for deletion - stays marked for a long retention period, around 95% of the rows have a value, and around 5% have null
- we query and operate on the 5% of records with deleted_at = null, most of our indexes are partial on that as well
I'll give a simplified example with two users, one being extremely dominant w/ 99.9% of the data; the next holds much less, but still accounts to around 200k rows.
From pg_stats:
attname | user
n_distinct | 2.0
most_common_vals | {A,B}
most_common_freqs | [0.9996333, 0.00036666667]
attname | row_type
n_distinct | 4.0
most_common_vals | {A,B}
most_common_freqs | [0.9968, 0.0025333334]
attname | deleted_at
n_distinct | 20761.0
null_frac | 0.043133333
<values / bounds are timestamps, omitted for brevity>
Querying before extended stats:
$> explain select 1 from my_rel where user ='B and row_type = 'A' and deleted_at is null;
Index Only Scan using idx_user_row_type_where_deleted_at_is_null on my_rel (cost=0.69..2851.78 rows=8213 width=4)
Index Cond: ((user = 'B') AND (row_type = 'B'))
The number is derived from: reltuples*user_b_freq*row_type_a_freq*deleted_at_null_frac = 520982816*0.00036666667*0.9968*0.043133333 = 8213.26586
When explain-analyzing, I get - (actual time=0.051..72.503 rows=174954 loops=1)
So in this specific case, the estimation is off by around 20x (note that this is a simplified case just to showcase the symptom).
I then create extended stats - I've tried to add them on all three columns in all combinations, and in pairs in all combinations - all leading to the same result,
I'll only showcase the three column variant for brevity:
$> create statistics s1 on user, row_type, deleted_at from my_rel;
$> analyze my_rel;$> explain select 1 from my_rel where user ='B' and row_type = 'A' and deleted_at is null;
Index Only Scan using idx_user_row_type_where_deleted_at_is_null on my_rel (cost=0.69..1560.01 rows=4491 width=4)
After analyzing, pg_stats contain different values, as the large table is sampled - I have user_b_freq = 0.0002, row_type_a_freq = 0.99686664, deleted_at_null_frac = 0.043233335,
thus the new calculation is: 520982816*0.0002*0.99686664*0.043233335 = 4490.64987.
Now it's off by around 40x - and it seems to still only consider the single column distributions.
Is there anything I'm missing ? I thought that maybe in my case, due to the high null fractions of deleted_at, the extended stats aren't used, but couldn't find an obvious hint from the code that would suggest that.
Appreciate your time !
Danny
