On Mon, Mar 30, 2020 at 06:14:42PM +0200, Pavel Stehule wrote:
po 30. 3. 2020 v 18:06 odesÃlatel Pavel Stehule <pavel.steh...@gmail.com>
napsal:
Hi
when I was in talk with Silvio Moioli, I found strange hash join. Hash was
created from bigger table.
https://www.postgresql.org/message-id/79dd683d-3296-1b21-ab4a-28fdc2d98807%40suse.de
Now it looks so materialized CTE disallow hash
create table bigger(a int);
create table smaller(a int);
insert into bigger select random()* 10000 from generate_series(1,100000);
insert into smaller select i from generate_series(1,100000) g(i);
analyze bigger, smaller;
-- no problem
explain analyze select * from bigger b join smaller s on b.a = s.a;
postgres=# explain analyze select * from bigger b join smaller s on b.a =
s.a;
QUERY PLAN
----------------------------------------------------------------------------------------------------------------------------
Hash Join (cost=3084.00..7075.00 rows=100000 width=8) (actual
time=32.937..87.276 rows=99994 loops=1)
Hash Cond: (b.a = s.a)
-> Seq Scan on bigger b (cost=0.00..1443.00 rows=100000 width=4)
(actual time=0.028..8.546 rows=100000 loops=1)
-> Hash (cost=1443.00..1443.00 rows=100000 width=4) (actual
time=32.423..32.423 rows=100000 loops=1)
Buckets: 131072 Batches: 2 Memory Usage: 2785kB
-> Seq Scan on smaller s (cost=0.00..1443.00 rows=100000
width=4) (actual time=0.025..9.931 rows=100000 loops=1)
Planning Time: 0.438 ms
Execution Time: 91.193 ms
(8 rows)
but with materialized CTE
postgres=# explain analyze with b as materialized (select * from bigger),
s as materialized (select * from smaller) select * from b join s on b.a =
s.a;
QUERY PLAN
----------------------------------------------------------------------------------------------------------------------
Merge Join (cost=23495.64..773995.64 rows=50000000 width=8) (actual
time=141.242..193.375 rows=99994 loops=1)
Merge Cond: (b.a = s.a)
CTE b
-> Seq Scan on bigger (cost=0.00..1443.00 rows=100000 width=4)
(actual time=0.026..11.083 rows=100000 loops=1)
CTE s
-> Seq Scan on smaller (cost=0.00..1443.00 rows=100000 width=4)
(actual time=0.015..9.161 rows=100000 loops=1)
-> Sort (cost=10304.82..10554.82 rows=100000 width=4) (actual
time=78.775..90.953 rows=100000 loops=1)
Sort Key: b.a
Sort Method: external merge Disk: 1376kB
-> CTE Scan on b (cost=0.00..2000.00 rows=100000 width=4)
(actual time=0.033..39.274 rows=100000 loops=1)
-> Sort (cost=10304.82..10554.82 rows=100000 width=4) (actual
time=62.453..74.004 rows=99996 loops=1)
Sort Key: s.a
Sort Method: external sort Disk: 1768kB
-> CTE Scan on s (cost=0.00..2000.00 rows=100000 width=4)
(actual time=0.018..31.669 rows=100000 loops=1)
Planning Time: 0.303 ms
Execution Time: 199.919 ms
(16 rows)
It doesn't use hash join - the estimations are perfect, but plan is
suboptimal
I was wrong, the estimation on CTE is ok, but JOIN estimation is bad
Merge Join (cost=23495.64..773995.64 rows=50000000 width=8) (actual
time=141.242..193.375 rows=99994 loops=1)
That's because eqjoinsel_inner won't have any statistics for either side
of the join, so it'll use default ndistinct values (200), resulting in
estimate of 0.5% for the join condition.
But this should not affect the choice of join algorithm, I think,
because that's only the output of the join.
regards
--
Tomas Vondra http://www.2ndQuadrant.com
PostgreSQL Development, 24x7 Support, Remote DBA, Training & Services
