Marc Millas <marc.millas@mokadb.com> writes: > 2023=# explain (analyze, buffers) select A.x, count(B.x) from A left join B > on st_within(B.geom, A.geom) group by A.x;
So the problem with this is that the only decently-performant way to do the join is like
> -> Nested Loop (cost=0.13..6275745.36 rows=4971999 width=16) (actual time=0.505..11781.817 rows=210651 loops=2) > -> Parallel Seq Scan on B (cost=0.00..18454.99 rows=248699 width=40) (actual time=0.005..22.859 rows=211395 loops=2) > -> Index Scan using A_geom_idx on A (cost=0.13..25.15 rows=1 width=17752) (actual time=0.054..0.055 rows=1 loops=422789) > Index Cond: (geom ~ B.geom) > Filter: st_within(B.geom, geom)
(Ignore the parallelism, it's not very relevant here.) There's no chance for merge or hash join because those require simple equality join conditions. The only way to avoid a stupid compare-every-row-of-A-to-every-row-of-B nestloop is to use a parameterized inner indexscan, as this plan does. But that only works if the join is inner or has the indexed table on the nullable side. We have no support for nestloop right join, which is what would be needed to make things run fast with no index on B.
