Tyrrill, Ed wrote:
QUERY PLAN
------------------------------------------------------------------------
------------------------------------------------------------------------
-------------------
Merge Left Join (cost=38725295.93..42505394.70 rows=13799645 width=8)
(actual time=6503583.342..8220629.311 rows=93524 loops=1)
Merge Cond: ("outer".record_id = "inner".record_id)
Filter: ("inner".record_id IS NULL)
-> Index Scan using backupobjects_pkey on backupobjects
(cost=0.00..521525.10 rows=13799645 width=8) (actual
time=15.955..357813.621 rows=13799645 loops=1)
-> Sort (cost=38725295.93..39262641.69 rows=214938304 width=8)
(actual time=6503265.293..7713657.750 rows=214938308 loops=1)
Sort Key: backup_location.record_id
-> Seq Scan on backup_location (cost=0.00..3311212.04
rows=214938304 width=8) (actual time=11.175..1881179.825 rows=214938308
loops=1)
Total runtime: 8229178.269 ms
(8 rows)
I ran vacuum analyze after the last time any inserts, deletes, or
updates were done, and before I ran the query above. I've attached my
postgresql.conf. The machine has 4 GB of RAM.
I thought maybe someone with more expertise than me might answer this, but since they haven't I'll just make a comment. It looks to me like the sort of 214 million rows is what's killing you. I suppose you could try to increase the sort memory, but that's a lot of memory. It seems to me an index merge of a relation this large would be faster, but that's a topic for the experts.
On a theoretical level, the problem is that it's sorting the largest table. Perhaps you could re-cast the query so that it only has to sort the smaller table, something like
select a.id from a where a.id not in (select distinct b.id from b)
where "b" is the smaller table. There's still no guarantee that it won't do a sort on "a", though. In fact one of the clever things about Postgres is that it can convert a query like the one above into a regular join, unless you do something like "select ... offset 0" which blocks the optimizer from doing the rearrangement.
But I think the first approach is to try to tune for a better plan using your original query.
Craig
