Re: Slow query with a lot of data - Mailing list pgsql-performance

Am 20.08.2008 um 20:06 schrieb Scott Carey:

Ok, so the problem boils down to the sort at the end.

The query up through the merge join on domain is as fast as its going to get.  The sort at the end however, should not happen ideally.  There are not that many rows returned, and it should hash_aggregate if it thinks there is enough space to do so.

The query planner is going to choose the sort > agg over the hash-agg if it estimates the total number of resulting rows to be large enough so that the hash won't fit in work_mem.   However, there seems to be another factor here based on this:


GroupAggregate  (cost=11745105.66..12277396.
81 rows=28704 width=12)"
"  ->  Sort  (cost=11745105.66..11878034.93 rows=53171707 width=12)"

"        Sort Key: a."user", b.category"
"        ->  Merge Join  (cost=149241.25..1287278.89 rows=53171707 width=12)"

"              Merge Cond: (b.domain = a.domain)"


The planner actually thinks there will only be 28704 rows returned of width 12.  But it chooses to sort 53 million rows before aggregating.  Thats either a bug or there's something else wrong here.   That is the wrong way to aggregate those results no matter how much work_mem you have unless I'm completely missing something...

You can try rearranging the query just to see if you can work around this.  What happens if you compare the explain on:

select
 a."user", b.category, sum(1.0/b.cat_count)::float
 from result a, domain_categories b
 where a."domain" = b."domain"
 and b.depth < 4
 and a.results > 100
 and a."user" < 30000
 group by a."user", b.category

"HashAggregate  (cost=1685527.69..1686101.77 rows=28704 width=12)"
"  ->  Merge Join  (cost=148702.25..1286739.89 rows=53171707 width=12)"

"        Merge Cond: (b.domain = a.domain)"
"        ->  Index Scan using domain_categories_domain on domain_categories b  (cost=0.00..421716.32 rows=5112568 width=12)"
"              Filter: (depth < 4)"

"        ->  Sort  (cost=148415.16..148513.60 rows=39376 width=8)"

"              Sort Key: a.domain"
"              ->  Bitmap Heap Scan on result a  (cost=1249.93..145409.79 rows=39376 width=8)"
"                    Recheck Cond: ("user" < 30000)"
"                    Filter: (results > 100)"
"                    ->  Bitmap Index Scan on result_user_idx  (cost=0.00..1240.08 rows=66881 width=0)"
"                          Index Cond: ("user" < 30000)"

to

select
 c."user", c.category, sum(1.0/c.cat_count)::float
 from (select a."user", b.category, b.cat_count
  from result a, domain_categories b
    where a."domain" = b."domain"
      and b.depth < 4
      and a.results > 100
      and a."user" < 30000 ) c
 group by c."user", c.category

"HashAggregate  (cost=1685527.69..1686101.77 rows=28704 width=12)"
"  ->  Merge Join  (cost=148702.25..1286739.89 rows=53171707 width=12)"

"        Merge Cond: (b.domain = a.domain)"
"        ->  Index Scan using domain_categories_domain on domain_categories b  (cost=0.00..421716.32 rows=5112568 width=12)"
"              Filter: (depth < 4)"

"        ->  Sort  (cost=148415.16..148513.60 rows=39376 width=8)"

"              Sort Key: a.domain"
"              ->  Bitmap Heap Scan on result a  (cost=1249.93..145409.79 rows=39376 width=8)"
"                    Recheck Cond: ("user" < 30000)"
"                    Filter: (results > 100)"
"                    ->  Bitmap Index Scan on result_user_idx  (cost=0.00..1240.08 rows=66881 width=0)"
"                          Index Cond: ("user" < 30000)"

It shouldn't make a difference, but I've seen things like this help before so its worth a try.  Make sure work_mem is reasonably sized for this test.

It's exactly the same. work_mem was set to 3000MB.

Another thing that won't be that fast, but may avoid the sort, is to select the subselection above into a temporary table, analyze it, and then do the outer select.  Make sure your settings for temporary space (temp_buffers in 8.3) are large enough for the intermediate results (700MB should do it).  That won't be that fast, but it will most likely be faster than sorting 50 million + rows.  There are lots of problems with this approach but it may be worth the experiment.

I'll try this.

Thanks so far!

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