Re: Speeding up aggregates - Mailing list pgsql-performance

Hannu Krosing wrote:
> On Sun, 2002-12-08 at 19:31, Joe Conway wrote:
>
>
>>parts=# explain analyze select i.part_id, sum(w.qty_oh) as total_oh from inv
>>i, iwhs w where i.part_id = w.part_id group by i.part_id having sum(w.qty_oh) > 0;
>>                                                             QUERY PLAN
>
> ...
>
>>  Total runtime: 3282.27 msec
>>(10 rows)
>>
>>
>>Note that similar to Josh, I saw a nice improvement when using the
>>HashAggregate on the simpler case, but as soon as I added a HAVING clause the
>>optimizer switched back to GroupAggregate.
>>
>>I'll try to play around with this a bit more later today.
>
>
> Try turning the having into subquery + where:
>
> explain analyze
> select * from (
>     select i.part_id, sum(w.qty_oh) as total_oh
>       from inv i, iwhs w
>      where i.part_id = w.part_id
>      group by i.part_id) sub
> where total_oh > 0;
>

Pretty much the same result. See below.

Joe

======================================
parts=# set sort_mem to 8000;
SET
parts=# explain analyze select * from (select i.part_id, sum(w.qty_oh) as
total_oh from inv i, iwhs w where i.part_id = w.part_id group by i.part_id)
sub where total_oh > 0;
                                                                QUERY PLAN


----------------------------------------------------------------------------------------------------------------------------------------
  Subquery Scan sub  (cost=11111.93..12015.10 rows=35528 width=36) (actual
time=2779.16..3212.46 rows=4189 loops=1)
    ->  GroupAggregate  (cost=11111.93..12015.10 rows=35528 width=36) (actual
time=2779.15..3202.97 rows=4189 loops=1)
          Filter: (sum(qty_oh) > 0::double precision)
          ->  Sort  (cost=11111.93..11293.31 rows=72553 width=36) (actual
time=2778.90..2878.33 rows=72548 loops=1)
                Sort Key: i.part_id
                ->  Hash Join  (cost=1319.10..5254.45 rows=72553 width=36)
(actual time=155.80..1235.32 rows=72548 loops=1)
                      Hash Cond: ("outer".part_id = "inner".part_id)
                      ->  Seq Scan on iwhs w  (cost=0.00..2121.53 rows=72553
width=22) (actual time=0.01..282.38 rows=72553 loops=1)
                      ->  Hash  (cost=1230.28..1230.28 rows=35528 width=14)
(actual time=155.56..155.56 rows=0 loops=1)
                            ->  Seq Scan on inv i  (cost=0.00..1230.28
rows=35528 width=14) (actual time=0.02..86.69 rows=35528 loops=1)
  Total runtime: 3232.84 msec
(11 rows)

parts=# set sort_mem to 12000;
SET
parts=# explain analyze select * from (select i.part_id, sum(w.qty_oh) as
total_oh from inv i, iwhs w where i.part_id = w.part_id group by i.part_id)
sub where total_oh > 0;
                                                             QUERY PLAN


----------------------------------------------------------------------------------------------------------------------------------
  Subquery Scan sub  (cost=5617.22..5794.86 rows=35528 width=36) (actual
time=1439.24..1565.47 rows=4189 loops=1)
    ->  HashAggregate  (cost=5617.22..5794.86 rows=35528 width=36) (actual
time=1439.23..1555.65 rows=4189 loops=1)
          Filter: (sum(qty_oh) > 0::double precision)
          ->  Hash Join  (cost=1319.10..5073.07 rows=72553 width=36) (actual
time=159.39..1098.30 rows=72548 loops=1)
                Hash Cond: ("outer".part_id = "inner".part_id)
                ->  Seq Scan on iwhs w  (cost=0.00..2121.53 rows=72553
width=22) (actual time=0.01..259.48 rows=72553 loops=1)
                ->  Hash  (cost=1230.28..1230.28 rows=35528 width=14) (actual
time=159.11..159.11 rows=0 loops=1)
                      ->  Seq Scan on inv i  (cost=0.00..1230.28 rows=35528
width=14) (actual time=0.03..87.74 rows=35528 loops=1)
  Total runtime: 1609.91 msec
(9 rows)