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Re: Speeding up aggregates

From: Joe Conway <mail(at)joeconway(dot)com>
To: Tom Lane <tgl(at)sss(dot)pgh(dot)pa(dot)us>
Cc: Josh Berkus <josh(at)agliodbs(dot)com>, pgsql-performance(at)postgresql(dot)org
Subject: Re: Speeding up aggregates
Date: 2002-12-08 19:37:47
Message-ID: 3DF39F8B.4050801@joeconway.com (view raw or flat)
Thread:
Lists: pgsql-performance
Tom Lane wrote:
> Note that even though there's no SORT, the sort_mem setting is used
> to determine the allowable hashtable size, so a too-small sort_mem
> might discourage the planner from selecting hashed aggregation.
> Use EXPLAIN to see which query plan gets chosen.
> 

Just to follow up on my last post, I did indeed find that bumping up sort_mem 
caused a switch back to HashAggregate, and a big improvement:

parts=# show sort_mem ;
  sort_mem
----------
  8192
(1 row)

parts=# set sort_mem to 32000;
SET
parts=# show sort_mem ;
  sort_mem
----------
  32000
(1 row)

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
----------------------------------------------------------------------------------------------------------------------------
  HashAggregate  (cost=5254.46..5432.10 rows=35528 width=36) (actual 
time=1286.89..1399.36 rows=4189 loops=1)
    Filter: (sum(qty_oh) > 0::double precision)
    ->  Hash Join  (cost=1319.10..4710.31 rows=72553 width=36) (actual 
time=163.36..947.54 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..266.20 rows=72553 loops=1)
          ->  Hash  (cost=1230.28..1230.28 rows=35528 width=14) (actual 
time=162.70..162.70 rows=0 loops=1)
                ->  Seq Scan on inv i  (cost=0.00..1230.28 rows=35528 
width=14) (actual time=0.04..88.98 rows=35528 loops=1)
  Total runtime: 1443.93 msec
(8 rows)

parts=# set sort_mem to 8192;
SET
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 

----------------------------------------------------------------------------------------------------------------------------------
  GroupAggregate  (cost=11111.93..12015.10 rows=35528 width=36) (actual 
time=2836.98..3261.66 rows=4189 loops=1)
    Filter: (sum(qty_oh) > 0::double precision)
    ->  Sort  (cost=11111.93..11293.31 rows=72553 width=36) (actual 
time=2836.73..2937.78 rows=72548 loops=1)
          Sort Key: i.part_id
          ->  Hash Join  (cost=1319.10..5254.45 rows=72553 width=36) (actual 
time=155.42..1258.40 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..308.57 rows=72553 loops=1)
                ->  Hash  (cost=1230.28..1230.28 rows=35528 width=14) (actual 
time=155.19..155.19 rows=0 loops=1)
                      ->  Seq Scan on inv i  (cost=0.00..1230.28 rows=35528 
width=14) (actual time=0.02..86.82 rows=35528 loops=1)
  Total runtime: 3281.75 msec
(10 rows)


So when it gets used, HashAggregate has provided a factor of two improvement 
on this test case at least. Nice work, Tom!

Joe


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Subject: is insertion and movement times are correlated to the size of the database?
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