Jesper -- I apologize for top-quoting -- a challenged reader. This doesn't directly address your question, but I can't help but notice that the estimates for rows is _wildly_ off the actual number in each and every query. How often / recently have you run ANALYZE on this table ? Are the timing results consistent over several runs ? It is possible that caching effects are entering into the time results. Greg Williamson ----- Original Message ---- From: Jesper Krogh <jesper@xxxxxxxx> To: pgsql-performance@xxxxxxxxxxxxxx Sent: Fri, January 1, 2010 3:48:43 AM Subject: Message queue table - strange performance drop with changing limit size. Hi. I have a "message queue" table, that contains in the order of 1-10m "messages". It is implemented using TheSchwartz: http://search.cpan.org/~bradfitz/TheSchwartz-1.07/lib/TheSchwartz.pm So when a "worker" picks the next job it goes into the "job" table an select the top X highest priority messages with the "funcid" that it can work on. The table looks like this: db=# \d workqueue.job Table "workqueue.job" Column | Type | Modifiers ---------------+----------+--------------------------------------------------------------- jobid | integer | not null default nextval('workqueue.job_jobid_seq'::regclass) funcid | integer | not null arg | bytea | uniqkey | text | insert_time | integer | run_after | integer | not null grabbed_until | integer | not null priority | smallint | coalesce | text | Indexes: "job_funcid_key" UNIQUE, btree (funcid, uniqkey) "funcid_coalesce_priority" btree (funcid, "coalesce", priority) "funcid_prority_idx2" btree (funcid, priority) "job_jobid_idx" btree (jobid) efam=# explain ANALYZe select jobid from workqueue.job where job.funcid in (3) order by priority asc limit 1000; QUERY PLAN ------------------------------------------------------------------------------------------------------------------------------------------------ Limit (cost=0.00..2008.53 rows=1000 width=6) (actual time=0.077..765.169 rows=1000 loops=1) -> Index Scan using funcid_prority_idx2 on job (cost=0.00..7959150.95 rows=3962674 width=6) (actual time=0.074..763.664 rows=1000 loops=1) Index Cond: (funcid = 3) Total runtime: 766.104 ms (4 rows) efam=# explain ANALYZe select jobid from workqueue.job where job.funcid in (3) order by priority asc limit 50; QUERY PLAN ---------------------------------------------------------------------------------------------------------------------------------------------- Limit (cost=0.00..100.43 rows=50 width=6) (actual time=0.037..505.765 rows=50 loops=1) -> Index Scan using funcid_prority_idx2 on job (cost=0.00..7959150.95 rows=3962674 width=6) (actual time=0.035..505.690 rows=50 loops=1) Index Cond: (funcid = 3) Total runtime: 505.959 ms (4 rows) efam=# explain ANALYZe select jobid from workqueue.job where job.funcid in (3) order by priority asc limit 10; QUERY PLAN -------------------------------------------------------------------------------------------------------------------------------------------- Limit (cost=0.00..20.09 rows=10 width=6) (actual time=0.056..0.653 rows=10 loops=1) -> Index Scan using funcid_prority_idx2 on job (cost=0.00..7959152.95 rows=3962674 width=6) (actual time=0.054..0.640 rows=10 loops=1) Index Cond: (funcid = 3) Total runtime: 0.687 ms (4 rows) So what I see is that "top 10" takes < 1ms, top 50 takes over 500 times more, and top 1000 only 1.5 times more than top 50. What can the reason be for the huge drop between limit 10 and limit 50 be? -- Jesper -- Sent via pgsql-performance mailing list (pgsql-performance@xxxxxxxxxxxxxx) To make changes to your subscription: http://www.postgresql.org/mailpref/pgsql-performance -- Sent via pgsql-performance mailing list (pgsql-performance@xxxxxxxxxxxxxx) To make changes to your subscription: http://www.postgresql.org/mailpref/pgsql-performance
