On Wednesday, May 22, 2013 3:24 AM fburgess wrote: > The SARS_ACTS table currently has 37,115,515 rows > we have indexed: idx_sars_acts_acts_run_id ON SARS_ACTS USING btree (sars_run_id) > we have pk constraint on the SARS_ACTS_RUN table; sars_acts_run_pkey PRIMARY KEY (id ) > serverdb=# explain select count(*) as y0_ from SARS_ACTS this_ inner join SARS_ACTS_RUN tr1_ on this_.SARS_RUN_ID=tr1_.ID where tr1_.ALGORITHM='SMAT'; > QUERY PLAN > -------------------------------------------------------------------------------------------------------------------------- > Aggregate (cost=4213952.17..4213952.18 rows=1 width=0) > -> Hash Join (cost=230573.06..4213943.93 rows=3296 width=0) > Hash Cond: (this_.SARS_RUN_ID=tr1_.ID) > -> Seq Scan om sars_acts this_ (cost=0.00..3844241.84 rows=37092284 width=8) > -> Hash (cost=230565.81..230565.81 rows=580 width=8) > -> Seq Scan on sars_acts_run tr1_ (cost=0.00..230565.81 rows=580 width=8) > Filter: ((algorithm)::text = 'SMAT'::text) > (7 rows) > This query executes in approximately 5.3 minutes to complete, very very slow, our users are not happy. > I did add an index on SARS_ACTS_RUN.ALGORITHM column but it didn't improve the run time. > The planner just changed the "Filter:" to an "Index Scan:" improving the cost of the Seq Scan > on the sars_acts_run table, but the overall run time remained the same. It seems like the bottleneck > is in the Seq Scan on the sars_acts table. > -> Seq Scan on sars_acts_run tr1_ (cost=0.00..230565.81 rows=580 width=8) > Filter: ((algorithm)::text = 'SMAT'::text) > Does anyone have suggestions about how to speed it up? Could you please once trying Analyzing both tables and then run the query to check which plan it uses: Analyze SARS_ACTS; Analyze SARS_ACTS_RUN; With Regards, Amit Kapila. -- Sent via pgsql-performance mailing list (pgsql-performance@xxxxxxxxxxxxxx) To make changes to your subscription: http://www.postgresql.org/mailpref/pgsql-performance
