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?
