Re: Performance of the Materialize operator in a query plan
От | PFC |
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Тема | Re: Performance of the Materialize operator in a query plan |
Дата | |
Msg-id | op.t94ke0tfcigqcu@apollo13.peufeu.com обсуждение исходный текст |
Ответ на | Re: Performance of the Materialize operator in a query plan (Viktor Rosenfeld <rosenfel@informatik.hu-berlin.de>) |
Ответы |
Re: Performance of the Materialize operator in a query plan
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Список | pgsql-performance |
> Do you mean, that the overhead is an artefact of timing the query? In > that case, the query should run faster than its evaluation with EXPLAIN > ANALYZE, correct? > > Is there a way to test this assumption regarding the speed of > gettimeofday? I'm on a Macbook and have no idea about the performance > of its implementation. Run EXPLAIN ANALYZE query Type \timing Run SELECT count(*) FROM (query) AS foo \timing gives timings as seen by the client. If you're local, and the result set is one single integer, client timings are not very different from server timings. If the client must retrieve lots of rows, this will be different, hence the fake count(*) above to prevent this. You might want to explain the count(*) also to be sure the same plan is used... And yes EXPLAIN ANALYZE has overhead, sometimes significant. Think Heisenberg... You will measure it easily with this dumb method ;) Here a very dumb query : SELECT count(*) FROM test; count ------- 99999 (1 ligne) Temps : 26,924 ms test=> EXPLAIN ANALYZE SELECT count(*) FROM test; QUERY PLAN -------------------------------------------------------------------------------- -------------------------------- Aggregate (cost=1692.99..1693.00 rows=1 width=0) (actual time=66.314..66.314 r ows=1 loops=1) -> Seq Scan on test (cost=0.00..1442.99 rows=99999 width=0) (actual time=0. 013..34.888 rows=99999 loops=1) Total runtime: 66.356 ms (3 lignes) Temps : 66,789 ms Apparently measuring the time it takes to get a row from the table takes 2x as long as actually getting the row from the table. Which is reassuring, in a way, since grabbing rows out of tables isn't such an unusual operation.
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