T E Schmitz escribió:
QUERY PLAN
GroupAggregate (cost=1370368.19..1435888.88 rows=5801 width=56) (actual
time=11945.030..13163.156 rows=5801 loops=1)
-> Sort (cost=1370368.19..1379715.86 rows=3739067 width=56) (actual
time=11944.753..12462.623 rows=120117 loops=1)
Sort Key: history.stock, history."day", history.high, history.low
-> Hash Left Join (cost=160.02..391554.63 rows=3739067
width=56) (actual time=52.746..3778.409 rows=120117 loops=1)
Hash Cond: ((("outer".stock)::text =
("inner".stock)::text) AND ("outer"."day" = "inner"."day"))
Join Filter: ("inner"."day" >= ("outer"."day" - 7))
-> Nested Loop Left Join (cost=0.00..204441.26
rows=3739067 width=57) (actual time=0.077..2313.375 rows=120117 loops=1)
It's estimating >3M, but it finds 120K rows.
Join Filter: (("inner".stock)::text =
("outer".stock)::text)
-> Seq Scan on history (cost=0.00..131.01
rows=5801 width=34) (actual time=0.016..34.845 rows=5801 loops=1)
-> Index Scan using idx_history_day on history
past_month (cost=0.00..22.32 rows=645 width=23) (actual
time=0.020..0.185 rows=21 loops=5801)
Index Cond: ((past_month."day" >=
("outer"."day" - 30)) AND (past_month."day" < "outer"."day"))
-> Hash (cost=131.01..131.01 rows=5801 width=23) (actual
time=52.608..52.608 rows=5801 loops=1)
-> Seq Scan on history past_week (cost=0.00..131.01
rows=5801 width=23) (actual time=0.010..25.110 rows=5801 loops=1)
Total runtime: 13187.729 ms
Try running a vacuum analyze on the database (or at least the tables
which differ in rows estimated and actual (history for example))
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select 'mmarques' || '@' || 'unl.edu.ar' AS email;
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Martín Marqués | Programador, DBA
Centro de Telemática | Administrador
Universidad Nacional
del Litoral
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