Improve PostgreSQL query performance - sql

Question

When running this query in my server it's very slow, and I can't understand why. Can anyone help me figure it out?
Query:
SELECT
"t_dat"."t_year" AS "c0",
"t_dat"."t_month" AS "c1",
"t_dat"."t_week" AS "c2",
"t_dat"."t_day" AS "c3",
"t_purs"."p_id" AS "c4",
sum("t_purs"."days") AS "m0",
sum("t_purs"."timecreated") AS "m1"
FROM "t_dat", "t_purs"
WHERE "t_purs"."created" = "t_dat"."t_key"
AND "t_dat"."t_year" = 2013
AND "t_dat"."t_month" = 3
AND "t_dat"."t_week" = 9
AND "t_dat"."t_day" IN (1,2)
AND "t_purs"."p_id" IN (
'4','15','18','19','20','29',
'31','35','46','56','72','78')
GROUP BY
"t_dat"."t_year",
"t_dat"."t_month",
"t_dat"."t_week",
"t_dat"."t_day",
"t_purs"."p_id"
Explain Analyze:
HashAggregate (cost=12252.04..12252.04 rows=1 width=28) (actualtime=10212.374..10212.384 rows=10 loops=1)
-> Nested Loop (cost=0.00..12252.03 rows=1 width=28) (actual time=3016.006..10212.249 rows=14 loops=1)
Join Filter: (t_dat.t_key = t_purs.created)
-> Seq Scan on t_dat (cost=0.00..129.90 rows=1 width=20) (actual time=0.745..2.040 rows=48 loops=1)
Filter: ((t_day = ANY ('{1,2}'::integer[])) AND (t_year = 2013) AND (t_month = 3) AND (t_week = 9))
-> Seq Scan on t_purs (cost=0.00..12087.49 rows=9900 width=16) (actual time=0.018..201.630 rows=14014 loops=48)
Filter: (p_id = ANY ('{4,15,18,19,20,29,31,35,46,56,72,78}'::integer[]))
Total runtime: 10212.470 ms

Answer 1

It is difficult to say what exactly you are missing, but if I were you, I would make sure that following index exists:
CREATE INDEX t_dat_id_date_idx
ON t_dat (t_key, t_year, t_month, t_week, t_day);
For t_purs, create this index:
CREATE INDEX t_purs_created_p_id_idx
ON t_purs (created, p_id);

Answer 2

Consider using a single column in your table:
t_date date
instead of (t_year, t_month, t_week, t_day). The data type date occupies 4 byte. That would shrink your table a bit, make the index smaller and faster and grouping a lot easier.
Year, month, week and day can easily and quickly be extracted from a date with extract(). Your query could then look like this and would be faster:
SELECT extract (year FROM t_date) AS c0
,extract (month FROM t_date) AS c1
,extract (week FROM t_date) AS c2
,extract (day FROM t_date) AS c3
,p.p_id AS c4
,sum(p.days) AS m0
,sum(p.timecreated) AS m1
FROM t_dat d
JOIN t_purs p ON p.created = d.t_key
WHERE d.t_date IN ('2013-03-01'::date, '2013-03-02'::date)
AND p.p_id IN (4,15,18,19,20,29,31,35,46,56,72,78)
GROUP BY d.t_date, p.p_id;
More important for performance is the index, which would then simply be:
CREATE INDEX t_dat_date_idx ON t_dat (t_key, t_date);
Or, depending on data distribution:
CREATE INDEX t_dat_date_idx ON t_dat (t_date, t_key);
The sequence of column matters. You may even create both.

Answer 3

Your query is having sequential scans on t_purs and t_dat. Creating the appropriate indexes will help you make this query faster and avoid sequential scans.
create index index_name on t_purs(created) where created is not null;
create index index_name on t_dat using btree(t_key, t_year, t_month, t_week, t_day)
Run explain analyze after running the above two queries. You'll see the plan time and execution time will be reduced.