#	exclusive	inclusive	rows x	rows	loops	node
1.	0.004	1272.393	↓ 0.0	0	1	→ Limit (cost=0.00..6815.39 rows=1 width=16) (actual time=1272.393..1272.393 rows=0 loops=1)
2.	1272.389	1272.389	↓ 0.0	0	1	→ Index Scan using phonestatus_callbackdate on dialapplet_predictivedialer_phonestatus s (cost=0.00..2651185.11 rows=389 width=16) (actual time=1272.389..1272.389 rows=0 loops=1) Index Cond: (callbackdate <= now()) Filter: ((campaignid = 554) AND (firstincontact = 1) AND (status = 'unassigned'::text))

Limit  (cost=0.00..6815.39 rows=1 width=16) (actual time=1272.393..1272.393 rows=0 loops=1)

  ->  Index Scan using phonestatus_callbackdate on dialapplet_predictivedialer_phonestatus s  (cost=0.00..2651185.11 rows=389 width=16) (actual time=1272.389..1272.389 rows=0 loops=1)

        Index Cond: (callbackdate <= now())

        Filter: ((campaignid = 554) AND (firstincontact = 1) AND (status = 'unassigned'::text))

Total runtime: 1272.435 ms

Per node type stats

node type	count	sum of times	% of query
Index Scan	1	1272.389 ms	100.0 %
Limit	1	0.004 ms	0.0 %

Per table stats

Table name	Scan count	Total time	% of query
scan type	count	sum of times	% of table
dialapplet_predictivedialer_phonestatus	1	1272.389 ms	100.0 %
Index Scan	1	1272.389 ms	100.0 %

explain.depesz.com

A tool for finding a real cause for slow queries.

Result: aOB

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Per node type stats

Per table stats