#	exclusive	inclusive	rows x	rows	loops	node
1.	4.958	1952.349	↑ 2.3	67	1	→ HashAggregate (cost=251315.390..251316.960 rows=157 width=4) (actual time=1952.282..1952.349 rows=67 loops=1)
2.	13.517	1947.391	↑ 29.4	3322	1	→ Nested Loop (cost=1678.360..250826.310 rows=97817 width=4) (actual time=39.496..1947.391 rows=3322 loops=1)
3.	878.785	1910.394	↑ 32.1	2935	1	→ Hash Left Join (cost=1678.360..75649.710 rows=94105 width=8) (actual time=39.477..1910.394 rows=2935 loops=1) Hash Cond: ((two_india.sierra)::text = (uniform.sierra)::text) Filter: ((two_india.two_juliet @ 'juliet_two'::seven_five) OR ((uniform.tango && 'juliet_two'::seven_five) AND november_papa(uniform.tango, 'juliet_two'::seven_five)))
4.	992.652	992.652	↓ 1.0	801934	1	→ Index Scan using india_five on two_india (cost=0.000..36121.240 rows=799554 width=125) (actual time=0.019..992.652 rows=801934 loops=1) Index Cond: ((seven_papa > 'two_foxtrot'::timestamp with time zone) AND (seven_papa < 'india_two'::timestamp with time zone))
5.	21.998	38.957	↓ 1.0	15848	1	→ Hash (cost=521.380..521.380 rows=15838 width=469) (actual time=38.957..38.957 rows=15848 loops=1) Buckets: 1024 Batches: 8 Memory Usage: 322kB
6.	16.959	16.959	↓ 1.0	15848	1	→ Seq Scan on uniform (cost=0.000..521.380 rows=15838 width=469) (actual time=0.007..16.959 rows=15848 loops=1)
7.	23.480	23.480	↑ 1.0	1	2935	→ Index Scan using xray on juliet_quebec (cost=0.000..1.850 rows=1 width=12) (actual time=0.007..0.008 rows=1 loops=2935) Index Cond: (november_whiskey = two_india.november_whiskey)

HashAggregate  (cost=251315.390..251316.960 rows=157 width=4) (actual time=1952.282..1952.349 rows=67 loops=1)
  ->  Nested Loop  (cost=1678.360..250826.310 rows=97817 width=4) (actual time=39.496..1947.391 rows=3322 loops=1)
        ->  Hash Left Join  (cost=1678.360..75649.710 rows=94105 width=8) (actual time=39.477..1910.394 rows=2935 loops=1)
                Hash Cond: ((two_india.sierra)::text = (uniform.sierra)::text)
                Filter: ((two_india.two_juliet @ 'juliet_two'::seven_five) OR ((uniform.tango && 'juliet_two'::seven_five) AND november_papa(uniform.tango, 'juliet_two'::seven_five)))
              ->  Index Scan using india_five on two_india  (cost=0.000..36121.240 rows=799554 width=125) (actual time=0.019..992.652 rows=801934 loops=1)
                      Index Cond: ((seven_papa > 'two_foxtrot'::timestamp with time zone) AND (seven_papa < 'india_two'::timestamp with time zone))
              ->  Hash  (cost=521.380..521.380 rows=15838 width=469) (actual time=38.957..38.957 rows=15848 loops=1)
                      Buckets: 1024  Batches: 8  Memory Usage: 322kB
                    ->  Seq Scan on uniform  (cost=0.000..521.380 rows=15838 width=469) (actual time=0.007..16.959 rows=15848 loops=1)
        ->  Index Scan using xray on juliet_quebec  (cost=0.000..1.850 rows=1 width=12) (actual time=0.007..0.008 rows=1 loops=2935)
                Index Cond: (november_whiskey = two_india.november_whiskey)

Per node type stats

node type	count	sum of times	% of query
Hash	1	21.998 ms	1.1 %
Hash Left Join	1	878.785 ms	45.0 %
HashAggregate	1	4.958 ms	0.3 %
Index Scan	2	1016.132 ms	52.0 %
Nested Loop	1	13.517 ms	0.7 %
Seq Scan	1	16.959 ms	0.9 %

Per table stats

Table name	Scan count	Total time	% of query
scan type	count	sum of times	% of table
juliet_quebec	1	23.480 ms	1.2 %
Index Scan	1	23.480 ms	100.0 %
two_india	1	992.652 ms	50.8 %
Index Scan	1	992.652 ms	100.0 %
uniform	1	16.959 ms	0.9 %
Seq Scan	1	16.959 ms	100.0 %

explain.depesz.com

A tool for finding a real cause for slow queries.

Result: ZPX

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Per node type stats

Per table stats