#	exclusive	inclusive	rows x	rows	loops	node
1.	166.136	4029.826	↓ 3944.0	3944	1	→ Sort (cost=16.660..16.670 rows=1 width=445) (actual time=4024.033..4029.826 rows=3944 loops=1) Sort Key: xray_uniform.victor, xray_uniform.six_victor_oscar, xray_uniform.foxtrot, xray_india.uniform, xray_uniform.november, xray_uniform.hotel, xray_uniform.four, xray_uniform.golf
2.	1148.497	3863.690	↓ 3944.0	3944	1	→ Nested Loop (cost=1.030..16.650 rows=1 width=445) (actual time=0.402..3863.690 rows=3944 loops=1) Join Filter: (("oscar".foxtrot = "charlie".foxtrot) AND ("oscar".uniform = "charlie".uniform))
3.	95.978	837.849	↓ 3944.0	3944	1	→ Nested Loop (cost=1.030..10.650 rows=1 width=244) (actual time=0.353..837.849 rows=3944 loops=1) Join Filter: ("oscar".november = "charlie".november)
4.	21.012	666.935	↓ 3944.0	3944	1	→ Nested Loop (cost=1.030..9.230 rows=1 width=229) (actual time=0.340..666.935 rows=3944 loops=1) Join Filter: ("oscar".hotel = "charlie".hotel)
5.	172.595	634.091	↓ 3944.0	3944	1	→ Nested Loop (cost=1.030..8.180 rows=1 width=236) (actual time=0.328..634.091 rows=3944 loops=1) Join Filter: (("charlie".hotel = "oscar".hotel) AND ("charlie".sierra_kilo = "oscar".sierra_kilo))
6.	25.683	315.568	↓ 3944.0	3944	1	→ Nested Loop (cost=1.030..7.130 rows=1 width=221) (actual time=0.275..315.568 rows=3944 loops=1) Join Filter: ("charlie".hotel = "oscar".hotel)
7.	13.805	13.805	↓ 3944.0	3944	1	→ Index Scan using yankee on alpha_sierra xray_uniform (cost=0.000..6.020 rows=1 width=203) (actual time=0.057..13.805 rows=3944 loops=1) Index Cond: ((victor = 2000::numeric) AND (six_victor_oscar = 2::numeric) AND (golf = 0::numeric))
8.	201.144	276.080	↑ 1.0	2	3944	→ GroupAggregate (cost=1.030..1.070 rows=2 width=18) (actual time=0.032..0.070 rows=2 loops=3944)
9.	74.889	74.936	↓ 16.5	33	3944	→ Sort (cost=1.030..1.030 rows=2 width=18) (actual time=0.001..0.019 rows=33 loops=3944) Sort Key: mike.bravo.hotel
10.	0.047	0.047	↓ 16.5	33	1	→ Seq Scan on bravo (cost=0.000..1.020 rows=2 width=18) (actual time=0.004..0.047 rows=33 loops=1)
11.	145.928	145.928	↓ 16.5	33	3944	→ Seq Scan on bravo (cost=0.000..1.020 rows=2 width=31) (actual time=0.002..0.037 rows=33 loops=3944)
12.	11.832	11.832	↑ 1.0	2	3944	→ Seq Scan on six_victor_yankee alpha_quebec (cost=0.000..1.020 rows=2 width=23) (actual time=0.001..0.003 rows=2 loops=3944)
13.	74.936	74.936	↑ 1.0	19	3944	→ Seq Scan on zulu sierra_three (cost=0.000..1.190 rows=19 width=24) (actual time=0.002..0.019 rows=19 loops=3944)
14.	1877.344	1877.344	↓ 232.0	232	3944	→ Index Scan using india_charlie on india_tango xray_india (cost=0.000..5.980 rows=1 width=248) (actual time=0.015..0.476 rows=232 loops=3944) Index Cond: ((2000::numeric = victor) AND (2::numeric = six_victor_oscar))

Sort  (cost=16.660..16.670 rows=1 width=445) (actual time=4024.033..4029.826 rows=3944 loops=1)
    Sort Key: xray_uniform.victor, xray_uniform.six_victor_oscar, xray_uniform.foxtrot, xray_india.uniform, xray_uniform.november, xray_uniform.hotel, xray_uniform.four, xray_uniform.golf
  ->  Nested Loop  (cost=1.030..16.650 rows=1 width=445) (actual time=0.402..3863.690 rows=3944 loops=1)
          Join Filter: (("oscar".foxtrot = "charlie".foxtrot) AND ("oscar".uniform = "charlie".uniform))
        ->  Nested Loop  (cost=1.030..10.650 rows=1 width=244) (actual time=0.353..837.849 rows=3944 loops=1)
                Join Filter: ("oscar".november = "charlie".november)
              ->  Nested Loop  (cost=1.030..9.230 rows=1 width=229) (actual time=0.340..666.935 rows=3944 loops=1)
                      Join Filter: ("oscar".hotel = "charlie".hotel)
                    ->  Nested Loop  (cost=1.030..8.180 rows=1 width=236) (actual time=0.328..634.091 rows=3944 loops=1)
                            Join Filter: (("charlie".hotel = "oscar".hotel) AND ("charlie".sierra_kilo = "oscar".sierra_kilo))
                          ->  Nested Loop  (cost=1.030..7.130 rows=1 width=221) (actual time=0.275..315.568 rows=3944 loops=1)
                                  Join Filter: ("charlie".hotel = "oscar".hotel)
                                ->  Index Scan using yankee on alpha_sierra xray_uniform  (cost=0.000..6.020 rows=1 width=203) (actual time=0.057..13.805 rows=3944 loops=1)
                                        Index Cond: ((victor = 2000::numeric) AND (six_victor_oscar = 2::numeric) AND (golf = 0::numeric))
                                ->  GroupAggregate  (cost=1.030..1.070 rows=2 width=18) (actual time=0.032..0.070 rows=2 loops=3944)
                                      ->  Sort  (cost=1.030..1.030 rows=2 width=18) (actual time=0.001..0.019 rows=33 loops=3944)
                                              Sort Key: mike.bravo.hotel
                                            ->  Seq Scan on bravo  (cost=0.000..1.020 rows=2 width=18) (actual time=0.004..0.047 rows=33 loops=1)
                          ->  Seq Scan on bravo  (cost=0.000..1.020 rows=2 width=31) (actual time=0.002..0.037 rows=33 loops=3944)
                    ->  Seq Scan on six_victor_yankee alpha_quebec  (cost=0.000..1.020 rows=2 width=23) (actual time=0.001..0.003 rows=2 loops=3944)
              ->  Seq Scan on zulu sierra_three  (cost=0.000..1.190 rows=19 width=24) (actual time=0.002..0.019 rows=19 loops=3944)
        ->  Index Scan using india_charlie on india_tango xray_india  (cost=0.000..5.980 rows=1 width=248) (actual time=0.015..0.476 rows=232 loops=3944)
                Index Cond: ((2000::numeric = victor) AND (2::numeric = six_victor_oscar))

Per node type stats

node type	count	sum of times	% of query
GroupAggregate	1	201.144 ms	5.0 %
Index Scan	2	1891.149 ms	46.9 %
Nested Loop	5	1463.765 ms	36.3 %
Seq Scan	4	232.743 ms	5.8 %
Sort	2	241.025 ms	6.0 %

Per table stats

Table name	Scan count	Total time	% of query
scan type	count	sum of times	% of table
alpha_sierra	1	13.805 ms	0.3 %
Index Scan	1	13.805 ms	100.0 %
bravo	2	145.975 ms	3.6 %
Seq Scan	2	145.975 ms	100.0 %
india_tango	1	1877.344 ms	46.6 %
Index Scan	1	1877.344 ms	100.0 %
six_victor_yankee	1	11.832 ms	0.3 %
Seq Scan	1	11.832 ms	100.0 %
zulu	1	74.936 ms	1.9 %
Seq Scan	1	74.936 ms	100.0 %

explain.depesz.com

A tool for finding a real cause for slow queries.

Result: kwv

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Per node type stats

Per table stats