#	exclusive	inclusive	rows x	rows	loops	node
1.	0.000	1,302.435	↑ 1.0	1	1	→ Aggregate (cost=69,582.29..69,582.30 rows=1 width=8) (actual time=1,302.435..1,302.435 rows=1 loops=1)
2.	27.162	1,310.421	↓ 75,072.0	75,072	1	→ Gather (cost=1,004.18..69,582.29 rows=1 width=4) (actual time=6.827..1,310.421 rows=75,072 loops=1) Workers Planned: 2 Workers Launched: 2
3.	147.463	1,283.259	↓ 25,024.0	25,024	3 / 3	→ Nested Loop Left Join (cost=4.18..68,582.19 rows=1 width=4) (actual time=7.583..1,283.259 rows=25,024 loops=3) Filter: (am.id IS NULL) Rows Removed by Filter: 338,971
4.	154.813	407.807	↓ 8.3	363,995	3 / 3	→ Hash Join (cost=3.75..47,607.62 rows=43,646 width=4) (actual time=0.274..407.807 rows=363,995 loops=3) Hash Cond: (sm.location_dest_id = sld.id) Join Filter: ((((sl.usage)::text <> ALL ('{internal,transit}'::text[])) AND ((sld.usage)::text = ANY ('{internal,transit}'::text[]))) OR (((sl.usage)::text = ANY ('{internal,transit}'::text[])) AND ((sld.usage)::text <> ALL ('{internal,transit}'::text[]))) OR (((sl.usage)::text = 'customer'::text) AND ((sld.usage)::text = 'supplier'::text)) OR (((sl.usage)::text = 'supplier'::text) AND ((sld.usage)::text = 'customer'::text)))
5.	93.583	252.925	↑ 1.2	363,995	3 / 3	→ Hash Join (cost=1.88..46,379.04 rows=454,047 width=40) (actual time=0.075..252.925 rows=363,995 loops=3) Hash Cond: (sm.location_id = sl.id)
6.	159.302	159.302	↑ 1.2	363,995	3 / 3	→ Parallel Seq Scan on stock_move sm (cost=0.00..45,055.78 rows=454,047 width=12) (actual time=0.019..159.302 rows=363,995 loops=3) Filter: ((price_unit <> '0'::double precision) AND ((state)::text = 'done'::text)) Rows Removed by Filter: 6,224
7.	0.020	0.040	↑ 1.0	39	3 / 3	→ Hash (cost=1.39..1.39 rows=39 width=36) (actual time=0.040..0.040 rows=39 loops=3) Buckets: 1,024 Batches: 1 Memory Usage: 10kB
8.	0.020	0.020	↑ 1.0	39	3 / 3	→ Seq Scan on stock_location sl (cost=0.00..1.39 rows=39 width=36) (actual time=0.005..0.020 rows=39 loops=3)
9.	0.026	0.069	↑ 1.0	39	3 / 3	→ Hash (cost=1.39..1.39 rows=39 width=36) (actual time=0.069..0.069 rows=39 loops=3) Buckets: 1,024 Batches: 1 Memory Usage: 10kB
10.	0.043	0.043	↑ 1.0	39	3 / 3	→ Seq Scan on stock_location sld (cost=0.00..1.39 rows=39 width=36) (actual time=0.028..0.043 rows=39 loops=3)
11.	727.989	727.989	↑ 1.0	1	1,091,984 / 3	→ Index Scan using account_move_stock_move_id_index on account_move am (cost=0.43..0.47 rows=1 width=8) (actual time=0.002..0.002 rows=1 loops=1,091,984) Index Cond: (stock_move_id = sm.id)

Planning time	:	1.283 ms
Execution time	:	1,318.143 ms

Aggregate  (cost=69582.29..69582.30 rows=1 width=8) (actual time=1302.435..1302.435 rows=1 loops=1)
  ->  Gather  (cost=1004.18..69582.29 rows=1 width=4) (actual time=6.827..1310.421 rows=75072 loops=1)
        Workers Planned: 2
        Workers Launched: 2
        ->  Nested Loop Left Join  (cost=4.18..68582.19 rows=1 width=4) (actual time=7.583..1283.259 rows=25024 loops=3)
              Filter: (am.id IS NULL)
              Rows Removed by Filter: 338971
              ->  Hash Join  (cost=3.75..47607.62 rows=43646 width=4) (actual time=0.274..407.807 rows=363995 loops=3)
                    Hash Cond: (sm.location_dest_id = sld.id)
                    Join Filter: ((((sl.usage)::text <> ALL ('{internal,transit}'::text[])) AND ((sld.usage)::text = ANY ('{internal,transit}'::text[]))) OR (((sl.usage)::text = ANY ('{internal,transit}'::text[])) AND ((sld.usage)::text <> ALL ('{internal,transit}'::text[]))) OR (((sl.usage)::text = 'customer'::text) AND ((sld.usage)::text = 'supplier'::text)) OR (((sl.usage)::text = 'supplier'::text) AND ((sld.usage)::text = 'customer'::text)))
                    ->  Hash Join  (cost=1.88..46379.04 rows=454047 width=40) (actual time=0.075..252.925 rows=363995 loops=3)
                          Hash Cond: (sm.location_id = sl.id)
                          ->  Parallel Seq Scan on stock_move sm  (cost=0.00..45055.78 rows=454047 width=12) (actual time=0.019..159.302 rows=363995 loops=3)
                                Filter: ((price_unit <> '0'::double precision) AND ((state)::text = 'done'::text))
                                Rows Removed by Filter: 6224
                          ->  Hash  (cost=1.39..1.39 rows=39 width=36) (actual time=0.040..0.040 rows=39 loops=3)
                                Buckets: 1024  Batches: 1  Memory Usage: 10kB
                                ->  Seq Scan on stock_location sl  (cost=0.00..1.39 rows=39 width=36) (actual time=0.005..0.020 rows=39 loops=3)
                    ->  Hash  (cost=1.39..1.39 rows=39 width=36) (actual time=0.069..0.069 rows=39 loops=3)
                          Buckets: 1024  Batches: 1  Memory Usage: 10kB
                          ->  Seq Scan on stock_location sld  (cost=0.00..1.39 rows=39 width=36) (actual time=0.028..0.043 rows=39 loops=3)
              ->  Index Scan using account_move_stock_move_id_index on account_move am  (cost=0.43..0.47 rows=1 width=8) (actual time=0.002..0.002 rows=1 loops=1091984)
                    Index Cond: (stock_move_id = sm.id)
Planning time: 1.283 ms
Execution time: 1318.143 ms

Per node type stats

node type	count	sum of times	% of query
Aggregate	1	0.000 ms	0.0 %
Gather	1	27.162 ms	2.1 %
Hash	2	0.046 ms	0.0 %
Hash Join	2	248.396 ms	19.1 %
Index Scan	1	727.989 ms	55.9 %
Nested Loop Left Join	1	147.463 ms	11.3 %
Parallel Seq Scan	1	159.302 ms	12.2 %
Seq Scan	2	0.063 ms	0.0 %

Per table stats

Table name	Scan count	Total time	% of query
scan type	count	sum of times	% of table
account_move	1	727.989 ms	55.9 %
Index Scan	1	727.989 ms	100.0 %
stock_location	2	0.063 ms	0.0 %
Seq Scan	2	0.063 ms	100.0 %
stock_move	1	159.302 ms	12.2 %
Parallel Seq Scan	1	159.302 ms	100.0 %

explain.depesz.com

PostgreSQL's explain analyze made readable

Result: M4up

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Per node type stats

Per table stats