#	exclusive	inclusive	rows x	rows	loops	node
1.	472.891	39,850.887	↑ 1.0	1	1	→ Aggregate (cost=1,174,236.59..1,174,236.60 rows=1 width=8) (actual time=39,850.887..39,850.887 rows=1 loops=1)
2.	3,743.600	39,377.996	↑ 2.5	1,389,957	1	→ GroupAggregate (cost=980,487.54..1,130,202.71 rows=3,522,710 width=194) (actual time=33,641.307..39,377.996 rows=1,389,957 loops=1) Group Key: ((salary_events.created_at)::date), salary_events.payment_id, salary_events.user_id, salary_events.location_id, salary_events.export_id, salary_events.salary_kind_id, locations.name, users.first_name, users.last_name, salary_kinds.name, salary_events.status
3.	11,561.159	35,634.396	↓ 1.0	3,529,669	1	→ Sort (cost=980,487.54..989,294.31 rows=3,522,710 width=82) (actual time=33,641.288..35,634.396 rows=3,529,669 loops=1) Sort Key: ((salary_events.created_at)::date), salary_events.payment_id, salary_events.user_id, salary_events.location_id, salary_events.export_id, salary_events.salary_kind_id, locations.name, users.first_name, users.last_name, salary_kinds.name, salary_events.status Sort Method: external merge Disk: 265656kB
4.	4,171.702	24,073.237	↓ 1.0	3,529,669	1	→ Hash Join (cost=444.50..428,853.08 rows=3,522,710 width=82) (actual time=4.246..24,073.237 rows=3,529,669 loops=1) Hash Cond: (salary_events.location_id = locations.id)
5.	10,357.614	19,901.447	↓ 1.0	3,529,669	1	→ Hash Join (cost=437.09..371,601.63 rows=3,522,710 width=75) (actual time=4.148..19,901.447 rows=3,529,669 loops=1) Hash Cond: (salary_events.salary_kind_id = salary_kinds.id) Join Filter: (((salary_events.payment_id)::text ~~* '%1%'::text) OR ((salary_events.created_at)::text ~~* '%1%'::text) OR (((users.first_name)::text \|\| COALESCE((' '::text \|\| (users.last_name)::text), ''::text)) ~~* '%1%'::text) OR ((salary_kinds.name)::text ~~* '%1%'::text))
6.	3,553.226	9,543.826	↓ 1.0	3,529,669	1	→ Hash Join (cost=435.98..235,095.51 rows=3,522,710 width=53) (actual time=4.121..9,543.826 rows=3,529,669 loops=1) Hash Cond: (salary_events.user_id = users.id)
7.	3,444.649	5,986.560	↓ 1.0	3,529,669	1	→ Merge Left Join (cost=0.72..186,222.99 rows=3,522,710 width=37) (actual time=0.042..5,986.560 rows=3,529,669 loops=1) Merge Cond: (salary_events.id = transactions_billing_record_price_factors.salary_event_id)
8.	2,518.304	2,518.304	↓ 1.0	3,529,669	1	→ Index Scan using salary_events_pkey on salary_events (cost=0.43..177,161.97 rows=3,522,710 width=41) (actual time=0.016..2,518.304 rows=3,529,669 loops=1)
9.	23.607	23.607	↑ 459.7	24,462	1	→ Index Only Scan using transactions_billing_record_price_factors_salary_event_idx on transactions_billing_record_price_factors (cost=0.29..535.92 rows=11,245,307 width=4) (actual time=0.022..23.607 rows=24,462 loops=1) Heap Fetches: 0
10.	1.940	4.040	↓ 1.0	5,168	1	→ Hash (cost=370.67..370.67 rows=5,167 width=20) (actual time=4.040..4.040 rows=5,168 loops=1) Buckets: 8192 Batches: 1 Memory Usage: 336kB
11.	2.100	2.100	↓ 1.0	5,168	1	→ Seq Scan on users (cost=0.00..370.67 rows=5,167 width=20) (actual time=0.003..2.100 rows=5,168 loops=1)
12.	0.004	0.007	↑ 1.0	5	1	→ Hash (cost=1.05..1.05 rows=5 width=26) (actual time=0.007..0.007 rows=5 loops=1) Buckets: 1024 Batches: 1 Memory Usage: 9kB
13.	0.003	0.003	↑ 1.0	5	1	→ Seq Scan on salary_kinds (cost=0.00..1.05 rows=5 width=26) (actual time=0.002..0.003 rows=5 loops=1)
14.	0.036	0.088	↑ 1.0	107	1	→ Hash (cost=6.07..6.07 rows=107 width=15) (actual time=0.088..0.088 rows=107 loops=1) Buckets: 1024 Batches: 1 Memory Usage: 14kB
15.	0.052	0.052	↑ 1.0	107	1	→ Seq Scan on locations (cost=0.00..6.07 rows=107 width=15) (actual time=0.006..0.052 rows=107 loops=1)

                                                                                                                                                   QUERY PLAN                                                                                                                                                   
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
 Aggregate  (cost=1174236.59..1174236.60 rows=1 width=8) (actual time=39850.887..39850.887 rows=1 loops=1)
   ->  GroupAggregate  (cost=980487.54..1130202.71 rows=3522710 width=194) (actual time=33641.307..39377.996 rows=1389957 loops=1)
         Group Key: ((salary_events.created_at)::date), salary_events.payment_id, salary_events.user_id, salary_events.location_id, salary_events.export_id, salary_events.salary_kind_id, locations.name, users.first_name, users.last_name, salary_kinds.name, salary_events.status
         ->  Sort  (cost=980487.54..989294.31 rows=3522710 width=82) (actual time=33641.288..35634.396 rows=3529669 loops=1)
               Sort Key: ((salary_events.created_at)::date), salary_events.payment_id, salary_events.user_id, salary_events.location_id, salary_events.export_id, salary_events.salary_kind_id, locations.name, users.first_name, users.last_name, salary_kinds.name, salary_events.status
               Sort Method: external merge  Disk: 265656kB
               ->  Hash Join  (cost=444.50..428853.08 rows=3522710 width=82) (actual time=4.246..24073.237 rows=3529669 loops=1)
                     Hash Cond: (salary_events.location_id = locations.id)
                     ->  Hash Join  (cost=437.09..371601.63 rows=3522710 width=75) (actual time=4.148..19901.447 rows=3529669 loops=1)
                           Hash Cond: (salary_events.salary_kind_id = salary_kinds.id)
                           Join Filter: (((salary_events.payment_id)::text ~~* '%1%'::text) OR ((salary_events.created_at)::text ~~* '%1%'::text) OR (((users.first_name)::text || COALESCE((' '::text || (users.last_name)::text), ''::text)) ~~* '%1%'::text) OR ((salary_kinds.name)::text ~~* '%1%'::text))
                           ->  Hash Join  (cost=435.98..235095.51 rows=3522710 width=53) (actual time=4.121..9543.826 rows=3529669 loops=1)
                                 Hash Cond: (salary_events.user_id = users.id)
                                 ->  Merge Left Join  (cost=0.72..186222.99 rows=3522710 width=37) (actual time=0.042..5986.560 rows=3529669 loops=1)
                                       Merge Cond: (salary_events.id = transactions_billing_record_price_factors.salary_event_id)
                                       ->  Index Scan using salary_events_pkey on salary_events  (cost=0.43..177161.97 rows=3522710 width=41) (actual time=0.016..2518.304 rows=3529669 loops=1)
                                       ->  Index Only Scan using transactions_billing_record_price_factors_salary_event_idx on transactions_billing_record_price_factors  (cost=0.29..535.92 rows=11245307 width=4) (actual time=0.022..23.607 rows=24462 loops=1)
                                             Heap Fetches: 0
                                 ->  Hash  (cost=370.67..370.67 rows=5167 width=20) (actual time=4.040..4.040 rows=5168 loops=1)
                                       Buckets: 8192  Batches: 1  Memory Usage: 336kB
                                       ->  Seq Scan on users  (cost=0.00..370.67 rows=5167 width=20) (actual time=0.003..2.100 rows=5168 loops=1)
                           ->  Hash  (cost=1.05..1.05 rows=5 width=26) (actual time=0.007..0.007 rows=5 loops=1)
                                 Buckets: 1024  Batches: 1  Memory Usage: 9kB
                                 ->  Seq Scan on salary_kinds  (cost=0.00..1.05 rows=5 width=26) (actual time=0.002..0.003 rows=5 loops=1)
                     ->  Hash  (cost=6.07..6.07 rows=107 width=15) (actual time=0.088..0.088 rows=107 loops=1)
                           Buckets: 1024  Batches: 1  Memory Usage: 14kB
                           ->  Seq Scan on locations  (cost=0.00..6.07 rows=107 width=15) (actual time=0.006..0.052 rows=107 loops=1)
 Planning time: 1.274 ms
 Execution time: 39919.473 ms

Per node type stats

node type	count	sum of times	% of query
Aggregate	1	472.891 ms	1.2 %
GroupAggregate	1	3,743.600 ms	9.4 %
Hash	3	1.980 ms	0.0 %
Hash Join	3	18,082.542 ms	45.4 %
Index Only Scan	1	23.607 ms	0.1 %
Index Scan	1	2,518.304 ms	6.3 %
Merge Left Join	1	3,444.649 ms	8.6 %
Seq Scan	3	2.155 ms	0.0 %
Sort	1	11,561.159 ms	29.0 %

Per table stats

Table name	Scan count	Total time	% of query
scan type	count	sum of times	% of table
locations	1	0.052 ms	0.0 %
Seq Scan	1	0.052 ms	100.0 %
salary_events	1	2,518.304 ms	6.3 %
Index Scan	1	2,518.304 ms	100.0 %
salary_kinds	1	0.003 ms	0.0 %
Seq Scan	1	0.003 ms	100.0 %
transactions_billing_record_price_factors	1	23.607 ms	0.1 %
Index Only Scan	1	23.607 ms	100.0 %
users	1	2.100 ms	0.0 %
Seq Scan	1	2.100 ms	100.0 %

explain.depesz.com

PostgreSQL's explain analyze made readable

Result: sYL5

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Per node type stats

Per table stats