#	exclusive	inclusive	rows x	rows	loops	node
1.	0.493	3,131,111.771	↓ 194.0	194	1	→ GroupAggregate (cost=1,861,262.55..1,861,264.50 rows=1 width=329) (actual time=3,131,111.201..3,131,111.771 rows=194 loops=1) Group Key: e.number, rg.id, ou.id, egs.name, pd.first_name, pd.last_name, e.employment_type, productcategories.group_name, suppliers.supplier_name, egb1.office_debit, egb1.benefit_tax, eb.accession_date, eb.resignation_date, bp.employer, bp.employer_zfss, bp.employee, bp.cofinanced_by_employeer, bp.cofinanced_by_zfss, payment.id_benefit_instance, bi1.id, spd.id, e.cost_place Filter: (sum(payment.amount) > '0'::numeric) Execution time: 3,131,112.616 ms(79 wierszy)
2.	0.839	3,131,111.278	↓ 225.0	225	1	→ Sort (cost=1,861,262.55..1,861,262.65 rows=1 width=256) (actual time=3,131,111.178..3,131,111.278 rows=225 loops=1) Sort Key: e.number, rg.id, ou.id, egs.name, pd.first_name, pd.last_name, e.employment_type, productcategories.group_name, suppliers.supplier_name, egb1.office_debit, egb1.benefit_tax, eb.accession_date, eb.resignation_date, bp.employer, bp.employer_zfss, bp.employee, bp.cofinanced_by_employeer, bp.cofinanced_by_zfss, bi1.id, spd.id, e.cost_place Sort Method: quicksort Memory: 98kB
3.	0.485	3,131,110.439	↓ 225.0	225	1	→ Nested Loop Left Join (cost=1,764,484.96..1,861,262.35 rows=1 width=256) (actual time=3,130,910.521..3,131,110.439 rows=225 loops=1)
4.	150.665	3,131,108.829	↓ 225.0	225	1	→ Hash Right Join (cost=1,764,476.46..1,861,253.46 rows=1 width=240) (actual time=3,130,909.889..3,131,108.829 rows=225 loops=1) Hash Cond: (bixspd.id_bi = bi1.id)
5.	147.392	147.392	↑ 1.0	314,787	1	→ Seq Scan on benefit_instance_x_simple_personal_data bixspd (cost=0.00..73,134.40 rows=315,232 width=16) (actual time=0.015..147.392 rows=314,787 loops=1)
6.	0.789	3,130,810.772	↓ 225.0	225	1	→ Hash (cost=1,764,476.21..1,764,476.21 rows=1 width=232) (actual time=3,130,810.772..3,130,810.772 rows=225 loops=1) Buckets: 1,024 Batches: 1 Memory Usage: 62kB
7.	0.907	3,130,809.983	↓ 225.0	225	1	→ Nested Loop Left Join (cost=3,818.25..1,764,476.21 rows=1 width=232) (actual time=11,641.304..3,130,809.983 rows=225 loops=1)
8.	0.945	3,130,807.501	↓ 225.0	225	1	→ Nested Loop Left Join (cost=3,812.60..1,764,470.16 rows=1 width=207) (actual time=11,641.277..3,130,807.501 rows=225 loops=1)
9.	0.778	3,130,804.306	↓ 225.0	225	1	→ Nested Loop Left Join (cost=3,804.10..1,764,460.36 rows=1 width=199) (actual time=11,641.245..3,130,804.306 rows=225 loops=1)
10.	0.771	3,130,803.303	↓ 225.0	225	1	→ Nested Loop Left Join (cost=3,798.50..1,764,454.38 rows=1 width=199) (actual time=11,641.224..3,130,803.303 rows=225 loops=1)
11.	0.825	3,130,802.307	↓ 225.0	225	1	→ Nested Loop Left Join (cost=3,792.95..1,764,448.48 rows=1 width=199) (actual time=11,641.205..3,130,802.307 rows=225 loops=1)
12.	45,217.439	3,130,799.232	↓ 225.0	225	1	→ Nested Loop (cost=3,784.50..1,764,438.75 rows=1 width=191) (actual time=11,641.180..3,130,799.232 rows=225 loops=1) Join Filter: (e.id_employee_account = bi1.id_employee_account) Rows Removed by Join Filter: 85,880,235
13.	1.744	10.153	↓ 516.0	516	1	→ Hash Join (cost=22.85..384.56 rows=1 width=60) (actual time=0.122..10.153 rows=516 loops=1) Hash Cond: (e.id_employee_group = egs.id)
14.	8.367	8.367	↓ 2.2	701	1	→ Index Scan using employees__id_company_idx on employees e (cost=8.55..288.45 rows=324 width=54) (actual time=0.069..8.367 rows=701 loops=1) Index Cond: (id_company = 194,330)
15.	0.007	0.042	↑ 1.0	2	1	→ Hash (cost=13.80..13.80 rows=2 width=22) (actual time=0.042..0.042 rows=2 loops=1) Buckets: 1,024 Batches: 1 Memory Usage: 9kB
16.	0.035	0.035	↑ 1.0	2	1	→ Index Scan using employee_groups_pkey on employee_groups egs (cost=5.60..13.80 rows=2 width=22) (actual time=0.032..0.035 rows=2 loops=1) Index Cond: (id = ANY ('{194394,194404}'::bigint[]))
17.	102,098.762	3,085,571.640	↓ 5.3	166,435	516	→ Hash Left Join (cost=3,761.66..1,756,192.44 rows=31,447 width=155) (actual time=127.564..5,979.790 rows=166,435 loops=516) Hash Cond: (benefits.id_product_category = productcategories.id)
18.	26,464.092	2,983,472.784	↓ 5.3	166,435	516	→ Gather (cost=3,726.66..1,748,310.08 rows=31,447 width=134) (actual time=127.562..5,781.924 rows=166,435 loops=516) Workers Planned: 2 Workers Launched: 2
19.	53,042.736	2,957,008.692	↓ 4.2	55,478	1,548 / 3	→ Nested Loop (cost=2,726.66..1,744,165.38 rows=13,103 width=134) (actual time=16.968..5,730.637 rows=55,478 loops=1,548)
20.	38,381.217	2,818,085.496	↓ 4.2	55,478	1,548 / 3	→ Hash Left Join (cost=2,718.16..1,628,178.64 rows=13,103 width=126) (actual time=16.914..5,461.406 rows=55,478 loops=1,548) Hash Cond: (bi1.id_benefit = benefits.id)
21.	61,238.364	2,776,259.568	↓ 4.2	55,478	1,548 / 3	→ Nested Loop Left Join (cost=45.40..1,622,197.91 rows=13,103 width=118) (actual time=10.069..5,380.348 rows=55,478 loops=1,548)
22.	58,748.664	2,629,140.744	↓ 4.2	55,478	1,548 / 3	→ Nested Loop Left Join (cost=36.80..1,503,531.41 rows=13,103 width=116) (actual time=10.029..5,095.234 rows=55,478 loops=1,548)
23.	45,318.560	2,484,511.620	↓ 4.2	55,478	1,548 / 3	→ Nested Loop Left Join (cost=28.20..1,385,891.84 rows=13,103 width=119) (actual time=9.986..4,814.945 rows=55,478 loops=1,548)
24.	245,018.472	2,324,685.780	↓ 4.2	55,478	1,548 / 3	→ Merge Join (cost=19.75..1,270,488.81 rows=13,103 width=111) (actual time=9.949..4,505.205 rows=55,478 loops=1,548) Merge Cond: (payment.id_benefit_instance = bi1.id)
25.	1,447,032.732	1,447,032.732	↓ 3.5	55,478	1,548 / 3	→ Parallel Index Scan using payment__id_benefit_instance_idx on payment (cost=11.25..3,439,069.07 rows=15,795 width=31) (actual time=5.799..2,804.327 rows=55,478 loops=1,548) Index Cond: (id_benefit_instance IS NOT NULL) Filter: ((NOT deleted) AND ((status)::text <> 'CANCELED'::text) AND ((payer)::text <> 'ONLINE_PROVISION'::text) AND ((date)::date >= '2019-06-01'::date) AND ((date)::date <= '2019-06-30'::date)) Rows Removed by Filter: 2,598,019
26.	632,634.576	632,634.576	↓ 1.2	747,420	1,548 / 3	→ Index Scan using benefit_instances_pkey on benefit_instances bi1 (cost=8.50..312,558.11 rows=632,918 width=80) (actual time=0.040..1,226.036 rows=747,420 loops=1,548) Filter: ((NOT temporary) AND ((NOT deleted) OR deleted) AND ((status)::text = 'ACTIVE'::text)) Rows Removed by Filter: 14,843
27.	114,507.280	114,507.280	↑ 1.0	1	85,880,460 / 3	→ Index Scan using employee_benefits_pkey on employee_benefits eb (cost=8.45..8.81 rows=1 width=24) (actual time=0.004..0.004 rows=1 loops=85,880,460) Index Cond: (bi1.id_employee_benefit = id)
28.	85,880.460	85,880.460	↑ 1.0	1	85,880,460 / 3	→ Index Scan using benefit_payments_pkey on benefit_payments bp (cost=8.60..8.98 rows=1 width=13) (actual time=0.003..0.003 rows=1 loops=85,880,460) Index Cond: (bi1.id_benefit_payments = id)
29.	85,880.460	85,880.460	↑ 1.0	1	85,880,460 / 3	→ Index Scan using employee_group_benefit_pkey on employee_group_benefit egb1 (cost=8.60..9.06 rows=1 width=18) (actual time=0.003..0.003 rows=1 loops=85,880,460) Index Cond: (bi1.id_emoloyee_group_benefit = id)
30.	1,016.472	3,444.711	↑ 1.0	4,365	1,033 / 3	→ Hash (cost=1,581.51..1,581.51 rows=4,365 width=24) (actual time=10.004..10.004 rows=4,365 loops=1,033) Buckets: 8,192 Batches: 1 Memory Usage: 293kB
31.	2,428.239	2,428.239	↑ 1.0	4,365	1,033 / 3	→ Index Scan using benefits_pkey on benefits (cost=5.65..1,581.51 rows=4,365 width=24) (actual time=0.045..7.052 rows=4,365 loops=1,033)
32.	85,880.460	85,880.460	↑ 1.0	1	85,880,460 / 3	→ Index Only Scan using employee_accounts_pkey on employee_accounts ea (cost=8.50..8.85 rows=1 width=8) (actual time=0.003..0.003 rows=1 loops=85,880,460) Index Cond: (id = bi1.id_employee_account) Heap Fetches: 4,177,193
33.	0.045	0.094	↑ 1.0	60	1	→ Hash (cost=20.00..20.00 rows=60 width=37) (actual time=0.094..0.094 rows=60 loops=1) Buckets: 1,024 Batches: 1 Memory Usage: 13kB
34.	0.049	0.049	↑ 1.0	60	1	→ Seq Scan on product_categories productcategories (cost=0.00..20.00 rows=60 width=37) (actual time=0.007..0.049 rows=60 loops=1)
35.	2.250	2.250	↓ 0.0	0	225	→ Index Scan using rentable_group_selections_employee_unique on rentable_group_selections rgs (cost=8.45..9.73 rows=1 width=16) (actual time=0.010..0.010 rows=0 loops=225) Index Cond: (id_employee = e.id)
36.	0.225	0.225	↓ 0.0	0	225	→ Index Only Scan using rentable_group_pkey on rentable_group rg (cost=5.55..5.90 rows=1 width=8) (actual time=0.001..0.001 rows=0 loops=225) Index Cond: (id = rgs.id_rentable_group) Heap Fetches: 0
37.	0.225	0.225	↓ 0.0	0	225	→ Index Only Scan using organization_units_pkey on organization_units ou (cost=5.60..5.98 rows=1 width=8) (actual time=0.001..0.001 rows=0 loops=225) Index Cond: (id = e.id_organization_unit) Heap Fetches: 0
38.	2.250	2.250	↑ 1.0	1	225	→ Index Scan using personal_data_pkey on personal_data pd (cost=8.50..9.80 rows=1 width=24) (actual time=0.010..0.010 rows=1 loops=225) Index Cond: (e.id = id)
39.	1.575	1.575	↑ 1.0	1	225	→ Index Scan using suppliers_pkey on suppliers (cost=5.65..6.05 rows=1 width=41) (actual time=0.007..0.007 rows=1 loops=225) Index Cond: (benefits.id_supplier = id)
40.	1.125	1.125	↓ 0.0	0	225	→ Index Scan using simple_personal_data_pkey on simple_personal_data spd (cost=8.50..8.89 rows=1 width=24) (actual time=0.005..0.005 rows=0 loops=225) Index Cond: (bixspd.id_spd = id)

Planning time	:	17.890 ms

                                                                                                                                                                                         QUERY PLAN                                                                                                                                                                                          
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
 GroupAggregate  (cost=1861262.55..1861264.50 rows=1 width=329) (actual time=3131111.201..3131111.771 rows=194 loops=1)
   Group Key: e.number, rg.id, ou.id, egs.name, pd.first_name, pd.last_name, e.employment_type, productcategories.group_name, suppliers.supplier_name, egb1.office_debit, egb1.benefit_tax, eb.accession_date, eb.resignation_date, bp.employer, bp.employer_zfss, bp.employee, bp.cofinanced_by_employeer, bp.cofinanced_by_zfss, payment.id_benefit_instance, bi1.id, spd.id, e.cost_place
   Filter: (sum(payment.amount) > '0'::numeric)
   ->  Sort  (cost=1861262.55..1861262.65 rows=1 width=256) (actual time=3131111.178..3131111.278 rows=225 loops=1)
         Sort Key: e.number, rg.id, ou.id, egs.name, pd.first_name, pd.last_name, e.employment_type, productcategories.group_name, suppliers.supplier_name, egb1.office_debit, egb1.benefit_tax, eb.accession_date, eb.resignation_date, bp.employer, bp.employer_zfss, bp.employee, bp.cofinanced_by_employeer, bp.cofinanced_by_zfss, bi1.id, spd.id, e.cost_place
         Sort Method: quicksort  Memory: 98kB
         ->  Nested Loop Left Join  (cost=1764484.96..1861262.35 rows=1 width=256) (actual time=3130910.521..3131110.439 rows=225 loops=1)
               ->  Hash Right Join  (cost=1764476.46..1861253.46 rows=1 width=240) (actual time=3130909.889..3131108.829 rows=225 loops=1)
                     Hash Cond: (bixspd.id_bi = bi1.id)
                     ->  Seq Scan on benefit_instance_x_simple_personal_data bixspd  (cost=0.00..73134.40 rows=315232 width=16) (actual time=0.015..147.392 rows=314787 loops=1)
                     ->  Hash  (cost=1764476.21..1764476.21 rows=1 width=232) (actual time=3130810.772..3130810.772 rows=225 loops=1)
                           Buckets: 1024  Batches: 1  Memory Usage: 62kB
                           ->  Nested Loop Left Join  (cost=3818.25..1764476.21 rows=1 width=232) (actual time=11641.304..3130809.983 rows=225 loops=1)
                                 ->  Nested Loop Left Join  (cost=3812.60..1764470.16 rows=1 width=207) (actual time=11641.277..3130807.501 rows=225 loops=1)
                                       ->  Nested Loop Left Join  (cost=3804.10..1764460.36 rows=1 width=199) (actual time=11641.245..3130804.306 rows=225 loops=1)
                                             ->  Nested Loop Left Join  (cost=3798.50..1764454.38 rows=1 width=199) (actual time=11641.224..3130803.303 rows=225 loops=1)
                                                   ->  Nested Loop Left Join  (cost=3792.95..1764448.48 rows=1 width=199) (actual time=11641.205..3130802.307 rows=225 loops=1)
                                                         ->  Nested Loop  (cost=3784.50..1764438.75 rows=1 width=191) (actual time=11641.180..3130799.232 rows=225 loops=1)
                                                               Join Filter: (e.id_employee_account = bi1.id_employee_account)
                                                               Rows Removed by Join Filter: 85880235
                                                               ->  Hash Join  (cost=22.85..384.56 rows=1 width=60) (actual time=0.122..10.153 rows=516 loops=1)
                                                                     Hash Cond: (e.id_employee_group = egs.id)
                                                                     ->  Index Scan using employees__id_company_idx on employees e  (cost=8.55..288.45 rows=324 width=54) (actual time=0.069..8.367 rows=701 loops=1)
                                                                           Index Cond: (id_company = 194330)
                                                                     ->  Hash  (cost=13.80..13.80 rows=2 width=22) (actual time=0.042..0.042 rows=2 loops=1)
                                                                           Buckets: 1024  Batches: 1  Memory Usage: 9kB
                                                                           ->  Index Scan using employee_groups_pkey on employee_groups egs  (cost=5.60..13.80 rows=2 width=22) (actual time=0.032..0.035 rows=2 loops=1)
                                                                                 Index Cond: (id = ANY ('{194394,194404}'::bigint[]))
                                                               ->  Hash Left Join  (cost=3761.66..1756192.44 rows=31447 width=155) (actual time=127.564..5979.790 rows=166435 loops=516)
                                                                     Hash Cond: (benefits.id_product_category = productcategories.id)
                                                                     ->  Gather  (cost=3726.66..1748310.08 rows=31447 width=134) (actual time=127.562..5781.924 rows=166435 loops=516)
                                                                           Workers Planned: 2
                                                                           Workers Launched: 2
                                                                           ->  Nested Loop  (cost=2726.66..1744165.38 rows=13103 width=134) (actual time=16.968..5730.637 rows=55478 loops=1548)
                                                                                 ->  Hash Left Join  (cost=2718.16..1628178.64 rows=13103 width=126) (actual time=16.914..5461.406 rows=55478 loops=1548)
                                                                                       Hash Cond: (bi1.id_benefit = benefits.id)
                                                                                       ->  Nested Loop Left Join  (cost=45.40..1622197.91 rows=13103 width=118) (actual time=10.069..5380.348 rows=55478 loops=1548)
                                                                                             ->  Nested Loop Left Join  (cost=36.80..1503531.41 rows=13103 width=116) (actual time=10.029..5095.234 rows=55478 loops=1548)
                                                                                                   ->  Nested Loop Left Join  (cost=28.20..1385891.84 rows=13103 width=119) (actual time=9.986..4814.945 rows=55478 loops=1548)
                                                                                                         ->  Merge Join  (cost=19.75..1270488.81 rows=13103 width=111) (actual time=9.949..4505.205 rows=55478 loops=1548)
                                                                                                               Merge Cond: (payment.id_benefit_instance = bi1.id)
                                                                                                               ->  Parallel Index Scan using payment__id_benefit_instance_idx on payment  (cost=11.25..3439069.07 rows=15795 width=31) (actual time=5.799..2804.327 rows=55478 loops=1548)
                                                                                                                     Index Cond: (id_benefit_instance IS NOT NULL)
                                                                                                                     Filter: ((NOT deleted) AND ((status)::text <> 'CANCELED'::text) AND ((payer)::text <> 'ONLINE_PROVISION'::text) AND ((date)::date >= '2019-06-01'::date) AND ((date)::date <= '2019-06-30'::date))
                                                                                                                     Rows Removed by Filter: 2598019
                                                                                                               ->  Index Scan using benefit_instances_pkey on benefit_instances bi1  (cost=8.50..312558.11 rows=632918 width=80) (actual time=0.040..1226.036 rows=747420 loops=1548)
                                                                                                                     Filter: ((NOT temporary) AND ((NOT deleted) OR deleted) AND ((status)::text = 'ACTIVE'::text))
                                                                                                                     Rows Removed by Filter: 14843
                                                                                                         ->  Index Scan using employee_benefits_pkey on employee_benefits eb  (cost=8.45..8.81 rows=1 width=24) (actual time=0.004..0.004 rows=1 loops=85880460)
                                                                                                               Index Cond: (bi1.id_employee_benefit = id)
                                                                                                   ->  Index Scan using benefit_payments_pkey on benefit_payments bp  (cost=8.60..8.98 rows=1 width=13) (actual time=0.003..0.003 rows=1 loops=85880460)
                                                                                                         Index Cond: (bi1.id_benefit_payments = id)
                                                                                             ->  Index Scan using employee_group_benefit_pkey on employee_group_benefit egb1  (cost=8.60..9.06 rows=1 width=18) (actual time=0.003..0.003 rows=1 loops=85880460)
                                                                                                   Index Cond: (bi1.id_emoloyee_group_benefit = id)
                                                                                       ->  Hash  (cost=1581.51..1581.51 rows=4365 width=24) (actual time=10.004..10.004 rows=4365 loops=1033)
                                                                                             Buckets: 8192  Batches: 1  Memory Usage: 293kB
                                                                                             ->  Index Scan using benefits_pkey on benefits  (cost=5.65..1581.51 rows=4365 width=24) (actual time=0.045..7.052 rows=4365 loops=1033)
                                                                                 ->  Index Only Scan using employee_accounts_pkey on employee_accounts ea  (cost=8.50..8.85 rows=1 width=8) (actual time=0.003..0.003 rows=1 loops=85880460)
                                                                                       Index Cond: (id = bi1.id_employee_account)
                                                                                       Heap Fetches: 4177193
                                                                     ->  Hash  (cost=20.00..20.00 rows=60 width=37) (actual time=0.094..0.094 rows=60 loops=1)
                                                                           Buckets: 1024  Batches: 1  Memory Usage: 13kB
                                                                           ->  Seq Scan on product_categories productcategories  (cost=0.00..20.00 rows=60 width=37) (actual time=0.007..0.049 rows=60 loops=1)
                                                         ->  Index Scan using rentable_group_selections_employee_unique on rentable_group_selections rgs  (cost=8.45..9.73 rows=1 width=16) (actual time=0.010..0.010 rows=0 loops=225)
                                                               Index Cond: (id_employee = e.id)
                                                   ->  Index Only Scan using rentable_group_pkey on rentable_group rg  (cost=5.55..5.90 rows=1 width=8) (actual time=0.001..0.001 rows=0 loops=225)
                                                         Index Cond: (id = rgs.id_rentable_group)
                                                         Heap Fetches: 0
                                             ->  Index Only Scan using organization_units_pkey on organization_units ou  (cost=5.60..5.98 rows=1 width=8) (actual time=0.001..0.001 rows=0 loops=225)
                                                   Index Cond: (id = e.id_organization_unit)
                                                   Heap Fetches: 0
                                       ->  Index Scan using personal_data_pkey on personal_data pd  (cost=8.50..9.80 rows=1 width=24) (actual time=0.010..0.010 rows=1 loops=225)
                                             Index Cond: (e.id = id)
                                 ->  Index Scan using suppliers_pkey on suppliers  (cost=5.65..6.05 rows=1 width=41) (actual time=0.007..0.007 rows=1 loops=225)
                                       Index Cond: (benefits.id_supplier = id)
               ->  Index Scan using simple_personal_data_pkey on simple_personal_data spd  (cost=8.50..8.89 rows=1 width=24) (actual time=0.005..0.005 rows=0 loops=225)
                     Index Cond: (bixspd.id_spd = id)
 Planning time: 17.890 ms
 Execution time: 3131112.616 ms
(79 wierszy)

Per node type stats

node type	count	sum of times	% of query
Gather	1	26,464.092 ms	0.8 %
GroupAggregate	1	0.493 ms	0.0 %
Hash	4	1,017.313 ms	0.0 %
Hash Join	1	1.744 ms	0.0 %
Hash Left Join	2	140,479.979 ms	4.5 %
Hash Right Join	1	150.665 ms	0.0 %
Index Only Scan	3	85,880.910 ms	2.7 %
Index Scan	11	921,346.617 ms	29.4 %
Merge Join	1	245,018.472 ms	7.8 %
Nested Loop	2	98,260.175 ms	3.1 %
Nested Loop Left Join	9	165,310.299 ms	5.3 %
Parallel Index Scan	1	1,447,032.732 ms	46.2 %
Seq Scan	2	147.441 ms	0.0 %
Sort	1	0.839 ms	0.0 %

Per table stats

Table name	Scan count	Total time	% of query
scan type	count	sum of times	% of table
benefit_instance_x_simple_personal_data	1	147.392 ms	0.0 %
Seq Scan	1	147.392 ms	100.0 %
benefit_instances	1	632,634.576 ms	20.2 %
Index Scan	1	632,634.576 ms	100.0 %
benefit_payments	1	85,880.460 ms	2.7 %
Index Scan	1	85,880.460 ms	100.0 %
benefits	1	2,428.239 ms	0.1 %
Index Scan	1	2,428.239 ms	100.0 %
employee_accounts	1	85,880.460 ms	2.7 %
Index Only Scan	1	85,880.460 ms	100.0 %
employee_benefits	1	114,507.280 ms	3.7 %
Index Scan	1	114,507.280 ms	100.0 %
employee_group_benefit	1	85,880.460 ms	2.7 %
Index Scan	1	85,880.460 ms	100.0 %
employee_groups	1	0.035 ms	0.0 %
Index Scan	1	0.035 ms	100.0 %
employees	1	8.367 ms	0.0 %
Index Scan	1	8.367 ms	100.0 %
organization_units	1	0.225 ms	0.0 %
Index Only Scan	1	0.225 ms	100.0 %
payment	1	1,447,032.732 ms	46.2 %
Parallel Index Scan	1	1,447,032.732 ms	100.0 %
personal_data	1	2.250 ms	0.0 %
Index Scan	1	2.250 ms	100.0 %
product_categories	1	0.049 ms	0.0 %
Seq Scan	1	0.049 ms	100.0 %
rentable_group	1	0.225 ms	0.0 %
Index Only Scan	1	0.225 ms	100.0 %
rentable_group_selections	1	2.250 ms	0.0 %
Index Scan	1	2.250 ms	100.0 %
simple_personal_data	1	1.125 ms	0.0 %
Index Scan	1	1.125 ms	100.0 %
suppliers	1	1.575 ms	0.0 %
Index Scan	1	1.575 ms	100.0 %

explain.depesz.com

PostgreSQL's explain analyze made readable

Result: 0PGJ

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Visible columns:

Per node type stats

Per table stats