#	exclusive	inclusive	rows x	rows	loops	node
1.	36.486	594.396	↑ 13.7	319	1	→ HashAggregate (cost=18303.52..18390.83 rows=4365 width=28) (actual time=594.047..594.396 rows=319 loops=1)
2.						Initplan (forHashAggregate)
3.	0.011	0.011	↑ 1.0	1	1	→ Result (cost=0.00..0.02 rows=1 width=0) (actual time=0.010..0.011 rows=1 loops=1)
4.	44.033	557.899	↓ 3.9	16878	1	→ Hash Join (cost=184.59..18248.94 rows=4365 width=28) (actual time=159.703..557.899 rows=16878 loops=1) Hash Cond: (v.appellation_id = a.id)
5.	24.256	513.357	↓ 3.9	16878	1	→ Hash Join (cost=174.42..18135.10 rows=4365 width=16) (actual time=159.151..513.357 rows=16878 loops=1) Hash Cond: (s.contenant_id = c.id)
6.	28.650	489.058	↓ 3.9	16878	1	→ Hash Join (cost=173.35..18074.01 rows=4365 width=16) (actual time=159.091..489.058 rows=16878 loops=1) Hash Cond: (s.vin_id = v.id)
7.	16.743	451.849	↓ 3.9	16878	1	→ Append (cost=0.00..17813.36 rows=4365 width=16) (actual time=150.491..451.849 rows=16878 loops=1)
8.	435.098	435.098	↓ 3.9	16878	1	→ Seq Scan on stock s (cost=0.00..17557.51 rows=4302 width=16) (actual time=150.489..435.098 rows=16878 loops=1) Filter: ((annee)::double precision = $0)
9.	0.002	0.002	↓ 0.0	0	1	→ Seq Scan on stock_1950 s (cost=0.00..36.55 rows=9 width=16) (actual time=0.002..0.002 rows=0 loops=1) Filter: ((annee)::double precision = $0)
10.	0.001	0.001	↓ 0.0	0	1	→ Seq Scan on stock_1960 s (cost=0.00..36.55 rows=9 width=16) (actual time=0.001..0.001 rows=0 loops=1) Filter: ((annee)::double precision = $0)
11.	0.001	0.001	↓ 0.0	0	1	→ Seq Scan on stock_1970 s (cost=0.00..36.55 rows=9 width=16) (actual time=0.001..0.001 rows=0 loops=1) Filter: ((annee)::double precision = $0)
12.	0.001	0.001	↓ 0.0	0	1	→ Seq Scan on stock_1980 s (cost=0.00..36.55 rows=9 width=16) (actual time=0.001..0.001 rows=0 loops=1) Filter: ((annee)::double precision = $0)
13.	0.001	0.001	↓ 0.0	0	1	→ Seq Scan on stock_1990 s (cost=0.00..36.55 rows=9 width=16) (actual time=0.001..0.001 rows=0 loops=1) Filter: ((annee)::double precision = $0)
14.	0.001	0.001	↓ 0.0	0	1	→ Seq Scan on stock_2000 s (cost=0.00..36.55 rows=9 width=16) (actual time=0.001..0.001 rows=0 loops=1) Filter: ((annee)::double precision = $0)
15.	0.001	0.001	↓ 0.0	0	1	→ Seq Scan on stock_2010 s (cost=0.00..36.55 rows=9 width=16) (actual time=0.001..0.001 rows=0 loops=1) Filter: ((annee)::double precision = $0)
16.	4.215	8.559	↑ 1.0	6060	1	→ Hash (cost=97.60..97.60 rows=6060 width=8) (actual time=8.559..8.559 rows=6060 loops=1) Buckets: 1024 Batches: 1 Memory Usage: 166kB
17.	4.344	4.344	↑ 1.0	6060	1	→ Seq Scan on vin v (cost=0.00..97.60 rows=6060 width=8) (actual time=0.012..4.344 rows=6060 loops=1)
18.	0.006	0.043	↑ 1.0	3	1	→ Hash (cost=1.03..1.03 rows=3 width=8) (actual time=0.043..0.043 rows=3 loops=1) Buckets: 1024 Batches: 1 Memory Usage: 1kB
19.	0.037	0.037	↑ 1.0	3	1	→ Seq Scan on contenant c (cost=0.00..1.03 rows=3 width=8) (actual time=0.002..0.037 rows=3 loops=1)
20.	0.269	0.509	↑ 1.0	319	1	→ Hash (cost=6.19..6.19 rows=319 width=20) (actual time=0.509..0.509 rows=319 loops=1) Buckets: 1024 Batches: 1 Memory Usage: 14kB
21.	0.240	0.240	↑ 1.0	319	1	→ Seq Scan on appellation a (cost=0.00..6.19 rows=319 width=20) (actual time=0.012..0.240 rows=319 loops=1)

                                                                 QUERY PLAN                                                                   
-----------------------------------------------------------------------------------------------------------------------------------------------
 HashAggregate  (cost=18303.52..18390.83 rows=4365 width=28) (actual time=594.047..594.396 rows=319 loops=1)
   InitPlan 1 (returns $0)
     ->  Result  (cost=0.00..0.02 rows=1 width=0) (actual time=0.010..0.011 rows=1 loops=1)
   ->  Hash Join  (cost=184.59..18248.94 rows=4365 width=28) (actual time=159.703..557.899 rows=16878 loops=1)
         Hash Cond: (v.appellation_id = a.id)
         ->  Hash Join  (cost=174.42..18135.10 rows=4365 width=16) (actual time=159.151..513.357 rows=16878 loops=1)
               Hash Cond: (s.contenant_id = c.id)
               ->  Hash Join  (cost=173.35..18074.01 rows=4365 width=16) (actual time=159.091..489.058 rows=16878 loops=1)
                     Hash Cond: (s.vin_id = v.id)
                     ->  Append  (cost=0.00..17813.36 rows=4365 width=16) (actual time=150.491..451.849 rows=16878 loops=1)
                           ->  Seq Scan on stock s  (cost=0.00..17557.51 rows=4302 width=16) (actual time=150.489..435.098 rows=16878 loops=1)
                                 Filter: ((annee)::double precision = $0)
                           ->  Seq Scan on stock_1950 s  (cost=0.00..36.55 rows=9 width=16) (actual time=0.002..0.002 rows=0 loops=1)
                                 Filter: ((annee)::double precision = $0)
                           ->  Seq Scan on stock_1960 s  (cost=0.00..36.55 rows=9 width=16) (actual time=0.001..0.001 rows=0 loops=1)
                                 Filter: ((annee)::double precision = $0)
                           ->  Seq Scan on stock_1970 s  (cost=0.00..36.55 rows=9 width=16) (actual time=0.001..0.001 rows=0 loops=1)
                                 Filter: ((annee)::double precision = $0)
                           ->  Seq Scan on stock_1980 s  (cost=0.00..36.55 rows=9 width=16) (actual time=0.001..0.001 rows=0 loops=1)
                                 Filter: ((annee)::double precision = $0)
                           ->  Seq Scan on stock_1990 s  (cost=0.00..36.55 rows=9 width=16) (actual time=0.001..0.001 rows=0 loops=1)
                                 Filter: ((annee)::double precision = $0)
                           ->  Seq Scan on stock_2000 s  (cost=0.00..36.55 rows=9 width=16) (actual time=0.001..0.001 rows=0 loops=1)
                                 Filter: ((annee)::double precision = $0)
                           ->  Seq Scan on stock_2010 s  (cost=0.00..36.55 rows=9 width=16) (actual time=0.001..0.001 rows=0 loops=1)
                                 Filter: ((annee)::double precision = $0)
                     ->  Hash  (cost=97.60..97.60 rows=6060 width=8) (actual time=8.559..8.559 rows=6060 loops=1)
                           Buckets: 1024  Batches: 1  Memory Usage: 166kB
                           ->  Seq Scan on vin v  (cost=0.00..97.60 rows=6060 width=8) (actual time=0.012..4.344 rows=6060 loops=1)
               ->  Hash  (cost=1.03..1.03 rows=3 width=8) (actual time=0.043..0.043 rows=3 loops=1)
                     Buckets: 1024  Batches: 1  Memory Usage: 1kB
                     ->  Seq Scan on contenant c  (cost=0.00..1.03 rows=3 width=8) (actual time=0.002..0.037 rows=3 loops=1)
         ->  Hash  (cost=6.19..6.19 rows=319 width=20) (actual time=0.509..0.509 rows=319 loops=1)
               Buckets: 1024  Batches: 1  Memory Usage: 14kB
               ->  Seq Scan on appellation a  (cost=0.00..6.19 rows=319 width=20) (actual time=0.012..0.240 rows=319 loops=1)
 Total runtime: 594.712 ms

Per node type stats

node type	count	sum of times	% of query
Append	1	16.743 ms	2.8 %
Hash	3	4.490 ms	0.8 %
Hash Join	3	96.939 ms	16.3 %
HashAggregate	1	36.486 ms	6.1 %
Result	1	0.011 ms	0.0 %
Seq Scan	11	439.727 ms	74.0 %

Per table stats

Table name	Scan count	Total time	% of query
scan type	count	sum of times	% of table
appellation	1	0.240 ms	0.0 %
Seq Scan	1	0.240 ms	100.0 %
contenant	1	0.037 ms	0.0 %
Seq Scan	1	0.037 ms	100.0 %
stock	1	435.098 ms	73.2 %
Seq Scan	1	435.098 ms	100.0 %
stock_1950	1	0.002 ms	0.0 %
Seq Scan	1	0.002 ms	100.0 %
stock_1960	1	0.001 ms	0.0 %
Seq Scan	1	0.001 ms	100.0 %
stock_1970	1	0.001 ms	0.0 %
Seq Scan	1	0.001 ms	100.0 %
stock_1980	1	0.001 ms	0.0 %
Seq Scan	1	0.001 ms	100.0 %
stock_1990	1	0.001 ms	0.0 %
Seq Scan	1	0.001 ms	100.0 %
stock_2000	1	0.001 ms	0.0 %
Seq Scan	1	0.001 ms	100.0 %
stock_2010	1	0.001 ms	0.0 %
Seq Scan	1	0.001 ms	100.0 %
vin	1	4.344 ms	0.7 %
Seq Scan	1	4.344 ms	100.0 %

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Per node type stats

Per table stats