#	exclusive	inclusive	rows x	rows	loops	node
1.	26.349	1828.338	↑ 1.0	319	1	→ HashAggregate (cost=18068.88..18075.26 rows=319 width=32) (actual time=1827.959..1828.338 rows=319 loops=1)
2.	51.328	1801.989	↓ 3.9	16802	1	→ Hash Left Join (cost=180.50..18015.15 rows=4299 width=32) (actual time=697.538..1801.989 rows=16802 loops=1) Hash Cond: (v.appellation_id = a.id)
3.	32.216	1749.928	↓ 3.9	16802	1	→ Hash Join (cost=170.32..17902.87 rows=4299 width=20) (actual time=696.754..1749.928 rows=16802 loops=1) Hash Cond: (s.contenant_id = c.id)
4.	34.570	1717.696	↓ 3.9	16802	1	→ Hash Join (cost=169.26..17842.69 rows=4299 width=20) (actual time=696.714..1717.696 rows=16802 loops=1) Hash Cond: (s.vin_id = v.id)
5.	931.273	1669.459	↓ 3.9	16802	1	→ Hash Join (cost=0.04..17587.49 rows=4299 width=20) (actual time=683.013..1669.459 rows=16802 loops=1) Hash Cond: ((s.annee)::double precision = ((round((random() * 50::double precision)) + 1950::double precision)))
6.	738.169	738.169	↑ 1.0	859764	1	→ Seq Scan on stock s (cost=0.00..13245.64 rows=859764 width=16) (actual time=0.004..738.169 rows=859764 loops=1)
7.	0.003	0.017	↑ 1.0	1	1	→ Hash (cost=0.03..0.03 rows=1 width=8) (actual time=0.017..0.017 rows=1 loops=1) Buckets: 1024 Batches: 1 Memory Usage: 1kB
8.	0.014	0.014	↑ 1.0	1	1	→ Result (cost=0.00..0.02 rows=1 width=0) (actual time=0.013..0.014 rows=1 loops=1)
9.	6.796	13.667	↑ 1.0	6054	1	→ Hash (cost=93.54..93.54 rows=6054 width=8) (actual time=13.667..13.667 rows=6054 loops=1) Buckets: 1024 Batches: 1 Memory Usage: 237kB
10.	6.871	6.871	↑ 1.0	6054	1	→ Seq Scan on vin v (cost=0.00..93.54 rows=6054 width=8) (actual time=0.007..6.871 rows=6054 loops=1)
11.	0.009	0.016	↑ 1.0	3	1	→ Hash (cost=1.03..1.03 rows=3 width=8) (actual time=0.016..0.016 rows=3 loops=1) Buckets: 1024 Batches: 1 Memory Usage: 1kB
12.	0.007	0.007	↑ 1.0	3	1	→ Seq Scan on contenant c (cost=0.00..1.03 rows=3 width=8) (actual time=0.003..0.007 rows=3 loops=1)
13.	0.369	0.733	↑ 1.0	319	1	→ Hash (cost=6.19..6.19 rows=319 width=20) (actual time=0.733..0.733 rows=319 loops=1) Buckets: 1024 Batches: 1 Memory Usage: 17kB
14.	0.364	0.364	↑ 1.0	319	1	→ Seq Scan on appellation a (cost=0.00..6.19 rows=319 width=20) (actual time=0.007..0.364 rows=319 loops=1)

 HashAggregate  (cost=18068.88..18075.26 rows=319 width=32) (actual time=1827.959..1828.338 rows=319 loops=1)
   ->  Hash Left Join  (cost=180.50..18015.15 rows=4299 width=32) (actual time=697.538..1801.989 rows=16802 loops=1)
         Hash Cond: (v.appellation_id = a.id)
         ->  Hash Join  (cost=170.32..17902.87 rows=4299 width=20) (actual time=696.754..1749.928 rows=16802 loops=1)
               Hash Cond: (s.contenant_id = c.id)
               ->  Hash Join  (cost=169.26..17842.69 rows=4299 width=20) (actual time=696.714..1717.696 rows=16802 loops=1)
                     Hash Cond: (s.vin_id = v.id)
                     ->  Hash Join  (cost=0.04..17587.49 rows=4299 width=20) (actual time=683.013..1669.459 rows=16802 loops=1)
                           Hash Cond: ((s.annee)::double precision = ((round((random() * 50::double precision)) + 1950::double precision)))
                           ->  Seq Scan on stock s  (cost=0.00..13245.64 rows=859764 width=16) (actual time=0.004..738.169 rows=859764 loops=1)
                           ->  Hash  (cost=0.03..0.03 rows=1 width=8) (actual time=0.017..0.017 rows=1 loops=1)
                                 Buckets: 1024  Batches: 1  Memory Usage: 1kB
                                 ->  Result  (cost=0.00..0.02 rows=1 width=0) (actual time=0.013..0.014 rows=1 loops=1)
                     ->  Hash  (cost=93.54..93.54 rows=6054 width=8) (actual time=13.667..13.667 rows=6054 loops=1)
                           Buckets: 1024  Batches: 1  Memory Usage: 237kB
                           ->  Seq Scan on vin v  (cost=0.00..93.54 rows=6054 width=8) (actual time=0.007..6.871 rows=6054 loops=1)
               ->  Hash  (cost=1.03..1.03 rows=3 width=8) (actual time=0.016..0.016 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=0.003..0.007 rows=3 loops=1)
         ->  Hash  (cost=6.19..6.19 rows=319 width=20) (actual time=0.733..0.733 rows=319 loops=1)
               Buckets: 1024  Batches: 1  Memory Usage: 17kB
               ->  Seq Scan on appellation a  (cost=0.00..6.19 rows=319 width=20) (actual time=0.007..0.364 rows=319 loops=1)
 Total runtime: 1828.743 ms

Per node type stats

node type	count	sum of times	% of query
Hash	4	7.177 ms	0.4 %
Hash Join	3	998.059 ms	54.6 %
Hash Left Join	1	51.328 ms	2.8 %
HashAggregate	1	26.349 ms	1.4 %
Result	1	0.014 ms	0.0 %
Seq Scan	4	745.411 ms	40.8 %

Per table stats

Table name	Scan count	Total time	% of query
scan type	count	sum of times	% of table
appellation	1	0.364 ms	0.0 %
Seq Scan	1	0.364 ms	100.0 %
contenant	1	0.007 ms	0.0 %
Seq Scan	1	0.007 ms	100.0 %
stock	1	738.169 ms	40.4 %
Seq Scan	1	738.169 ms	100.0 %
vin	1	6.871 ms	0.4 %
Seq Scan	1	6.871 ms	100.0 %

explain.depesz.com

A tool for finding a real cause for slow queries.

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Per node type stats

Per table stats