#	exclusive	inclusive	rows x	rows	loops	node
1.	3.324	504.140	↑ 1.0	319	1	→ GroupAggregate (cost=60,829.02..60,899.92 rows=319 width=48) (actual time=499.938..504.140 rows=319 loops=1) Group Key: ((((((round((random() * '50'::double precision)) + '1950'::double precision)))::text \|\| ' - '::text) \|\| a.libelle))
2.	26.846	500.816	↓ 3.9	16,829	1	→ Sort (cost=60,829.02..60,839.77 rows=4,301 width=40) (actual time=499.915..500.816 rows=16,829 loops=1) Sort Key: ((((((round((random() * '50'::double precision)) + '1950'::double precision)))::text \|\| ' - '::text) \|\| a.libelle)) Sort Method: quicksort Memory: 2105kB
3.	16.817	473.970	↓ 3.9	16,829	1	→ Hash Left Join (cost=531.06..60,569.45 rows=4,301 width=40) (actual time=7.502..473.970 rows=16,829 loops=1) Hash Cond: (v.appellation_id = a.id)
4.	4.891	456.755	↓ 3.9	16,829	1	→ Hash Join (cost=502.14..60,486.08 rows=4,301 width=20) (actual time=7.073..456.755 rows=16,829 loops=1) Hash Cond: (s.vin_id = v.id)
5.	4.477	444.997	↓ 3.9	16,829	1	→ Hash Join (cost=81.62..60,054.26 rows=4,301 width=20) (actual time=0.184..444.997 rows=16,829 loops=1) Hash Cond: (s.contenant_id = c.id)
6.	106.761	440.505	↓ 3.9	16,829	1	→ Hash Join (cost=0.47..59,961.77 rows=4,301 width=20) (actual time=0.157..440.505 rows=16,829 loops=1) Hash Cond: ((s.annee)::double precision = ((round((random() * '50'::double precision)) + '1950'::double precision)))
7.	333.732	333.732	↑ 1.0	860,129	1	→ Index Scan using stock_pkey on stock s (cost=0.42..55,618.08 rows=860,129 width=16) (actual time=0.039..333.732 rows=860,129 loops=1)
8.	0.006	0.012	↑ 1.0	1	1	→ Hash (cost=0.03..0.03 rows=1 width=8) (actual time=0.011..0.012 rows=1 loops=1) Buckets: 1024 Batches: 1 Memory Usage: 9kB
9.	0.006	0.006	↑ 1.0	1	1	→ Result (cost=0.00..0.02 rows=1 width=8) (actual time=0.005..0.006 rows=1 loops=1)
10.	0.004	0.015	↑ 400.0	3	1	→ Hash (cost=66.15..66.15 rows=1,200 width=8) (actual time=0.015..0.015 rows=3 loops=1) Buckets: 2048 Batches: 1 Memory Usage: 17kB
11.	0.011	0.011	↑ 400.0	3	1	→ Index Scan using contenant_pkey on contenant c (cost=0.15..66.15 rows=1,200 width=8) (actual time=0.007..0.011 rows=3 loops=1)
12.	2.756	6.867	↑ 1.1	6,058	1	→ Hash (cost=334.96..334.96 rows=6,845 width=8) (actual time=6.867..6.867 rows=6,058 loops=1) Buckets: 8192 Batches: 1 Memory Usage: 301kB
13.	4.111	4.111	↑ 1.1	6,058	1	→ Index Scan using vin_pkey on vin v (cost=0.28..334.96 rows=6,845 width=8) (actual time=0.020..4.111 rows=6,058 loops=1)
14.	0.184	0.398	↑ 1.0	319	1	→ Hash (cost=24.93..24.93 rows=319 width=36) (actual time=0.398..0.398 rows=319 loops=1) Buckets: 1024 Batches: 1 Memory Usage: 25kB
15.	0.214	0.214	↑ 1.0	319	1	→ Index Scan using appellation_pkey on appellation a (cost=0.15..24.93 rows=319 width=36) (actual time=0.018..0.214 rows=319 loops=1)

                                                                               QUERY PLAN                                                                                
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------
 GroupAggregate  (cost=60829.02..60899.92 rows=319 width=48) (actual time=499.938..504.140 rows=319 loops=1)
   Group Key: ((((((round((random() * '50'::double precision)) + '1950'::double precision)))::text || ' - '::text) || a.libelle))
   ->  Sort  (cost=60829.02..60839.77 rows=4301 width=40) (actual time=499.915..500.816 rows=16829 loops=1)
         Sort Key: ((((((round((random() * '50'::double precision)) + '1950'::double precision)))::text || ' - '::text) || a.libelle))
         Sort Method: quicksort  Memory: 2105kB
         ->  Hash Left Join  (cost=531.06..60569.45 rows=4301 width=40) (actual time=7.502..473.970 rows=16829 loops=1)
               Hash Cond: (v.appellation_id = a.id)
               ->  Hash Join  (cost=502.14..60486.08 rows=4301 width=20) (actual time=7.073..456.755 rows=16829 loops=1)
                     Hash Cond: (s.vin_id = v.id)
                     ->  Hash Join  (cost=81.62..60054.26 rows=4301 width=20) (actual time=0.184..444.997 rows=16829 loops=1)
                           Hash Cond: (s.contenant_id = c.id)
                           ->  Hash Join  (cost=0.47..59961.77 rows=4301 width=20) (actual time=0.157..440.505 rows=16829 loops=1)
                                 Hash Cond: ((s.annee)::double precision = ((round((random() * '50'::double precision)) + '1950'::double precision)))
                                 ->  Index Scan using stock_pkey on stock s  (cost=0.42..55618.08 rows=860129 width=16) (actual time=0.039..333.732 rows=860129 loops=1)
                                 ->  Hash  (cost=0.03..0.03 rows=1 width=8) (actual time=0.011..0.012 rows=1 loops=1)
                                       Buckets: 1024  Batches: 1  Memory Usage: 9kB
                                       ->  Result  (cost=0.00..0.02 rows=1 width=8) (actual time=0.005..0.006 rows=1 loops=1)
                           ->  Hash  (cost=66.15..66.15 rows=1200 width=8) (actual time=0.015..0.015 rows=3 loops=1)
                                 Buckets: 2048  Batches: 1  Memory Usage: 17kB
                                 ->  Index Scan using contenant_pkey on contenant c  (cost=0.15..66.15 rows=1200 width=8) (actual time=0.007..0.011 rows=3 loops=1)
                     ->  Hash  (cost=334.96..334.96 rows=6845 width=8) (actual time=6.867..6.867 rows=6058 loops=1)
                           Buckets: 8192  Batches: 1  Memory Usage: 301kB
                           ->  Index Scan using vin_pkey on vin v  (cost=0.28..334.96 rows=6845 width=8) (actual time=0.020..4.111 rows=6058 loops=1)
               ->  Hash  (cost=24.93..24.93 rows=319 width=36) (actual time=0.398..0.398 rows=319 loops=1)
                     Buckets: 1024  Batches: 1  Memory Usage: 25kB
                     ->  Index Scan using appellation_pkey on appellation a  (cost=0.15..24.93 rows=319 width=36) (actual time=0.018..0.214 rows=319 loops=1)
 Planning Time: 1.301 ms
 Execution Time: 504.299 ms
(28 rows)

Per node type stats

node type	count	sum of times	% of query
GroupAggregate	1	3.324 ms	0.7 %
Hash	4	2.950 ms	0.6 %
Hash Join	3	116.129 ms	23.0 %
Hash Left Join	1	16.817 ms	3.3 %
Index Scan	4	338.068 ms	67.1 %
Result	1	0.006 ms	0.0 %
Sort	1	26.846 ms	5.3 %

Per table stats

Table name	Scan count	Total time	% of query
scan type	count	sum of times	% of table
appellation	1	0.214 ms	0.0 %
Index Scan	1	0.214 ms	100.0 %
contenant	1	0.011 ms	0.0 %
Index Scan	1	0.011 ms	100.0 %
stock	1	333.732 ms	66.2 %
Index Scan	1	333.732 ms	100.0 %
vin	1	4.111 ms	0.8 %
Index Scan	1	4.111 ms	100.0 %

explain.depesz.com

PostgreSQL's explain analyze made readable

Result: oEp : Optimization for: plan #k3S6

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Visible columns:

Optimization path:

Optimization(s) for this plan:

Per node type stats

Per table stats