#	exclusive	inclusive	rows x	rows	loops	node
1.	60.878	11182.289	↓ 5.1	319	1	→ HashAggregate (cost=20987.57..20988.81 rows=62 width=32) (actual time=11181.542..11182.289 rows=319 loops=1)
2.	272.370	11121.411	↓ 271.0	16803	1	→ Nested Loop Left Join (cost=1.07..20986.80 rows=62 width=32) (actual time=2657.876..11121.411 rows=16803 loops=1)
3.	199.979	10748.223	↓ 271.0	16803	1	→ Nested Loop (cost=1.07..20968.63 rows=62 width=20) (actual time=2657.813..10748.223 rows=16803 loops=1)
4.	1863.081	10430.623	↓ 271.0	16803	1	→ Nested Loop (cost=1.07..20951.22 rows=62 width=20) (actual time=2657.779..10430.623 rows=16803 loops=1) Join Filter: ((s.annee)::double precision = ((round((random() * 50::double precision)) + 1950::double precision)))
5.	0.020	0.020	↑ 1.0	1	1	→ Result (cost=0.00..0.02 rows=1 width=0) (actual time=0.018..0.020 rows=1 loops=1)
6.	3587.113	8567.522	↓ 69.7	860874	1	→ Hash Join (cost=1.07..20765.81 rows=12359 width=16) (actual time=6.564..8567.522 rows=860874 loops=1) Hash Cond: (s.contenant_id = c.id)
7.	3171.846	4980.379	↓ 1.0	860874	1	→ Append (cost=0.00..17551.38 rows=823938 width=16) (actual time=6.458..4980.379 rows=860874 loops=1)
8.	6.458	6.458	↑ 1.0	3	1	→ Seq Scan on stock s (cost=0.00..4657.03 rows=3 width=16) (actual time=6.451..6.458 rows=3 loops=1)
9.	352.089	352.089	↓ 1.0	169085	1	→ Seq Scan on stock_1950 s (cost=0.00..2531.78 rows=161778 width=16) (actual time=0.012..352.089 rows=169085 loops=1)
10.	356.918	356.918	↓ 1.0	168524	1	→ Seq Scan on stock_1960 s (cost=0.00..2523.47 rows=161247 width=16) (actual time=0.011..356.918 rows=168524 loops=1)
11.	354.020	354.020	↓ 1.0	168487	1	→ Seq Scan on stock_1970 s (cost=0.00..2523.47 rows=161247 width=16) (actual time=0.010..354.020 rows=168487 loops=1)
12.	347.712	347.712	↓ 1.0	168789	1	→ Seq Scan on stock_1980 s (cost=0.00..2529.01 rows=161601 width=16) (actual time=0.010..347.712 rows=168789 loops=1)
13.	355.067	355.067	↓ 1.0	168497	1	→ Seq Scan on stock_1990 s (cost=0.00..2523.47 rows=161247 width=16) (actual time=0.011..355.067 rows=168497 loops=1)
14.	36.269	36.269	↓ 1.0	17489	1	→ Seq Scan on stock_2000 s (cost=0.00..263.15 rows=16815 width=16) (actual time=0.010..36.269 rows=17489 loops=1)
15.	0.016	0.030	↑ 1.0	3	1	→ Hash (cost=1.03..1.03 rows=3 width=8) (actual time=0.030..0.030 rows=3 loops=1) Buckets: 1024 Batches: 1 Memory Usage: 1kB
16.	0.014	0.014	↑ 1.0	3	1	→ Seq Scan on contenant c (cost=0.00..1.03 rows=3 width=8) (actual time=0.007..0.014 rows=3 loops=1)
17.	117.621	117.621	↑ 1.0	1	16803	→ Index Scan using vin_pkey on vin v (cost=0.00..0.27 rows=1 width=8) (actual time=0.005..0.007 rows=1 loops=16803) Index Cond: (id = s.vin_id)
18.	100.818	100.818	↑ 1.0	1	16803	→ Index Scan using appellation_pkey on appellation a (cost=0.00..0.27 rows=1 width=20) (actual time=0.004..0.006 rows=1 loops=16803) Index Cond: (v.appellation_id = id)

-------------------------
 HashAggregate  (cost=20987.57..20988.81 rows=62 width=32) (actual time=11181.542..11182.289 rows=319 loops=1)
   ->  Nested Loop Left Join  (cost=1.07..20986.80 rows=62 width=32) (actual time=2657.876..11121.411 rows=16803 loops=1)
         ->  Nested Loop  (cost=1.07..20968.63 rows=62 width=20) (actual time=2657.813..10748.223 rows=16803 loops=1)
               ->  Nested Loop  (cost=1.07..20951.22 rows=62 width=20) (actual time=2657.779..10430.623 rows=16803 loops=1)
                     Join Filter: ((s.annee)::double precision = ((round((random() * 50::double precision)) + 1950::double precision)))
                     ->  Result  (cost=0.00..0.02 rows=1 width=0) (actual time=0.018..0.020 rows=1 loops=1)
                     ->  Hash Join  (cost=1.07..20765.81 rows=12359 width=16) (actual time=6.564..8567.522 rows=860874 loops=1)
                           Hash Cond: (s.contenant_id = c.id)
                           ->  Append  (cost=0.00..17551.38 rows=823938 width=16) (actual time=6.458..4980.379 rows=860874 loops=1)
                                 ->  Seq Scan on stock s  (cost=0.00..4657.03 rows=3 width=16) (actual time=6.451..6.458 rows=3 loops=1)
                                 ->  Seq Scan on stock_1950 s  (cost=0.00..2531.78 rows=161778 width=16) (actual time=0.012..352.089 rows=169085 loops=1)
                                 ->  Seq Scan on stock_1960 s  (cost=0.00..2523.47 rows=161247 width=16) (actual time=0.011..356.918 rows=168524 loops=1)
                                 ->  Seq Scan on stock_1970 s  (cost=0.00..2523.47 rows=161247 width=16) (actual time=0.010..354.020 rows=168487 loops=1)
                                 ->  Seq Scan on stock_1980 s  (cost=0.00..2529.01 rows=161601 width=16) (actual time=0.010..347.712 rows=168789 loops=1)
                                 ->  Seq Scan on stock_1990 s  (cost=0.00..2523.47 rows=161247 width=16) (actual time=0.011..355.067 rows=168497 loops=1)
                                 ->  Seq Scan on stock_2000 s  (cost=0.00..263.15 rows=16815 width=16) (actual time=0.010..36.269 rows=17489 loops=1)
                           ->  Hash  (cost=1.03..1.03 rows=3 width=8) (actual time=0.030..0.030 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=0.007..0.014 rows=3 loops=1)
               ->  Index Scan using vin_pkey on vin v  (cost=0.00..0.27 rows=1 width=8) (actual time=0.005..0.007 rows=1 loops=16803)
                     Index Cond: (id = s.vin_id)
         ->  Index Scan using appellation_pkey on appellation a  (cost=0.00..0.27 rows=1 width=20) (actual time=0.004..0.006 rows=1 loops=16803)
               Index Cond: (v.appellation_id = id)
 Total runtime: 11183.519 ms
(24 lignes)

Per node type stats

node type	count	sum of times	% of query
Append	1	3171.846 ms	28.4 %
Hash	1	0.016 ms	0.0 %
Hash Join	1	3587.113 ms	32.1 %
HashAggregate	1	60.878 ms	0.5 %
Index Scan	2	218.439 ms	2.0 %
Nested Loop	2	2063.060 ms	18.4 %
Nested Loop Left Join	1	272.370 ms	2.4 %
Result	1	0.020 ms	0.0 %
Seq Scan	8	1808.547 ms	16.2 %

Per table stats

Table name	Scan count	Total time	% of query
scan type	count	sum of times	% of table
appellation	1	100.818 ms	0.9 %
Index Scan	1	100.818 ms	100.0 %
contenant	1	0.014 ms	0.0 %
Seq Scan	1	0.014 ms	100.0 %
stock	1	6.458 ms	0.1 %
Seq Scan	1	6.458 ms	100.0 %
stock_1950	1	352.089 ms	3.1 %
Seq Scan	1	352.089 ms	100.0 %
stock_1960	1	356.918 ms	3.2 %
Seq Scan	1	356.918 ms	100.0 %
stock_1970	1	354.020 ms	3.2 %
Seq Scan	1	354.020 ms	100.0 %
stock_1980	1	347.712 ms	3.1 %
Seq Scan	1	347.712 ms	100.0 %
stock_1990	1	355.067 ms	3.2 %
Seq Scan	1	355.067 ms	100.0 %
stock_2000	1	36.269 ms	0.3 %
Seq Scan	1	36.269 ms	100.0 %
vin	1	117.621 ms	1.1 %
Index Scan	1	117.621 ms	100.0 %

explain.depesz.com

A tool for finding a real cause for slow queries.

Result: sNE

Colorize:

Columns visibility:

Per node type stats

Per table stats