explain.depesz.com

A tool for finding a real cause for slow queries.

Result: 4T9o

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# exclusive inclusive rows x rows loops node
1. 36.486 594.396 ↑ 13.7 319 1

HashAggregate (cost=18,303.52..18,390.83 rows=4,365 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 16,878 1

Hash Join (cost=184.59..18,248.94 rows=4,365 width=28) (actual time=159.703..557.899 rows=16,878 loops=1)

  • Hash Cond: (v.appellation_id = a.id)
5. 24.256 513.357 ↓ 3.9 16,878 1

Hash Join (cost=174.42..18,135.10 rows=4,365 width=16) (actual time=159.151..513.357 rows=16,878 loops=1)

  • Hash Cond: (s.contenant_id = c.id)
6. 28.650 489.058 ↓ 3.9 16,878 1

Hash Join (cost=173.35..18,074.01 rows=4,365 width=16) (actual time=159.091..489.058 rows=16,878 loops=1)

  • Hash Cond: (s.vin_id = v.id)
7. 16.743 451.849 ↓ 3.9 16,878 1

Append (cost=0.00..17,813.36 rows=4,365 width=16) (actual time=150.491..451.849 rows=16,878 loops=1)

8. 435.098 435.098 ↓ 3.9 16,878 1

Seq Scan on stock s (cost=0.00..17,557.51 rows=4,302 width=16) (actual time=150.489..435.098 rows=16,878 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 6,060 1

Hash (cost=97.60..97.60 rows=6,060 width=8) (actual time=8.559..8.559 rows=6,060 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 166kB
17. 4.344 4.344 ↑ 1.0 6,060 1

Seq Scan on vin v (cost=0.00..97.60 rows=6,060 width=8) (actual time=0.012..4.344 rows=6,060 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)