explain.depesz.com

A tool for finding a real cause for slow queries.

Result: sNE

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# exclusive inclusive rows x rows loops node
1. 60.878 11,182.289 ↓ 5.1 319 1

HashAggregate (cost=20,987.57..20,988.81 rows=62 width=32) (actual time=11,181.542..11,182.289 rows=319 loops=1)

2. 272.370 11,121.411 ↓ 271.0 16,803 1

Nested Loop Left Join (cost=1.07..20,986.80 rows=62 width=32) (actual time=2,657.876..11,121.411 rows=16,803 loops=1)

3. 199.979 10,748.223 ↓ 271.0 16,803 1

Nested Loop (cost=1.07..20,968.63 rows=62 width=20) (actual time=2,657.813..10,748.223 rows=16,803 loops=1)

4. 1,863.081 10,430.623 ↓ 271.0 16,803 1

Nested Loop (cost=1.07..20,951.22 rows=62 width=20) (actual time=2,657.779..10,430.623 rows=16,803 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. 3,587.113 8,567.522 ↓ 69.7 860,874 1

Hash Join (cost=1.07..20,765.81 rows=12,359 width=16) (actual time=6.564..8,567.522 rows=860,874 loops=1)

  • Hash Cond: (s.contenant_id = c.id)
7. 3,171.846 4,980.379 ↓ 1.0 860,874 1

Append (cost=0.00..17,551.38 rows=823,938 width=16) (actual time=6.458..4,980.379 rows=860,874 loops=1)

8. 6.458 6.458 ↑ 1.0 3 1

Seq Scan on stock s (cost=0.00..4,657.03 rows=3 width=16) (actual time=6.451..6.458 rows=3 loops=1)

9. 352.089 352.089 ↓ 1.0 169,085 1

Seq Scan on stock_1950 s (cost=0.00..2,531.78 rows=161,778 width=16) (actual time=0.012..352.089 rows=169,085 loops=1)

10. 356.918 356.918 ↓ 1.0 168,524 1

Seq Scan on stock_1960 s (cost=0.00..2,523.47 rows=161,247 width=16) (actual time=0.011..356.918 rows=168,524 loops=1)

11. 354.020 354.020 ↓ 1.0 168,487 1

Seq Scan on stock_1970 s (cost=0.00..2,523.47 rows=161,247 width=16) (actual time=0.010..354.020 rows=168,487 loops=1)

12. 347.712 347.712 ↓ 1.0 168,789 1

Seq Scan on stock_1980 s (cost=0.00..2,529.01 rows=161,601 width=16) (actual time=0.010..347.712 rows=168,789 loops=1)

13. 355.067 355.067 ↓ 1.0 168,497 1

Seq Scan on stock_1990 s (cost=0.00..2,523.47 rows=161,247 width=16) (actual time=0.011..355.067 rows=168,497 loops=1)

14. 36.269 36.269 ↓ 1.0 17,489 1

Seq Scan on stock_2000 s (cost=0.00..263.15 rows=16,815 width=16) (actual time=0.010..36.269 rows=17,489 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 16,803

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=16,803)

  • Index Cond: (id = s.vin_id)
18. 100.818 100.818 ↑ 1.0 1 16,803

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=16,803)

  • Index Cond: (v.appellation_id = id)