explain.depesz.com

A tool for finding a real cause for slow queries.

Result: bl7

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# exclusive inclusive rows x rows loops node
1. 70.245 11,726.554 ↓ 2.5 319 1

HashAggregate (cost=33,192.68..33,195.20 rows=126 width=32) (actual time=11,725.796..11,726.554 rows=319 loops=1)

2. 271.649 11,656.309 ↓ 133.3 16,795 1

Nested Loop Left Join (cost=1.07..33,191.10 rows=126 width=32) (actual time=1,281.582..11,656.309 rows=16,795 loops=1)

3. 220.945 11,267.095 ↓ 133.3 16,795 1

Nested Loop (cost=1.07..33,154.19 rows=126 width=20) (actual time=1,281.519..11,267.095 rows=16,795 loops=1)

4. 1,946.904 10,911.790 ↓ 133.3 16,795 1

Nested Loop (cost=1.07..33,118.81 rows=126 width=20) (actual time=1,281.484..10,911.790 rows=16,795 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=0.017..0.020 rows=1 loops=1)

6. 3,765.569 8,964.866 ↓ 34.1 860,874 1

Hash Join (cost=1.07..32,739.58 rows=25,280 width=16) (actual time=6.718..8,964.866 rows=860,874 loops=1)

  • Hash Cond: (s.contenant_id = c.id)
7. 3,291.492 5,199.266 ↑ 2.0 860,874 1

Append (cost=0.00..26,165.61 rows=1,685,361 width=16) (actual time=6.594..5,199.266 rows=860,874 loops=1)

8. 6.594 6.594 ↑ 287,142.0 3 1

Seq Scan on stock s (cost=0.00..13,271.26 rows=861,426 width=16) (actual time=6.588..6.594 rows=3 loops=1)

9. 382.134 382.134 ↓ 1.0 169,085 1

Seq Scan on stock_1950 s (cost=0.00..2,531.78 rows=161,778 width=16) (actual time=0.011..382.134 rows=169,085 loops=1)

10. 361.539 361.539 ↓ 1.0 168,524 1

Seq Scan on stock_1960 s (cost=0.00..2,523.47 rows=161,247 width=16) (actual time=0.012..361.539 rows=168,524 loops=1)

11. 373.675 373.675 ↓ 1.0 168,487 1

Seq Scan on stock_1970 s (cost=0.00..2,523.47 rows=161,247 width=16) (actual time=0.014..373.675 rows=168,487 loops=1)

12. 374.972 374.972 ↓ 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..374.972 rows=168,789 loops=1)

13. 367.025 367.025 ↓ 1.0 168,497 1

Seq Scan on stock_1990 s (cost=0.00..2,523.47 rows=161,247 width=16) (actual time=0.010..367.025 rows=168,497 loops=1)

14. 41.835 41.835 ↓ 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..41.835 rows=17,489 loops=1)

15. 0.018 0.031 ↑ 1.0 3 1

Hash (cost=1.03..1.03 rows=3 width=8) (actual time=0.031..0.031 rows=3 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 1kB
16. 0.013 0.013 ↑ 1.0 3 1

Seq Scan on contenant c (cost=0.00..1.03 rows=3 width=8) (actual time=0.006..0.013 rows=3 loops=1)

17. 134.360 134.360 ↑ 1.0 1 16,795

Index Scan using vin_pkey on vin v (cost=0.00..0.27 rows=1 width=8) (actual time=0.006..0.008 rows=1 loops=16,795)

  • Index Cond: (id = s.vin_id)
18. 117.565 117.565 ↑ 1.0 1 16,795

Index Scan using appellation_pkey on appellation a (cost=0.00..0.27 rows=1 width=20) (actual time=0.004..0.007 rows=1 loops=16,795)

  • Index Cond: (v.appellation_id = id)