explain.depesz.com

PostgreSQL's explain analyze made readable

Result: 3INb

Settings
# exclusive inclusive rows x rows loops node
1. 14.111 162.631 ↑ 1.0 3,056 1

Subquery Scan on vue_pvofull (cost=2,110.93..87,572.04 rows=3,064 width=42) (actual time=7.562..162.631 rows=3,056 loops=1)

2. 75.587 148.520 ↑ 1.0 3,056 1

Hash Left Join (cost=2,110.93..87,269.47 rows=3,064 width=688) (actual time=7.541..148.520 rows=3,056 loops=1)

  • Hash Cond: (vehicule.cle = indicateur.vehicule_cle)
3.          

Initplan (for Hash Left Join)

4. 0.002 0.008 ↑ 1.0 1 1

Aggregate (cost=0.02..0.03 rows=1 width=32) (actual time=0.008..0.008 rows=1 loops=1)

5. 0.006 0.006 ↓ 0.0 0 1

Sort (cost=0.01..0.02 rows=0 width=36) (actual time=0.006..0.006 rows=0 loops=1)

  • Sort Key: id
  • Sort Method: quicksort Memory: 25kB
6. 0.000 0.000 ↓ 0.0 0 1

Result (cost=0.00..0.00 rows=0 width=36) (actual time=0.000..0.000 rows=0 loops=1)

  • One-Time Filter: false
7. 1.299 48.459 ↑ 1.0 3,056 1

Hash Left Join (cost=2,082.00..15,073.32 rows=3,064 width=257) (actual time=7.328..48.459 rows=3,056 loops=1)

  • Hash Cond: (vehicule.warranty_id = warranty.id)
8. 1.507 44.173 ↑ 1.0 3,056 1

Hash Join (cost=1,784.27..14,742.65 rows=3,064 width=237) (actual time=4.275..44.173 rows=3,056 loops=1)

  • Hash Cond: (vehicule.lieu_cle = lieu.cle)
9. 1.465 42.646 ↑ 1.0 3,056 1

Hash Join (cost=1,780.44..14,696.69 rows=3,064 width=237) (actual time=4.241..42.646 rows=3,056 loops=1)

  • Hash Cond: (vehicule.site_cle = site.cle)
10. 1.401 41.168 ↑ 1.0 3,056 1

Hash Join (cost=1,776.62..14,650.74 rows=3,064 width=237) (actual time=4.218..41.168 rows=3,056 loops=1)

  • Hash Cond: (vehicule.affectation = affectation.cle)
11. 1.669 39.758 ↑ 1.0 3,056 1

Hash Left Join (cost=1,775.55..14,607.54 rows=3,064 width=241) (actual time=4.199..39.758 rows=3,056 loops=1)

  • Hash Cond: (vehicule.origin_code = origine.code_enum)
12. 2.474 38.074 ↑ 1.0 3,056 1

Nested Loop Left Join (cost=1,774.26..14,568.63 rows=3,064 width=241) (actual time=4.168..38.074 rows=3,056 loops=1)

13. 1.080 32.544 ↑ 1.0 3,056 1

Nested Loop Left Join (cost=1,773.84..9,304.55 rows=3,064 width=233) (actual time=4.166..32.544 rows=3,056 loops=1)

14. 5.258 22.296 ↑ 1.0 3,056 1

Hash Join (cost=1,773.42..4,040.47 rows=3,064 width=225) (actual time=4.156..22.296 rows=3,056 loops=1)

  • Hash Cond: (modeleperso.modele_cle = vehicule.modele_perso_cle)
15. 12.910 12.910 ↑ 1.0 19,084 1

Seq Scan on modele_perso_mv modeleperso (cost=0.00..2,164.84 rows=19,084 width=146) (actual time=0.003..12.910 rows=19,084 loops=1)

16. 1.022 4.128 ↑ 1.0 3,056 1

Hash (cost=1,735.12..1,735.12 rows=3,064 width=91) (actual time=4.128..4.128 rows=3,056 loops=1)

  • Buckets: 4,096 Batches: 1 Memory Usage: 424kB
17. 3.106 3.106 ↑ 1.0 3,056 1

Index Scan using idx_stock on vehicule (cost=0.29..1,735.12 rows=3,064 width=91) (actual time=0.012..3.106 rows=3,056 loops=1)

  • Index Cond: ((stock)::text = ANY ('{ST,IM}'::text[]))
  • Filter: (deletion_date IS NULL)
  • Rows Removed by Filter: 11
18. 9.168 9.168 ↑ 1.0 1 3,056

Index Scan using pk_prix on prix pv (cost=0.42..1.71 rows=1 width=16) (actual time=0.003..0.003 rows=1 loops=3,056)

  • Index Cond: (vehicule.prix_vente = cle)
19. 3.056 3.056 ↑ 1.0 1 3,056

Index Scan using pk_prix on prix pm (cost=0.42..1.71 rows=1 width=16) (actual time=0.001..0.001 rows=1 loops=3,056)

  • Index Cond: (vehicule.prix_marchand = cle)
20. 0.006 0.015 ↑ 1.0 13 1

Hash (cost=1.13..1.13 rows=13 width=14) (actual time=0.015..0.015 rows=13 loops=1)

  • Buckets: 1,024 Batches: 1 Memory Usage: 9kB
21. 0.009 0.009 ↑ 1.0 13 1

Seq Scan on origine (cost=0.00..1.13 rows=13 width=14) (actual time=0.006..0.009 rows=13 loops=1)

22. 0.003 0.009 ↑ 1.0 3 1

Hash (cost=1.03..1.03 rows=3 width=4) (actual time=0.009..0.009 rows=3 loops=1)

  • Buckets: 1,024 Batches: 1 Memory Usage: 9kB
23. 0.006 0.006 ↑ 1.0 3 1

Seq Scan on affectation (cost=0.00..1.03 rows=3 width=4) (actual time=0.006..0.006 rows=3 loops=1)

24. 0.006 0.013 ↑ 1.0 54 1

Hash (cost=3.15..3.15 rows=54 width=4) (actual time=0.013..0.013 rows=54 loops=1)

  • Buckets: 1,024 Batches: 1 Memory Usage: 10kB
25. 0.007 0.007 ↑ 1.0 54 1

Index Only Scan using pk_localisation on localisation site (cost=0.14..3.15 rows=54 width=4) (actual time=0.001..0.007 rows=54 loops=1)

  • Heap Fetches: 0
26. 0.010 0.020 ↑ 1.0 54 1

Hash (cost=3.15..3.15 rows=54 width=4) (actual time=0.020..0.020 rows=54 loops=1)

  • Buckets: 1,024 Batches: 1 Memory Usage: 10kB
27. 0.010 0.010 ↑ 1.0 54 1

Index Only Scan using pk_localisation on localisation lieu (cost=0.14..3.15 rows=54 width=4) (actual time=0.005..0.010 rows=54 loops=1)

  • Heap Fetches: 0
28. 1.594 2.987 ↑ 1.0 9,366 1

Hash (cost=180.66..180.66 rows=9,366 width=28) (actual time=2.987..2.987 rows=9,366 loops=1)

  • Buckets: 16,384 Batches: 1 Memory Usage: 695kB
29. 1.393 1.393 ↑ 1.0 9,366 1

Seq Scan on warranty (cost=0.00..180.66 rows=9,366 width=28) (actual time=0.004..1.393 rows=9,366 loops=1)

30. 0.001 0.003 ↓ 0.0 0 1

Hash (cost=18.40..18.40 rows=840 width=12) (actual time=0.003..0.003 rows=0 loops=1)

  • Buckets: 1,024 Batches: 1 Memory Usage: 8kB
31. 0.002 0.002 ↓ 0.0 0 1

Seq Scan on indicateur_argus indicateur (cost=0.00..18.40 rows=840 width=12) (actual time=0.002..0.002 rows=0 loops=1)

32.          

SubPlan (for Hash Left Join)

33. 3.056 6.112 ↑ 1.0 1 3,056

Aggregate (cost=1.06..1.07 rows=1 width=8) (actual time=0.002..0.002 rows=1 loops=3,056)

34. 3.056 3.056 ↓ 0.0 0 3,056

Seq Scan on publication (cost=0.00..1.05 rows=4 width=0) (actual time=0.001..0.001 rows=0 loops=3,056)

  • Filter: (vehicule_cle = vehicule.cle)
  • Rows Removed by Filter: 4
35. 0.004 0.009 ↑ 1.0 1 1

Aggregate (cost=17.19..17.20 rows=1 width=8) (actual time=0.009..0.009 rows=1 loops=1)

36. 0.005 0.005 ↑ 14.0 1 1

Index Scan using idx_photo_vehicule_cle on photo (cost=0.42..17.16 rows=14 width=8) (actual time=0.005..0.005 rows=1 loops=1)

  • Index Cond: (vehicule_cle = vehicule.cle)
37. 0.002 0.004 ↑ 1.0 1 1

Limit (cost=0.00..0.26 rows=1 width=8) (actual time=0.003..0.004 rows=1 loops=1)

38. 0.002 0.002 ↑ 4.0 1 1

Seq Scan on publication publication_1 (cost=0.00..1.05 rows=4 width=8) (actual time=0.002..0.002 rows=1 loops=1)

  • Filter: (vehicule_cle = vehicule.cle)
39. 0.000 0.002 ↑ 1.0 1 1

Limit (cost=0.00..0.26 rows=1 width=8) (actual time=0.002..0.002 rows=1 loops=1)

40. 0.002 0.002 ↑ 4.0 1 1

Seq Scan on publication publication_2 (cost=0.00..1.05 rows=4 width=8) (actual time=0.002..0.002 rows=1 loops=1)

  • Filter: (vehicule_cle = vehicule.cle)
41. 0.000 6.112 ↑ 1.0 1 3,056

Aggregate (cost=1.80..1.81 rows=1 width=8) (actual time=0.002..0.002 rows=1 loops=3,056)

42. 6.112 6.112 ↑ 14.0 1 3,056

Index Only Scan using idx_photo_vehicule_cle on photo photo_1 (cost=0.42..1.76 rows=14 width=0) (actual time=0.002..0.002 rows=1 loops=3,056)

  • Index Cond: (vehicule_cle = vehicule.cle)
  • Heap Fetches: 0
43. 3.056 6.112 ↑ 1.0 1 3,056

Aggregate (cost=1.80..1.81 rows=1 width=4) (actual time=0.002..0.002 rows=1 loops=3,056)

44. 3.056 3.056 ↑ 14.0 1 3,056

Index Only Scan using idx_photo_vehicule_cle on photo photo_2 (cost=0.42..1.76 rows=14 width=0) (actual time=0.001..0.001 rows=1 loops=3,056)

  • Index Cond: (vehicule_cle = vehicule.cle)
  • Heap Fetches: 0
45. 3.056 3.056 ↑ 1.0 1 3,056

Result (cost=0.00..0.02 rows=1 width=8) (actual time=0.001..0.001 rows=1 loops=3,056)

46. 3.056 3.056 ↑ 1.0 1 3,056

Result (cost=0.00..0.02 rows=1 width=8) (actual time=0.000..0.001 rows=1 loops=3,056)

Planning time : 5.035 ms
Execution time : 163.432 ms