explain.depesz.com

PostgreSQL's explain analyze made readable

Result: KeBI

Settings
# exclusive inclusive rows x rows loops node
1. 0.035 260.235 ↑ 1.0 100 1

Limit (cost=0.71..107,790.66 rows=100 width=1,792) (actual time=14.544..260.235 rows=100 loops=1)

2. 1.280 260.200 ↑ 3.6 100 1

Nested Loop (cost=0.71..386,966.64 rows=359 width=1,792) (actual time=14.544..260.200 rows=100 loops=1)

3. 34.020 34.020 ↑ 4.8 100 1

Index Scan using pk_vehicule on vehicule vehicle0_ (cost=0.29..4,318.57 rows=475 width=1,568) (actual time=11.394..34.020 rows=100 loops=1)

  • Index Cond: (cle > 0)
  • Filter: (canal_particulier AND ((stock)::text = 'ST'::text))
  • Rows Removed by Filter: 21503
4. 0.800 0.800 ↑ 1.0 1 100

Index Scan using pk_prix on prix price1_ (cost=0.42..2.37 rows=1 width=4) (actual time=0.007..0.008 rows=1 loops=100)

  • Index Cond: (cle = vehicle0_.prix_vente)
  • Filter: (valeur > '0'::numeric)
5.          

SubPlan (for Nested Loop)

6. 0.100 2.400 ↓ 0.0 0 100

Limit (cost=51.16..51.16 rows=1 width=8) (actual time=0.024..0.024 rows=0 loops=100)

7. 0.300 2.300 ↓ 0.0 0 100

Sort (cost=51.16..51.18 rows=8 width=8) (actual time=0.023..0.023 rows=0 loops=100)

  • Sort Key: p.date
  • Sort Method: quicksort Memory: 25kB
8. 2.000 2.000 ↓ 0.0 0 100

Index Scan using idx_prix_vehicule_cle on prix p (cost=0.42..51.12 rows=8 width=8) (actual time=0.018..0.020 rows=0 loops=100)

  • Index Cond: (vehicule_cle = vehicle0_.cle)
  • Filter: ((valeur > '0'::numeric) AND (type = 3))
  • Rows Removed by Filter: 9
9. 0.100 1.200 ↑ 1.0 1 100

Limit (cost=51.15..51.15 rows=1 width=8) (actual time=0.011..0.012 rows=1 loops=100)

10. 0.300 1.100 ↑ 6.0 1 100

Sort (cost=51.15..51.16 rows=6 width=8) (actual time=0.011..0.011 rows=1 loops=100)

  • Sort Key: p_1.date
  • Sort Method: quicksort Memory: 25kB
11. 0.800 0.800 ↑ 1.5 4 100

Index Scan using idx_prix_vehicule_cle on prix p_1 (cost=0.42..51.12 rows=6 width=8) (actual time=0.003..0.008 rows=4 loops=100)

  • Index Cond: (vehicule_cle = vehicle0_.cle)
  • Filter: ((valeur > '0'::numeric) AND (type = 2))
  • Rows Removed by Filter: 5
12. 0.100 1.100 ↑ 1.0 1 100

Limit (cost=51.14..51.14 rows=1 width=40) (actual time=0.011..0.011 rows=1 loops=100)

13. 0.200 1.000 ↑ 5.0 1 100

Sort (cost=51.14..51.15 rows=5 width=40) (actual time=0.010..0.010 rows=1 loops=100)

  • Sort Key: p_2.date DESC
  • Sort Method: quicksort Memory: 25kB
14. 0.800 0.800 ↑ 5.0 1 100

Index Scan using idx_prix_vehicule_cle on prix p_2 (cost=0.42..51.12 rows=5 width=40) (actual time=0.004..0.008 rows=1 loops=100)

  • Index Cond: (vehicule_cle = vehicle0_.cle)
  • Filter: (((statutvehicule)::text = 'ST'::text) AND (type = 3))
  • Rows Removed by Filter: 8
15. 0.100 1.100 ↑ 1.0 1 100

Limit (cost=51.14..51.14 rows=1 width=40) (actual time=0.010..0.011 rows=1 loops=100)

16. 0.200 1.000 ↑ 4.0 1 100

Sort (cost=51.14..51.15 rows=4 width=40) (actual time=0.010..0.010 rows=1 loops=100)

  • Sort Key: p_3.date DESC
  • Sort Method: quicksort Memory: 25kB
17. 0.800 0.800 ↑ 1.3 3 100

Index Scan using idx_prix_vehicule_cle on prix p_3 (cost=0.42..51.12 rows=4 width=40) (actual time=0.003..0.008 rows=3 loops=100)

  • Index Cond: (vehicule_cle = vehicle0_.cle)
  • Filter: (((statutvehicule)::text = 'ST'::text) AND (type = 2))
  • Rows Removed by Filter: 5
18. 0.400 205.600 ↑ 1.0 1 100

Aggregate (cost=671.62..671.63 rows=1 width=8) (actual time=2.056..2.056 rows=1 loops=100)

19. 205.200 205.200 ↑ 7.0 1 100

Seq Scan on photo_annexe p_4 (cost=0.00..671.60 rows=7 width=0) (actual time=1.940..2.052 rows=1 loops=100)

  • Filter: (vehicule_cle = vehicle0_.cle)
  • Rows Removed by Filter: 11730
20. 0.200 0.800 ↑ 1.0 1 100

Aggregate (cost=3.28..3.29 rows=1 width=8) (actual time=0.008..0.008 rows=1 loops=100)

21. 0.600 0.600 ↑ 1.0 1 100

Index Scan using idx_offre_vovendu_cle on offre o (cost=0.29..3.28 rows=1 width=0) (actual time=0.005..0.006 rows=1 loops=100)

  • Index Cond: (vovendu_cle = vehicle0_.cle)
  • Filter: ((motif_annulation)::text <> 'MODIFICATION'::text)
22. 0.100 0.500 ↑ 1.0 1 100

Aggregate (cost=2.31..2.32 rows=1 width=8) (actual time=0.005..0.005 rows=1 loops=100)

23. 0.400 0.400 ↓ 0.0 0 100

Index Scan using idx_offre_vorepris_cle on offre o_1 (cost=0.29..2.31 rows=1 width=0) (actual time=0.004..0.004 rows=0 loops=100)

  • Index Cond: (vorepris_cle = vehicle0_.cle)
  • Filter: ((motif_annulation)::text <> 'MODIFICATION'::text)
  • Rows Removed by Filter: 0
24. 0.200 4.700 ↑ 1.0 1 100

Aggregate (cost=82.78..82.79 rows=1 width=8) (actual time=0.047..0.047 rows=1 loops=100)

25. 4.500 4.500 ↓ 1.5 3 100

Index Scan using idx_vehicule_equipements_vehicule_cle on vehicule_equipements ve (cost=0.43..82.77 rows=2 width=0) (actual time=0.019..0.045 rows=3 loops=100)

  • Index Cond: (vehicule_cle = vehicle0_.cle)
  • Filter: (serie_option = 'O'::bpchar)
  • Rows Removed by Filter: 81
26. 0.300 2.200 ↑ 1.0 1 100

Aggregate (cost=2.28..2.29 rows=1 width=8) (actual time=0.021..0.022 rows=1 loops=100)

27. 1.900 1.900 ↑ 2.1 20 100

Index Only Scan using idx_photo_vehicule_cle on photo p_5 (cost=0.42..2.18 rows=43 width=0) (actual time=0.007..0.019 rows=20 loops=100)

  • Index Cond: (vehicule_cle = vehicle0_.cle)
  • Heap Fetches: 1087
28. 0.200 2.800 ↑ 1.0 1 100

Aggregate (cost=82.78..82.79 rows=1 width=8) (actual time=0.027..0.028 rows=1 loops=100)

29. 2.600 2.600 ↓ 1.5 3 100

Index Scan using idx_vehicule_equipements_vehicule_cle on vehicule_equipements ve_1 (cost=0.43..82.77 rows=2 width=8) (actual time=0.010..0.026 rows=3 loops=100)

  • Index Cond: (vehicule_cle = vehicle0_.cle)
  • Filter: (serie_option = 'O'::bpchar)
  • Rows Removed by Filter: 81
30. 0.100 0.900 ↑ 1.0 1 100

Aggregate (cost=4.33..4.34 rows=1 width=32) (actual time=0.009..0.009 rows=1 loops=100)

31. 0.800 0.800 ↑ 1.0 1 100

Index Scan using idx_frais_vehicule_cle on frais f (cost=0.29..4.33 rows=1 width=5) (actual time=0.006..0.008 rows=1 loops=100)

  • Index Cond: (vehicule_cle = vehicle0_.cle)
  • Filter: (type = 2)
  • Rows Removed by Filter: 2
32. 0.100 0.400 ↑ 1.0 1 100

Aggregate (cost=4.33..4.34 rows=1 width=32) (actual time=0.004..0.004 rows=1 loops=100)

33. 0.300 0.300 ↑ 1.0 1 100

Index Scan using idx_frais_vehicule_cle on frais f_1 (cost=0.29..4.33 rows=1 width=5) (actual time=0.002..0.003 rows=1 loops=100)

  • Index Cond: (vehicule_cle = vehicle0_.cle)
  • Filter: (type = 0)
  • Rows Removed by Filter: 1
34. 0.200 0.400 ↑ 1.0 1 100

Aggregate (cost=4.33..4.34 rows=1 width=32) (actual time=0.004..0.004 rows=1 loops=100)

35. 0.200 0.200 ↓ 0.0 0 100

Index Scan using idx_frais_vehicule_cle on frais f_2 (cost=0.29..4.33 rows=1 width=5) (actual time=0.002..0.002 rows=0 loops=100)

  • Index Cond: (vehicule_cle = vehicle0_.cle)
  • Filter: (type = 1)
  • Rows Removed by Filter: 2
Planning time : 1.299 ms
Execution time : 260.575 ms