explain.depesz.com

A tool for finding a real cause for slow queries.

Result: bKS

options
# exclusive inclusive rows x rows loops node
1. 0.003 69841.950 ↓ 0.0 0 1
→

Unique (cost=46.63..46.68 rows=1 width=428) (actual time=69841.950..69841.950 rows=0 loops=1)

2.          

CTE q1
3. 0.070 69841.927 ↓ 0.0 0 1
→

Hash Join (cost=38.76..46.60 rows=1 width=193) (actual time=69841.927..69841.927 rows=0 loops=1)

  • Hash Cond: (reglement.individu_id = o.objet_id)
4. 0.885 0.885 ↓ 1.2 21 1
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Seq Scan on t_reglements reglement (cost=0.00..7.76 rows=18 width=84) (actual time=0.025..0.885 rows=21 loops=1)

  • Filter: (((libelle)::text ~~* '%session%'::text) AND (total_ttc <> 0::numeric))
5. 0.170 69840.972 ↓ 35.0 35 1
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Hash (cost=38.75..38.75 rows=1 width=125) (actual time=69840.972..69840.972 rows=35 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 5kB
6. 0.213 69840.802 ↓ 35.0 35 1
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Nested Loop Left Join (cost=9.24..38.75 rows=1 width=125) (actual time=1280.661..69840.802 rows=35 loops=1)

7. 0.196 69840.239 ↓ 35.0 35 1
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Nested Loop Left Join (cost=9.24..36.38 rows=1 width=119) (actual time=1280.645..69840.239 rows=35 loops=1)

8. 0.194 69839.763 ↓ 35.0 35 1
→

Nested Loop Left Join (cost=9.24..34.09 rows=1 width=113) (actual time=1280.633..69839.763 rows=35 loops=1)

9. 146.333 69839.359 ↓ 35.0 35 1
→

Nested Loop (cost=9.24..31.81 rows=1 width=113) (actual time=1280.622..69839.359 rows=35 loops=1)

  • Join Filter: (r2.source_id = r1.rel_objet_id)
10. 3.927 9.326 ↓ 2180.0 2180 1
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Nested Loop (cost=0.00..4.84 rows=1 width=4) (actual time=0.068..9.326 rows=2180 loops=1)

11. 0.037 0.037 ↑ 1.0 1 1
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Index Scan using idx_t_objets_type_code on t_objets type_coordonnee (cost=0.00..2.43 rows=1 width=4) (actual time=0.035..0.037 rows=1 loops=1)

  • Index Cond: (((type)::text = 'type_coordonnee'::text) AND ((code)::text = 'MAIL_PERS'::text))
  • Filter: ((NOT efface) AND (NOT obsolete))
12. 5.362 5.362 ↓ 2180.0 2180 1
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Index Scan using idx_t_relations_dest_nom on t_relations r2 (cost=0.00..2.40 rows=1 width=8) (actual time=0.029..5.362 rows=2180 loops=1)

  • Index Cond: ((r2.dest_id = type_coordonnee.objet_id) AND ((r2.relation_nom)::text = 'individu-coordonnee-type_coordonnee'::text))
13. 405.480 69683.700 ↓ 46.0 46 2180
→

Nested Loop (cost=9.24..26.96 rows=1 width=117) (actual time=29.444..31.965 rows=46 loops=2180)

14. 342.260 68438.920 ↓ 35.0 35 2180
→

Nested Loop Left Join (cost=9.24..24.55 rows=1 width=105) (actual time=29.423..31.394 rows=35 loops=2180)

15. 392.400 67715.160 ↓ 35.0 35 2180
→

Nested Loop Left Join (cost=9.24..22.27 rows=1 width=90) (actual time=29.411..31.062 rows=35 loops=2180)

16. 390.220 67017.560 ↓ 35.0 35 2180
→

Nested Loop Left Join (cost=9.24..19.99 rows=1 width=40) (actual time=29.399..30.742 rows=35 loops=2180)

17. 6688.240 66245.840 ↓ 35.0 35 2180
→

Nested Loop (cost=9.24..17.62 rows=1 width=35) (actual time=29.386..30.388 rows=35 loops=2180)

18. 9698.820 41193.280 ↓ 936.0 936 2180
→

Nested Loop (cost=9.24..15.52 rows=1 width=39) (actual time=0.064..18.896 rows=936 loops=2180)

19. 10051.980 23332.540 ↓ 936.0 936 2180
→

Nested Loop Left Join (cost=9.24..13.23 rows=1 width=16) (actual time=0.056..10.703 rows=936 loops=2180)

  • Filter: (NOT COALESCE(d1.attribut_valbool, false))
20. 2986.475 3165.360 ↓ 1160.0 1160 2180
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Hash Join (cost=9.24..10.91 rows=1 width=20) (actual time=0.046..1.452 rows=1160 loops=2180)

  • Hash Cond: (concours.objet_id = inscription_concours.concours_id)
21. 91.560 91.560 ↑ 1.0 48 2180
→

Seq Scan on t_concours concours (cost=0.00..1.48 rows=48 width=8) (actual time=0.002..0.042 rows=48 loops=2180)

22. 2.383 87.325 ↓ 1160.0 1160 1
→

Hash (cost=9.23..9.23 rows=1 width=20) (actual time=87.325..87.325 rows=1160 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 50kB
23. 18.457 84.942 ↓ 1160.0 1160 1
→

Nested Loop (cost=0.00..9.23 rows=1 width=20) (actual time=0.078..84.942 rows=1160 loops=1)

24. 21.706 56.533 ↓ 4976.0 4976 1
→

Nested Loop (cost=0.00..7.12 rows=1 width=8) (actual time=0.050..56.533 rows=4976 loops=1)

25. 8.288 14.923 ↓ 4976.0 4976 1
→

Nested Loop (cost=0.00..4.84 rows=1 width=4) (actual time=0.040..14.923 rows=4976 loops=1)

26. 0.025 0.025 ↑ 1.0 1 1
→

Index Scan using idx_t_objets_type_code on t_objets type_adresse (cost=0.00..2.43 rows=1 width=4) (actual time=0.021..0.025 rows=1 loops=1)

  • Index Cond: (((type)::text = 'type_adresse'::text) AND ((code)::text = 'DOMICILE'::text))
  • Filter: ((NOT efface) AND (NOT obsolete))
27. 6.610 6.610 ↓ 4976.0 4976 1
→

Index Scan using idx_t_relations_dest_nom on t_relations r4 (cost=0.00..2.40 rows=1 width=8) (actual time=0.015..6.610 rows=4976 loops=1)

  • Index Cond: ((r4.dest_id = type_adresse.objet_id) AND ((r4.relation_nom)::text = 'individu-adresse-type_adresse'::text))
28. 19.904 19.904 ↑ 1.0 1 4976
→

Index Scan using t_individus_adresses_pkey on t_individus_adresses r3 (cost=0.00..2.27 rows=1 width=12) (actual time=0.003..0.004 rows=1 loops=4976)

  • Index Cond: (r3.objet_id = r4.source_id)
29. 9.952 9.952 ↓ 0.0 0 4976
→

Index Scan using t_inscriptions_concours_apprenant_id_concours_id_key on t_inscriptions_concours inscription_concours (cost=0.00..2.10 rows=1 width=12) (actual time=0.002..0.002 rows=0 loops=4976)

  • Index Cond: (inscription_concours.apprenant_id = r3.individu_id)
30. 10115.200 10115.200 ↓ 0.0 0 2528800
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Index Scan using idx_t_donnees_objet_id_attribut_id on t_donnees d1 (cost=0.00..2.31 rows=1 width=5) (actual time=0.004..0.004 rows=0 loops=2528800)

  • Index Cond: ((d1.objet_id = inscription_concours.objet_id) AND (d1.attribut_id = 44210))
31. 8161.920 8161.920 ↑ 1.0 1 2040480
→

Index Scan using t_individus_pkey on t_individus o (cost=0.00..2.27 rows=1 width=23) (actual time=0.003..0.004 rows=1 loops=2040480)

  • Index Cond: (o.objet_id = r3.individu_id)
32. 18364.320 18364.320 ↓ 0.0 0 2040480
→

Index Scan using t_cours_pkey on t_cours cours (cost=0.00..2.09 rows=1 width=4) (actual time=0.009..0.009 rows=0 loops=2040480)

  • Index Cond: (cours.objet_id = concours.cours_id)
  • Filter: (((cours.code)::text ~~* '12%MARS%'::text) OR ((cours.code)::text ~~* '12%MAI%'::text) OR ((cours.code)::text ~~* '12%JUIN%'::text))
33. 381.500 381.500 ↑ 1.0 1 76300
→

Index Scan using t_objets_pkey on t_objets titre (cost=0.00..2.36 rows=1 width=13) (actual time=0.004..0.005 rows=1 loops=76300)

  • Index Cond: (titre.objet_id = o.titre_id)
  • Filter: ((NOT titre.efface) AND (NOT titre.obsolete) AND ((titre.type)::text = 'titre'::text))
34. 305.200 305.200 ↑ 1.0 1 76300
→

Index Scan using t_adresses_pkey on t_adresses adresse (cost=0.00..2.27 rows=1 width=58) (actual time=0.003..0.004 rows=1 loops=76300)

  • Index Cond: (adresse.objet_id = r3.adresse_id)
35. 381.500 381.500 ↑ 1.0 1 76300
→

Index Scan using t_villes_pkey on t_villes ville (cost=0.00..2.27 rows=1 width=23) (actual time=0.004..0.005 rows=1 loops=76300)

  • Index Cond: (ville.objet_id = adresse.ville_id)
36. 839.300 839.300 ↑ 1.0 1 76300
→

Index Scan using idx_t_relations_source_nom on t_relations r1 (cost=0.00..2.40 rows=1 width=12) (actual time=0.010..0.011 rows=1 loops=76300)

  • Index Cond: ((r1.source_id = o.objet_id) AND ((r1.relation_nom)::text = 'individu-coordonnee'::text))
37. 0.210 0.210 ↑ 1.0 1 35
→

Index Scan using t_pays_pkey on t_pays pays (cost=0.00..2.27 rows=1 width=4) (actual time=0.005..0.006 rows=1 loops=35)

  • Index Cond: (pays.objet_id = adresse.pays_id)
38. 0.280 0.280 ↑ 1.0 1 35
→

Index Scan using idx_t_donnees_objet_id_attribut_id on t_donnees d2 (cost=0.00..2.28 rows=1 width=14) (actual time=0.007..0.008 rows=1 loops=35)

  • Index Cond: ((d2.objet_id = pays.objet_id) AND (d2.attribut_id = 181306))
39. 0.350 0.350 ↑ 1.0 1 35
→

Index Scan using t_objets_pkey on t_objets coordonnee (cost=0.00..2.36 rows=1 width=14) (actual time=0.009..0.010 rows=1 loops=35)

  • Index Cond: (coordonnee.objet_id = r1.dest_id)
  • Filter: ((NOT coordonnee.efface) AND (NOT coordonnee.obsolete) AND ((coordonnee.type)::text = 'coordonnee'::text))
40. 0.016 69841.947 ↓ 0.0 0 1
→

Sort (cost=0.03..0.04 rows=1 width=428) (actual time=69841.947..69841.947 rows=0 loops=1)

  • Sort Key: q1."Nom.Individu", q1.id_interne_aurion, q1."id.Individu", q1."Code.Titre", q1."Prénom.Individu", q1."Rue (ligne 1).Adresse", q1."Rue (ligne 2).Adresse", q1."Rue (ligne 3).Adresse", q1."Rue (ligne 4).Adresse", q1."Code ISO.Pays", q1."Code postal.Ville", q1."Nom.Ville", q1."Coordonnée.Coordonnée", q1."Date de règlement.Règlement", q1."Libellé.Règlement", q1."Montant total initial.Règlement
  • Sort Method: quicksort Memory: 17kB
41. 69841.931 69841.931 ↓ 0.0 0 1
→

CTE Scan on q1 (cost=0.00..0.02 rows=1 width=428) (actual time=69841.931..69841.931 rows=0 loops=1)