explain.depesz.com

A tool for finding a real cause for slow queries.

Result: ESA

options
Did it help? Consider supporting us - Bitcoin address: 12v2hUztAk2LgzQ9H9LMwuU32urHMjZQnq
# exclusive inclusive rows x rows loops node
1. 0.003 70472.695 ↓ 0.0 0 1

Unique (cost=46.63..46.68 rows=1 width=428) (actual time=70472.695..70472.695 rows=0 loops=1)

2.          

CTE q1

3. 0.030 70472.671 ↓ 0.0 0 1

Hash Join (cost=38.76..46.60 rows=1 width=193) (actual time=70472.671..70472.671 rows=0 loops=1)

  • Hash Cond: (reglement.individu_id = o.objet_id)
4. 0.897 0.897 ↓ 1.2 21 1

Seq Scan on t_reglements reglement (cost=0.00..7.76 rows=18 width=84) (actual time=0.026..0.897 rows=21 loops=1)

  • Filter: (((libelle)::text ~~* '%session%'::text) AND (total_ttc <> 0::numeric))
5. 0.180 70471.744 ↓ 35.0 35 1

Hash (cost=38.75..38.75 rows=1 width=125) (actual time=70471.744..70471.744 rows=35 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 5kB
6. 0.216 70471.564 ↓ 35.0 35 1

Nested Loop Left Join (cost=9.24..38.75 rows=1 width=125) (actual time=1307.409..70471.564 rows=35 loops=1)

7. 0.207 70470.998 ↓ 35.0 35 1

Nested Loop Left Join (cost=9.24..36.38 rows=1 width=119) (actual time=1307.394..70470.998 rows=35 loops=1)

8. 0.193 70470.511 ↓ 35.0 35 1

Nested Loop Left Join (cost=9.24..34.09 rows=1 width=113) (actual time=1307.375..70470.511 rows=35 loops=1)

9. 151.132 70470.108 ↓ 35.0 35 1

Nested Loop (cost=9.24..31.81 rows=1 width=113) (actual time=1307.365..70470.108 rows=35 loops=1)

  • Join Filter: (r2.source_id = r1.rel_objet_id)
10. 3.953 9.616 ↓ 2180.0 2180 1

Nested Loop (cost=0.00..4.84 rows=1 width=4) (actual time=0.070..9.616 rows=2180 loops=1)

11. 0.038 0.038 ↑ 1.0 1 1

Index Scan using idx_t_objets_type_code on t_objets type_coordonnee (cost=0.00..2.43 rows=1 width=4) (actual time=0.036..0.038 rows=1 loops=1)

  • Index Cond: (((type)::text = 'type_coordonnee'::text) AND ((code)::text = 'MAIL_PERS'::text))
  • Filter: ((NOT efface) AND (NOT obsolete))
12. 5.625 5.625 ↓ 2180.0 2180 1

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..5.625 rows=2180 loops=1)

  • Index Cond: ((r2.dest_id = type_coordonnee.objet_id) AND ((r2.relation_nom)::text = 'individu-coordonnee-type_coordonnee'::text))
13. 418.560 70309.360 ↓ 46.0 46 2180

Nested Loop (cost=9.24..26.96 rows=1 width=117) (actual time=29.705..32.252 rows=46 loops=2180)

14. 344.440 69051.500 ↓ 35.0 35 2180

Nested Loop Left Join (cost=9.24..24.55 rows=1 width=105) (actual time=29.682..31.675 rows=35 loops=2180)

15. 392.400 68325.560 ↓ 35.0 35 2180

Nested Loop Left Join (cost=9.24..22.27 rows=1 width=90) (actual time=29.671..31.342 rows=35 loops=2180)

16. 396.760 67627.960 ↓ 35.0 35 2180

Nested Loop Left Join (cost=9.24..19.99 rows=1 width=40) (actual time=29.659..31.022 rows=35 loops=2180)

17. 6886.620 66849.700 ↓ 35.0 35 2180

Nested Loop (cost=9.24..17.62 rows=1 width=35) (actual time=29.643..30.665 rows=35 loops=2180)

18. 9812.180 41598.760 ↓ 936.0 936 2180

Nested Loop (cost=9.24..15.52 rows=1 width=39) (actual time=0.064..19.082 rows=936 loops=2180)

19. 10320.120 23624.660 ↓ 936.0 936 2180

Nested Loop Left Join (cost=9.24..13.23 rows=1 width=16) (actual time=0.056..10.837 rows=936 loops=2180)

  • Filter: (NOT COALESCE(d1.attribut_valbool, false))
20. 3009.998 3189.340 ↓ 1160.0 1160 2180

Hash Join (cost=9.24..10.91 rows=1 width=20) (actual time=0.045..1.463 rows=1160 loops=2180)

  • Hash Cond: (concours.objet_id = inscription_concours.concours_id)
21. 93.740 93.740 ↑ 1.0 48 2180

Seq Scan on t_concours concours (cost=0.00..1.48 rows=48 width=8) (actual time=0.002..0.043 rows=48 loops=2180)

22. 1.269 85.602 ↓ 1160.0 1160 1

Hash (cost=9.23..9.23 rows=1 width=20) (actual time=85.602..85.602 rows=1160 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 50kB
23. 18.334 84.333 ↓ 1160.0 1160 1

Nested Loop (cost=0.00..9.23 rows=1 width=20) (actual time=0.080..84.333 rows=1160 loops=1)

24. 21.666 56.047 ↓ 4976.0 4976 1

Nested Loop (cost=0.00..7.12 rows=1 width=8) (actual time=0.052..56.047 rows=4976 loops=1)

25. 7.360 14.477 ↓ 4976.0 4976 1

Nested Loop (cost=0.00..4.84 rows=1 width=4) (actual time=0.042..14.477 rows=4976 loops=1)

26. 0.026 0.026 ↑ 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=0.022..0.026 rows=1 loops=1)

  • Index Cond: (((type)::text = 'type_adresse'::text) AND ((code)::text = 'DOMICILE'::text))
  • Filter: ((NOT efface) AND (NOT obsolete))
27. 7.091 7.091 ↓ 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=0.016..7.091 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

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. 0.017 70472.692 ↓ 0.0 0 1

Sort (cost=0.03..0.04 rows=1 width=428) (actual time=70472.692..70472.692 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. 70472.675 70472.675 ↓ 0.0 0 1

CTE Scan on q1 (cost=0.00..0.02 rows=1 width=428) (actual time=70472.675..70472.675 rows=0 loops=1)