Location karcher à 59283 Raimbeaucourt

1. 0.0001

   SELECT cate_title_slug FROM category WHERE fami_id IN (3,5) AND cate_title_slug<>'' AND cate_title_slug IS NOT NULL

2. 0.0001

   SELECT cate_id FROM category WHERE fami_id='5' AND cate_title_slug='materiel-nettoyage'

3. 0.0001

   SELECT cate_title_slug, cate_title,cate_title_aff FROM category WHERE cate_id=110

4. 0

   SELECT filtre_dptnom FROM filtredpt WHERE filtre_dpt='59'

5. 0.0001

   SELECT ville_nom FROM villes WHERE ville_cp='59283' AND ville_nom like '%raimbeaucourt%' ORDER BY ville_population_2012 DESC LIMIT 0,1

6. 0.0005

   SELECT DISTINCT ssrubr_slug FROM ssrubr WHERE ssrubr_visu=1 AND fami_id IN (3,5) AND ssrubr_slug IS NOT NULL AND ssrubr_slug<>''

7. 0.0002

   SELECT ssrubr_id, ssrubr_keywords, ssrubr_title, ssrubr_slug, ssrubr_mail, ssrubr_photo FROM ssrubr WHERE (ssrubr_keywords like '%nettoyeur%haute%pression%' OR ssrubr_title like '%nettoyeur%haute%pression%' OR ssrubr_slug like '%nettoyeur%haute%pression%') AND cate_id='110' ORDER BY ssrubr_id LIMIT 0,1

8. 0.0001

   SHOW TABLES LIKE 'pays'

9. 0.0002

   SHOW COLUMNS FROM pays LIKE 'pays_tel_%'

10. 0.0003

    SELECT pays_code, pays_nom, pays_actif, pays_NDD, pays_drapeau, pays_lang, pays_tel, pays_nb_annonces, pays_tel_masque, pays_tel_max, pays_tel_fmt_paires, pays_tel_mode FROM pays ORDER BY pays_nom ASC

11. 0.0001

    SELECT ville_cp, ville_latitude_deg,ville_longitude_deg FROM villes v WHERE ville_cp='59283'

12. 0.0022

    SELECT ville_cp, ROUND((((acos(sin((50.45*pi()/180)) * sin((ville_latitude_deg*pi()/180))+cos((50.45*pi()/180)) * cos((ville_latitude_deg*pi()/180)) * cos(((3.08333- ville_longitude_deg)* pi()/180))))*180/pi())*60*1.1515* 1.609344),2) as distance FROM villes WHERE ville_latitude_deg>50.270135678816 AND ville_latitude_deg<50.629864321184 AND ville_longitude_deg>2.8008577016393 AND ville_longitude_deg<3.3658022983607 HAVING distance <= 20 ORDER BY distance ASC

13. 0.0001

    SELECT keywords_id FROM ssrubr_keywords WHERE keywords_title='nettoyeur haute pression; karcher; kärcher; nettoy'

14. 0.0002

    SELECT count(wish_id) as nbwish FROM wish WHERE fami_id='5' AND cate_id='110'AND MATCH (wish_title,wish_desc) AGAINST ('+nettoyeur haute pression; karcher; kärcher; nettoy*' IN BOOLEAN MODE) AND wish_visu IN (1,6,10) AND wish_cp like '59283%'

15. 0.0001

    SELECT * FROM texte WHERE cate_id=110

16. 0.0002

    SELECT ssrubr_texte_haut FROM ssrubr WHERE fami_id='5' AND (ssrubr_keywords='nettoyeur haute pression; karcher; kärcher; nettoy' OR ssrubr_slug='nettoyeur-haute-pression;-karcher;-kärcher;-nettoy' OR (ssrubr_title like '%nettoyeur haute pression; karcher; kärcher; nettoy%')) AND cate_id='110' ORDER BY ssrubr_id LIMIT 0,1

17. 0.0001

    SELECT ssrubr_texte_haut, ssrubr_texte_bas FROM ssrubr WHERE ssrubr_slug='nettoyeur-haute-pression' AND cate_id='110'

18. 0.0001

    SELECT ville_cp, ville_latitude_deg, ville_longitude_deg FROM villes v WHERE ville_cp='59283'

19. 0.0029

    SELECT ville_cp, ROUND((((acos(sin((50.45*pi()/180)) * sin((ville_latitude_deg*pi()/180))+cos((50.45*pi()/180)) * cos((ville_latitude_deg*pi()/180)) * cos(((3.08333- ville_longitude_deg)* pi()/180))))*180/pi())*60*1.1515* 1.609344),2) as distance FROM villes WHERE ville_latitude_deg>50.270135678816 AND ville_latitude_deg<50.629864321184 AND ville_longitude_deg>2.8008577016393 AND ville_longitude_deg<3.3658022983607 HAVING distance <= 20 ORDER BY distance ASC

20. 0.0164

    SELECT ville_cp, ROUND((((acos(sin((50.45*pi()/180)) * sin((ville_latitude_deg*pi()/180))+cos((50.45*pi()/180)) * cos((ville_latitude_deg*pi()/180)) * cos(((3.08333- ville_longitude_deg)* pi()/180))))*180/pi())*60*1.1515* 1.609344),2) as distance FROM villes WHERE ville_latitude_deg>50.000339197041 AND ville_latitude_deg<50.899660802959 AND ville_longitude_deg>2.3771431645938 AND ville_longitude_deg<3.7895168354062 HAVING distance <= 50 ORDER BY distance ASC

21. 0.0655

    SELECT wish.*, ui.util_photo, ui.util_prenom, ui.util_nom, ui.stripe_account_id, ui.util_verify, ville_cp,ville_nom,ville_latitude_deg,ville_longitude_deg, ROUND((((acos(sin((50.45*pi()/180)) * sin((ville_latitude_deg*pi()/180))+cos((50.45*pi()/180)) * cos((ville_latitude_deg*pi()/180)) * cos(((3.08333- ville_longitude_deg)* pi()/180))))*180/pi())*60*1.1515* 1.609344),2) as distance FROM wish LEFT JOIN util_info ui ON wish.util_id=ui.util_id LEFT JOIN villes v ON wish.wish_cp=v.ville_cp WHERE wish_id IS NOT NULL AND fami_id='5' AND cate_id='110' AND MATCH (wish_desc) AGAINST ('(+nettoyeur +haute +pression; +karcher; +kärcher; +nettoy*)' IN BOOLEAN MODE) AND wish_visu IN (1,6,10) AND wish_cp IN (59283,59283,59283,62790,59246,59162,59286,59310,62141,59239,59950,59261,59194,59551,62820,62970,59235,59239,59710,59128,59194,62590,62119,59148,59119,59710,59551,59553,59553,62950,59310,59133,62220,59310,59710,59500,59242,59167,59450,59710,59133,59553,62710,62640,59310,59552,59242,59112,62110,59552,59187,59147,62490,59273,59113,59182,62880,59870,62740,59870,59146,59287,59182,59287,62440,59169,59175,59310,59147,59175,62112,59242,59251,62490,62420,62117,59169,59830,59147,59176,59185,62320,62320,59112,59263,59273,62880,62880,59310,59176,59310,62490,59870,59155,59169,62320,59870,59810,59139,62112,59490,62410,62490,59830,62880,62218,62221,59139,59310,62430,59262,59169,59310,59221,62320,59830,59272,62580,62680,59830,59870,59790,59151,59165,62490,59169,62490,62410,59490,62580,62138,59830,59184,59151,59870,59830,59136,59580,59320,59230,7611,59178,62300,59496,62580,59179,62580,62410,59310,59690,59870,59320,59234,59830,59151,62210,59151,59152,59152,62118,59260,62490,59230,59274,59780,59171,59580,59178,59120,59152,59211,59234,62118,62138,62580,59226,62410,62300,62490,7610,59320,62580,59496,59171) AND type_id=1 GROUP BY wish_id

22. 0.0002

    SELECT COALESCE(SUM(CASE WHEN M.match_step <> 3 THEN 1 ELSE 0 END), 0) AS h, COALESCE(SUM(CASE WHEN M.match_step = 3 THEN 1 ELSE 0 END), 0) AS cancelled FROM `match` AS M INNER JOIN wish AS W ON W.wish_id = M.wish_id WHERE ((W.util_id = 127128 AND W.type_id = 1) OR (W.util_id <> 127128 AND W.type_id = 2)) AND (M.post_util_id = 127128 OR M.dest_util_id = 127128) AND M.buy_stripe_pi_id IS NOT NULL AND M.buy_stripe_pi_id <> ''

23. 0.0001

    SELECT ROUND(AVG(note), 1) AS note_moy, COUNT(note) AS nb_avis FROM `note` WHERE dest_util_id=127128 AND note_visu IN (1,3,4)

24. 0.0001

    SELECT COALESCE(SUM(CASE WHEN M.match_step <> 3 THEN 1 ELSE 0 END), 0) AS h, COALESCE(SUM(CASE WHEN M.match_step = 3 THEN 1 ELSE 0 END), 0) AS cancelled FROM `match` AS M INNER JOIN wish AS W ON W.wish_id = M.wish_id WHERE ((W.util_id = 146004 AND W.type_id = 1) OR (W.util_id <> 146004 AND W.type_id = 2)) AND (M.post_util_id = 146004 OR M.dest_util_id = 146004) AND M.buy_stripe_pi_id IS NOT NULL AND M.buy_stripe_pi_id <> ''

25. 0.0001

    SELECT ROUND(AVG(note), 1) AS note_moy, COUNT(note) AS nb_avis FROM `note` WHERE dest_util_id=146004 AND note_visu IN (1,3,4)

26. 0.0001

    SELECT COALESCE(SUM(CASE WHEN M.match_step <> 3 THEN 1 ELSE 0 END), 0) AS h, COALESCE(SUM(CASE WHEN M.match_step = 3 THEN 1 ELSE 0 END), 0) AS cancelled FROM `match` AS M INNER JOIN wish AS W ON W.wish_id = M.wish_id WHERE ((W.util_id = 29270 AND W.type_id = 1) OR (W.util_id <> 29270 AND W.type_id = 2)) AND (M.post_util_id = 29270 OR M.dest_util_id = 29270) AND M.buy_stripe_pi_id IS NOT NULL AND M.buy_stripe_pi_id <> ''

27. 0.0001

    SELECT ROUND(AVG(note), 1) AS note_moy, COUNT(note) AS nb_avis FROM `note` WHERE dest_util_id=29270 AND note_visu IN (1,3,4)

28. 0.0001

    SELECT COALESCE(SUM(CASE WHEN M.match_step <> 3 THEN 1 ELSE 0 END), 0) AS h, COALESCE(SUM(CASE WHEN M.match_step = 3 THEN 1 ELSE 0 END), 0) AS cancelled FROM `match` AS M INNER JOIN wish AS W ON W.wish_id = M.wish_id WHERE ((W.util_id = 323872 AND W.type_id = 1) OR (W.util_id <> 323872 AND W.type_id = 2)) AND (M.post_util_id = 323872 OR M.dest_util_id = 323872) AND M.buy_stripe_pi_id IS NOT NULL AND M.buy_stripe_pi_id <> ''

29. 0.0001

    SELECT ROUND(AVG(note), 1) AS note_moy, COUNT(note) AS nb_avis FROM `note` WHERE dest_util_id=323872 AND note_visu IN (1,3,4)

30. 0.0004

    SELECT COALESCE(SUM(CASE WHEN M.match_step <> 3 THEN 1 ELSE 0 END), 0) AS h, COALESCE(SUM(CASE WHEN M.match_step = 3 THEN 1 ELSE 0 END), 0) AS cancelled FROM `match` AS M INNER JOIN wish AS W ON W.wish_id = M.wish_id WHERE ((W.util_id = 339897 AND W.type_id = 1) OR (W.util_id <> 339897 AND W.type_id = 2)) AND (M.post_util_id = 339897 OR M.dest_util_id = 339897) AND M.buy_stripe_pi_id IS NOT NULL AND M.buy_stripe_pi_id <> ''

31. 0.0001

    SELECT ROUND(AVG(note), 1) AS note_moy, COUNT(note) AS nb_avis FROM `note` WHERE dest_util_id=339897 AND note_visu IN (1,3,4)

32. 0.0001

    SELECT COALESCE(SUM(CASE WHEN M.match_step <> 3 THEN 1 ELSE 0 END), 0) AS h, COALESCE(SUM(CASE WHEN M.match_step = 3 THEN 1 ELSE 0 END), 0) AS cancelled FROM `match` AS M INNER JOIN wish AS W ON W.wish_id = M.wish_id WHERE ((W.util_id = 496237 AND W.type_id = 1) OR (W.util_id <> 496237 AND W.type_id = 2)) AND (M.post_util_id = 496237 OR M.dest_util_id = 496237) AND M.buy_stripe_pi_id IS NOT NULL AND M.buy_stripe_pi_id <> ''

33. 0.0001

    SELECT ROUND(AVG(note), 1) AS note_moy, COUNT(note) AS nb_avis FROM `note` WHERE dest_util_id=496237 AND note_visu IN (1,3,4)

34. 0.0001

    SELECT COALESCE(SUM(CASE WHEN M.match_step <> 3 THEN 1 ELSE 0 END), 0) AS h, COALESCE(SUM(CASE WHEN M.match_step = 3 THEN 1 ELSE 0 END), 0) AS cancelled FROM `match` AS M INNER JOIN wish AS W ON W.wish_id = M.wish_id WHERE ((W.util_id = 542014 AND W.type_id = 1) OR (W.util_id <> 542014 AND W.type_id = 2)) AND (M.post_util_id = 542014 OR M.dest_util_id = 542014) AND M.buy_stripe_pi_id IS NOT NULL AND M.buy_stripe_pi_id <> ''

35. 0.0001

    SELECT ROUND(AVG(note), 1) AS note_moy, COUNT(note) AS nb_avis FROM `note` WHERE dest_util_id=542014 AND note_visu IN (1,3,4)

36. 0.0001

    SELECT ville_cp, ville_latitude_deg, ville_longitude_deg FROM villes v WHERE ville_cp='59283'

37. 0.002

    SELECT ville_cp, ROUND((((acos(sin((50.45*pi()/180)) * sin((ville_latitude_deg*pi()/180))+cos((50.45*pi()/180)) * cos((ville_latitude_deg*pi()/180)) * cos(((3.08333- ville_longitude_deg)* pi()/180))))*180/pi())*60*1.1515* 1.609344),2) as distance FROM villes WHERE ville_latitude_deg>50.270135678816 AND ville_latitude_deg<50.629864321184 AND ville_longitude_deg>2.8008577016393 AND ville_longitude_deg<3.3658022983607 HAVING distance <= 20 ORDER BY distance ASC

38. 0.0146

    SELECT ville_cp, ROUND((((acos(sin((50.45*pi()/180)) * sin((ville_latitude_deg*pi()/180))+cos((50.45*pi()/180)) * cos((ville_latitude_deg*pi()/180)) * cos(((3.08333- ville_longitude_deg)* pi()/180))))*180/pi())*60*1.1515* 1.609344),2) as distance FROM villes WHERE ville_latitude_deg>50.000339197041 AND ville_latitude_deg<50.899660802959 AND ville_longitude_deg>2.3771431645938 AND ville_longitude_deg<3.7895168354062 HAVING distance <= 50 ORDER BY distance ASC

39. 0.0001

    SELECT ssr.ssrubr_id, ssr.ssrubr_slug, ssr.ssrubr_mail, ssr.ssrubr_mail_bis, ssr.ssrubr_keywords, ssr.ssrubr_bt2, ssr.ssrubr_photo, c.cate_title_slug, f.fami_url FROM ssrubr ssr, category c, family f WHERE ssr.cate_id=c.cate_id AND c.fami_id=f.fami_id AND ssr.ssrubr_id IN (745)

40. 0.0002

    SELECT util_id FROM util_abo WHERE abo_type=4 AND abo_actif=1 AND abo_expire>SYSDATE() AND util_id IN (29270,146004,339897,323872,127128,542014,496237)

41. 0.0001

    SELECT util_id, util_photo FROM util_info WHERE util_id IN (29270,146004,339897,323872,127128,542014,496237)

42. 0.0002

    SELECT ssrubr_id, ssrubr_title, ssrubr_slug, ssrubr_photo, cate_title_slug FROM ssrubr ss, category c WHERE ss.cate_id=c.cate_id AND ssrubr_visu=1 AND ss.cate_id=110 AND ss.fami_id=5 ORDER BY ssrubr_order LIMIT 6,100

43. 0.0003

    SELECT ss.fami_id, fami_url, ss.cate_id, cate_title_slug, keywords_title FROM `ssrubr_keywords` sk, ssrubr ss, category c, family f WHERE sk.ssrubr_id=ss.ssrubr_id AND ss.cate_id=c.cate_id AND c.fami_id=f.fami_id AND (ssrubr_slug='nettoyeur haute pression; karcher; kärcher; nettoy' OR ssrubr_slug like '%nettoyeur haute pression; karcher; kärcher; nettoy%' OR MATCH (ssrubr_slug) AGAINST ('(+nettoyeur +haute +pression; +karcher; +kärcher; +nettoy)')) AND sk.ssrubr_id='745' AND keywords_visu=1 ORDER BY keywords_nb DESC, keywords_id ASC LIMIT 0,10

44. 0.0003

    SELECT ss.ssrubr_id, ss.ssrubr_slug, ss.ssrubr_title, ss.ssrubr_keywords, ss.ssrubr_mail, c.cate_title_slug FROM ssrubr ss INNER JOIN category c ON ss.cate_id = c.cate_id WHERE ss.ssrubr_visu = 1 AND ss.fami_id = 5 AND ss.cate_id = 110 AND ss.ssrubr_id <> 745 ORDER BY ss.ssrubr_order ASC, ss.ssrubr_id ASC LIMIT 10

45. 0.0001

    SELECT * FROM actu WHERE ssrubr_id='745' AND actu_niveau=0 ORDER BY actu_ordre DESC

46. 0.0001

    SELECT fami_id as id, fami_title as title, fami_tutotitle, fami_tutodesc, fami_buttontitle, fami_url as slug FROM `family`

47. 0.0001

    SELECT cate_id as id, cate_title as title, cate_title_slug as slug, fami_id FROM `category` WHERE cate_visu=1 AND fami_id='5' AND cate_cate_id IS NULL ORDER BY cate_order ASC

48. 0.0002

    SELECT ssrubr_id AS id, ssrubr_title as title, ssrubr_slug as slug, ssrubr_photo, cate_id, fami_id FROM ssrubr WHERE ssrubr_visu=1 AND cate_id IN (110) ORDER BY ssrubr_order ASC

49. 0.001

    SELECT AVG(note) as note_moy FROM note WHERE note_visu IN (1,2,3,4) AND post_util_id<>7

50. 0.0252

    SELECT COUNT(*) AS nbavis FROM note n INNER JOIN util_info dest ON n.dest_util_id=dest.util_id INNER JOIN util_info post ON n.post_util_id=post.util_id INNER JOIN wish w ON n.wish_id=w.wish_id WHERE n.note_visu IN (1,2,3,4) AND n.post_util_id<>'7'

51. 0.022

    SELECT count(*) as nbwish FROM wish WHERE wish_visu IN (1,6,10)

51 requetes sql en 0.158