Location karcher à 37390 Cerelles

1. 0.0002

   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

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

4. 0.0001

   SELECT filtre_dptnom FROM filtredpt WHERE CONVERT(filtre_dpt USING utf8mb4) COLLATE utf8mb4_general_ci = CONVERT('37' USING utf8mb4) COLLATE utf8mb4_general_ci

5. 0.0001

   SELECT ville_nom FROM villes WHERE ville_cp='37390' AND ville_nom like '%cerelles%' 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. 0.0001

   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. 0.0002

    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='37390'

12. 0.0019

    SELECT ville_cp, ROUND((((acos(sin((47.4667*pi()/180)) * sin((ville_latitude_deg*pi()/180))+cos((47.4667*pi()/180)) * cos((ville_latitude_deg*pi()/180)) * cos(((0.7- ville_longitude_deg)* pi()/180))))*180/pi())*60*1.1515* 1.609344),2) as distance FROM villes WHERE ville_latitude_deg>47.286835678816 AND ville_latitude_deg<47.646564321184 AND ville_longitude_deg>0.43393540074042 AND ville_longitude_deg<0.96606459925958 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. 0.0004

    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 '37390%'

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='37390'

19. 0.0016

    SELECT ville_cp, ROUND((((acos(sin((47.4667*pi()/180)) * sin((ville_latitude_deg*pi()/180))+cos((47.4667*pi()/180)) * cos((ville_latitude_deg*pi()/180)) * cos(((0.7- ville_longitude_deg)* pi()/180))))*180/pi())*60*1.1515* 1.609344),2) as distance FROM villes WHERE ville_latitude_deg>47.286835678816 AND ville_latitude_deg<47.646564321184 AND ville_longitude_deg>0.43393540074042 AND ville_longitude_deg<0.96606459925958 HAVING distance <= 20 ORDER BY distance ASC

20. 0.0051

    SELECT ville_cp, ROUND((((acos(sin((47.4667*pi()/180)) * sin((ville_latitude_deg*pi()/180))+cos((47.4667*pi()/180)) * cos((ville_latitude_deg*pi()/180)) * cos(((0.7- ville_longitude_deg)* pi()/180))))*180/pi())*60*1.1515* 1.609344),2) as distance FROM villes WHERE ville_latitude_deg>47.017039197041 AND ville_latitude_deg<47.916360802959 AND ville_longitude_deg>0.034832766061774 AND ville_longitude_deg<1.3651672339382 HAVING distance <= 50 ORDER BY distance ASC

21. 0.0626

    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((47.4667*pi()/180)) * sin((ville_latitude_deg*pi()/180))+cos((47.4667*pi()/180)) * cos((ville_latitude_deg*pi()/180)) * cos(((0.7- 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 (37390,37390,44440,37390,37390,37390,37210,37390,37360,37390,37360,37210,37380,37540,37700,37390,37210,37200,37100,37000,37360,37700,37380,37520,37230,37550,37360,37510,37210,37210,37270,37300,37270,37380,37270,37380,44540,37380,37360,37230,37170,37230,37230,37210,37510,37110,37370,37370,37370,37510,37380,37270,37510,37400,37110,37110,37530,37340,37360,37530,37110) 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 = 59677 AND W.type_id = 1) OR (W.util_id <> 59677 AND W.type_id = 2)) AND (M.post_util_id = 59677 OR M.dest_util_id = 59677) 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=59677 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 = 249241 AND W.type_id = 1) OR (W.util_id <> 249241 AND W.type_id = 2)) AND (M.post_util_id = 249241 OR M.dest_util_id = 249241) 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=249241 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 = 421844 AND W.type_id = 1) OR (W.util_id <> 421844 AND W.type_id = 2)) AND (M.post_util_id = 421844 OR M.dest_util_id = 421844) 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=421844 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 = 430077 AND W.type_id = 1) OR (W.util_id <> 430077 AND W.type_id = 2)) AND (M.post_util_id = 430077 OR M.dest_util_id = 430077) 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=430077 AND note_visu IN (1,3,4)

30. 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 = 498843 AND W.type_id = 1) OR (W.util_id <> 498843 AND W.type_id = 2)) AND (M.post_util_id = 498843 OR M.dest_util_id = 498843) 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=498843 AND note_visu IN (1,3,4)

32. 0.0001

    SELECT ville_cp, ville_latitude_deg, ville_longitude_deg FROM villes v WHERE ville_cp='37390'

33. 0.0015

    SELECT ville_cp, ROUND((((acos(sin((47.4667*pi()/180)) * sin((ville_latitude_deg*pi()/180))+cos((47.4667*pi()/180)) * cos((ville_latitude_deg*pi()/180)) * cos(((0.7- ville_longitude_deg)* pi()/180))))*180/pi())*60*1.1515* 1.609344),2) as distance FROM villes WHERE ville_latitude_deg>47.286835678816 AND ville_latitude_deg<47.646564321184 AND ville_longitude_deg>0.43393540074042 AND ville_longitude_deg<0.96606459925958 HAVING distance <= 20 ORDER BY distance ASC

34. 0.006

    SELECT ville_cp, ROUND((((acos(sin((47.4667*pi()/180)) * sin((ville_latitude_deg*pi()/180))+cos((47.4667*pi()/180)) * cos((ville_latitude_deg*pi()/180)) * cos(((0.7- ville_longitude_deg)* pi()/180))))*180/pi())*60*1.1515* 1.609344),2) as distance FROM villes WHERE ville_latitude_deg>47.017039197041 AND ville_latitude_deg<47.916360802959 AND ville_longitude_deg>0.034832766061774 AND ville_longitude_deg<1.3651672339382 HAVING distance <= 50 ORDER BY distance ASC

35. 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)

36. 0.0002

    SELECT util_id FROM util_abo WHERE abo_type=4 AND abo_actif=1 AND abo_expire>SYSDATE() AND util_id IN (59677,498843,249241,421844,430077)

37. 0.0001

    SELECT util_id, util_photo FROM util_info WHERE util_id IN (59677,498843,249241,421844,430077)

38. 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

39. 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

40. 0.0002

    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

41. 0.0001

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

42. 0.0001

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

43. 0.0002

    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

44. 0.0003

    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

45. 0.0013

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

46. 0.0221

    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'

47. 0.0165

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

47 requetes sql en 0.1243