Location marteau piqueur hilti à 75001 Paris

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-bricolage'

3. 0.0001

   SELECT cate_title_slug, cate_title,cate_title_aff FROM category WHERE cate_id=87

4. 0.0001

   SELECT filtre_dptnom FROM filtredpt WHERE CONVERT(filtre_dpt USING utf8mb4) COLLATE utf8mb4_general_ci = CONVERT('75' USING utf8mb4) COLLATE utf8mb4_general_ci

5. 0.0001

   SELECT ville_nom FROM villes WHERE ville_cp='75001' AND ville_nom like '%paris%' ORDER BY ville_population_2012 DESC LIMIT 0,1

6. 0.0006

   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. 0.003

   SELECT ssrubr_id, ssrubr_keywords, ssrubr_title, ssrubr_slug, ssrubr_mail, ssrubr_photo FROM ssrubr WHERE (ssrubr_keywords like '%marteau%piqueur%hilti%' OR ssrubr_title like '%marteau%piqueur%hilti%' OR ssrubr_slug like '%marteau%piqueur%hilti%') AND cate_id='87' ORDER BY ssrubr_id LIMIT 0,1

8. 0.0001

   SELECT cate_title_form, cate_title FROM category WHERE cate_title='marteau%piqueur%hilti' OR cate_title_aff='marteau%piqueur%hilti' LIMIT 0,1

9. 0.0001

   SHOW TABLES LIKE 'pays'

10. 0.0002

    SHOW COLUMNS FROM pays LIKE 'pays_tel_%'

11. 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

12. 0.0001

    SELECT ville_cp, ville_latitude_deg,ville_longitude_deg FROM villes v WHERE ville_cp='75001'

13. 0.0024

    SELECT ville_cp, ROUND((((acos(sin((48.86*pi()/180)) * sin((ville_latitude_deg*pi()/180))+cos((48.86*pi()/180)) * cos((ville_latitude_deg*pi()/180)) * cos(((2.34445- ville_longitude_deg)* pi()/180))))*180/pi())*60*1.1515* 1.609344),2) as distance FROM villes WHERE ville_latitude_deg>48.680135678816 AND ville_latitude_deg<49.039864321184 AND ville_longitude_deg>2.0710584569437 AND ville_longitude_deg<2.6178415430563 HAVING distance <= 20 ORDER BY distance ASC

14. 0.0001

    SELECT keywords_id FROM ssrubr_keywords WHERE keywords_title='marteau piqueur hilti'

15. 0.0016

    SELECT count(wish_id) as nbwish FROM wish WHERE fami_id='5' AND cate_id='87'AND MATCH (wish_title,wish_desc) AGAINST ('+marteau piqueur hilti*' IN BOOLEAN MODE) AND wish_visu IN (1,6,10) AND wish_cp like '75001%'

16. 0.0001

    SELECT * FROM texte WHERE cate_id=87

17. 0.0003

    SELECT ssrubr_texte_haut FROM ssrubr WHERE fami_id='5' AND (ssrubr_keywords='marteau piqueur hilti' OR ssrubr_slug='marteau-piqueur-hilti' OR (ssrubr_title like '%marteau piqueur hilti%')) AND cate_id='87' ORDER BY ssrubr_id LIMIT 0,1

18. 0.0001

    SELECT ssrubr_texte_haut, ssrubr_texte_bas FROM ssrubr WHERE ssrubr_slug='marteau-piqueur-hilti' AND cate_id='87'

19. 0.0001

    SELECT ville_cp, ville_latitude_deg, ville_longitude_deg FROM villes v WHERE ville_cp='75001'

20. 0.001

    SELECT ville_cp, ROUND((((acos(sin((48.86*pi()/180)) * sin((ville_latitude_deg*pi()/180))+cos((48.86*pi()/180)) * cos((ville_latitude_deg*pi()/180)) * cos(((2.34445- ville_longitude_deg)* pi()/180))))*180/pi())*60*1.1515* 1.609344),2) as distance FROM villes WHERE ville_latitude_deg>48.797047487586 AND ville_latitude_deg<48.922952512414 AND ville_longitude_deg>2.2487630978401 AND ville_longitude_deg<2.4401369021599 HAVING distance <= 7 ORDER BY distance ASC

21. 0.0074

    SELECT ville_cp, ROUND((((acos(sin((48.86*pi()/180)) * sin((ville_latitude_deg*pi()/180))+cos((48.86*pi()/180)) * cos((ville_latitude_deg*pi()/180)) * cos(((2.34445- ville_longitude_deg)* pi()/180))))*180/pi())*60*1.1515* 1.609344),2) as distance FROM villes WHERE ville_latitude_deg>48.410339197041 AND ville_latitude_deg<49.309660802959 AND ville_longitude_deg>1.6609652486165 AND ville_longitude_deg<3.0279347513835 HAVING distance <= 50 ORDER BY distance ASC

22. 0.1129

    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((48.86*pi()/180)) * sin((ville_latitude_deg*pi()/180))+cos((48.86*pi()/180)) * cos((ville_latitude_deg*pi()/180)) * cos(((2.34445- 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='87' AND MATCH (wish_desc) AGAINST ('(+marteau +piqueur +hilti*)' IN BOOLEAN MODE) AND wish_visu IN (1,6,10) AND wish_cp IN (75001,75007,75002,75000,75001,75017,75006,75116,75020,75018,75015,75019,75003,75004,75014,75013,75012,75011,75010,75009,75008,75016,75005,92170,93400,93310,94250,94270,92120,93170,92110,94205,94200,92240,93500,94160,92200,92300,93100,94300,94110,94800,93120,93300,93230,93260,92600,94220,92220,92130,92100,92400,92320,92800,92150,93450,92390,92250,93200,93210,92270,94140,94233,94235,94231,94232,94236,94237,94234,94230,94239,94238,94240,92140,92230,93000,93130,94700,92700,94400,92360,92190,92210,94410,92260,92330,92340,93350,93140,94340,93110,94120,92350,93800,94550,93240,94130,94320,92500,93430,93700,93150,93440,94170,92000,92310,94600,93250,92290,92410,95360,92380,93380,95880,95100,94150,78800,94000,95870,94260,92370,93360,92160,95218,95210,92420,92430,94390,94310,94360,95140,94100,94210,94340,78420,93320,93340,91370,94500,95500,78140,95410,95110,95170,93330,93220,95230,91320,94430,78170,78400,94380,78220,95400,91300,95120,94290,94460,94350,91570,78500,78290,78380,78360,95160,95350,95500,95400,95200,95580,95580,95130,91550,93270,94190,93600,95600,93460,93190,93390,93160,91200,78150,78350,78430,95530,94480,78000,93370,78560,91430,78150,94420,78110,94380,94370,95240,94490,94880,94880,91120,78600,94450,95350,95130,94470,91430,95440,91560,91420,95500,95680,78600,95370,91160,91380,93410,93420,95390,78230,91270,93470,77181,95500,95720,78530,78350,91160,77420,95320,77500,91140,78160,78870,91400,78750,95330,95460,91230,94510,95190,91330,78100,91260,95720) AND type_id=1 GROUP BY wish_id

23. 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 = 202102 AND W.type_id = 1) OR (W.util_id <> 202102 AND W.type_id = 2)) AND (M.post_util_id = 202102 OR M.dest_util_id = 202102) AND M.buy_stripe_pi_id IS NOT NULL AND M.buy_stripe_pi_id <> ''

24. 0.0001

    SELECT ROUND(AVG(note), 1) AS note_moy, COUNT(note) AS nb_avis FROM `note` WHERE dest_util_id=202102 AND note_visu IN (1,3,4)

25. 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 = 172204 AND W.type_id = 1) OR (W.util_id <> 172204 AND W.type_id = 2)) AND (M.post_util_id = 172204 OR M.dest_util_id = 172204) AND M.buy_stripe_pi_id IS NOT NULL AND M.buy_stripe_pi_id <> ''

26. 0.0001

    SELECT ROUND(AVG(note), 1) AS note_moy, COUNT(note) AS nb_avis FROM `note` WHERE dest_util_id=172204 AND note_visu IN (1,3,4)

27. 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 = 246323 AND W.type_id = 1) OR (W.util_id <> 246323 AND W.type_id = 2)) AND (M.post_util_id = 246323 OR M.dest_util_id = 246323) AND M.buy_stripe_pi_id IS NOT NULL AND M.buy_stripe_pi_id <> ''

28. 0.0001

    SELECT ROUND(AVG(note), 1) AS note_moy, COUNT(note) AS nb_avis FROM `note` WHERE dest_util_id=246323 AND note_visu IN (1,3,4)

29. 0.0001

    SELECT ville_cp, ville_latitude_deg, ville_longitude_deg FROM villes v WHERE ville_cp='75001'

30. 0.0007

    SELECT ville_cp, ROUND((((acos(sin((48.86*pi()/180)) * sin((ville_latitude_deg*pi()/180))+cos((48.86*pi()/180)) * cos((ville_latitude_deg*pi()/180)) * cos(((2.34445- ville_longitude_deg)* pi()/180))))*180/pi())*60*1.1515* 1.609344),2) as distance FROM villes WHERE ville_latitude_deg>48.797047487586 AND ville_latitude_deg<48.922952512414 AND ville_longitude_deg>2.2487630978401 AND ville_longitude_deg<2.4401369021599 HAVING distance <= 7 ORDER BY distance ASC

31. 0.0067

    SELECT ville_cp, ROUND((((acos(sin((48.86*pi()/180)) * sin((ville_latitude_deg*pi()/180))+cos((48.86*pi()/180)) * cos((ville_latitude_deg*pi()/180)) * cos(((2.34445- ville_longitude_deg)* pi()/180))))*180/pi())*60*1.1515* 1.609344),2) as distance FROM villes WHERE ville_latitude_deg>48.410339197041 AND ville_latitude_deg<49.309660802959 AND ville_longitude_deg>1.6609652486165 AND ville_longitude_deg<3.0279347513835 HAVING distance <= 50 ORDER BY distance ASC

32. 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 (58)

33. 0.0002

    SELECT util_id FROM util_abo WHERE abo_type=4 AND abo_actif=1 AND abo_expire>SYSDATE() AND util_id IN (202102,246323,172204)

34. 0.0001

    SELECT util_id, util_photo FROM util_info WHERE util_id IN (202102,246323,172204)

35. 0.0003

    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=87 AND ss.fami_id=5 ORDER BY ssrubr_order LIMIT 6,100

36. 0.0002

    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='marteau piqueur hilti' OR ssrubr_slug like '%marteau piqueur hilti%' OR MATCH (ssrubr_slug) AGAINST ('(+marteau +piqueur +hilti)')) AND sk.ssrubr_id='' AND keywords_visu=1 ORDER BY keywords_nb DESC, keywords_id ASC LIMIT 0,10

37. 0.0001

    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_slug = 'marteau-piqueur-hilti' AND ss.cate_id = 87 LIMIT 1

38. 0.0004

    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 = 87 ORDER BY ss.ssrubr_order ASC, ss.ssrubr_id ASC LIMIT 10

39. 0.0001

    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_slug = 'marteau-piqueur-hilti' AND ss.cate_id = 87 LIMIT 1

40. 0.0001

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

41. 0

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

42. 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

43. 0.0004

    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 (87) ORDER BY ssrubr_order ASC

44. 0.0017

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

45. 0.0295

    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'

46. 0.018

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

46 requetes sql en 0.1906