Location autres outils à 34440 Nissan-lez-enserune

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-jardinage'

3. 0

   SELECT cate_title_slug, cate_title,cate_title_aff FROM category WHERE cate_id=88

4. 0.0001

   SELECT filtre_dptnom FROM filtredpt WHERE CONVERT(filtre_dpt USING utf8mb4) COLLATE utf8mb4_general_ci = CONVERT('34' USING utf8mb4) COLLATE utf8mb4_general_ci

5. 0.0001

   SELECT ville_nom FROM villes WHERE ville_cp='34440' AND ville_nom like '%nissan%lez%enserune%' ORDER BY ville_population_2012 DESC LIMIT 0,1

6. 0.0007

   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. 0.0031

   SELECT ssrubr_id, ssrubr_keywords, ssrubr_title, ssrubr_slug, ssrubr_mail, ssrubr_photo FROM ssrubr WHERE (ssrubr_keywords like '%autres%outils%' OR ssrubr_title like '%autres%outils%' OR ssrubr_slug like '%autres%outils%') AND cate_id='88' ORDER BY ssrubr_id LIMIT 0,1

8. 0.0002

   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='34440'

12. 0.0009

    SELECT ville_cp, ROUND((((acos(sin((41.0318*pi()/180)) * sin((ville_latitude_deg*pi()/180))+cos((41.0318*pi()/180)) * cos((ville_latitude_deg*pi()/180)) * cos(((28.9684- ville_longitude_deg)* pi()/180))))*180/pi())*60*1.1515* 1.609344),2) as distance FROM villes WHERE ville_latitude_deg>40.851935678816 AND ville_latitude_deg<41.211664321184 AND ville_longitude_deg>28.729961970776 AND ville_longitude_deg<29.206838029224 HAVING distance <= 20 ORDER BY distance ASC

13. 0.0001

    SELECT keywords_id FROM ssrubr_keywords WHERE keywords_title='autres outils'

14. 0.0002

    SELECT count(wish_id) as nbwish FROM wish WHERE fami_id='5' AND cate_id='88'AND MATCH (wish_title,wish_desc) AGAINST ('+autres outils*' IN BOOLEAN MODE) AND wish_visu IN (1,6,10) AND wish_cp like '34440%'

15. 0.0001

    SELECT * FROM texte WHERE cate_id=88

16. 0.0002

    SELECT ssrubr_texte_haut FROM ssrubr WHERE fami_id='5' AND (ssrubr_keywords='autres outils' OR ssrubr_slug='autres-outils' OR (ssrubr_title like '%autres outils%')) AND cate_id='88' ORDER BY ssrubr_id LIMIT 0,1

17. 0.0001

    SELECT ssrubr_texte_haut, ssrubr_texte_bas FROM ssrubr WHERE ssrubr_slug='autres-outils' AND cate_id='88'

18. 0.0001

    SELECT ville_cp, ville_latitude_deg, ville_longitude_deg FROM villes v WHERE ville_cp='34440'

19. 0.001

    SELECT ville_cp, ROUND((((acos(sin((41.0318*pi()/180)) * sin((ville_latitude_deg*pi()/180))+cos((41.0318*pi()/180)) * cos((ville_latitude_deg*pi()/180)) * cos(((28.9684- ville_longitude_deg)* pi()/180))))*180/pi())*60*1.1515* 1.609344),2) as distance FROM villes WHERE ville_latitude_deg>40.851935678816 AND ville_latitude_deg<41.211664321184 AND ville_longitude_deg>28.729961970776 AND ville_longitude_deg<29.206838029224 HAVING distance <= 20 ORDER BY distance ASC

20. 0.0013

    SELECT ville_cp, ROUND((((acos(sin((41.0318*pi()/180)) * sin((ville_latitude_deg*pi()/180))+cos((41.0318*pi()/180)) * cos((ville_latitude_deg*pi()/180)) * cos(((28.9684- ville_longitude_deg)* pi()/180))))*180/pi())*60*1.1515* 1.609344),2) as distance FROM villes WHERE ville_latitude_deg>40.582139197041 AND ville_latitude_deg<41.481460802959 AND ville_longitude_deg>28.372299786721 AND ville_longitude_deg<29.564500213279 HAVING distance <= 50 ORDER BY distance ASC

21. 0.0583

    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((41.0318*pi()/180)) * sin((ville_latitude_deg*pi()/180))+cos((41.0318*pi()/180)) * cos((ville_latitude_deg*pi()/180)) * cos(((28.9684- 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='88' AND MATCH (wish_desc) AGAINST ('(+autres +outils*)' IN BOOLEAN MODE) AND wish_visu IN (1,6,10) AND wish_cp IN (34440,34440,34437,34110,34430,34435,34080,34087,34083,34433,34116,34112,34682,34445,34373,34375,34371,34377,34379,34091,34126,34367,34384,34120,34096,34134,34130,34365,34122,34093,34015,34010,34381,34104,34173,34098,34353,34357,34035,34040,34040,34045,34025,34020,34055,34387,34349,34050,34668,34403,34870,34394,34347,34674,34060,34065,34664,34340,34107,34182,34714,34716,34718,34710,34676,34662,34330,34415,34418,34421,34425,34427,34410,34413,34660,34160,34164,34165,34335,34345,34680,34230,34235,34240,34245,34220,34225,34342,34337,34255,34250,34382,34684,34200,34204,34209,34203,34398,34396,34470,34688,34893,34180,34192,34810,34726,34728,34724,34720,34722,34188,34460,34196,34194,34158,34270,34197,34899,34890,34212,34210,34467,34214,34740,34873,34218,34805,34464,34457,34730,34742,34744,34755,34750,34758,34752,34734,34736,34732,34800,34140,34145,34146,34142,34149,34147,34453,34295,34738,34746,34764,34760,34762,34768,34707,34700,34704,34766,34075,34840,34303,34307,34480,34830,34977,34153,34825,34829,34820,34290,34076,34070,34473,34450,34450,34773,34774,34775,34779,34776,34771,34490,34782,34848,34844,34843,34841,34077,34858,34854,34852,34975,34360,34788,34973,34494,34494) AND type_id=1 GROUP BY wish_id

22. 0.0001

    SELECT util_id, util_photo, util_prenom, util_nom, stripe_account_id, util_verify FROM util_info WHERE util_id IN (362489,244426,472017)

23. 0.0003

    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 = 362489 AND W.type_id = 1) OR (W.util_id <> 362489 AND W.type_id = 2)) AND (M.post_util_id = 362489 OR M.dest_util_id = 362489) 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=362489 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 = 244426 AND W.type_id = 1) OR (W.util_id <> 244426 AND W.type_id = 2)) AND (M.post_util_id = 244426 OR M.dest_util_id = 244426) 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=244426 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 = 472017 AND W.type_id = 1) OR (W.util_id <> 472017 AND W.type_id = 2)) AND (M.post_util_id = 472017 OR M.dest_util_id = 472017) 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=472017 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='34440'

30. 0.0011

    SELECT ville_cp, ROUND((((acos(sin((41.0318*pi()/180)) * sin((ville_latitude_deg*pi()/180))+cos((41.0318*pi()/180)) * cos((ville_latitude_deg*pi()/180)) * cos(((28.9684- ville_longitude_deg)* pi()/180))))*180/pi())*60*1.1515* 1.609344),2) as distance FROM villes WHERE ville_latitude_deg>40.851935678816 AND ville_latitude_deg<41.211664321184 AND ville_longitude_deg>28.729961970776 AND ville_longitude_deg<29.206838029224 HAVING distance <= 20 ORDER BY distance ASC

31. 0.0018

    SELECT ville_cp, ROUND((((acos(sin((41.0318*pi()/180)) * sin((ville_latitude_deg*pi()/180))+cos((41.0318*pi()/180)) * cos((ville_latitude_deg*pi()/180)) * cos(((28.9684- ville_longitude_deg)* pi()/180))))*180/pi())*60*1.1515* 1.609344),2) as distance FROM villes WHERE ville_latitude_deg>40.582139197041 AND ville_latitude_deg<41.481460802959 AND ville_longitude_deg>28.372299786721 AND ville_longitude_deg<29.564500213279 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 (1175)

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 (362489,244426,472017)

34. 0.0001

    SELECT util_id, util_photo FROM util_info WHERE util_id IN (362489,244426,472017)

35. 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='autres outils' OR ssrubr_slug like '%autres outils%' OR MATCH (ssrubr_slug) AGAINST ('(+autres +outils)')) AND sk.ssrubr_id='1175' AND keywords_visu=1 ORDER BY keywords_nb DESC, keywords_id ASC LIMIT 0,10

36. 0.0005

    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 = 88 AND ss.ssrubr_id <> 1175 ORDER BY ss.ssrubr_order ASC, ss.ssrubr_id ASC LIMIT 10

37. 0.0001

    SELECT * FROM actu WHERE ssrubr_id='1175' AND actu_niveau=0 ORDER BY actu_ordre DESC

38. 0.0001

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

39. 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

40. 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 (88) ORDER BY ssrubr_order ASC

41. 0.0016

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

42. 0.0265

    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'

43. 0.0168

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

43 requetes sql en 0.118