Location micro à 59552 Courchelettes

1. 0.0001

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

2. 0

   SELECT cate_title_slug, cate_title,cate_title_aff FROM category WHERE cate_id=232

3. 0

   SELECT filtre_dptnom FROM filtredpt WHERE filtre_dpt='59'

4. 0.0001

   SELECT ville_nom FROM villes WHERE ville_cp='59552' AND ville_nom like '%courchelettes%' ORDER BY ville_population_2012 DESC LIMIT 0,1

5. 0.0011

   SELECT ssrubr_id, ssrubr_keywords, ssrubr_title, ssrubr_slug, ssrubr_mail, ssrubr_photo FROM ssrubr WHERE (ssrubr_keywords like '%micro%' OR ssrubr_title like '%micro%' OR ssrubr_slug like '%micro%') AND cate_id='232' ORDER BY ssrubr_id LIMIT 0,1

6. 0.0001

   SELECT ville_cp, ville_latitude_deg,ville_longitude_deg FROM villes v WHERE ville_cp='59552'

7. 0.0015

   SELECT ville_cp, ROUND((((acos(sin((50.3531*pi()/180)) * sin((ville_latitude_deg*pi()/180))+cos((50.3531*pi()/180)) * cos((ville_latitude_deg*pi()/180)) * cos(((3.06778- ville_longitude_deg)* pi()/180))))*180/pi())*60*1.1515* 1.609344),2) as distance FROM villes WHERE ville_latitude_deg>50.173235678816 AND ville_latitude_deg<50.532964321184 AND ville_longitude_deg>2.7858846128999 AND ville_longitude_deg<3.3496753871001 HAVING distance <= 20 ORDER BY distance ASC

8. 0.0001

   SELECT keywords_id FROM ssrubr_keywords WHERE keywords_title='micro'

9. 0.0003

   SELECT count(wish_id) as nbwish FROM wish WHERE fami_id='5' AND cate_id='232'AND MATCH (wish_title,wish_desc) AGAINST ('+micro*' IN BOOLEAN MODE) AND wish_visu IN (1,6,10) AND wish_cp like '59552%'

10. 0.0001

    SELECT * FROM texte WHERE cate_id=232

11. 0.0001

    SELECT ssrubr_texte_haut FROM ssrubr WHERE fami_id='5' AND (ssrubr_keywords='micro' OR ssrubr_slug='micro' OR (ssrubr_title like '%micro%')) AND cate_id='232' ORDER BY ssrubr_id LIMIT 0,1

12. 0

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

13. 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

14. 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 (232) ORDER BY ssrubr_order ASC

15. 0.0001

    SELECT ssrubr_texte_haut, ssrubr_texte_bas FROM ssrubr WHERE ssrubr_slug='micro' AND cate_id='232'

16. 0.0001

    SELECT ville_cp, ville_latitude_deg, ville_longitude_deg FROM villes v WHERE ville_cp='59552'

17. 0.0014

    SELECT ville_cp, ROUND((((acos(sin((50.3531*pi()/180)) * sin((ville_latitude_deg*pi()/180))+cos((50.3531*pi()/180)) * cos((ville_latitude_deg*pi()/180)) * cos(((3.06778- ville_longitude_deg)* pi()/180))))*180/pi())*60*1.1515* 1.609344),2) as distance FROM villes WHERE ville_latitude_deg>50.173235678816 AND ville_latitude_deg<50.532964321184 AND ville_longitude_deg>2.7858846128999 AND ville_longitude_deg<3.3496753871001 HAVING distance <= 20 ORDER BY distance ASC

18. 0.0051

    SELECT ville_cp, ROUND((((acos(sin((50.3531*pi()/180)) * sin((ville_latitude_deg*pi()/180))+cos((50.3531*pi()/180)) * cos((ville_latitude_deg*pi()/180)) * cos(((3.06778- ville_longitude_deg)* pi()/180))))*180/pi())*60*1.1515* 1.609344),2) as distance FROM villes WHERE ville_latitude_deg>49.903439197041 AND ville_latitude_deg<50.802760802959 AND ville_longitude_deg>2.3630354551822 AND ville_longitude_deg<3.7725245448178 HAVING distance <= 50 ORDER BY distance ASC

19. 0.0402

    SELECT count(wish_id) as nbwish FROM wish WHERE wish_id IS NOT NULL AND fami_id='5' AND cate_id='232' AND MATCH (wish_desc) AGAINST ('(+micro*)' IN BOOLEAN MODE) AND wish_visu IN (1,6,10) AND wish_cp IN (59552,59552,59552,59500,59169,62112,59187,59553,59553,59450,59553,62117,62112,59169,62490,59119,59128,59287,59151,62490,62490,59169,62490,59169,59182,59287,59286,59950,59167,59194,59151,62970,59151,59169,62490,59151,59194,62490,59182,62790,59283,62490,62141,62950,62118,59176,62860,59259,62490,59234,62118,59176,59151,62580,62156,62119,59283,59162,59146,62320,62320,62156,62860,62860,62860,59148,62860,62110,59265,59165,62860,62156,59234,59234,62118,62320,62156,59870,59490,62640,62580,59580,62580,62118,62580,59265,59310,62320,62860,62156,59580,62118,62860,62420,62740,62590,62156,59246,59310,59247,62580,62118,62710,59870,59239,59261,59247,62820,62156,59252,62182,62680,62860,62860,62860,62118,59490,62580,59235,62580,59870,62182,62430,62440,59252,62220,59551,59239,59870,59247,59179,62880,59215,62580,59710,59265,62218,62221,59265,62860,59252,62128,62860,59310,59310,62223,62182,62128,62210,59172,62223,59171,59554,59265,62580,59111,62880,62880,59551,62128,59870,59554,59268,59710,59133,62128,62300,59171,62182,62580,62880,59295,59295,59133,62300,59554,59111,59554,59242,59310,62128,59112,59172,59870,62860,62223)

20. 0.0477

    SELECT wish.*, ui.util_photo, ui.util_prenom, ui.util_nom, ui.stripe_account_id, ville_cp,ville_nom,ville_latitude_deg,ville_longitude_deg, ROUND((((acos(sin((50.3531*pi()/180)) * sin((ville_latitude_deg*pi()/180))+cos((50.3531*pi()/180)) * cos((ville_latitude_deg*pi()/180)) * cos(((3.06778- 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='232' AND MATCH (wish_desc) AGAINST ('(+micro*)' IN BOOLEAN MODE) AND wish_visu IN (1,6,10) AND wish_cp IN (59552,59552,59552,59500,59169,62112,59187,59553,59553,59450,59553,62117,62112,59169,62490,59119,59128,59287,59151,62490,62490,59169,62490,59169,59182,59287,59286,59950,59167,59194,59151,62970,59151,59169,62490,59151,59194,62490,59182,62790,59283,62490,62141,62950,62118,59176,62860,59259,62490,59234,62118,59176,59151,62580,62156,62119,59283,59162,59146,62320,62320,62156,62860,62860,62860,59148,62860,62110,59265,59165,62860,62156,59234,59234,62118,62320,62156,59870,59490,62640,62580,59580,62580,62118,62580,59265,59310,62320,62860,62156,59580,62118,62860,62420,62740,62590,62156,59246,59310,59247,62580,62118,62710,59870,59239,59261,59247,62820,62156,59252,62182,62680,62860,62860,62860,62118,59490,62580,59235,62580,59870,62182,62430,62440,59252,62220,59551,59239,59870,59247,59179,62880,59215,62580,59710,59265,62218,62221,59265,62860,59252,62128,62860,59310,59310,62223,62182,62128,62210,59172,62223,59171,59554,59265,62580,59111,62880,62880,59551,62128,59870,59554,59268,59710,59133,62128,62300,59171,62182,62580,62880,59295,59295,59133,62300,59554,59111,59554,59242,59310,62128,59112,59172,59870,62860,62223) AND type_id=1 GROUP BY wish_id

21. 0.0001

    SELECT ville_cp, ville_latitude_deg, ville_longitude_deg FROM villes v WHERE ville_cp='59552'

22. 0.0014

    SELECT ville_cp, ROUND((((acos(sin((50.3531*pi()/180)) * sin((ville_latitude_deg*pi()/180))+cos((50.3531*pi()/180)) * cos((ville_latitude_deg*pi()/180)) * cos(((3.06778- ville_longitude_deg)* pi()/180))))*180/pi())*60*1.1515* 1.609344),2) as distance FROM villes WHERE ville_latitude_deg>50.173235678816 AND ville_latitude_deg<50.532964321184 AND ville_longitude_deg>2.7858846128999 AND ville_longitude_deg<3.3496753871001 HAVING distance <= 20 ORDER BY distance ASC

23. 0.0049

    SELECT ville_cp, ROUND((((acos(sin((50.3531*pi()/180)) * sin((ville_latitude_deg*pi()/180))+cos((50.3531*pi()/180)) * cos((ville_latitude_deg*pi()/180)) * cos(((3.06778- ville_longitude_deg)* pi()/180))))*180/pi())*60*1.1515* 1.609344),2) as distance FROM villes WHERE ville_latitude_deg>49.903439197041 AND ville_latitude_deg<50.802760802959 AND ville_longitude_deg>2.3630354551822 AND ville_longitude_deg<3.7725245448178 HAVING distance <= 50 ORDER BY distance ASC

24. 0.0001

    SELECT M.match_step FROM `match` AS M LEFT JOIN wish AS W ON W.wish_id = M.wish_id WHERE ((W.util_id = 331837 AND W.type_id = 1) OR (W.util_id != 331837 AND W.type_id = 2)) AND (`post_util_id` = 331837 OR `dest_util_id` = 331837) AND buy_stripe_pi_id != '' AND buy_stripe_pi_id IS NOT NULL

25. 0.0001

    SELECT ROUND(AVG(note), 1) AS note_moy, COUNT(note) AS nb_avis FROM `note` WHERE dest_util_id=331837 AND note_visu IN (1,3,4)

26. 0.0001

    SELECT ssrubr_slug, ssrubr_mail, ssrubr_mail_bis, ssrubr_keywords, ssrubr_bt2, cate_title_slug, fami_url, ssrubr_photo 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='673' LIMIT 0,1

27. 0.0001

    SELECT M.match_step FROM `match` AS M LEFT JOIN wish AS W ON W.wish_id = M.wish_id WHERE ((W.util_id = 331837 AND W.type_id = 1) OR (W.util_id != 331837 AND W.type_id = 2)) AND (`post_util_id` = 331837 OR `dest_util_id` = 331837) AND buy_stripe_pi_id != '' AND buy_stripe_pi_id IS NOT NULL

28. 0.0002

    SELECT abo_id FROM util_abo WHERE abo_type=4 AND abo_actif=1 AND abo_expire>SYSDATE() AND util_id = 331837

29. 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=232 AND ss.fami_id=5 ORDER BY ssrubr_order LIMIT 6,100

30. 0.0001

    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='micro' OR ssrubr_slug like '%micro%' OR MATCH (ssrubr_slug) AGAINST ('(+micro)')) AND sk.ssrubr_id='673' AND keywords_visu=1 ORDER BY keywords_nb DESC, keywords_id ASC LIMIT 0,10

31. 0.0001

    SELECT * FROM actu WHERE ssrubr_id='673' AND actu_niveau=0 ORDER BY actu_ordre DESC

32. 0.0009

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

33. 0

    SELECT count(*) as nbwish FROM wish

34. 0.0506

    SELECT note.*, post.util_prenom as postp, post.util_nom as postn, dest.util_prenom as destp, dest.util_nom as destn, w.wish_id, w.wish_desc, w.fami_id FROM note, util_info dest, util_info post, wish w WHERE note.wish_id=w.wish_id AND note.post_util_id=post.util_id AND note.dest_util_id=dest.util_id AND note_visu IN (1,2,3,4) AND post_util_id<>'7' ORDER BY note_date DESC, note_hour DESC

34 requetes sql en 0.1573