explain.depesz.com

PostgreSQL's explain analyze made readable

Result: Qmb3

Settings
# exclusive inclusive rows x rows loops node
1. 20.395 116,523.202 ↑ 1.4 178 1

Hash Left Join (cost=52,879.86..8,374,191.96 rows=243 width=2,545) (actual time=2,192.270..116,523.202 rows=178 loops=1)

  • Hash Cond: (sbd.action = pa.productactionid)
2. 0.346 1,640.465 ↑ 1.4 178 1

Hash Left Join (cost=52,864.91..58,314.06 rows=243 width=2,316) (actual time=1,606.008..1,640.465 rows=178 loops=1)

  • Hash Cond: (qi.status = qits.itemstatusid)
3. 0.571 1,640.111 ↑ 1.4 178 1

Nested Loop Left Join (cost=52,863.82..58,310.45 rows=243 width=2,305) (actual time=1,605.992..1,640.111 rows=178 loops=1)

4. 0.593 1,637.626 ↓ 1.4 174 1

Nested Loop Left Join (cost=52,863.40..58,226.04 rows=121 width=2,298) (actual time=1,605.976..1,637.626 rows=174 loops=1)

5. 0.530 1,635.293 ↓ 1.4 174 1

Hash Left Join (cost=52,863.12..58,171.17 rows=121 width=2,266) (actual time=1,605.960..1,635.293 rows=174 loops=1)

  • Hash Cond: (q.status = qs.quotationstatusid)
6. 0.375 1,634.739 ↓ 1.4 174 1

Hash Left Join (cost=52,847.94..58,154.33 rows=121 width=2,037) (actual time=1,605.925..1,634.739 rows=174 loops=1)

  • Hash Cond: (sbd.office = o2.officeid)
7. 0.522 1,634.307 ↓ 1.4 174 1

Hash Left Join (cost=52,839.45..58,145.38 rows=121 width=2,049) (actual time=1,605.858..1,634.307 rows=174 loops=1)

  • Hash Cond: (cs.officeid = o1.officeid)
8. 0.627 1,633.722 ↓ 1.4 174 1

Hash Join (cost=52,830.97..58,135.26 rows=121 width=2,048) (actual time=1,605.773..1,633.722 rows=174 loops=1)

  • Hash Cond: (sbd.status = ps.productstatusid)
9. 0.561 1,633.031 ↑ 5.2 225 1

Nested Loop Left Join (cost=52,827.50..58,126.21 rows=1,166 width=2,035) (actual time=1,605.698..1,633.031 rows=225 loops=1)

10. 0.644 1,630.670 ↑ 5.2 225 1

Nested Loop Left Join (cost=52,827.22..57,683.19 rows=1,166 width=2,028) (actual time=1,605.683..1,630.670 rows=225 loops=1)

11. 19.623 1,628.001 ↑ 5.2 225 1

Hash Right Join (cost=52,826.93..57,260.19 rows=1,166 width=2,022) (actual time=1,605.646..1,628.001 rows=225 loops=1)

  • Hash Cond: (q.productid = pd.productid)
12. 13.045 13.045 ↓ 1.0 106,330 1

Seq Scan on quotation q (cost=0.00..4,024.35 rows=105,935 width=41) (actual time=0.002..13.045 rows=106,330 loops=1)

13. 0.187 1,595.333 ↑ 3.9 78 1

Hash (cost=52,823.15..52,823.15 rows=302 width=1,989) (actual time=1,595.333..1,595.333 rows=78 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 30kB
14. 0.110 1,595.146 ↑ 3.9 78 1

Hash Left Join (cost=43,459.96..52,823.15 rows=302 width=1,989) (actual time=1,391.593..1,595.146 rows=78 loops=1)

  • Hash Cond: ((pd.seasonclient = sc.seasonclientid) AND (pd.client = sc.clientid))
15. 0.077 1,594.755 ↑ 3.9 78 1

Nested Loop Left Join (cost=43,440.86..52,801.70 rows=302 width=1,996) (actual time=1,391.289..1,594.755 rows=78 loops=1)

16. 6.493 1,594.366 ↑ 3.9 78 1

Nested Loop (cost=43,440.57..52,699.43 rows=302 width=1,983) (actual time=1,391.264..1,594.366 rows=78 loops=1)

  • Join Filter: (pd.client = c.clientid)
  • Rows Removed by Join Filter: 27427
17. 0.017 0.017 ↑ 1.0 1 1

Index Only Scan using ixclient90 on dbclient c (cost=0.14..8.83 rows=1 width=8) (actual time=0.015..0.017 rows=1 loops=1)

  • Index Cond: (companyshort = 'SVH'::text)
  • Heap Fetches: 1
18. 11.907 1,587.856 ↓ 1.0 27,505 1

Hash Left Join (cost=43,440.43..52,347.59 rows=27,441 width=1,975) (actual time=1,203.166..1,587.856 rows=27,505 loops=1)

  • Hash Cond: (sbd.sustainability = sa.sustainabilityid)
19. 40.685 1,575.934 ↓ 1.0 27,505 1

Hash Left Join (cost=43,425.26..52,175.24 rows=27,441 width=1,766) (actual time=1,203.132..1,575.934 rows=27,505 loops=1)

  • Hash Cond: (pd.productid = md.productid)
20. 16.488 1,510.439 ↓ 1.0 27,505 1

Hash Left Join (cost=42,160.23..50,328.50 rows=27,441 width=1,518) (actual time=1,178.294..1,510.439 rows=27,505 loops=1)

  • Hash Cond: (sbd.packaging = packaging.packagingid)
21. 13.026 1,493.928 ↓ 1.0 27,505 1

Hash Left Join (cost=42,158.49..49,965.93 rows=27,441 width=1,514) (actual time=1,178.259..1,493.928 rows=27,505 loops=1)

  • Hash Cond: (sbd.wittmkz = mkz.smmkzid)
22. 21.836 1,480.739 ↓ 1.0 27,505 1

Hash Left Join (cost=42,147.75..49,727.34 rows=27,441 width=1,496) (actual time=1,178.073..1,480.739 rows=27,505 loops=1)

  • Hash Cond: (sbd.rebuying = rebuying.ldapusermappingid)
23. 19.048 1,441.787 ↓ 1.0 27,505 1

Hash Left Join (cost=40,920.18..47,876.03 rows=27,441 width=1,482) (actual time=1,160.933..1,441.787 rows=27,505 loops=1)

  • Hash Cond: (sbd.sourcingbuyer = sourcingbuyer.ldapusermappingid)
24. 19.819 1,405.874 ↓ 1.0 27,505 1

Hash Left Join (cost=39,692.61..46,074.75 rows=27,441 width=1,468) (actual time=1,144.046..1,405.874 rows=27,505 loops=1)

  • Hash Cond: (sbd.qa = development.ldapusermappingid)
25. 13.013 1,370.215 ↓ 1.0 27,505 1

Hash Left Join (cost=38,465.05..44,274.21 rows=27,441 width=1,454) (actual time=1,128.181..1,370.215 rows=27,505 loops=1)

  • Hash Cond: (sbd.designer = designer.ldapusermappingid)
26. 22.782 1,341.318 ↓ 1.0 27,505 1

Hash Left Join (cost=37,237.48..42,668.58 rows=27,441 width=1,449) (actual time=1,112.281..1,341.318 rows=27,505 loops=1)

  • Hash Cond: (sbd.creativebuyer = creativebuyer.ldapusermappingid)
27. 24.171 1,302.435 ↓ 1.0 27,505 1

Hash Left Join (cost=36,009.91..40,822.92 rows=27,441 width=1,435) (actual time=1,096.158..1,302.435 rows=27,505 loops=1)

  • Hash Cond: (pd.productid = sr.productid)
28. 8.766 202.784 ↓ 1.0 27,505 1

Hash Left Join (cost=1,499.44..6,209.54 rows=27,441 width=1,403) (actual time=20.670..202.784 rows=27,505 loops=1)

  • Hash Cond: (sbd.calculationsizeoption = calculationsizeoption.calculationsizeoptionid)
29. 9.366 194.007 ↓ 1.0 27,505 1

Hash Left Join (cost=1,484.94..6,091.35 rows=27,441 width=1,194) (actual time=20.654..194.007 rows=27,505 loops=1)

  • Hash Cond: (sbd.subbrandcategory = subbrandcategory.subbrandcategoryid)
30. 8.247 184.633 ↓ 1.0 27,505 1

Hash Left Join (cost=1,470.22..5,972.94 rows=27,441 width=1,171) (actual time=20.642..184.633 rows=27,505 loops=1)

  • Hash Cond: (sbd.styleorigin = styleorigin.styleoriginid)
31. 8.122 176.376 ↓ 1.0 27,505 1

Hash Left Join (cost=1,455.49..5,854.56 rows=27,441 width=1,148) (actual time=20.628..176.376 rows=27,505 loops=1)

  • Hash Cond: (sbd.assortment = assortment.assortmentid)
32. 8.067 168.250 ↓ 1.0 27,505 1

Hash Left Join (cost=1,440.77..5,736.18 rows=27,441 width=1,125) (actual time=20.620..168.250 rows=27,505 loops=1)

  • Hash Cond: (sbd.seasonname = seasonname.seasonnameid)
33. 8.241 160.171 ↓ 1.0 27,505 1

Hash Left Join (cost=1,426.04..5,617.77 rows=27,441 width=884) (actual time=20.602..160.171 rows=27,505 loops=1)

  • Hash Cond: (sbd.bodyshapetype = bodyshapetype.bodyshapetypeid)
34. 8.277 151.908 ↓ 1.0 27,505 1

Hash Left Join (cost=1,411.32..5,499.52 rows=27,441 width=861) (actual time=20.576..151.908 rows=27,505 loops=1)

  • Hash Cond: (sbd.subbrand = subbrand.subbrandid)
35. 8.295 143.626 ↓ 1.0 27,505 1

Hash Left Join (cost=1,396.59..5,381.11 rows=27,441 width=838) (actual time=20.567..143.626 rows=27,505 loops=1)

  • Hash Cond: (sbd.fashionlevel = fashionlevel.fashionlevelid)
36. 9.833 135.324 ↓ 1.0 27,505 1

Hash Left Join (cost=1,381.87..5,262.74 rows=27,441 width=815) (actual time=20.552..135.324 rows=27,505 loops=1)

  • Hash Cond: (sbd.tactemplate = template.templateid)
37. 16.098 125.467 ↓ 1.0 27,505 1

Hash Left Join (cost=1,380.28..5,108.38 rows=27,441 width=801) (actual time=20.521..125.467 rows=27,505 loops=1)

  • Hash Cond: (sbd.genre = genre.genreid)
38. 43.276 109.302 ↓ 1.0 27,505 1

Hash Join (cost=1,374.91..4,743.66 rows=27,441 width=792) (actual time=20.434..109.302 rows=27,505 loops=1)

  • Hash Cond: (sbd.productid = pd.productid)
39. 9.576 45.660 ↑ 1.0 27,505 1

Merge Left Join (cost=20.49..2,805.16 rows=27,526 width=719) (actual time=0.053..45.660 rows=27,505 loops=1)

  • Merge Cond: ((sbd.package)::text = ((packageoption.packageoptionid)::text))
40. 36.058 36.058 ↑ 1.0 27,505 1

Index Scan using ixfk_sbd_package on sourcerbriefingdetails sbd (cost=0.29..2,315.36 rows=27,526 width=511) (actual time=0.035..36.058 rows=27,505 loops=1)

41. 0.021 0.026 ↑ 2.6 81 1

Sort (cost=20.20..20.72 rows=210 width=238) (actual time=0.016..0.026 rows=81 loops=1)

  • Sort Key: ((packageoption.packageoptionid)::text)
  • Sort Method: quicksort Memory: 25kB
42. 0.005 0.005 ↑ 52.5 4 1

Seq Scan on packageoption (cost=0.00..12.10 rows=210 width=238) (actual time=0.004..0.005 rows=4 loops=1)

43. 10.384 20.366 ↓ 1.0 27,505 1

Hash (cost=1,011.41..1,011.41 rows=27,441 width=81) (actual time=20.366..20.366 rows=27,505 loops=1)

  • Buckets: 4096 Batches: 1 Memory Usage: 3083kB
44. 9.982 9.982 ↓ 1.0 27,505 1

Seq Scan on product pd (cost=0.00..1,011.41 rows=27,441 width=81) (actual time=0.003..9.982 rows=27,505 loops=1)

45. 0.033 0.067 ↓ 1.1 159 1

Hash (cost=3.50..3.50 rows=150 width=18) (actual time=0.067..0.067 rows=159 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 8kB
46. 0.034 0.034 ↓ 1.1 159 1

Seq Scan on genre (cost=0.00..3.50 rows=150 width=18) (actual time=0.006..0.034 rows=159 loops=1)

47. 0.012 0.024 ↓ 1.7 44 1

Hash (cost=1.26..1.26 rows=26 width=32) (actual time=0.024..0.024 rows=44 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 4kB
48. 0.012 0.012 ↓ 1.7 44 1

Seq Scan on template (cost=0.00..1.26 rows=26 width=32) (actual time=0.005..0.012 rows=44 loops=1)

49. 0.002 0.007 ↑ 42.0 5 1

Hash (cost=12.10..12.10 rows=210 width=52) (actual time=0.007..0.007 rows=5 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 1kB
50. 0.005 0.005 ↑ 42.0 5 1

Seq Scan on fashionlevel (cost=0.00..12.10 rows=210 width=52) (actual time=0.004..0.005 rows=5 loops=1)

51. 0.001 0.005 ↑ 210.0 1 1

Hash (cost=12.10..12.10 rows=210 width=52) (actual time=0.005..0.005 rows=1 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 1kB
52. 0.004 0.004 ↑ 210.0 1 1

Seq Scan on subbrand (cost=0.00..12.10 rows=210 width=52) (actual time=0.003..0.004 rows=1 loops=1)

53. 0.012 0.022 ↑ 6.6 32 1

Hash (cost=12.10..12.10 rows=210 width=52) (actual time=0.022..0.022 rows=32 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 2kB
54. 0.010 0.010 ↑ 6.6 32 1

Seq Scan on bodyshapetype (cost=0.00..12.10 rows=210 width=52) (actual time=0.007..0.010 rows=32 loops=1)

55. 0.004 0.012 ↑ 10.5 20 1

Hash (cost=12.10..12.10 rows=210 width=270) (actual time=0.012..0.012 rows=20 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 2kB
56. 0.008 0.008 ↑ 10.5 20 1

Seq Scan on seasonname (cost=0.00..12.10 rows=210 width=270) (actual time=0.003..0.008 rows=20 loops=1)

57. 0.001 0.004 ↑ 105.0 2 1

Hash (cost=12.10..12.10 rows=210 width=52) (actual time=0.004..0.004 rows=2 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 1kB
58. 0.003 0.003 ↑ 105.0 2 1

Seq Scan on assortment (cost=0.00..12.10 rows=210 width=52) (actual time=0.002..0.003 rows=2 loops=1)

59. 0.005 0.010 ↑ 16.2 13 1

Hash (cost=12.10..12.10 rows=210 width=52) (actual time=0.010..0.010 rows=13 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 1kB
60. 0.005 0.005 ↑ 16.2 13 1

Seq Scan on styleorigin (cost=0.00..12.10 rows=210 width=52) (actual time=0.003..0.005 rows=13 loops=1)

61. 0.003 0.008 ↑ 16.2 13 1

Hash (cost=12.10..12.10 rows=210 width=52) (actual time=0.008..0.008 rows=13 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 1kB
62. 0.005 0.005 ↑ 16.2 13 1

Seq Scan on subbrandcategory (cost=0.00..12.10 rows=210 width=52) (actual time=0.003..0.005 rows=13 loops=1)

63. 0.004 0.011 ↑ 12.5 16 1

Hash (cost=12.00..12.00 rows=200 width=238) (actual time=0.011..0.011 rows=16 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 1kB
64. 0.007 0.007 ↑ 12.5 16 1

Seq Scan on calculationsizeoption (cost=0.00..12.00 rows=200 width=238) (actual time=0.004..0.007 rows=16 loops=1)

65. 1.474 1,075.480 ↓ 6,290.0 6,290 1

Hash (cost=34,510.45..34,510.45 rows=1 width=40) (actual time=1,075.480..1,075.480 rows=6,290 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 302kB
66. 0.756 1,074.006 ↓ 6,290.0 6,290 1

Subquery Scan on sr (cost=34,510.43..34,510.45 rows=1 width=40) (actual time=1,071.309..1,074.006 rows=6,290 loops=1)

67. 5.169 1,073.250 ↓ 6,290.0 6,290 1

HashAggregate (cost=34,510.43..34,510.44 rows=1 width=40) (actual time=1,071.308..1,073.250 rows=6,290 loops=1)

  • Group Key: p.productid
68. 9.227 1,068.081 ↓ 6,645.0 6,645 1

HashAggregate (cost=34,510.39..34,510.42 rows=1 width=19) (actual time=1,066.597..1,068.081 rows=6,645 loops=1)

  • Group Key: p.productid, isz.sizerangecode, CASE WHEN (((isz0.dim2)::text || (isz0.dim1)::text) = ((isz1.dim2)::text || (isz1.dim1)::text)) THEN ((isz0.dim2)::text || (isz0.dim1)::text) ELSE (((((isz0.dim2)::text || (isz0.dim1)::text) || '-'::text) || (isz1.dim2)::text) || (isz1.dim1)::text) END
69. 62.466 1,058.854 ↓ 13,493.0 13,493 1

Hash Join (cost=31,796.20..34,510.38 rows=1 width=19) (actual time=987.535..1,058.854 rows=13,493 loops=1)

  • Hash Cond: ((isz1.itemid = isz0.itemid) AND ((isz1.sizerangecode)::text = (isz0.sizerangecode)::text) AND (isz1.sortkeyinrange = (max(isz.sortkeyinrange))))
70. 8.890 8.890 ↑ 1.0 82,811 1

Seq Scan on itemsize isz1 (cost=0.00..1,779.13 rows=83,113 width=19) (actual time=0.005..8.890 rows=82,811 loops=1)

71. 5.431 987.498 ↓ 1,938.7 13,571 1

Hash (cost=31,796.07..31,796.07 rows=7 width=38) (actual time=987.498..987.498 rows=13,571 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 978kB
72. 53.199 982.067 ↓ 1,938.7 13,571 1

Merge Join (cost=30,133.74..31,796.07 rows=7 width=38) (actual time=920.637..982.067 rows=13,571 loops=1)

  • Merge Cond: (((isz0.sizerangecode)::text = (isz.sizerangecode)::text) AND (i.productid = p.productid) AND (isz0.sortkeyinrange = (min(isz.sortkeyinrange))))
73. 637.983 736.646 ↑ 1.0 82,811 1

Sort (cost=12,588.03..12,795.82 rows=83,113 width=35) (actual time=729.930..736.646 rows=82,811 loops=1)

  • Sort Key: isz0.sizerangecode, i.productid, isz0.sortkeyinrange
  • Sort Method: quicksort Memory: 9542kB
74. 55.639 98.663 ↑ 1.0 82,811 1

Hash Join (cost=2,355.15..5,796.54 rows=83,113 width=35) (actual time=32.038..98.663 rows=82,811 loops=1)

  • Hash Cond: (isz0.itemid = i.itemid)
75. 11.007 11.007 ↑ 1.0 82,811 1

Seq Scan on itemsize isz0 (cost=0.00..1,779.13 rows=83,113 width=19) (actual time=0.002..11.007 rows=82,811 loops=1)

76. 21.124 32.017 ↓ 1.0 52,241 1

Hash (cost=1,702.29..1,702.29 rows=52,229 width=16) (actual time=32.017..32.017 rows=52,241 loops=1)

  • Buckets: 8192 Batches: 1 Memory Usage: 2551kB
77. 10.893 10.893 ↓ 1.0 52,241 1

Seq Scan on item i (cost=0.00..1,702.29 rows=52,229 width=16) (actual time=0.004..10.893 rows=52,241 loops=1)

78. 26.200 192.222 ↑ 6.1 13,568 1

Sort (cost=17,545.71..17,753.49 rows=83,113 width=19) (actual time=190.697..192.222 rows=13,568 loops=1)

  • Sort Key: isz.sizerangecode, p.productid, (min(isz.sortkeyinrange))
  • Sort Method: quicksort Memory: 714kB
79. 40.269 166.022 ↑ 12.4 6,681 1

HashAggregate (cost=9,091.96..9,923.09 rows=83,113 width=15) (actual time=164.306..166.022 rows=6,681 loops=1)

  • Group Key: p.productid, isz.sizerangecode
80. 49.780 125.753 ↑ 1.0 82,811 1

Hash Join (cost=4,819.44..8,260.83 rows=83,113 width=15) (actual time=66.594..125.753 rows=82,811 loops=1)

  • Hash Cond: (isz.itemid = i_1.itemid)
81. 9.418 9.418 ↑ 1.0 82,811 1

Seq Scan on itemsize isz (cost=0.00..1,779.13 rows=83,113 width=15) (actual time=0.018..9.418 rows=82,811 loops=1)

82. 11.545 66.555 ↓ 1.0 52,241 1

Hash (cost=4,166.58..4,166.58 rows=52,229 width=16) (actual time=66.555..66.555 rows=52,241 loops=1)

  • Buckets: 8192 Batches: 1 Memory Usage: 2551kB
83. 33.367 55.010 ↓ 1.0 52,241 1

Hash Join (cost=1,354.42..4,166.58 rows=52,229 width=16) (actual time=11.028..55.010 rows=52,241 loops=1)

  • Hash Cond: (i_1.productid = p.productid)
84. 10.636 10.636 ↓ 1.0 52,241 1

Seq Scan on item i_1 (cost=0.00..1,702.29 rows=52,229 width=16) (actual time=0.006..10.636 rows=52,241 loops=1)

85. 5.465 11.007 ↓ 1.0 27,505 1

Hash (cost=1,011.41..1,011.41 rows=27,441 width=8) (actual time=11.007..11.007 rows=27,505 loops=1)

  • Buckets: 4096 Batches: 1 Memory Usage: 1102kB
86. 5.542 5.542 ↓ 1.0 27,505 1

Seq Scan on product p (cost=0.00..1,011.41 rows=27,441 width=8) (actual time=0.004..5.542 rows=27,505 loops=1)

87. 9.500 16.101 ↑ 1.0 34,916 1

Hash (cost=790.03..790.03 rows=35,003 width=22) (actual time=16.101..16.101 rows=34,916 loops=1)

  • Buckets: 4096 Batches: 1 Memory Usage: 1880kB
88. 6.601 6.601 ↑ 1.0 34,916 1

Seq Scan on dbldapusermapping creativebuyer (cost=0.00..790.03 rows=35,003 width=22) (actual time=0.009..6.601 rows=34,916 loops=1)

89. 9.284 15.884 ↑ 1.0 34,916 1

Hash (cost=790.03..790.03 rows=35,003 width=22) (actual time=15.884..15.884 rows=34,916 loops=1)

  • Buckets: 4096 Batches: 1 Memory Usage: 1880kB
90. 6.600 6.600 ↑ 1.0 34,916 1

Seq Scan on dbldapusermapping designer (cost=0.00..790.03 rows=35,003 width=22) (actual time=0.005..6.600 rows=34,916 loops=1)

91. 9.188 15.840 ↑ 1.0 34,916 1

Hash (cost=790.03..790.03 rows=35,003 width=22) (actual time=15.840..15.840 rows=34,916 loops=1)

  • Buckets: 4096 Batches: 1 Memory Usage: 1880kB
92. 6.652 6.652 ↑ 1.0 34,916 1

Seq Scan on dbldapusermapping development (cost=0.00..790.03 rows=35,003 width=22) (actual time=0.005..6.652 rows=34,916 loops=1)

93. 10.259 16.865 ↑ 1.0 34,916 1

Hash (cost=790.03..790.03 rows=35,003 width=22) (actual time=16.865..16.865 rows=34,916 loops=1)

  • Buckets: 4096 Batches: 1 Memory Usage: 1880kB
94. 6.606 6.606 ↑ 1.0 34,916 1

Seq Scan on dbldapusermapping sourcingbuyer (cost=0.00..790.03 rows=35,003 width=22) (actual time=0.005..6.606 rows=34,916 loops=1)

95. 10.491 17.116 ↑ 1.0 34,916 1

Hash (cost=790.03..790.03 rows=35,003 width=22) (actual time=17.116..17.116 rows=34,916 loops=1)

  • Buckets: 4096 Batches: 1 Memory Usage: 1880kB
96. 6.625 6.625 ↑ 1.0 34,916 1

Seq Scan on dbldapusermapping rebuying (cost=0.00..790.03 rows=35,003 width=22) (actual time=0.005..6.625 rows=34,916 loops=1)

97. 0.091 0.163 ↑ 1.3 270 1

Hash (cost=6.44..6.44 rows=344 width=27) (actual time=0.163..0.163 rows=270 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 17kB
98. 0.072 0.072 ↑ 1.3 270 1

Seq Scan on dbsmmkz mkz (cost=0.00..6.44 rows=344 width=27) (actual time=0.010..0.072 rows=270 loops=1)

99. 0.013 0.023 ↓ 1.1 35 1

Hash (cost=1.33..1.33 rows=33 width=22) (actual time=0.023..0.023 rows=35 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 2kB
100. 0.010 0.010 ↓ 1.1 35 1

Seq Scan on packaging (cost=0.00..1.33 rows=33 width=22) (actual time=0.007..0.010 rows=35 loops=1)

101. 8.564 24.810 ↓ 1.0 27,493 1

Hash (cost=923.77..923.77 rows=27,300 width=256) (actual time=24.810..24.810 rows=27,493 loops=1)

  • Buckets: 4096 Batches: 1 Memory Usage: 1713kB
102. 6.815 16.246 ↓ 1.0 27,493 1

Hash Left Join (cost=16.30..923.77 rows=27,300 width=256) (actual time=0.035..16.246 rows=27,493 loops=1)

  • Hash Cond: (md.shellweightunit = swu.weightunitid)
103. 9.411 9.411 ↓ 1.0 27,493 1

Seq Scan on materialdetails md (cost=0.00..704.00 rows=27,300 width=83) (actual time=0.007..9.411 rows=27,493 loops=1)

104. 0.015 0.020 ↑ 11.7 24 1

Hash (cost=12.80..12.80 rows=280 width=238) (actual time=0.020..0.020 rows=24 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 2kB
105. 0.005 0.005 ↑ 11.7 24 1

Seq Scan on weightunit swu (cost=0.00..12.80 rows=280 width=238) (actual time=0.004..0.005 rows=24 loops=1)

106. 0.008 0.015 ↑ 12.8 18 1

Hash (cost=12.30..12.30 rows=230 width=238) (actual time=0.015..0.015 rows=18 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 1kB
107. 0.007 0.007 ↑ 12.8 18 1

Seq Scan on sustainability sa (cost=0.00..12.30 rows=230 width=238) (actual time=0.005..0.007 rows=18 loops=1)

108. 0.312 0.312 ↑ 1.0 1 78

Index Scan using pk_department on dbdepartment d (cost=0.29..0.33 rows=1 width=29) (actual time=0.004..0.004 rows=1 loops=78)

  • Index Cond: (pd.department = departmentid)
  • Filter: (pd.client = clientid)
109. 0.173 0.281 ↓ 1.1 591 1

Hash (cost=11.24..11.24 rows=524 width=16) (actual time=0.281..0.281 rows=591 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 29kB
110. 0.108 0.108 ↓ 1.1 591 1

Seq Scan on dbseasonclient sc (cost=0.00..11.24 rows=524 width=16) (actual time=0.006..0.108 rows=591 loops=1)

111. 2.025 2.025 ↑ 1.0 1 225

Index Scan using pk_clients on dbclientsupplier cs (cost=0.29..0.35 rows=1 width=23) (actual time=0.008..0.009 rows=1 loops=225)

  • Index Cond: (q.clientsupplier = clientsupplierid)
112. 1.800 1.800 ↑ 1.0 1 225

Index Scan using pk_supplier on dbsupplier s (cost=0.29..0.37 rows=1 width=15) (actual time=0.007..0.008 rows=1 loops=225)

  • Index Cond: (cs.supplierid = supplierid)
113. 0.003 0.064 ↑ 2.8 4 1

Hash (cost=3.33..3.33 rows=11 width=22) (actual time=0.064..0.064 rows=4 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 1kB
114. 0.061 0.061 ↑ 2.8 4 1

Seq Scan on productstatus ps (cost=0.00..3.33 rows=11 width=22) (actual time=0.027..0.061 rows=4 loops=1)

  • Filter: ((code)::text ~~* 'Free for Quotation'::text)
  • Rows Removed by Filter: 34
115. 0.034 0.063 ↑ 1.2 131 1

Hash (cost=6.55..6.55 rows=155 width=8) (actual time=0.063..0.063 rows=131 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 6kB
116. 0.029 0.029 ↑ 1.2 131 1

Seq Scan on dboffice o1 (cost=0.00..6.55 rows=155 width=8) (actual time=0.004..0.029 rows=131 loops=1)

117. 0.039 0.057 ↑ 1.2 131 1

Hash (cost=6.55..6.55 rows=155 width=8) (actual time=0.057..0.057 rows=131 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 6kB
118. 0.018 0.018 ↑ 1.2 131 1

Seq Scan on dboffice o2 (cost=0.00..6.55 rows=155 width=8) (actual time=0.001..0.018 rows=131 loops=1)

119. 0.013 0.024 ↑ 9.6 24 1

Hash (cost=12.30..12.30 rows=230 width=238) (actual time=0.024..0.024 rows=24 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 2kB
120. 0.011 0.011 ↑ 9.6 24 1

Seq Scan on quotationstatus qs (cost=0.00..12.30 rows=230 width=238) (actual time=0.005..0.011 rows=24 loops=1)

121. 1.740 1.740 ↑ 1.0 1 174

Index Scan using iteminfo_pkey on iteminfo ii (cost=0.29..0.44 rows=1 width=40) (actual time=0.008..0.010 rows=1 loops=174)

  • Index Cond: (productid = pd.productid)
122. 1.914 1.914 ↑ 3.0 1 174

Index Scan using ix_quotationitem_quotionid on quotationitem qi (cost=0.42..0.67 rows=3 width=23) (actual time=0.009..0.011 rows=1 loops=174)

  • Index Cond: (q.quotationid = quotationid)
123. 0.005 0.008 ↓ 1.2 5 1

Hash (cost=1.04..1.04 rows=4 width=20) (actual time=0.008..0.008 rows=5 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 1kB
124. 0.003 0.003 ↓ 1.2 5 1

Seq Scan on itemstatus qits (cost=0.00..1.04 rows=4 width=20) (actual time=0.003..0.003 rows=5 loops=1)

125. 0.005 0.010 ↑ 24.4 9 1

Hash (cost=12.20..12.20 rows=220 width=238) (actual time=0.010..0.010 rows=9 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 1kB
126. 0.005 0.005 ↑ 24.4 9 1

Seq Scan on productaction pa (cost=0.00..12.20 rows=220 width=238) (actual time=0.005..0.005 rows=9 loops=1)

127.          

SubPlan (for Hash Left Join)

128. 2.670 2.670 ↑ 60.0 1 178

Index Scan using pk_18ndscrptn on dbi18ndescription (cost=0.57..181.73 rows=60 width=24) (actual time=0.015..0.015 rows=1 loops=178)

  • Index Cond: ((rid = (sbd.brand)::numeric) AND (languageid = 3064863::numeric))
129. 0.712 2,150.952 ↑ 2.0 1 178

Unique (cost=1,849.48..1,849.49 rows=2 width=6) (actual time=12.083..12.084 rows=1 loops=178)

130. 1.780 2,150.240 ↑ 2.0 1 178

Sort (cost=1,849.48..1,849.49 rows=2 width=6) (actual time=12.080..12.080 rows=1 loops=178)

  • Sort Key: dbsmfabric.code
  • Sort Method: quicksort Memory: 25kB
131. 1.754 2,148.460 ↑ 2.0 1 178

Nested Loop Left Join (cost=0.28..1,849.47 rows=2 width=6) (actual time=10.359..12.070 rows=1 loops=178)

132. 2,144.366 2,144.366 ↑ 2.0 1 178

Seq Scan on item it (cost=0.00..1,832.86 rows=2 width=9) (actual time=10.337..12.047 rows=1 loops=178)

  • Filter: (productid = pd.productid)
  • Rows Removed by Filter: 52240
133. 2.340 2.340 ↑ 1.0 1 195

Index Scan using pk_smfabric on dbsmfabric (cost=0.28..8.29 rows=1 width=14) (actual time=0.011..0.012 rows=1 loops=195)

  • Index Cond: (smfabricid = it.coloroption)
134. 0.534 2,225.178 ↑ 2.0 1 178

Unique (cost=1,836.57..1,836.58 rows=2 width=15) (actual time=12.500..12.501 rows=1 loops=178)

135. 1.958 2,224.644 ↑ 2.0 1 178

Sort (cost=1,836.57..1,836.58 rows=2 width=15) (actual time=12.498..12.498 rows=1 loops=178)

  • Sort Key: ft.en
  • Sort Method: quicksort Memory: 25kB
136. 2.602 2,222.686 ↑ 2.0 1 178

Nested Loop Left Join (cost=0.00..1,836.56 rows=2 width=15) (actual time=10.536..12.487 rows=1 loops=178)

  • Join Filter: (it_1.fashiontheme = ft.fashionthemeid)
  • Rows Removed by Join Filter: 50
137. 2,219.304 2,219.304 ↑ 2.0 1 178

Seq Scan on item it_1 (cost=0.00..1,832.86 rows=2 width=9) (actual time=10.519..12.468 rows=1 loops=178)

  • Filter: (productid = pd.productid)
  • Rows Removed by Filter: 52240
138. 0.768 0.780 ↓ 1.1 46 195

Materialize (cost=0.00..2.60 rows=40 width=24) (actual time=0.001..0.004 rows=46 loops=195)

139. 0.012 0.012 ↓ 1.1 46 1

Seq Scan on fashiontheme ft (cost=0.00..2.40 rows=40 width=24) (actual time=0.004..0.012 rows=46 loops=1)

140. 0.534 2,156.826 ↑ 2.0 1 178

Unique (cost=1,851.80..1,851.81 rows=2 width=32) (actual time=12.117..12.117 rows=1 loops=178)

141. 4.450 2,156.292 ↑ 2.0 1 178

Sort (cost=1,851.80..1,851.80 rows=2 width=32) (actual time=12.114..12.114 rows=1 loops=178)

  • Sort Key: cam.en
  • Sort Method: quicksort Memory: 25kB
142. 2.441 2,151.842 ↑ 2.0 1 178

Nested Loop Left Join (cost=0.00..1,851.79 rows=2 width=32) (actual time=10.507..12.089 rows=1 loops=178)

  • Join Filter: (it_2.campaign = cam.campaignid)
  • Rows Removed by Join Filter: 32
143. 2,148.816 2,148.816 ↑ 2.0 1 178

Seq Scan on item it_2 (cost=0.00..1,832.86 rows=2 width=9) (actual time=10.493..12.072 rows=1 loops=178)

  • Filter: (productid = pd.productid)
  • Rows Removed by Filter: 52240
144. 0.576 0.585 ↑ 7.0 30 195

Materialize (cost=0.00..13.15 rows=210 width=52) (actual time=0.001..0.003 rows=30 loops=195)

145. 0.009 0.009 ↑ 7.0 30 1

Seq Scan on campaign cam (cost=0.00..12.10 rows=210 width=52) (actual time=0.005..0.009 rows=30 loops=1)

146. 2.492 101,875.274 ↑ 1.0 1 178

Aggregate (cost=22,967.94..22,967.95 rows=1 width=267) (actual time=572.333..572.333 rows=1 loops=178)

147. 0.890 101,872.782 ↑ 1.0 2 178

Nested Loop Left Join (cost=18,802.91..22,967.88 rows=2 width=267) (actual time=571.980..572.319 rows=2 loops=178)

148. 171.592 101,870.290 ↑ 1.0 2 178

Nested Loop Left Join (cost=18,802.76..22,967.53 rows=2 width=58) (actual time=571.970..572.305 rows=2 loops=178)

149. 2,471.530 101,696.028 ↑ 1.0 2 178

Merge Right Join (cost=18,802.62..22,967.20 rows=2 width=58) (actual time=570.994..571.326 rows=2 loops=178)

  • Merge Cond: (mce.materialcomponentid = mc.materialcomponentid)
150. 39,053.200 93,139.568 ↓ 1.0 66,068 178

GroupAggregate (cost=14,933.42..18,291.96 rows=64,481 width=25) (actual time=291.119..523.256 rows=66,068 loops=178)

  • Group Key: mce.materialcomponentid
151. 40,576.702 54,086.368 ↑ 1.1 112,687 178

Sort (cost=14,933.42..15,252.49 rows=127,626 width=25) (actual time=291.091..303.856 rows=112,687 loops=178)

  • Sort Key: mce.materialcomponentid
  • Sort Method: quicksort Memory: 12994kB
152. 10,749.911 13,509.666 ↑ 1.0 126,983 178

Hash Join (cost=21.62..4,109.74 rows=127,626 width=25) (actual time=0.011..75.897 rows=126,983 loops=178)

  • Hash Cond: (mce.materialid = mat.materialid)
153. 2,759.534 2,759.534 ↑ 1.0 126,983 178

Seq Scan on materialcomponentelement mce (cost=0.00..2,333.26 rows=127,626 width=22) (actual time=0.005..15.503 rows=126,983 loops=178)

154. 0.121 0.221 ↑ 1.3 482 1

Hash (cost=13.50..13.50 rows=650 width=21) (actual time=0.221..0.221 rows=482 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 26kB
155. 0.100 0.100 ↑ 1.3 482 1

Seq Scan on material mat (cost=0.00..13.50 rows=650 width=21) (actual time=0.006..0.100 rows=482 loops=1)

156. 2.670 6,084.930 ↑ 1.0 2 178

Sort (cost=3,869.20..3,869.20 rows=2 width=35) (actual time=34.185..34.185 rows=2 loops=178)

  • Sort Key: mc.materialcomponentid
  • Sort Method: quicksort Memory: 25kB
157. 2,162.344 6,082.260 ↑ 1.0 2 178

Hash Right Join (cost=1,832.89..3,869.19 rows=2 width=35) (actual time=31.908..34.170 rows=2 loops=178)

  • Hash Cond: (mc.objectid = it_3.itemid)
158. 1,531.868 1,531.868 ↑ 1.0 75,010 178

Seq Scan on materialcomponent mc (cost=0.00..1,748.75 rows=76,675 width=43) (actual time=0.006..8.606 rows=75,010 loops=178)

159. 1.068 2,388.048 ↑ 2.0 1 178

Hash (cost=1,832.86..1,832.86 rows=2 width=8) (actual time=13.416..13.416 rows=1 loops=178)

  • Buckets: 1024 Batches: 1 Memory Usage: 1kB
160. 2,386.980 2,386.980 ↑ 2.0 1 178

Seq Scan on item it_3 (cost=0.00..1,832.86 rows=2 width=8) (actual time=11.797..13.410 rows=1 loops=178)

  • Filter: (productid = pd.productid)
  • Rows Removed by Filter: 52240
161. 2.670 2.670 ↑ 1.0 1 267

Index Scan using component_pkey on component c_1 (cost=0.14..0.16 rows=1 width=18) (actual time=0.010..0.010 rows=1 loops=267)

  • Index Cond: (mc.component = componentid)
162. 1.602 1.602 ↑ 1.0 1 267

Index Scan using pk_weightunitid on weightunit wu (cost=0.15..0.17 rows=1 width=238) (actual time=0.005..0.006 rows=1 loops=267)

  • Index Cond: (weightunitid = mc.weightunitid)
163. 0.712 2,172.846 ↑ 2.0 1 178

Unique (cost=1,839.34..1,839.35 rows=2 width=13) (actual time=12.206..12.207 rows=1 loops=178)

164. 1.246 2,172.134 ↑ 2.0 1 178

Sort (cost=1,839.34..1,839.34 rows=2 width=13) (actual time=12.203..12.203 rows=1 loops=178)

  • Sort Key: fc.code
  • Sort Method: quicksort Memory: 25kB
165. 4.865 2,170.888 ↑ 2.0 1 178

Nested Loop Left Join (cost=0.00..1,839.33 rows=2 width=13) (actual time=10.546..12.196 rows=1 loops=178)

  • Join Filter: (it_4.fabriccode = fc.fabriccodeid)
  • Rows Removed by Join Filter: 114
166. 2,164.658 2,164.658 ↑ 2.0 1 178

Seq Scan on item it_4 (cost=0.00..1,832.86 rows=2 width=9) (actual time=10.527..12.161 rows=1 loops=178)

  • Filter: (productid = pd.productid)
  • Rows Removed by Filter: 52240
167. 1.349 1.365 ↑ 1.0 105 195

Materialize (cost=0.00..3.57 rows=105 width=22) (actual time=0.001..0.007 rows=105 loops=195)

168. 0.016 0.016 ↑ 1.0 105 1

Seq Scan on fabriccode fc (cost=0.00..3.05 rows=105 width=22) (actual time=0.005..0.016 rows=105 loops=1)

169. 1.958 2,135.466 ↑ 1.0 1 178

HashAggregate (cost=1,832.87..1,832.88 rows=1 width=6) (actual time=11.996..11.997 rows=1 loops=178)

  • Group Key: item.vk
170. 2,133.508 2,133.508 ↑ 2.0 1 178

Seq Scan on item (cost=0.00..1,832.86 rows=2 width=6) (actual time=10.046..11.986 rows=1 loops=178)

  • Filter: (productid = pd.productid)
  • Rows Removed by Filter: 52240
171. 0.712 2,141.340 ↑ 2.0 1 178

Unique (cost=1,853.50..1,853.51 rows=2 width=218) (actual time=12.030..12.030 rows=1 loops=178)

172. 1.780 2,140.628 ↑ 2.0 1 178

Sort (cost=1,853.50..1,853.50 rows=2 width=218) (actual time=12.026..12.026 rows=1 loops=178)

  • Sort Key: dro.code
  • Sort Method: quicksort Memory: 25kB
173. 1.763 2,138.848 ↑ 2.0 1 178

Nested Loop Left Join (cost=0.00..1,853.49 rows=2 width=218) (actual time=10.397..12.016 rows=1 loops=178)

  • Join Filter: (it_5.dropoption = dro.dropoptionid)
  • Rows Removed by Join Filter: 7
174. 2,136.890 2,136.890 ↑ 2.0 1 178

Seq Scan on item it_5 (cost=0.00..1,832.86 rows=2 width=9) (actual time=10.387..12.005 rows=1 loops=178)

  • Filter: (productid = pd.productid)
  • Rows Removed by Filter: 52240
175. 0.190 0.195 ↑ 35.7 7 195

Materialize (cost=0.00..13.75 rows=250 width=238) (actual time=0.001..0.001 rows=7 loops=195)

176. 0.005 0.005 ↑ 35.7 7 1

Seq Scan on dropoption dro (cost=0.00..12.50 rows=250 width=238) (actual time=0.004..0.005 rows=7 loops=1)

177. 1.780 1.780 ↓ 0.0 0 178

Index Only Scan using pk_productstatususer on productstatususer psu (cost=0.28..8.30 rows=1 width=9) (actual time=0.010..0.010 rows=0 loops=178)

  • Index Cond: (productid = pd.productid)
  • Heap Fetches: 0
Planning time : 37.568 ms
Execution time : 116,525.362 ms