explain.depesz.com

PostgreSQL's explain analyze made readable

Result: IRtJ

Settings
# exclusive inclusive rows x rows loops node
1. 0.000 0.000 ↓ 0.0

Nested Loop (cost=114,325.74..119,676.98 rows=1 width=89) (actual rows= loops=)

  • Join Filter: (((ft2_1.codice)::text = (fm2_1.feature_member_type)::text) AND (fm.reporting_year = fm2_1.reporting_year) AND (fm.region_fk = fm2_1.region_fk) AND (fm.feature_member_version = fm2_1.feature_member_version))
2. 0.000 0.000 ↓ 0.0

Nested Loop (cost=114,325.32..119,673.72 rows=1 width=154) (actual rows= loops=)

3. 0.000 0.000 ↓ 0.0

Nested Loop (cost=114,325.19..119,672.52 rows=7 width=130) (actual rows= loops=)

  • Join Filter: ((max(fm2_4.feature_member_version)) = fm.feature_member_version)
4. 0.000 0.000 ↓ 0.0

Unique (cost=21,063.58..22,442.04 rows=1,470 width=38) (actual rows= loops=)

5. 0.000 0.000 ↓ 0.0

Sort (cost=21,063.58..21,408.20 rows=137,846 width=38) (actual rows= loops=)

  • Sort Key: fm.reporting_year, fm.region_fk, ft.codice_dataset, fm.feature_member_version DESC
6. 0.000 0.000 ↓ 0.0

Hash Join (cost=1.23..5,527.07 rows=137,846 width=38) (actual rows= loops=)

  • Hash Cond: ((fm.feature_member_type)::text = (ft.codice)::text)
7. 0.000 0.000 ↓ 0.0

Seq Scan on feature_members fm (cost=0.00..3,630.46 rows=137,846 width=18) (actual rows= loops=)

8. 0.000 0.000 ↓ 0.0

Hash (cost=1.10..1.10 rows=10 width=48) (actual rows= loops=)

9. 0.000 0.000 ↓ 0.0

Seq Scan on feature_member_types ft (cost=0.00..1.10 rows=10 width=48) (actual rows= loops=)

10. 0.000 0.000 ↓ 0.0

Materialize (cost=93,261.61..97,193.73 rows=1 width=92) (actual rows= loops=)

11. 0.000 0.000 ↓ 0.0

Nested Loop (cost=93,261.61..97,193.73 rows=1 width=92) (actual rows= loops=)

  • Join Filter: (rf.from_id = fm2.feature_member_id)
12. 0.000 0.000 ↓ 0.0

Nested Loop (cost=69,664.58..71,065.14 rows=1 width=8) (actual rows= loops=)

  • Join Filter: ((ft_1.codice_dataset)::text = (ft2.codice_dataset)::text)
13. 0.000 0.000 ↓ 0.0

Merge Join (cost=69,664.45..71,064.98 rows=1 width=36) (actual rows= loops=)

  • Merge Cond: ((fm_1.reporting_year = fm2.reporting_year) AND (fm_1.region_fk = fm2.region_fk))
  • Join Filter: (fm2.feature_member_version = fm_1.feature_member_version)
14. 0.000 0.000 ↓ 0.0

Unique (cost=21,063.58..22,442.04 rows=1,470 width=38) (actual rows= loops=)

15. 0.000 0.000 ↓ 0.0

Sort (cost=21,063.58..21,408.20 rows=137,846 width=38) (actual rows= loops=)

  • Sort Key: fm_1.reporting_year, fm_1.region_fk, ft_1.codice_dataset, fm_1.feature_member_version DESC
16. 0.000 0.000 ↓ 0.0

Hash Join (cost=1.23..5,527.07 rows=137,846 width=38) (actual rows= loops=)

  • Hash Cond: ((fm_1.feature_member_type)::text = (ft_1.codice)::text)
17. 0.000 0.000 ↓ 0.0

Seq Scan on feature_members fm_1 (cost=0.00..3,630.46 rows=137,846 width=18) (actual rows= loops=)

18. 0.000 0.000 ↓ 0.0

Hash (cost=1.10..1.10 rows=10 width=48) (actual rows= loops=)

19. 0.000 0.000 ↓ 0.0

Seq Scan on feature_member_types ft_1 (cost=0.00..1.10 rows=10 width=48) (actual rows= loops=)

20. 0.000 0.000 ↓ 0.0

Sort (cost=48,600.86..48,600.87 rows=1 width=34) (actual rows= loops=)

  • Sort Key: fm2.reporting_year, fm2.region_fk
21. 0.000 0.000 ↓ 0.0

Merge Join (cost=48,600.82..48,600.85 rows=1 width=34) (actual rows= loops=)

  • Merge Cond: ((fm2.feature_member_version = (max(fm2_3.feature_member_version))) AND (fm2.local_id = fm2_3.local_id))
22. 0.000 0.000 ↓ 0.0

Sort (cost=25,012.11..25,012.12 rows=1 width=143) (actual rows= loops=)

  • Sort Key: fm2.feature_member_version, fm2.local_id
23. 0.000 0.000 ↓ 0.0

Nested Loop (cost=24,915.30..25,012.10 rows=1 width=143) (actual rows= loops=)

  • Join Filter: (q1.local_id = fm2.local_id)
24. 0.000 0.000 ↓ 0.0

Hash Join (cost=24,914.88..25,006.95 rows=1 width=82) (actual rows= loops=)

  • Hash Cond: (f.local_id = q1.local_id)
25. 0.000 0.000 ↓ 0.0

HashAggregate (cost=1,326.17..1,364.93 rows=3,876 width=39) (actual rows= loops=)

  • Group Key: f.local_id
26. 0.000 0.000 ↓ 0.0

Merge Join (cost=887.09..1,316.48 rows=3,876 width=39) (actual rows= loops=)

  • Merge Cond: (f.feature_member_id = s.feature_member_fk)
27. 0.000 0.000 ↓ 0.0

Index Scan using feature_members_feature_member_id_idx on feature_members f (cost=0.42..8,750.68 rows=137,846 width=43) (actual rows= loops=)

28. 0.000 0.000 ↓ 0.0

Sort (cost=548.46..558.18 rows=3,887 width=4) (actual rows= loops=)

  • Sort Key: s.feature_member_fk
29. 0.000 0.000 ↓ 0.0

Seq Scan on sampling_points s (cost=0.00..316.71 rows=3,887 width=4) (actual rows= loops=)

  • Filter: sampling_point_used_aqd
30. 0.000 0.000 ↓ 0.0

Hash (cost=23,588.70..23,588.70 rows=1 width=43) (actual rows= loops=)

31. 0.000 0.000 ↓ 0.0

Subquery Scan on q1 (cost=23,588.68..23,588.70 rows=1 width=43) (actual rows= loops=)

32. 0.000 0.000 ↓ 0.0

HashAggregate (cost=23,588.68..23,588.69 rows=1 width=43) (actual rows= loops=)

  • Group Key: fm2_2.local_id
33. 0.000 0.000 ↓ 0.0

Nested Loop (cost=21,203.03..23,588.68 rows=1 width=43) (actual rows= loops=)

34. 0.000 0.000 ↓ 0.0

Nested Loop (cost=21,063.58..22,479.92 rows=7 width=38) (actual rows= loops=)

  • Join Filter: ((ft2_2.codice_dataset)::text = (ft_2.codice_dataset)::text)
35. 0.000 0.000 ↓ 0.0

Unique (cost=21,063.58..22,442.04 rows=1,470 width=38) (actual rows= loops=)

36. 0.000 0.000 ↓ 0.0

Sort (cost=21,063.58..21,408.20 rows=137,846 width=38) (actual rows= loops=)

  • Sort Key: fm_2.reporting_year, fm_2.region_fk, ft_2.codice_dataset, fm_2.feature_member_version DESC
37. 0.000 0.000 ↓ 0.0

Hash Join (cost=1.23..5,527.07 rows=137,846 width=38) (actual rows= loops=)

  • Hash Cond: ((fm_2.feature_member_type)::text = (ft_2.codice)::text)
38. 0.000 0.000 ↓ 0.0

Seq Scan on feature_members fm_2 (cost=0.00..3,630.46 rows=137,846 width=18) (actual rows= loops=)

39. 0.000 0.000 ↓ 0.0

Hash (cost=1.10..1.10 rows=10 width=48) (actual rows= loops=)

40. 0.000 0.000 ↓ 0.0

Seq Scan on feature_member_types ft_2 (cost=0.00..1.10 rows=10 width=48) (actual rows= loops=)

41. 0.000 0.000 ↓ 0.0

Materialize (cost=0.00..1.13 rows=1 width=48) (actual rows= loops=)

42. 0.000 0.000 ↓ 0.0

Seq Scan on feature_member_types ft2_2 (cost=0.00..1.12 rows=1 width=48) (actual rows= loops=)

  • Filter: ((codice)::text = 'SPO'::text)
43. 0.000 0.000 ↓ 0.0

Bitmap Heap Scan on feature_members fm2_2 (cost=139.44..158.20 rows=19 width=57) (actual rows= loops=)

  • Recheck Cond: ((region_fk = fm_2.region_fk) AND (feature_member_version = fm_2.feature_member_version))
  • Filter: (((feature_member_type)::text = 'SPO'::text) AND (fm_2.reporting_year = reporting_year))
44. 0.000 0.000 ↓ 0.0

BitmapAnd (cost=139.44..139.44 rows=547 width=0) (actual rows= loops=)

45. 0.000 0.000 ↓ 0.0

Bitmap Index Scan on feature_members_region_fk_idx (cost=0.00..51.14 rows=6,564 width=0) (actual rows= loops=)

  • Index Cond: (region_fk = fm_2.region_fk)
46. 0.000 0.000 ↓ 0.0

Bitmap Index Scan on feature_members_feature_member_version_idx (cost=0.00..88.04 rows=11,487 width=0) (actual rows= loops=)

  • Index Cond: (feature_member_version = fm_2.feature_member_version)
47. 0.000 0.000 ↓ 0.0

Index Scan using feature_members_local_id_idx on feature_members fm2 (cost=0.42..5.10 rows=4 width=61) (actual rows= loops=)

  • Index Cond: (local_id = f.local_id)
  • Filter: (local_id <> ALL ('{SPO.IT0502A_9_chemi_2005-12-08_00:00:00,SPO.IT1678A_38_chemi_2004-01-13_00:00:00,SPO.IT1678A_9_chemi_2004-01-13_00:00:00,SPO.IT2279A_38_chemi_2018-01-01_00:00:00,SPO.IT2279A_8_chemi_2018-06-06_00:00:00,SPO.IT2279A_9_chemi_2018-06-06_00:00:00,SPO.IT0502A_38_chemi_2005-12-08_00:00:00,SPO.IT0507A_9_chemi_2004-01-01_00:00:00,SPO.IT1800A_21_other_2006-02-01_00:00:00,SPO.IT1802A_21_other_2006-02-01_00:00:00,SPO.IT1802A_482_other_2006-02-01_00:00:00,SPO.IT1800A_482_other_2006-02-01_00:00:00,SPO.IT1537A_38_chemi_2010-06-15_00:00:00,SPO.IT1137A_10_IR-GFC_1997-09-02_00:00:00,SPO.IT1137A_38_chemi_1997-09-02_00:00:00,SPO.IT1137A_5_BETA_2002-02-19_00:00:00,SPO.IT1137A_8_chemi_1997-09-02_00:00:00,SPO.IT2280A_38_chemi_2017-05-24_00:00:00,SPO.IT2280A_6001_BETA_2017-05-23_00:00:00,SPO.IT1905A_5_gravi_2009-12-30_00:00:00,SPO.IT1041A_38_chemi_1996-03-01_00:00:00,SPO.IT1487A_9_chemi_2015-09-01_00:00:00,SPO.IT1488A_38_chemi_1996-03-02_00:00:00,SPO.IT1488A_9_chemi_2015-08-19_19:00:00,SPO.IT1496A_38_chemi_1994-01-02_00:00:00,SPO.IT1041A_9_chemi_1996-03-01_00:00:00,SPO.IT1488A_38_chemi_2015-08-19_19:00:00,SPO.IT0934A_38_chemi_1994-01-02_00:00:00,SPO.IT0934A_9_chemi_1994-01-02_00:00:00,SPO.IT1486A_38_chemi_1994-07-02_00:00:00,SPO.IT2198A_1_UV-FL_2014-05-23_00:00:00,SPO.IT2197A_1_UV-FL_2014-05-26_00:00:00,SPO.IT1486A_9_chemi_1994-07-02_00:00:00,SPO.IT2199A_1_UV-FL_2014-02-14_00:00:00,SPO.IT2196A_1_UV-FL_2014-07-17_00:00:00,SPO.IT1041A_5018_GF-AAS_1996-03-01_00:00:00,SPO.IT1041A_5015_GF-AAS_1996-03-01_00:00:00,SPO.IT0898A_9_chemi_1993-06-02_00:00:00,SPO.IT1496A_9_chemi_1994-01-02_00:00:00,SPO.IT1041A_5014_GF-AAS_1996-03-01_00:00:00,SPO.IT0898A_38_chemi_1993-06-02_00:00:00,SPO.IT1041A_12_GF-AAS_1996-03-01_00:00:00,SPO.IT1041A_5029_HPLC-FLD_1996-03-01_00:00:00,SPO.IT2277A_1_UV-FL_2017-09-01_00:00:00,SPO.IT2277A_10_NDIR_2017-09-01_00:00:00,SPO.IT1491A_6001_BETA_2016-03-09_20:00:00,SPO.IT1476A_4_BETA_2004-01-01_00:00:00,SPO.IT1477A_4_BETA_2004-01-09_00:00:00,SPO.IT1476A_38_chemi_2001-12-12_00:00:00,SPO.IT1477A_38_chemi_2001-12-12_00:00:00,SPO.IT1475A_4_BETA_2005-01-01_00:00:00,SPO.IT1882A_5_BETA_2008-01-01_00:00:00,SPO.IT0441A_8_chemi_2015-01-01_00:00:00,SPO.IT0441A_9_chemi_2015-01-01_00:00:00,SPO.IT0441A_7_UV-P_2015-01-01_00:00:00,SPO.IT0441A_38_chemi_2015-01-01_00:00:00,SPO.IT0441A_10_NDIR_2015-01-01_00:00:00,SPO.IT0441A_5_BETA_2015-01-01_00:00:00,SPO.IT1343A_38_chemi_1994-01-02_00:00:00,SPO.IT1343A_9_chemi_1994-01-02_00:00:00,SPO.IT2183A_9_chemi_2014-09-26_00:00:00,SPO.IT1553A_38_chemi_2000-12-01_00:00:00,SPO.IT1553A_9_chemi_2000-12-01_00:00:00,SPO.IT1654A_9_chemi_1997-11-04_00:00:00,SPO.IT2183A_38_chemi_2014-09-26_00:00:00,SPO.IT1592A_38_chemi_2016-01-14_00:00:00,SPO.IT2154A_9_chemi_2014-04-10_00:00:00,SPO.IT2154A_38_chemi_2014-04-10_00:00:00,SPO.IT2153A_9_chemi_2014-01-01_00:00:00,SPO.IT0860A_9_chemi_1992-12-18_00:00:00,SPO.IT0862A_9_chemi_1992-12-22_00:00:00,SPO.IT2153A_38_chemi_2014-01-01_00:00:00,SPO.IT0860A_38_chemi_1992-12-18_00:00:00,SPO.IT1110A_38_chemi_1998-03-10_00:00:00,SPO.IT0063A_9_chemi_2000-04-16_00:00:00,SPO.IT1560A_38_chemi_2002-01-15_00:00:00,SPO.IT0862A_38_chemi_1992-12-22_00:00:00,SPO.IT1557A_9_chemi_2002-01-01_00:00:00,SPO.IT1557A_38_chemi_2002-01-01_00:00:00,SPO.IT1571A_9_chemi_2000-12-01_00:00:00,SPO.IT1654A_38_chemi_1997-11-04_00:00:00,SPO.IT0063A_38_chemi_2000-04-16_00:00:00,SPO.IT1592A_9_chemi_2016-01-14_00:00:00,SPO.IT1110A_9_chemi_1998-03-10_00:00:00,SPO.IT2264A_8_chemi_2012-05-06_00:00:00,SPO.IT2255A_8_chemi_2018-06-04_00:00:00,SPO.IT2257A_5_BETA_2016-05-26_00:00:00,SPO.IT0524A_38_chemi_1984-12-10_00:00:00,SPO.IT0524A_8_chemi_1984-12-10_00:00:00,SPO.IT0524A_7_UV-P_2006-01-01_00:00:00,SPO.IT0524A_9_chemi_1984-12-10_00:00:00,SPO.IT0623A_7_UV-P_1989-10-01_00:00:00,SPO.IT0524A_1_UV-FL_1983-01-01_00:00:00,SPO.IT2162A_5_BETA_2017-01-01_00:00:00,SPO.IT0852A_9_chemi_1995-01-01_00:00:00,SPO.IT0858A_9_chemi_1993-08-01_00:00:00,SPO.IT0858A_431_other_2008-01-01_00:00:00,SPO.IT0858A_482_other_2008-01-01_00:00:00,SPO.IT0858A_81_other_2012-01-01_00:00:00,SPO.IT0858A_21_other_1998-05-05_00:00:00,SPO.IT0858A_80_other_2012-01-01_00:00:00,SPO.IT1465A_7_UV-P_1997-10-01_00:00:00,SPO.IT1718A_5012_spectro_2013-01-01_00:00:00,SPO.IT1718A_5015_spectro_2013-01-01_00:00:00,SPO.IT1718A_5014_spectro_2013-01-01_00:00:00,SPO.IT1718A_5018_spectro_2013-01-01_00:00:00,SPO.IT0983A_21_FID_1996-06-01_00:00:00,SPO.IT0983A_78_FID_1996-06-01_00:00:00,SPO.IT1029A_431_GC-FID_2017-03-01_00:00:00,SPO.IT1159A_431_GC-FID_2017-03-01_00:00:00,SPO.IT1587A_10_IR-GFC_2003-09-17_00:00:00,SPO.IT1393A_7_UV-P_1999-09-01_00:00:00}'::text[]))
48. 0.000 0.000 ↓ 0.0

Sort (cost=23,588.71..23,588.72 rows=1 width=47) (actual rows= loops=)

  • Sort Key: (max(fm2_3.feature_member_version)), fm2_3.local_id
49. 0.000 0.000 ↓ 0.0

HashAggregate (cost=23,588.68..23,588.69 rows=1 width=47) (actual rows= loops=)

  • Group Key: fm2_3.local_id
50. 0.000 0.000 ↓ 0.0

Nested Loop (cost=21,203.03..23,588.68 rows=1 width=47) (actual rows= loops=)

51. 0.000 0.000 ↓ 0.0

Nested Loop (cost=21,063.58..22,479.92 rows=7 width=38) (actual rows= loops=)

  • Join Filter: ((ft2_3.codice_dataset)::text = (ft_3.codice_dataset)::text)
52. 0.000 0.000 ↓ 0.0

Unique (cost=21,063.58..22,442.04 rows=1,470 width=38) (actual rows= loops=)

53. 0.000 0.000 ↓ 0.0

Sort (cost=21,063.58..21,408.20 rows=137,846 width=38) (actual rows= loops=)

  • Sort Key: fm_3.reporting_year, fm_3.region_fk, ft_3.codice_dataset, fm_3.feature_member_version DESC
54. 0.000 0.000 ↓ 0.0

Hash Join (cost=1.23..5,527.07 rows=137,846 width=38) (actual rows= loops=)

  • Hash Cond: ((fm_3.feature_member_type)::text = (ft_3.codice)::text)
55. 0.000 0.000 ↓ 0.0

Seq Scan on feature_members fm_3 (cost=0.00..3,630.46 rows=137,846 width=18) (actual rows= loops=)

56. 0.000 0.000 ↓ 0.0

Hash (cost=1.10..1.10 rows=10 width=48) (actual rows= loops=)

57. 0.000 0.000 ↓ 0.0

Seq Scan on feature_member_types ft_3 (cost=0.00..1.10 rows=10 width=48) (actual rows= loops=)

58. 0.000 0.000 ↓ 0.0

Materialize (cost=0.00..1.13 rows=1 width=48) (actual rows= loops=)

59. 0.000 0.000 ↓ 0.0

Seq Scan on feature_member_types ft2_3 (cost=0.00..1.12 rows=1 width=48) (actual rows= loops=)

  • Filter: ((codice)::text = 'SPO'::text)
60. 0.000 0.000 ↓ 0.0

Bitmap Heap Scan on feature_members fm2_3 (cost=139.44..158.20 rows=19 width=57) (actual rows= loops=)

  • Recheck Cond: ((region_fk = fm_3.region_fk) AND (feature_member_version = fm_3.feature_member_version))
  • Filter: (((feature_member_type)::text = 'SPO'::text) AND (fm_3.reporting_year = reporting_year))
61. 0.000 0.000 ↓ 0.0

BitmapAnd (cost=139.44..139.44 rows=547 width=0) (actual rows= loops=)

62. 0.000 0.000 ↓ 0.0

Bitmap Index Scan on feature_members_region_fk_idx (cost=0.00..51.14 rows=6,564 width=0) (actual rows= loops=)

  • Index Cond: (region_fk = fm_3.region_fk)
63. 0.000 0.000 ↓ 0.0

Bitmap Index Scan on feature_members_feature_member_version_idx (cost=0.00..88.04 rows=11,487 width=0) (actual rows= loops=)

  • Index Cond: (feature_member_version = fm_3.feature_member_version)
64. 0.000 0.000 ↓ 0.0

Index Scan using feature_member_types_codice_idx on feature_member_types ft2 (cost=0.14..0.15 rows=1 width=48) (actual rows= loops=)

  • Index Cond: ((codice)::text = (fm2.feature_member_type)::text)
65. 0.000 0.000 ↓ 0.0

Hash Join (cost=23,597.02..26,128.53 rows=4 width=92) (actual rows= loops=)

  • Hash Cond: (rf.to_id = spo.feature_member_fk)
66. 0.000 0.000 ↓ 0.0

Seq Scan on resolve_feature_member rf (cost=0.00..2,009.34 rows=139,234 width=8) (actual rows= loops=)

67. 0.000 0.000 ↓ 0.0

Hash (cost=23,597.01..23,597.01 rows=1 width=92) (actual rows= loops=)

68. 0.000 0.000 ↓ 0.0

Nested Loop (cost=23,588.96..23,597.01 rows=1 width=92) (actual rows= loops=)

69. 0.000 0.000 ↓ 0.0

HashAggregate (cost=23,588.68..23,588.69 rows=1 width=47) (actual rows= loops=)

  • Group Key: fm2_4.local_id
70. 0.000 0.000 ↓ 0.0

Nested Loop (cost=21,203.03..23,588.68 rows=1 width=47) (actual rows= loops=)

71. 0.000 0.000 ↓ 0.0

Nested Loop (cost=21,063.58..22,479.92 rows=7 width=38) (actual rows= loops=)

  • Join Filter: ((ft2_4.codice_dataset)::text = (ft_4.codice_dataset)::text)
72. 0.000 0.000 ↓ 0.0

Unique (cost=21,063.58..22,442.04 rows=1,470 width=38) (actual rows= loops=)

73. 0.000 0.000 ↓ 0.0

Sort (cost=21,063.58..21,408.20 rows=137,846 width=38) (actual rows= loops=)

  • Sort Key: fm_4.reporting_year, fm_4.region_fk, ft_4.codice_dataset, fm_4.feature_member_version DESC
74. 0.000 0.000 ↓ 0.0

Hash Join (cost=1.23..5,527.07 rows=137,846 width=38) (actual rows= loops=)

  • Hash Cond: ((fm_4.feature_member_type)::text = (ft_4.codice)::text)
75. 0.000 0.000 ↓ 0.0

Seq Scan on feature_members fm_4 (cost=0.00..3,630.46 rows=137,846 width=18) (actual rows= loops=)

76. 0.000 0.000 ↓ 0.0

Hash (cost=1.10..1.10 rows=10 width=48) (actual rows= loops=)

77. 0.000 0.000 ↓ 0.0

Seq Scan on feature_member_types ft_4 (cost=0.00..1.10 rows=10 width=48) (actual rows= loops=)

78. 0.000 0.000 ↓ 0.0

Materialize (cost=0.00..1.13 rows=1 width=48) (actual rows= loops=)

79. 0.000 0.000 ↓ 0.0

Seq Scan on feature_member_types ft2_4 (cost=0.00..1.12 rows=1 width=48) (actual rows= loops=)

  • Filter: ((codice)::text = 'SAM'::text)
80. 0.000 0.000 ↓ 0.0

Bitmap Heap Scan on feature_members fm2_4 (cost=139.44..158.20 rows=19 width=57) (actual rows= loops=)

  • Recheck Cond: ((region_fk = fm_4.region_fk) AND (feature_member_version = fm_4.feature_member_version))
  • Filter: (((feature_member_type)::text = 'SAM'::text) AND (fm_4.reporting_year = reporting_year))
81. 0.000 0.000 ↓ 0.0

BitmapAnd (cost=139.44..139.44 rows=547 width=0) (actual rows= loops=)

82. 0.000 0.000 ↓ 0.0

Bitmap Index Scan on feature_members_region_fk_idx (cost=0.00..51.14 rows=6,564 width=0) (actual rows= loops=)

  • Index Cond: (region_fk = fm_4.region_fk)
83. 0.000 0.000 ↓ 0.0

Bitmap Index Scan on feature_members_feature_member_version_idx (cost=0.00..88.04 rows=11,487 width=0) (actual rows= loops=)

  • Index Cond: (feature_member_version = fm_4.feature_member_version)
84. 0.000 0.000 ↓ 0.0

Index Scan using sampling_points_oc_feature_of_interest_sample_local_id_idx on sampling_points spo (cost=0.28..8.30 rows=1 width=45) (actual rows= loops=)

  • Index Cond: (oc_feature_of_interest_sample_local_id = fm2_4.local_id)
85. 0.000 0.000 ↓ 0.0

Index Scan using feature_member_types_codice_dataset_idx on feature_member_types ft2_1 (cost=0.14..0.16 rows=1 width=48) (actual rows= loops=)

  • Index Cond: ((codice_dataset)::text = (ft.codice_dataset)::text)
86. 0.000 0.000 ↓ 0.0

Index Scan using feature_members_local_id_idx on feature_members fm2_1 (cost=0.42..3.18 rows=4 width=61) (actual rows= loops=)

  • Index Cond: (local_id = spo.oc_feature_of_interest_sample_local_id)