explain.depesz.com

PostgreSQL's explain analyze made readable

Result: sADZ

Settings
# exclusive inclusive rows x rows loops node
1. 0.620 6.924 ↓ 6.9 208 1

Sort (cost=742.78..742.86 rows=30 width=1,127) (actual time=6.916..6.924 rows=208 loops=1)

  • Sort Key: pat.id
  • Sort Method: quicksort Memory: 234kB
2. 0.639 6.304 ↓ 6.9 208 1

HashAggregate (cost=741.75..742.05 rows=30 width=1,127) (actual time=6.209..6.304 rows=208 loops=1)

3. 0.011 5.665 ↓ 6.9 208 1

Append (cost=27.53..736.50 rows=30 width=1,127) (actual time=0.552..5.665 rows=208 loops=1)

4. 0.068 2.253 ↓ 3.3 63 1

Nested Loop Left Join (cost=27.53..346.61 rows=19 width=1,025) (actual time=0.552..2.253 rows=63 loops=1)

  • Join Filter: ((tc.id)::text = (mc.triage_category_id)::text)
  • Rows Removed by Join Filter: 315
5. 0.021 2.185 ↓ 3.3 63 1

Hash Left Join (cost=27.53..344.12 rows=19 width=873) (actual time=0.543..2.185 rows=63 loops=1)

  • Hash Cond: ((ac.id)::text = (wacl.attendance_chain_key)::text)
  • Filter: ((wacl.* IS NULL) OR (NOT wb.deleted))
6. 0.007 2.115 ↓ 1.7 63 1

Nested Loop Left Join (cost=24.52..340.96 rows=38 width=820) (actual time=0.489..2.115 rows=63 loops=1)

7. 0.009 1.910 ↓ 1.3 33 1

Nested Loop Left Join (cost=24.23..328.59 rows=26 width=778) (actual time=0.482..1.910 rows=33 loops=1)

8. 0.000 1.811 ↑ 1.0 18 1

Nested Loop Left Join (cost=23.95..320.03 rows=18 width=736) (actual time=0.473..1.811 rows=18 loops=1)

9. 0.011 1.723 ↑ 1.0 18 1

Nested Loop Left Join (cost=23.67..312.88 rows=18 width=767) (actual time=0.465..1.723 rows=18 loops=1)

10. 0.067 1.640 ↑ 1.0 18 1

Nested Loop Left Join (cost=23.39..295.11 rows=18 width=760) (actual time=0.457..1.640 rows=18 loops=1)

11. 0.014 1.483 ↑ 1.0 18 1

Nested Loop Left Join (cost=23.11..282.66 rows=18 width=753) (actual time=0.385..1.483 rows=18 loops=1)

12. 0.011 1.397 ↑ 1.0 18 1

Nested Loop Left Join (cost=22.96..279.25 rows=18 width=733) (actual time=0.359..1.397 rows=18 loops=1)

13. 0.000 1.260 ↑ 1.0 18 1

Nested Loop Left Join (cost=22.69..267.50 rows=18 width=665) (actual time=0.329..1.260 rows=18 loops=1)

14. 0.016 1.243 ↑ 1.0 18 1

Nested Loop Left Join (cost=22.40..180.83 rows=18 width=612) (actual time=0.328..1.243 rows=18 loops=1)

15. 0.013 1.227 ↑ 1.0 18 1

Nested Loop Left Join (cost=22.13..175.02 rows=18 width=561) (actual time=0.326..1.227 rows=18 loops=1)

16. 0.001 1.142 ↑ 1.0 18 1

Nested Loop Left Join (cost=21.86..168.06 rows=18 width=561) (actual time=0.318..1.142 rows=18 loops=1)

17. 0.017 1.051 ↑ 1.0 18 1

Nested Loop (cost=21.58..159.90 rows=18 width=473) (actual time=0.310..1.051 rows=18 loops=1)

18. 0.023 0.944 ↑ 1.0 18 1

Nested Loop (cost=21.30..152.39 rows=18 width=436) (actual time=0.301..0.944 rows=18 loops=1)

19. 0.126 0.781 ↓ 1.1 28 1

Hash Join (cost=21.02..143.02 rows=25 width=436) (actual time=0.222..0.781 rows=28 loops=1)

  • Hash Cond: ((mc.attendant_organization_id)::text = (mu.id)::text)
20. 0.557 0.557 ↑ 1.0 1,399 1

Seq Scan on medical_cases mc (cost=0.00..116.40 rows=1,425 width=396) (actual time=0.008..0.557 rows=1,399 loops=1)

  • Filter: ((NOT deleted) AND (((status)::text = ANY ('{ACTIVE,DISCHARGED}'::text[])) OR (((status)::text = 'CLOSED'::text) AND (close_date >= '2019-01-09 00:00:00'::timestamp without time zone) AND (close_date <= '2019-01-10 00:00:00'::timestamp without time zone))))
  • Rows Removed by Filter: 256
21. 0.003 0.098 ↑ 1.2 6 1

Hash (cost=20.94..20.94 rows=7 width=77) (actual time=0.098..0.098 rows=6 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 1kB
22. 0.095 0.095 ↑ 1.2 6 1

Seq Scan on medical_units mu (cost=0.00..20.94 rows=7 width=77) (actual time=0.006..0.095 rows=6 loops=1)

  • Filter: ((special_medical_unit_type)::text = 'EMERGENCY'::text)
  • Rows Removed by Filter: 353
23. 0.140 0.140 ↑ 1.0 1 28

Index Only Scan using outpatient_medical_cases_pkey on outpatient_medical_cases omc (cost=0.28..0.36 rows=1 width=37) (actual time=0.005..0.005 rows=1 loops=28)

  • Index Cond: (id = (mc.id)::text)
  • Heap Fetches: 18
24. 0.090 0.090 ↑ 1.0 1 18

Index Scan using attendance_chain_pk on attendance_chain ac (cost=0.28..0.41 rows=1 width=74) (actual time=0.005..0.005 rows=1 loops=18)

  • Index Cond: ((id)::text = (mc.attendance_chain_id)::text)
25. 0.090 0.090 ↑ 1.0 1 18

Index Scan using financial_cases_pk on financial_cases fc (cost=0.28..0.44 rows=1 width=125) (actual time=0.005..0.005 rows=1 loops=18)

  • Index Cond: ((mc.financial_case_id)::text = (id)::text)
26. 0.072 0.072 ↑ 1.0 1 18

Index Scan using outpatient_financial_cases_pk on outpatient_financial_cases ofc (cost=0.28..0.38 rows=1 width=74) (actual time=0.004..0.004 rows=1 loops=18)

  • Index Cond: ((id)::text = (fc.id)::text)
27. 0.000 0.000 ↓ 0.0 0 18

Index Scan using referral_notes_pkey on referral_notes refn (cost=0.27..0.31 rows=1 width=125) (actual time=0.000..0.000 rows=0 loops=18)

  • Index Cond: ((mc.referral_note_id)::text = (id)::text)
28. 0.018 0.018 ↓ 0.0 0 18

Index Scan using diagnoses_pkey on diagnoses diag (cost=0.29..4.80 rows=1 width=90) (actual time=0.001..0.001 rows=0 loops=18)

  • Index Cond: ((refn.sender_diagnosis_id)::text = (id)::text)
29. 0.126 0.126 ↑ 1.0 1 18

Index Scan using patients_pkey on patients pat (cost=0.28..0.64 rows=1 width=105) (actual time=0.007..0.007 rows=1 loops=18)

  • Index Cond: ((id)::text = (mc.patient_id)::text)
30. 0.072 0.072 ↑ 1.0 1 18

Index Scan using employees_pkey on employees emp (cost=0.14..0.18 rows=1 width=57) (actual time=0.004..0.004 rows=1 loops=18)

  • Index Cond: ((id)::text = (mc.attendant_employee_id)::text)
31. 0.090 0.090 ↑ 1.0 1 18

Index Scan using dictionary_items_pkey on dictionary_items payment_cat (cost=0.28..0.68 rows=1 width=44) (actual time=0.005..0.005 rows=1 loops=18)

  • Index Cond: ((id)::text = (fc.payment_category_id)::text)
32. 0.072 0.072 ↑ 1.0 1 18

Index Scan using dictionary_items_pkey on dictionary_items attendance_type (cost=0.28..0.98 rows=1 width=44) (actual time=0.004..0.004 rows=1 loops=18)

  • Index Cond: ((id)::text = (ofc.attendance_type_id)::text)
33. 0.090 0.090 ↑ 1.0 1 18

Index Scan using ojote_result_pk on ojote_result ojote_res (cost=0.28..0.39 rows=1 width=43) (actual time=0.004..0.005 rows=1 loops=18)

  • Index Cond: ((id)::text = (fc.ojote_result_id)::text)
34. 0.090 0.090 ↓ 2.0 2 18

Index Scan using dictionary_translations_dictionary_item_id_language_key on dictionary_translations payment_cat_tr (cost=0.28..0.47 rows=1 width=79) (actual time=0.004..0.005 rows=2 loops=18)

  • Index Cond: ((payment_cat.id)::text = (dictionary_item_id)::text)
35. 0.198 0.198 ↓ 2.0 2 33

Index Scan using dictionary_translations_dictionary_item_id_language_key on dictionary_translations attendance_type_tr (cost=0.28..0.47 rows=1 width=79) (actual time=0.005..0.006 rows=2 loops=33)

  • Index Cond: ((attendance_type.id)::text = (dictionary_item_id)::text)
36. 0.007 0.049 ↓ 1.6 28 1

Hash (cost=2.80..2.80 rows=17 width=483) (actual time=0.049..0.049 rows=28 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 7kB
37. 0.011 0.042 ↓ 1.6 28 1

Hash Right Join (cost=1.38..2.80 rows=17 width=483) (actual time=0.033..0.042 rows=28 loops=1)

  • Hash Cond: ((wb.treatment_id)::text = (wacl.treatment_id)::text)
38. 0.003 0.003 ↑ 1.0 18 1

Seq Scan on wristbands wb (cost=0.00..1.18 rows=18 width=181) (actual time=0.001..0.003 rows=18 loops=1)

39. 0.022 0.028 ↑ 1.0 17 1

Hash (cost=1.17..1.17 rows=17 width=482) (actual time=0.028..0.028 rows=17 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 5kB
40. 0.006 0.006 ↑ 1.0 17 1

Seq Scan on wristband_attendance_chain_links wacl (cost=0.00..1.17 rows=17 width=482) (actual time=0.004..0.006 rows=17 loops=1)

41. 0.000 0.000 ↑ 1.0 5 63

Materialize (cost=0.00..1.08 rows=5 width=332) (actual time=0.000..0.000 rows=5 loops=63)

42. 0.002 0.002 ↑ 1.0 5 1

Seq Scan on triage_categories tc (cost=0.00..1.05 rows=5 width=332) (actual time=0.002..0.002 rows=5 loops=1)

43. 0.048 3.401 ↓ 13.2 145 1

Hash Left Join (cost=362.05..389.59 rows=11 width=1,303) (actual time=2.482..3.401 rows=145 loops=1)

  • Hash Cond: ((wacl_1.treatment_id)::text = (wb_1.treatment_id)::text)
  • Filter: ((wacl_1.* IS NULL) OR (NOT wb_1.deleted))
44. 0.021 3.345 ↓ 6.6 145 1

Nested Loop Left Join (cost=360.65..387.88 rows=22 width=1,605) (actual time=2.469..3.345 rows=145 loops=1)

45. 0.026 2.886 ↓ 4.9 73 1

Hash Left Join (cost=360.36..380.75 rows=15 width=1,563) (actual time=2.457..2.886 rows=73 loops=1)

  • Hash Cond: ((ac_1.id)::text = (wacl_1.attendance_chain_key)::text)
46. 0.030 2.802 ↓ 4.9 73 1

Hash Left Join (cost=358.98..379.30 rows=15 width=1,208) (actual time=2.394..2.802 rows=73 loops=1)

  • Hash Cond: ((da.diet_id)::text = (diet.id)::text)
47. 0.065 2.768 ↓ 4.9 73 1

Nested Loop Left Join (cost=357.85..378.06 rows=15 width=1,160) (actual time=2.382..2.768 rows=73 loops=1)

48. 0.041 2.411 ↓ 4.9 73 1

Hash Right Join (cost=357.57..372.09 rows=15 width=1,191) (actual time=2.374..2.411 rows=73 loops=1)

  • Hash Cond: ((ifre.medical_case_id)::text = (imc.id)::text)
  • Filter: ((ifre.* IS NULL) OR ((ifre.* IS NOT NULL) AND ((mc_1.status)::text = 'ACTIVE'::text)))
49. 0.000 0.000 ↓ 0.0 0 1

Seq Scan on inpat_fin_rep_errors ifre (cost=0.00..13.20 rows=320 width=334) (actual time=0.000..0.000 rows=0 loops=1)

50. 0.073 2.370 ↓ 3.5 73 1

Hash (cost=357.31..357.31 rows=21 width=1,191) (actual time=2.370..2.370 rows=73 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 61kB
51. 0.028 2.297 ↓ 3.5 73 1

Hash Left Join (cost=341.64..357.31 rows=21 width=1,191) (actual time=2.226..2.297 rows=73 loops=1)

  • Hash Cond: ((dml.diet_assignment_id)::text = (da.id)::text)
  • Filter: ((da.* IS NULL) OR da.present)
  • Rows Removed by Filter: 16
52. 0.052 2.234 ↓ 2.1 89 1

Hash Right Join (cost=340.26..355.73 rows=42 width=1,191) (actual time=2.186..2.234 rows=89 loops=1)

  • Hash Cond: ((dml.medical_case_id)::text = (imc.id)::text)
53. 0.002 0.002 ↑ 25.3 15 1

Seq Scan on dias_meca_link dml (cost=0.00..13.80 rows=380 width=180) (actual time=0.001..0.002 rows=15 loops=1)

54. 0.081 2.180 ↓ 1.7 73 1

Hash (cost=339.74..339.74 rows=42 width=1,101) (actual time=2.180..2.180 rows=73 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 61kB
55. 0.041 2.099 ↓ 1.7 73 1

Nested Loop Left Join (cost=31.91..339.74 rows=42 width=1,101) (actual time=0.360..2.099 rows=73 loops=1)

56. 0.007 1.836 ↓ 1.3 37 1

Nested Loop Left Join (cost=31.63..325.94 rows=29 width=1,059) (actual time=0.351..1.836 rows=37 loops=1)

57. 0.009 1.715 ↑ 1.1 19 1

Nested Loop Left Join (cost=31.35..316.43 rows=20 width=1,017) (actual time=0.343..1.715 rows=19 loops=1)

58. 0.011 1.706 ↓ 1.4 19 1

Nested Loop Left Join (cost=31.06..309.77 rows=14 width=975) (actual time=0.342..1.706 rows=19 loops=1)

59. 0.012 1.600 ↓ 1.4 19 1

Nested Loop Left Join (cost=30.78..293.84 rows=14 width=968) (actual time=0.334..1.600 rows=19 loops=1)

60. 0.007 1.493 ↓ 1.4 19 1

Nested Loop Left Join (cost=30.50..277.92 rows=14 width=961) (actual time=0.326..1.493 rows=19 loops=1)

61. 0.009 1.391 ↓ 1.4 19 1

Nested Loop Left Join (cost=30.22..261.99 rows=14 width=954) (actual time=0.320..1.391 rows=19 loops=1)

62. 0.004 1.382 ↓ 1.4 19 1

Nested Loop Left Join (cost=29.93..246.07 rows=14 width=947) (actual time=0.318..1.382 rows=19 loops=1)

63. 0.004 1.318 ↑ 1.0 10 1

Nested Loop Left Join (cost=29.65..241.31 rows=10 width=905) (actual time=0.310..1.318 rows=10 loops=1)

64. 0.007 1.254 ↑ 1.0 10 1

Nested Loop Left Join (cost=29.37..234.39 rows=10 width=898) (actual time=0.301..1.254 rows=10 loops=1)

65. 0.010 1.247 ↑ 1.0 10 1

Nested Loop Left Join (cost=29.08..186.24 rows=10 width=845) (actual time=0.299..1.247 rows=10 loops=1)

66. 0.005 1.237 ↑ 1.0 10 1

Nested Loop Left Join (cost=28.81..183.02 rows=10 width=794) (actual time=0.294..1.237 rows=10 loops=1)

67. 0.003 1.182 ↑ 1.0 10 1

Nested Loop Left Join (cost=28.54..179.21 rows=10 width=683) (actual time=0.284..1.182 rows=10 loops=1)

68. 0.006 1.109 ↑ 1.0 10 1

Nested Loop Left Join (cost=28.26..174.67 rows=10 width=595) (actual time=0.278..1.109 rows=10 loops=1)

69. 0.006 1.103 ↑ 1.0 10 1

Nested Loop Left Join (cost=28.13..172.87 rows=10 width=574) (actual time=0.276..1.103 rows=10 loops=1)

  • Join Filter: ((nc.nursing_room_id)::text = (nr.id)::text)
  • Rows Removed by Join Filter: 80
70. 0.006 1.087 ↑ 1.0 10 1

Hash Left Join (cost=28.13..170.57 rows=10 width=553) (actual time=0.271..1.087 rows=10 loops=1)

  • Hash Cond: ((imc.id)::text = (nc.inpatient_medical_case_id)::text)
71. 0.005 1.041 ↑ 1.0 10 1

Nested Loop Left Join (cost=22.00..164.39 rows=10 width=516) (actual time=0.228..1.041 rows=10 loops=1)

72. 0.000 1.006 ↑ 1.0 10 1

Nested Loop (cost=21.85..162.49 rows=10 width=496) (actual time=0.223..1.006 rows=10 loops=1)

73. 0.007 0.926 ↑ 1.0 10 1

Nested Loop (cost=21.58..158.32 rows=10 width=459) (actual time=0.217..0.926 rows=10 loops=1)

74. 0.018 0.849 ↑ 1.0 10 1

Nested Loop (cost=21.30..151.77 rows=10 width=391) (actual time=0.210..0.849 rows=10 loops=1)

75. 0.129 0.719 ↓ 1.1 28 1

Hash Join (cost=21.02..143.02 rows=25 width=346) (actual time=0.199..0.719 rows=28 loops=1)

  • Hash Cond: ((mc_1.attendant_organization_id)::text = (mu_1.id)::text)
76. 0.498 0.498 ↑ 1.0 1,399 1

Seq Scan on medical_cases mc_1 (cost=0.00..116.40 rows=1,425 width=306) (actual time=0.003..0.498 rows=1,399 loops=1)

  • Filter: ((NOT deleted) AND (((status)::text = ANY ('{ACTIVE,DISCHARGED}'::text[])) OR (((status)::text = 'CLOSED'::text) AND (close_date >= '2019-01-09 00:00:00'::timestamp without time zone) AND (close_date <= '2019-01-10 00:00:00'::timestamp without time zone))))
  • Rows Removed by Filter: 256
77. 0.003 0.092 ↑ 1.2 6 1

Hash (cost=20.94..20.94 rows=7 width=77) (actual time=0.092..0.092 rows=6 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 1kB
78. 0.089 0.089 ↑ 1.2 6 1

Seq Scan on medical_units mu_1 (cost=0.00..20.94 rows=7 width=77) (actual time=0.005..0.089 rows=6 loops=1)

  • Filter: ((special_medical_unit_type)::text = 'EMERGENCY'::text)
  • Rows Removed by Filter: 353
79. 0.112 0.112 ↓ 0.0 0 28

Index Scan using in_patient_medical_cases_pkey on inpatient_medical_cases imc (cost=0.28..0.34 rows=1 width=82) (actual time=0.004..0.004 rows=0 loops=28)

  • Index Cond: ((id)::text = (mc_1.id)::text)
80. 0.070 0.070 ↑ 1.0 1 10

Index Scan using patients_pkey on patients pat_1 (cost=0.28..0.65 rows=1 width=105) (actual time=0.007..0.007 rows=1 loops=10)

  • Index Cond: ((id)::text = (mc_1.patient_id)::text)
81. 0.080 0.080 ↑ 1.0 1 10

Index Scan using attendance_chain_pk on attendance_chain ac_1 (cost=0.28..0.41 rows=1 width=74) (actual time=0.007..0.008 rows=1 loops=10)

  • Index Cond: ((id)::text = (mc_1.attendance_chain_id)::text)
82. 0.030 0.030 ↑ 1.0 1 10

Index Scan using employees_pkey on employees emp_1 (cost=0.14..0.18 rows=1 width=57) (actual time=0.003..0.003 rows=1 loops=10)

  • Index Cond: ((mc_1.attendant_employee_id)::text = (id)::text)
83. 0.017 0.040 ↓ 1.0 145 1

Hash (cost=4.39..4.39 rows=139 width=74) (actual time=0.040..0.040 rows=145 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 11kB
84. 0.023 0.023 ↓ 1.0 145 1

Seq Scan on nursing_cases nc (cost=0.00..4.39 rows=139 width=74) (actual time=0.002..0.023 rows=145 loops=1)

85. 0.008 0.010 ↑ 1.0 8 10

Materialize (cost=0.00..1.12 rows=8 width=148) (actual time=0.000..0.001 rows=8 loops=10)

86. 0.002 0.002 ↑ 1.0 8 1

Seq Scan on nursing_room nr (cost=0.00..1.08 rows=8 width=148) (actual time=0.001..0.002 rows=8 loops=1)

87. 0.000 0.000 ↓ 0.0 0 10

Index Scan using rooms_pk on rooms room (cost=0.14..0.17 rows=1 width=148) (actual time=0.000..0.000 rows=0 loops=10)

  • Index Cond: ((id)::text = (imc.room_id)::text)
88. 0.070 0.070 ↑ 1.0 1 10

Index Scan using financial_cases_pk on financial_cases fc_1 (cost=0.28..0.44 rows=1 width=125) (actual time=0.007..0.007 rows=1 loops=10)

  • Index Cond: ((mc_1.financial_case_id)::text = (id)::text)
89. 0.050 0.050 ↑ 1.0 1 10

Index Scan using inpat_financial_cases_pkey on inpatient_financial_cases ifc (cost=0.28..0.37 rows=1 width=185) (actual time=0.005..0.005 rows=1 loops=10)

  • Index Cond: ((id)::text = (fc_1.id)::text)
90. 0.000 0.000 ↓ 0.0 0 10

Index Scan using referral_notes_pkey on referral_notes refn_1 (cost=0.27..0.31 rows=1 width=125) (actual time=0.000..0.000 rows=0 loops=10)

  • Index Cond: ((mc_1.referral_note_id)::text = (id)::text)
91. 0.000 0.000 ↓ 0.0 0 10

Index Scan using diagnoses_pkey on diagnoses diag_1 (cost=0.29..4.80 rows=1 width=90) (actual time=0.000..0.000 rows=0 loops=10)

  • Index Cond: ((id)::text = (refn_1.sender_diagnosis_id)::text)
92. 0.060 0.060 ↑ 1.0 1 10

Index Scan using dictionary_items_pkey on dictionary_items payment_cat_1 (cost=0.28..0.68 rows=1 width=44) (actual time=0.006..0.006 rows=1 loops=10)

  • Index Cond: ((id)::text = (fc_1.payment_category_id)::text)
93. 0.060 0.060 ↓ 2.0 2 10

Index Scan using dictionary_translations_dictionary_item_id_language_key on dictionary_translations payment_cat_tr_1 (cost=0.28..0.47 rows=1 width=79) (actual time=0.005..0.006 rows=2 loops=10)

  • Index Cond: ((payment_cat_1.id)::text = (dictionary_item_id)::text)
94. 0.000 0.000 ↓ 0.0 0 19

Index Scan using dictionary_items_pkey on dictionary_items paymant_partial_pretence (cost=0.28..1.13 rows=1 width=44) (actual time=0.000..0.000 rows=0 loops=19)

  • Index Cond: ((id)::text = (ifc.paymant_partial_pretence_id)::text)
95. 0.095 0.095 ↑ 1.0 1 19

Index Scan using dictionary_items_pkey on dictionary_items admission_source (cost=0.28..1.13 rows=1 width=44) (actual time=0.005..0.005 rows=1 loops=19)

  • Index Cond: ((id)::text = (ifc.admission_source_id)::text)
96. 0.095 0.095 ↑ 1.0 1 19

Index Scan using dictionary_items_pkey on dictionary_items admission_type_a (cost=0.28..1.13 rows=1 width=44) (actual time=0.005..0.005 rows=1 loops=19)

  • Index Cond: ((id)::text = (ifc.addmission_type_a_id)::text)
97. 0.095 0.095 ↑ 1.0 1 19

Index Scan using dictionary_items_pkey on dictionary_items admission_type_b (cost=0.28..1.13 rows=1 width=44) (actual time=0.005..0.005 rows=1 loops=19)

  • Index Cond: ((id)::text = (ifc.addmission_type_b_id)::text)
98. 0.000 0.000 ↓ 0.0 0 19

Index Scan using dictionary_translations_dictionary_item_id_language_key on dictionary_translations paymant_partial_pretence_tr (cost=0.28..0.47 rows=1 width=79) (actual time=0.000..0.000 rows=0 loops=19)

  • Index Cond: ((paymant_partial_pretence.id)::text = (dictionary_item_id)::text)
99. 0.114 0.114 ↓ 2.0 2 19

Index Scan using dictionary_translations_dictionary_item_id_language_key on dictionary_translations admission_source_tr (cost=0.28..0.47 rows=1 width=79) (actual time=0.005..0.006 rows=2 loops=19)

  • Index Cond: ((admission_source.id)::text = (dictionary_item_id)::text)
100. 0.222 0.222 ↓ 2.0 2 37

Index Scan using dictionary_translations_dictionary_item_id_language_key on dictionary_translations admission_type_a_tr (cost=0.28..0.47 rows=1 width=79) (actual time=0.006..0.006 rows=2 loops=37)

  • Index Cond: ((admission_type_a.id)::text = (dictionary_item_id)::text)
101. 0.030 0.035 ↑ 1.0 17 1

Hash (cost=1.17..1.17 rows=17 width=1,032) (actual time=0.035..0.035 rows=17 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 6kB
102. 0.005 0.005 ↑ 1.0 17 1

Seq Scan on diet_assignments da (cost=0.00..1.17 rows=17 width=1,032) (actual time=0.003..0.005 rows=17 loops=1)

103. 0.292 0.292 ↑ 1.0 1 73

Index Scan using ojote_result_pk on ojote_result ojote_res_1 (cost=0.28..0.39 rows=1 width=43) (actual time=0.004..0.004 rows=1 loops=73)

  • Index Cond: ((id)::text = (fc_1.ojote_result_id)::text)
104. 0.001 0.004 ↑ 1.0 6 1

Hash (cost=1.06..1.06 rows=6 width=228) (actual time=0.004..0.004 rows=6 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 1kB
105. 0.003 0.003 ↑ 1.0 6 1

Seq Scan on diets diet (cost=0.00..1.06 rows=6 width=228) (actual time=0.001..0.003 rows=6 loops=1)

106. 0.004 0.058 ↑ 1.0 17 1

Hash (cost=1.17..1.17 rows=17 width=482) (actual time=0.058..0.058 rows=17 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 5kB
107. 0.054 0.054 ↑ 1.0 17 1

Seq Scan on wristband_attendance_chain_links wacl_1 (cost=0.00..1.17 rows=17 width=482) (actual time=0.052..0.054 rows=17 loops=1)

108. 0.438 0.438 ↓ 2.0 2 73

Index Scan using dictionary_translations_dictionary_item_id_language_key on dictionary_translations admission_type_b_tr (cost=0.28..0.47 rows=1 width=79) (actual time=0.005..0.006 rows=2 loops=73)

  • Index Cond: ((admission_type_b.id)::text = (dictionary_item_id)::text)
109. 0.005 0.008 ↑ 1.0 18 1

Hash (cost=1.18..1.18 rows=18 width=181) (actual time=0.008..0.008 rows=18 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 2kB
110. 0.003 0.003 ↑ 1.0 18 1

Seq Scan on wristbands wb_1 (cost=0.00..1.18 rows=18 width=181) (actual time=0.001..0.003 rows=18 loops=1)