explain.depesz.com

PostgreSQL's explain analyze made readable

Result: yTnP

Settings
# exclusive inclusive rows x rows loops node
1. 0.033 9,160.484 ↑ 1.0 24 1

Limit (cost=95,660.78..95,668.70 rows=24 width=2,611) (actual time=9,160.354..9,160.484 rows=24 loops=1)

2. 0.176 9,160.451 ↑ 112.3 24 1

Result (cost=95,660.78..96,550.46 rows=2,696 width=2,611) (actual time=9,160.353..9,160.451 rows=24 loops=1)

3. 128.923 9,160.275 ↑ 112.3 24 1

Sort (cost=95,660.78..95,667.52 rows=2,696 width=2,579) (actual time=9,160.274..9,160.275 rows=24 loops=1)

  • Sort Key: pc.associated_date DESC NULLS LAST
  • Sort Method: top-N heapsort Memory: 67kB
4. 342.696 9,031.352 ↓ 39.4 106,088 1

Hash Left Join (cost=66,395.27..95,585.49 rows=2,696 width=2,579) (actual time=6,719.727..9,031.352 rows=106,088 loops=1)

  • Hash Cond: (pc.candidate_id = tgc_1.candidate_id)
5. 54.212 8,610.917 ↓ 51.3 69,241 1

Nested Loop Left Join (cost=53,560.28..79,804.72 rows=1,351 width=2,593) (actual time=6,640.028..8,610.917 rows=69,241 loops=1)

6. 57.006 8,279.741 ↓ 256.4 69,241 1

Nested Loop Left Join (cost=53,560.28..73,952.47 rows=270 width=2,585) (actual time=6,640.021..8,279.741 rows=69,241 loops=1)

7. 57.491 8,153.494 ↓ 256.4 69,241 1

Nested Loop Left Join (cost=53,560.00..73,871.02 rows=270 width=2,591) (actual time=6,639.999..8,153.494 rows=69,241 loops=1)

8. 118.348 7,680.557 ↓ 256.4 69,241 1

Nested Loop Left Join (cost=53,538.01..57,304.92 rows=270 width=2,583) (actual time=6,639.935..7,680.557 rows=69,241 loops=1)

  • Join Filter: (offer.id = offer_approval.offer_id)
  • Rows Removed by Join Filter: 1315568
9. 438.067 7,562.209 ↓ 256.4 69,241 1

Hash Left Join (cost=53,536.58..57,142.48 rows=270 width=2,558) (actual time=6,639.495..7,562.209 rows=69,241 loops=1)

  • Hash Cond: (pc.candidate_id = tgc.candidate_id)
  • Filter: ((tgc.id IS NULL) OR (tgc.team_group_id = ANY ('{1133,1121}'::integer[])))
  • Rows Removed by Filter: 141404
10. 128.627 7,026.142 ↓ 199.5 135,095 1

Nested Loop Left Join (cost=40,341.60..41,304.60 rows=677 width=2,558) (actual time=6,541.452..7,026.142 rows=135,095 loops=1)

11. 137.304 6,897.515 ↓ 199.5 135,094 1

Nested Loop Left Join (cost=40,341.45..41,185.89 rows=677 width=2,288) (actual time=6,541.439..6,897.515 rows=135,094 loops=1)

12. 127.324 6,760.211 ↓ 199.5 135,094 1

Merge Left Join (cost=40,341.31..41,068.46 rows=677 width=2,280) (actual time=6,541.413..6,760.211 rows=135,094 loops=1)

  • Merge Cond: (pc.candidate_id = cd.candidate_id)
13. 528.693 6,476.071 ↓ 199.5 135,094 1

Sort (cost=13,779.88..13,781.57 rows=677 width=2,239) (actual time=6,396.921..6,476.071 rows=135,094 loops=1)

  • Sort Key: pc.candidate_id
  • Sort Method: external merge Disk: 78408kB
14. 70.912 5,947.378 ↓ 199.5 135,094 1

Hash Left Join (cost=3,638.98..13,748.05 rows=677 width=2,239) (actual time=27.978..5,947.378 rows=135,094 loops=1)

  • Hash Cond: ((interview_1.id = ih.interview_id) AND (interview_1.interview_no = ih.interview_no))
15. 144.192 5,872.892 ↓ 199.5 135,084 1

Nested Loop Left Join (cost=3,154.13..13,238.53 rows=677 width=2,231) (actual time=24.349..5,872.892 rows=135,084 loops=1)

16. 77.862 5,593.617 ↓ 199.5 135,083 1

Hash Left Join (cost=3,153.56..12,763.30 rows=677 width=2,185) (actual time=24.339..5,593.617 rows=135,083 loops=1)

  • Hash Cond: (c.user_account_id = user_temp.id)
17. 160.136 5,515.623 ↓ 199.5 135,083 1

Nested Loop Left Join (cost=3,066.66..12,674.60 rows=677 width=2,172) (actual time=24.203..5,515.623 rows=135,083 loops=1)

18. 61.989 5,220.404 ↓ 199.5 135,083 1

Hash Left Join (cost=3,066.37..12,453.99 rows=677 width=2,140) (actual time=24.182..5,220.404 rows=135,083 loops=1)

  • Hash Cond: (offer.id = offer_fee_model.offer_id)
19. 105.744 5,158.308 ↓ 199.5 135,083 1

Nested Loop Left Join (cost=3,051.52..12,435.99 rows=677 width=2,128) (actual time=24.070..5,158.308 rows=135,083 loops=1)

20. 63.968 5,052.564 ↓ 199.5 135,083 1

Hash Left Join (cost=3,051.23..12,125.82 rows=677 width=2,072) (actual time=24.064..5,052.564 rows=135,083 loops=1)

  • Hash Cond: (offer.id = invoice.offer_id)
21. 91.030 4,987.778 ↓ 199.5 135,083 1

Nested Loop Left Join (cost=2,958.22..12,031.03 rows=677 width=2,058) (actual time=23.229..4,987.778 rows=135,083 loops=1)

22. 62.405 4,896.748 ↓ 199.5 135,083 1

Hash Left Join (cost=2,957.95..11,832.71 rows=677 width=2,041) (actual time=23.220..4,896.748 rows=135,083 loops=1)

  • Hash Cond: (pd.vertical_id = vertical.id)
23. 131.748 4,834.315 ↓ 199.5 135,083 1

Hash Left Join (cost=2,955.78..11,828.73 rows=677 width=2,023) (actual time=23.181..4,834.315 rows=135,083 loops=1)

  • Hash Cond: (pd.company_location_id = company_location.id)
24. 72.227 4,681.996 ↓ 199.5 135,083 1

Hash Left Join (cost=366.49..8,410.67 rows=677 width=1,955) (actual time=2.410..4,681.996 rows=135,083 loops=1)

  • Hash Cond: (c.candidate_source_id = candidate_source.id)
25. 58.241 4,609.745 ↓ 199.5 135,083 1

Hash Left Join (cost=364.66..8,406.86 rows=677 width=1,443) (actual time=2.382..4,609.745 rows=135,083 loops=1)

  • Hash Cond: (c.deleted_by_user_id = ua_deleted_by.id)
26. 66.205 4,551.419 ↓ 199.5 135,083 1

Hash Left Join (cost=277.76..8,318.19 rows=677 width=1,430) (actual time=2.293..4,551.419 rows=135,083 loops=1)

  • Hash Cond: (pc.created_by = ua.id)
27. 86.927 4,485.134 ↓ 199.5 135,083 1

Nested Loop Left Join (cost=190.87..8,229.50 rows=677 width=1,413) (actual time=2.210..4,485.134 rows=135,083 loops=1)

28. 70.005 4,263.124 ↓ 199.5 135,083 1

Hash Left Join (cost=190.58..8,022.80 rows=677 width=1,305) (actual time=2.204..4,263.124 rows=135,083 loops=1)

  • Hash Cond: ((cl.country_code)::text = (co.code)::text)
29. 82.908 4,193.053 ↓ 199.5 135,083 1

Nested Loop Left Join (cost=181.89..8,012.32 rows=677 width=1,297) (actual time=2.133..4,193.053 rows=135,083 loops=1)

30. 33.220 3,434.730 ↓ 199.5 135,083 1

Nested Loop Left Join (cost=181.47..7,649.10 rows=677 width=1,263) (actual time=2.107..3,434.730 rows=135,083 loops=1)

31. 52.441 3,131.344 ↓ 199.5 135,083 1

Hash Left Join (cost=181.18..7,433.70 rows=677 width=1,218) (actual time=2.087..3,131.344 rows=135,083 loops=1)

  • Hash Cond: (pc.sent_user_id = sent_user_account.id)
32. 57.716 3,078.818 ↓ 199.5 135,083 1

Hash Left Join (cost=94.28..7,345.03 rows=677 width=1,201) (actual time=1.998..3,078.818 rows=135,083 loops=1)

  • Hash Cond: (pc.shortlisted_user_id = shortlisted_user_account.id)
33. 130.321 3,020.965 ↓ 199.5 135,083 1

Nested Loop (cost=7.39..7,256.35 rows=677 width=1,184) (actual time=1.855..3,020.965 rows=135,083 loops=1)

  • Join Filter: (pc.candidate_id = c.id)
34. 26.807 1,528.994 ↓ 199.9 136,165 1

Nested Loop (cost=6.96..6,027.29 rows=681 width=994) (actual time=1.828..1,528.994 rows=136,165 loops=1)

35. 78.382 821.362 ↓ 199.9 136,165 1

Nested Loop (cost=6.54..5,621.87 rows=681 width=544) (actual time=1.803..821.362 rows=136,165 loops=1)

36. 1.087 356.190 ↓ 199.9 25,786 1

Nested Loop Left Join (cost=6.12..2,447.33 rows=129 width=147) (actual time=1.773..356.190 rows=25,786 loops=1)

37. 40.639 200.387 ↓ 199.9 25,786 1

Nested Loop Left Join (cost=5.70..1,451.22 rows=129 width=119) (actual time=1.756..200.387 rows=25,786 loops=1)

38. 29.366 30.818 ↓ 199.9 25,786 1

Bitmap Heap Scan on position_description pd (cost=5.29..435.10 rows=129 width=102) (actual time=1.719..30.818 rows=25,786 loops=1)

  • Recheck Cond: (position_category = 1)
  • Heap Blocks: exact=2895
39. 1.452 1.452 ↓ 199.9 25,786 1

Bitmap Index Scan on position_description_position_category (cost=0.00..5.25 rows=129 width=0) (actual time=1.452..1.452 rows=25,786 loops=1)

  • Index Cond: (position_category = 1)
40. 128.930 128.930 ↑ 1.0 1 25,786

Index Scan using contact__pkey on contact (cost=0.42..7.88 rows=1 width=21) (actual time=0.005..0.005 rows=1 loops=25,786)

  • Index Cond: (id = pd.contact_id)
41. 154.716 154.716 ↑ 1.0 1 25,786

Index Scan using company__pkey on company (cost=0.42..7.72 rows=1 width=32) (actual time=0.006..0.006 rows=1 loops=25,786)

  • Index Cond: (id = pd.company_id)
42. 386.790 386.790 ↑ 1.8 5 25,786

Index Scan using index_pc_pd on position_candidate pc (cost=0.42..24.52 rows=9 width=405) (actual time=0.005..0.015 rows=5 loops=25,786)

  • Index Cond: (position_description_id = pd.id)
  • Filter: ((status >= 102) AND (associated_date <= now()) AND (associated_date >= (date_trunc('year'::text, now()) - '5 years'::interval)))
43. 680.825 680.825 ↑ 1.0 1 136,165

Index Scan using candidate_extension_candidate_id__pkey on candidate_extension ce (cost=0.42..0.60 rows=1 width=450) (actual time=0.005..0.005 rows=1 loops=136,165)

  • Index Cond: (candidate_id = pc.candidate_id)
44. 1,361.650 1,361.650 ↑ 1.0 1 136,165

Index Scan using candidate_pkey on candidate c (cost=0.42..1.79 rows=1 width=198) (actual time=0.010..0.010 rows=1 loops=136,165)

  • Index Cond: (id = ce.candidate_id)
  • Filter: (deleted_timestamp IS NULL)
  • Rows Removed by Filter: 0
45. 0.036 0.137 ↓ 1.0 352 1

Hash (cost=82.51..82.51 rows=351 width=21) (actual time=0.137..0.137 rows=352 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 28kB
46. 0.101 0.101 ↓ 1.0 352 1

Seq Scan on user_account shortlisted_user_account (cost=0.00..82.51 rows=351 width=21) (actual time=0.011..0.101 rows=352 loops=1)

47. 0.035 0.085 ↓ 1.0 352 1

Hash (cost=82.51..82.51 rows=351 width=21) (actual time=0.085..0.085 rows=352 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 28kB
48. 0.050 0.050 ↓ 1.0 352 1

Seq Scan on user_account sent_user_account (cost=0.00..82.51 rows=351 width=21) (actual time=0.007..0.050 rows=352 loops=1)

49. 270.166 270.166 ↑ 1.0 1 135,083

Index Scan using position_extension__pkey on position_extension pe (cost=0.29..0.32 rows=1 width=53) (actual time=0.002..0.002 rows=1 loops=135,083)

  • Index Cond: (pc.position_description_id = position_id)
50. 675.415 675.415 ↑ 1.0 1 135,083

Index Scan using common_location_pkey on common_location cl (cost=0.42..0.54 rows=1 width=46) (actual time=0.005..0.005 rows=1 loops=135,083)

  • Index Cond: (c.current_location_id = id)
51. 0.029 0.066 ↑ 1.0 253 1

Hash (cost=5.53..5.53 rows=253 width=13) (actual time=0.065..0.066 rows=253 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 20kB
52. 0.037 0.037 ↑ 1.0 253 1

Seq Scan on country co (cost=0.00..5.53 rows=253 width=13) (actual time=0.016..0.037 rows=253 loops=1)

53. 135.083 135.083 ↓ 0.0 0 135,083

Index Scan using offer_valid_position_candidate_id__unq on offer (cost=0.28..0.31 rows=1 width=112) (actual time=0.001..0.001 rows=0 loops=135,083)

  • Index Cond: (pc.id = position_candidate_id)
54. 0.036 0.080 ↓ 1.0 352 1

Hash (cost=82.51..82.51 rows=351 width=21) (actual time=0.080..0.080 rows=352 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 28kB
55. 0.044 0.044 ↓ 1.0 352 1

Seq Scan on user_account ua (cost=0.00..82.51 rows=351 width=21) (actual time=0.005..0.044 rows=352 loops=1)

56. 0.042 0.085 ↓ 1.0 352 1

Hash (cost=82.51..82.51 rows=351 width=21) (actual time=0.085..0.085 rows=352 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 28kB
57. 0.043 0.043 ↓ 1.0 352 1

Seq Scan on user_account ua_deleted_by (cost=0.00..82.51 rows=351 width=21) (actual time=0.005..0.043 rows=352 loops=1)

58. 0.007 0.024 ↓ 1.0 38 1

Hash (cost=1.37..1.37 rows=37 width=520) (actual time=0.024..0.024 rows=38 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 11kB
59. 0.017 0.017 ↓ 1.0 38 1

Seq Scan on candidate_source (cost=0.00..1.37 rows=37 width=520) (actual time=0.014..0.017 rows=38 loops=1)

60. 7.992 20.571 ↑ 1.0 42,546 1

Hash (cost=1,558.46..1,558.46 rows=42,546 width=72) (actual time=20.571..20.571 rows=42,546 loops=1)

  • Buckets: 65536 Batches: 2 Memory Usage: 2552kB
61. 12.579 12.579 ↑ 1.0 42,546 1

Seq Scan on company_location (cost=0.00..1,558.46 rows=42,546 width=72) (actual time=0.016..12.579 rows=42,546 loops=1)

62. 0.012 0.028 ↑ 1.0 52 1

Hash (cost=1.52..1.52 rows=52 width=22) (actual time=0.028..0.028 rows=52 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 11kB
63. 0.016 0.016 ↑ 1.0 52 1

Seq Scan on vertical (cost=0.00..1.52 rows=52 width=22) (actual time=0.012..0.016 rows=52 loops=1)

64. 0.000 0.000 ↓ 0.0 0 135,083

Index Scan using client_account_pkey on user_account (cost=0.27..0.29 rows=1 width=21) (actual time=0.000..0.000 rows=0 loops=135,083)

  • Index Cond: (company.company_owner_id = id)
65. 0.297 0.818 ↑ 1.0 2,845 1

Hash (cost=57.45..57.45 rows=2,845 width=18) (actual time=0.818..0.818 rows=2,845 loops=1)

  • Buckets: 4096 Batches: 1 Memory Usage: 155kB
66. 0.521 0.521 ↑ 1.0 2,845 1

Seq Scan on invoice (cost=0.00..57.45 rows=2,845 width=18) (actual time=0.014..0.521 rows=2,845 loops=1)

67. 0.000 0.000 ↓ 0.0 0 135,083

Index Scan using offer_personal_info__offer__fkey on offer_personal_info (cost=0.28..0.46 rows=1 width=60) (actual time=0.000..0.000 rows=0 loops=135,083)

  • Index Cond: (offer_id = offer.id)
68. 0.043 0.107 ↑ 1.0 438 1

Hash (cost=9.38..9.38 rows=438 width=16) (actual time=0.107..0.107 rows=438 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 26kB
69. 0.064 0.064 ↑ 1.0 438 1

Seq Scan on offer_fee_model (cost=0.00..9.38 rows=438 width=16) (actual time=0.010..0.064 rows=438 loops=1)

70. 135.083 135.083 ↑ 1.0 1 135,083

Index Scan using compensation_position_id__uidx on compensation (cost=0.29..0.33 rows=1 width=36) (actual time=0.001..0.001 rows=1 loops=135,083)

  • Index Cond: (pc.position_description_id = position_id)
71. 0.043 0.132 ↓ 1.0 352 1

Hash (cost=82.51..82.51 rows=351 width=21) (actual time=0.131..0.132 rows=352 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 28kB
72. 0.089 0.089 ↓ 1.0 352 1

Seq Scan on user_account user_temp (cost=0.00..82.51 rows=351 width=21) (actual time=0.008..0.089 rows=352 loops=1)

73. 0.000 135.083 ↓ 0.0 0 135,083

Nested Loop Left Join (cost=0.57..0.69 rows=1 width=50) (actual time=0.001..0.001 rows=0 loops=135,083)

74. 135.083 135.083 ↓ 0.0 0 135,083

Index Scan using interview_position_candidate_id on interview interview_1 (cost=0.29..0.31 rows=1 width=41) (actual time=0.001..0.001 rows=0 loops=135,083)

  • Index Cond: (position_candidate_id = pc.id)
  • Filter: ((pc.status - interview_no) = 103)
  • Rows Removed by Filter: 0
75. 26.739 26.739 ↑ 1.0 1 8,913

Index Scan using interview_time__interview_id_fkey on interview_time (cost=0.29..0.36 rows=1 width=20) (actual time=0.003..0.003 rows=1 loops=8,913)

  • Index Cond: (interview_1.id = interview_id)
  • Filter: ((selected = 1) OR (index_num = 1))
  • Rows Removed by Filter: 0
76. 1.500 3.574 ↑ 1.0 12,594 1

Hash (cost=295.94..295.94 rows=12,594 width=24) (actual time=3.574..3.574 rows=12,594 loops=1)

  • Buckets: 16384 Batches: 1 Memory Usage: 857kB
77. 2.074 2.074 ↑ 1.0 12,594 1

Seq Scan on interview_history ih (cost=0.00..295.94 rows=12,594 width=24) (actual time=0.015..2.074 rows=12,594 loops=1)

78. 5.887 156.816 ↓ 1.5 52,536 1

Unique (cost=26,561.42..26,837.78 rows=35,488 width=53) (actual time=144.288..156.816 rows=52,536 loops=1)

79. 50.523 150.929 ↑ 1.0 54,059 1

Sort (cost=26,561.42..26,699.60 rows=55,271 width=53) (actual time=144.286..150.929 rows=54,059 loops=1)

  • Sort Key: cd.candidate_id, cd.created DESC
  • Sort Method: external merge Disk: 3496kB
80. 95.712 100.406 ↑ 1.0 54,062 1

Bitmap Heap Scan on candidate_document cd (cost=1,072.78..20,317.66 rows=55,271 width=53) (actual time=6.143..100.406 rows=54,062 loops=1)

  • Recheck Cond: ((document_type)::text = 'candidate_photo'::text)
  • Heap Blocks: exact=12845
81. 4.694 4.694 ↑ 1.0 54,062 1

Bitmap Index Scan on candidate_document_document_type_idx (cost=0.00..1,058.96 rows=55,271 width=0) (actual time=4.694..4.694 rows=54,062 loops=1)

  • Index Cond: ((document_type)::text = 'candidate_photo'::text)
82. 0.000 0.000 ↓ 0.0 0 135,094

Index Scan using compensation_fee_model_compensation_id__fkey on compensation_fee_model (cost=0.14..0.16 rows=1 width=12) (actual time=0.000..0.000 rows=0 loops=135,094)

  • Index Cond: (compensation_id = compensation.id)
83. 0.000 0.000 ↓ 0.0 0 135,094

Index Scan using company_response__position_candidate_id__fkey on position_candidate_company_response (cost=0.15..0.17 rows=1 width=274) (actual time=0.000..0.000 rows=0 loops=135,094)

  • Index Cond: (position_candidate_id = pc.id)
84. 50.619 98.000 ↑ 1.0 369,418 1

Hash (cost=6,411.66..6,411.66 rows=369,466 width=20) (actual time=98.000..98.000 rows=369,418 loops=1)

  • Buckets: 65536 Batches: 8 Memory Usage: 2880kB
85. 47.381 47.381 ↑ 1.0 369,418 1

Seq Scan on team_group_candidate tgc (cost=0.00..6,411.66 rows=369,466 width=20) (actual time=0.024..47.381 rows=369,418 loops=1)

86. 0.000 0.000 ↑ 1.0 19 69,241

Materialize (cost=1.43..85.54 rows=19 width=29) (actual time=0.000..0.000 rows=19 loops=69,241)

87. 0.041 0.429 ↑ 1.0 19 1

Hash Right Join (cost=1.43..85.44 rows=19 width=29) (actual time=0.259..0.429 rows=19 loops=1)

  • Hash Cond: (approver.id = offer_approval.user_account_id)
88. 0.357 0.357 ↓ 1.0 352 1

Seq Scan on user_account approver (cost=0.00..82.51 rows=351 width=21) (actual time=0.015..0.357 rows=352 loops=1)

89. 0.010 0.031 ↑ 1.0 19 1

Hash (cost=1.19..1.19 rows=19 width=12) (actual time=0.030..0.031 rows=19 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 9kB
90. 0.021 0.021 ↑ 1.0 19 1

Seq Scan on offer_approval (cost=0.00..1.19 rows=19 width=12) (actual time=0.019..0.021 rows=19 loops=1)

91. 0.000 415.446 ↓ 0.0 0 69,241

GroupAggregate (cost=21.99..61.34 rows=1 width=12) (actual time=0.006..0.006 rows=0 loops=69,241)

  • Group Key: cd_1.candidate_id
92. 0.000 415.446 ↓ 0.0 0 69,241

Result (cost=21.99..61.32 rows=1 width=8) (actual time=0.006..0.006 rows=0 loops=69,241)

  • One-Time Filter: (pc.status >= 102)
93. 138.439 415.446 ↓ 0.0 0 69,241

Hash Join (cost=21.99..61.32 rows=1 width=8) (actual time=0.006..0.006 rows=0 loops=69,241)

  • Hash Cond: (cd_1.document_types_id = odt.document_type_id)
94. 0.043 0.043 ↑ 14.0 1 1

Index Scan using candidate_document_candidate_id_idx on candidate_document cd_1 (cost=0.42..39.69 rows=14 width=8) (actual time=0.043..0.043 rows=1 loops=1)

  • Index Cond: (pc.candidate_id = candidate_id)
95. 276.964 276.964 ↓ 0.0 0 69,241

Hash (cost=21.50..21.50 rows=5 width=4) (actual time=0.004..0.004 rows=0 loops=69,241)

  • Buckets: 1024 Batches: 1 Memory Usage: 8kB
96. 0.000 0.000 ↓ 0.0 0 69,241

Seq Scan on onboarding_document_type odt (cost=0.00..21.50 rows=5 width=4) (actual time=0.000..0.000 rows=0 loops=69,241)

  • Filter: (pc.position_description_id = job_id)
97. 69.241 69.241 ↓ 0.0 0 69,241

Index Scan using gdpr_compliance_pkey on candidate_gdpr_compliance cgc (cost=0.28..0.30 rows=1 width=6) (actual time=0.001..0.001 rows=0 loops=69,241)

  • Index Cond: (c.id = candidate_id)
  • Filter: (portal_status IS NOT NULL)
  • Rows Removed by Filter: 0
98. 276.964 276.964 ↓ 0.0 0 69,241

GroupAggregate (cost=0.00..21.57 rows=5 width=16) (actual time=0.004..0.004 rows=0 loops=69,241)

  • Group Key: onboarding_document_type.job_id
99. 0.000 0.000 ↓ 0.0 0 69,241

Seq Scan on onboarding_document_type (cost=0.00..21.50 rows=5 width=8) (actual time=0.000..0.000 rows=0 loops=69,241)

  • Filter: (job_id = pc.position_description_id)
100. 46.588 77.739 ↑ 1.0 369,418 1

Hash (cost=6,411.66..6,411.66 rows=369,466 width=12) (actual time=77.739..77.739 rows=369,418 loops=1)

  • Buckets: 131072 Batches: 8 Memory Usage: 3207kB
101. 31.151 31.151 ↑ 1.0 369,418 1

Seq Scan on team_group_candidate tgc_1 (cost=0.00..6,411.66 rows=369,466 width=12) (actual time=0.009..31.151 rows=369,418 loops=1)