explain.depesz.com

PostgreSQL's explain analyze made readable

Result: 9Y2u

Settings
# exclusive inclusive rows x rows loops node
1. 7,132.856 658,140.243 ↑ 1.0 1 1

Aggregate (cost=53,774,470.94..53,774,470.95 rows=1 width=8) (actual time=658,140.243..658,140.243 rows=1 loops=1)

2. 5,988.452 651,007.387 ↓ 6.9 87,514,183 1

Append (cost=64,497.21..53,616,875.24 rows=12,607,656 width=699) (actual time=685.181..651,007.387 rows=87,514,183 loops=1)

3. 31,207.492 633,453.009 ↓ 7.1 85,805,554 1

Subquery Scan on *SELECT* 1 (cost=64,497.21..51,929,271.26 rows=12,089,614 width=445) (actual time=685.181..633,453.009 rows=85,805,554 loops=1)

4. 79,878.532 602,245.517 ↓ 7.1 85,805,554 1

Hash Left Join (cost=64,497.21..51,778,151.09 rows=12,089,614 width=445) (actual time=685.175..602,245.517 rows=85,805,554 loops=1)

  • Hash Cond: ((v.id_dealer_pro)::text = (r.id_dealer)::text)
5. 0.000 522,364.027 ↓ 7.1 85,805,554 1

Gather (cost=64,098.83..20,947,725.61 rows=12,089,614 width=326) (actual time=682.205..522,364.027 rows=85,805,554 loops=1)

  • Workers Planned: 3
  • Workers Launched: 3
6. 8,938.720 581,347.755 ↓ 5.5 21,451,388 4

Parallel Hash Left Join (cost=63,098.83..19,737,764.21 rows=3,899,875 width=326) (actual time=675.101..581,347.755 rows=21,451,388 loops=4)

  • Hash Cond: (a.id_vendeur = v.id_vendeur)
7. 16,500.424 572,030.487 ↓ 5.5 21,451,388 4

Parallel Hash Left Join (cost=25,730.77..19,690,158.97 rows=3,899,875 width=319) (actual time=292.310..572,030.487 rows=21,451,388 loops=4)

  • Hash Cond: (a.id_veh = ve.id_veh)
8. 8,565.058 555,263.966 ↓ 5.5 21,451,388 4

Hash Left Join (cost=2,035.99..19,656,226.98 rows=3,899,875 width=261) (actual time=25.138..555,263.966 rows=21,451,388 loops=4)

  • Hash Cond: (a.id_boite = b.id_boite)
9. 8,475.713 546,698.892 ↓ 5.5 21,451,388 4

Hash Left Join (cost=2,034.94..19,624,443.95 rows=3,899,875 width=251) (actual time=25.107..546,698.892 rows=21,451,388 loops=4)

  • Hash Cond: (a.id_energie = e.id_energie)
10. 8,583.235 538,223.165 ↓ 5.5 21,451,388 4

Hash Left Join (cost=2,033.81..19,607,004.27 rows=3,899,875 width=244) (actual time=25.079..538,223.165 rows=21,451,388 loops=4)

  • Hash Cond: (a.id_carrosserie = c.id_carrosserie)
11. 11,320.350 529,639.901 ↓ 5.5 21,451,388 4

Hash Left Join (cost=2,032.38..19,594,630.40 rows=3,899,875 width=236) (actual time=25.022..529,639.901 rows=21,451,388 loops=4)

  • Hash Cond: (a.id_phase = p.id_phase)
12. 11,003.202 518,309.919 ↓ 5.5 21,451,388 4

Hash Left Join (cost=1,284.70..19,583,644.07 rows=3,899,875 width=232) (actual time=15.230..518,309.919 rows=21,451,388 loops=4)

  • Hash Cond: (a.id_generation = ge.id_generation)
13. 10,529.007 507,298.101 ↓ 5.5 21,451,388 4

Hash Left Join (cost=641.31..19,572,761.61 rows=3,899,875 width=234) (actual time=6.417..507,298.101 rows=21,451,388 loops=4)

  • Hash Cond: (a.id_smodele = sm.id_smodele)
14. 10,894.517 496,767.236 ↓ 5.5 21,451,388 4

Hash Left Join (cost=499.10..19,562,372.68 rows=3,899,875 width=231) (actual time=4.466..496,767.236 rows=21,451,388 loops=4)

  • Hash Cond: (a.id_modele = m.id_modele)
15. 485,786.139 485,871.568 ↓ 5.5 21,451,388 4

Nested Loop (cost=411.42..19,552,032.96 rows=3,899,875 width=222) (actual time=3.292..485,871.568 rows=21,451,388 loops=4)

16. 22.246 34.859 ↑ 1.3 9,489 4

Hash Left Join (cost=411.00..12,954.10 rows=12,156 width=29) (actual time=2.901..34.859 rows=9,489 loops=4)

  • Hash Cond: (g.id_pays = pays.id_pays)
17. 10.093 12.527 ↑ 1.3 9,489 4

Parallel Bitmap Heap Scan on geographie g (cost=406.88..12,782.84 rows=12,156 width=17) (actual time=2.710..12.527 rows=9,489 loops=4)

  • Recheck Cond: (id_pays = 'FR'::bpchar)
  • Heap Blocks: exact=428
18. 2.434 2.434 ↓ 1.0 37,955 1

Bitmap Index Scan on geographie_id_pays_id_geographie_idx (cost=0.00..397.46 rows=37,685 width=0) (actual time=2.433..2.434 rows=37,955 loops=1)

  • Index Cond: (id_pays = 'FR'::bpchar)
19. 0.004 0.086 ↑ 1.0 1 4

Hash (cost=4.10..4.10 rows=1 width=15) (actual time=0.086..0.086 rows=1 loops=4)

  • Buckets: 1024 Batches: 1 Memory Usage: 9kB
20. 0.005 0.082 ↑ 1.0 1 4

Nested Loop Left Join (cost=0.14..4.10 rows=1 width=15) (actual time=0.078..0.082 rows=1 loops=4)

21. 0.051 0.051 ↑ 1.0 1 4

Seq Scan on pays (cost=0.00..1.71 rows=1 width=7) (actual time=0.047..0.051 rows=1 loops=4)

  • Filter: (id_pays = 'FR'::bpchar)
  • Rows Removed by Filter: 56
22. 0.026 0.026 ↑ 1.0 1 4

Index Scan using devise_pkey on devise (cost=0.14..2.36 rows=1 width=12) (actual time=0.026..0.026 rows=1 loops=4)

  • Index Cond: (pays.id_devise = id_devise)
23. 0.238 50.570 ↑ 8.3 2,261 37,955

Append (cost=0.42..1,419.04 rows=18,832 width=200) (actual time=0.097..50.570 rows=2,261 loops=37,955)

24. 0.419 0.419 ↑ 6.3 21 37,955

Index Scan using annonce_dead_partition_2012_id_geographie_idx on annonce_dead_partition_2012 a (cost=0.42..4.88 rows=133 width=198) (actual time=0.022..0.419 rows=21 loops=37,955)

  • Index Cond: (id_geographie = g.id_geographie)
25. 2.840 2.840 ↑ 3.5 145 37,955

Index Scan using annonce_dead_partition_2013_id_geographie_idx on annonce_dead_partition_2013 a_1 (cost=0.43..23.81 rows=502 width=199) (actual time=0.067..2.840 rows=145 loops=37,955)

  • Index Cond: (id_geographie = g.id_geographie)
26. 3.029 3.029 ↑ 3.6 176 37,955

Index Scan using annonce_dead_partition_2014_id_geographie_idx on annonce_dead_partition_2014 a_2 (cost=0.43..30.43 rows=634 width=201) (actual time=0.067..3.029 rows=176 loops=37,955)

  • Index Cond: (id_geographie = g.id_geographie)
27. 2.152 2.152 ↑ 4.2 213 37,955

Index Scan using annonce_dead_partition_2015_id_geographie_idx1 on annonce_dead_partition_2015 a_3 (cost=0.43..41.15 rows=894 width=203) (actual time=0.065..2.152 rows=213 loops=37,955)

  • Index Cond: (id_geographie = g.id_geographie)
28. 9.038 9.038 ↑ 6.9 381 37,955

Index Scan using annonce_dead_partition_2016_id_geographie_idx on annonce_dead_partition_2016 a_4 (cost=0.56..161.60 rows=2,647 width=204) (actual time=0.095..9.038 rows=381 loops=37,955)

  • Index Cond: (id_geographie = g.id_geographie)
29. 8.378 8.378 ↑ 10.3 425 37,955

Index Scan using annonce_dead_partition_2017_id_geographie_idx on annonce_dead_partition_2017 a_5 (cost=0.56..247.24 rows=4,378 width=204) (actual time=0.093..8.378 rows=425 loops=37,955)

  • Index Cond: (id_geographie = g.id_geographie)
30. 10.443 10.443 ↑ 9.4 457 37,955

Index Scan using annonce_dead_partition_2018_id_geographie_idx on annonce_dead_partition_2018 a_6 (cost=0.57..314.81 rows=4,312 width=198) (actual time=0.098..10.443 rows=457 loops=37,955)

  • Index Cond: (id_geographie = g.id_geographie)
31. 14.033 14.033 ↑ 12.0 443 37,955

Index Scan using annonce_dead_partition_2019_id_geographie_idx on annonce_dead_partition_2019 a_7 (cost=0.57..500.96 rows=5,332 width=198) (actual time=0.102..14.033 rows=443 loops=37,955)

  • Index Cond: (id_geographie = g.id_geographie)
32. 0.613 1.151 ↑ 1.0 2,919 4

Hash (cost=51.19..51.19 rows=2,919 width=17) (actual time=1.151..1.151 rows=2,919 loops=4)

  • Buckets: 4096 Batches: 1 Memory Usage: 178kB
33. 0.538 0.538 ↑ 1.0 2,919 4

Seq Scan on modele m (cost=0.00..51.19 rows=2,919 width=17) (actual time=0.016..0.538 rows=2,919 loops=4)

34. 0.775 1.858 ↑ 1.0 4,098 4

Hash (cost=90.98..90.98 rows=4,098 width=11) (actual time=1.858..1.858 rows=4,098 loops=4)

  • Buckets: 8192 Batches: 1 Memory Usage: 245kB
35. 1.083 1.083 ↑ 1.0 4,098 4

Seq Scan on smodele sm (cost=0.00..90.98 rows=4,098 width=11) (actual time=0.014..1.083 rows=4,098 loops=4)

36. 4.093 8.616 ↑ 1.0 20,862 4

Hash (cost=382.62..382.62 rows=20,862 width=6) (actual time=8.616..8.616 rows=20,862 loops=4)

  • Buckets: 32768 Batches: 1 Memory Usage: 1072kB
37. 4.523 4.523 ↑ 1.0 20,862 4

Seq Scan on generation ge (cost=0.00..382.62 rows=20,862 width=6) (actual time=0.015..4.523 rows=20,862 loops=4)

38. 4.699 9.632 ↑ 1.0 25,319 4

Hash (cost=431.19..431.19 rows=25,319 width=8) (actual time=9.632..9.632 rows=25,319 loops=4)

  • Buckets: 32768 Batches: 1 Memory Usage: 1246kB
39. 4.933 4.933 ↑ 1.0 25,319 4

Seq Scan on phase p (cost=0.00..431.19 rows=25,319 width=8) (actual time=0.020..4.933 rows=25,319 loops=4)

40. 0.007 0.029 ↑ 1.0 19 4

Hash (cost=1.19..1.19 rows=19 width=12) (actual time=0.028..0.029 rows=19 loops=4)

  • Buckets: 1024 Batches: 1 Memory Usage: 9kB
41. 0.022 0.022 ↑ 1.0 19 4

Seq Scan on carrosserie c (cost=0.00..1.19 rows=19 width=12) (actual time=0.020..0.022 rows=19 loops=4)

42. 0.004 0.014 ↑ 1.0 6 4

Hash (cost=1.06..1.06 rows=6 width=11) (actual time=0.013..0.014 rows=6 loops=4)

  • Buckets: 1024 Batches: 1 Memory Usage: 9kB
43. 0.010 0.010 ↑ 1.0 6 4

Seq Scan on energie e (cost=0.00..1.06 rows=6 width=11) (actual time=0.009..0.010 rows=6 loops=4)

44. 0.003 0.016 ↑ 1.0 2 4

Hash (cost=1.02..1.02 rows=2 width=14) (actual time=0.016..0.016 rows=2 loops=4)

  • Buckets: 1024 Batches: 1 Memory Usage: 9kB
45. 0.013 0.013 ↑ 1.0 2 4

Seq Scan on boite b (cost=0.00..1.02 rows=2 width=14) (actual time=0.013..0.013 rows=2 loops=4)

46. 99.681 266.097 ↑ 1.3 178,139 4

Parallel Hash (cost=20,821.57..20,821.57 rows=229,857 width=62) (actual time=266.097..266.097 rows=178,139 loops=4)

  • Buckets: 1048576 Batches: 1 Memory Usage: 72640kB
47. 166.416 166.416 ↑ 1.3 178,139 4

Parallel Seq Scan on vehicule ve (cost=0.00..20,821.57 rows=229,857 width=62) (actual time=0.020..166.416 rows=178,139 loops=4)

48. 151.830 378.548 ↑ 1.0 382,292 4

Parallel Hash (cost=32,360.25..32,360.25 rows=400,625 width=15) (actual time=378.548..378.548 rows=382,292 loops=4)

  • Buckets: 2097152 Batches: 1 Memory Usage: 76384kB
49. 226.718 226.718 ↑ 1.0 382,292 4

Parallel Seq Scan on vendeur v (cost=0.00..32,360.25 rows=400,625 width=15) (actual time=0.094..226.718 rows=382,292 loops=4)

50. 0.775 2.946 ↑ 1.0 4,817 1

Hash (cost=338.17..338.17 rows=4,817 width=35) (actual time=2.945..2.946 rows=4,817 loops=1)

  • Buckets: 8192 Batches: 1 Memory Usage: 381kB
51. 2.171 2.171 ↑ 1.0 4,817 1

Seq Scan on dealer r (cost=0.00..338.17 rows=4,817 width=35) (actual time=0.004..2.171 rows=4,817 loops=1)

52.          

SubPlan (forHash Left Join)

53. 0.004 0.012 ↑ 50.0 2 1

Result (cost=0.00..2.52 rows=100 width=8) (actual time=0.011..0.012 rows=2 loops=1)

54. 0.007 0.008 ↑ 50.0 2 1

ProjectSet (cost=0.00..0.52 rows=100 width=4) (actual time=0.007..0.008 rows=2 loops=1)

55. 0.001 0.001 ↑ 1.0 1 1

Result (cost=0.00..0.01 rows=1 width=0) (actual time=0.001..0.001 rows=1 loops=1)

56. 2,328.480 11,565.926 ↓ 3.3 1,708,629 1

Gather (cost=64,497.22..1,619,385.28 rows=518,042 width=816) (actual time=633.095..11,565.926 rows=1,708,629 loops=1)

  • Workers Planned: 3
  • Workers Launched: 3
57. 146.347 9,237.446 ↓ 2.6 427,157 4

Hash Left Join (cost=63,497.22..240,393.56 rows=167,110 width=828) (actual time=626.808..9,237.446 rows=427,157 loops=4)

  • Hash Cond: ((v_1.id_dealer_pro)::text = (r_1.id_dealer)::text)
58. 210.567 9,081.104 ↓ 2.6 427,157 4

Parallel Hash Left Join (cost=63,098.84..239,556.52 rows=167,110 width=805) (actual time=616.723..9,081.104 rows=427,157 loops=4)

  • Hash Cond: (a_8.id_vendeur = v_1.id_vendeur)
59. 368.182 8,564.079 ↓ 2.6 427,157 4

Parallel Hash Left Join (cost=25,730.78..201,749.79 rows=167,110 width=798) (actual time=306.914..8,564.079 rows=427,157 loops=4)

  • Hash Cond: (a_8.id_veh = ve_1.id_veh)
60. 173.493 7,911.946 ↓ 2.6 427,157 4

Hash Left Join (cost=2,036.00..177,616.35 rows=167,110 width=740) (actual time=21.861..7,911.946 rows=427,157 loops=4)

  • Hash Cond: (a_8.id_boite = b_1.id_boite)
61. 171.437 7,738.441 ↓ 2.6 427,157 4

Hash Left Join (cost=2,034.95..176,261.65 rows=167,110 width=730) (actual time=21.789..7,738.441 rows=427,157 loops=4)

  • Hash Cond: (a_8.id_energie = e_1.id_energie)
62. 163.542 7,566.988 ↓ 2.6 427,157 4

Hash Left Join (cost=2,033.82..175,513.64 rows=167,110 width=723) (actual time=21.760..7,566.988 rows=427,157 loops=4)

  • Hash Cond: (a_8.id_carrosserie = c_1.id_carrosserie)
63. 235.854 7,403.418 ↓ 2.6 427,157 4

Hash Left Join (cost=2,032.39..174,989.71 rows=167,110 width=715) (actual time=21.715..7,403.418 rows=427,157 loops=4)

  • Hash Cond: (a_8.id_phase = p_1.id_phase)
64. 231.762 7,159.300 ↓ 2.6 427,157 4

Hash Left Join (cost=1,284.71..173,803.31 rows=167,110 width=711) (actual time=13.337..7,159.300 rows=427,157 loops=4)

  • Hash Cond: (a_8.id_generation = ge_1.id_generation)
65. 208.893 6,919.968 ↓ 2.6 427,157 4

Hash Left Join (cost=641.32..172,721.17 rows=167,110 width=713) (actual time=5.637..6,919.968 rows=427,157 loops=4)

  • Hash Cond: (a_8.id_smodele = sm_1.id_smodele)
66. 219.740 6,709.474 ↓ 2.6 427,157 4

Hash Left Join (cost=499.11..172,139.93 rows=167,110 width=710) (actual time=4.002..6,709.474 rows=427,157 loops=4)

  • Hash Cond: (a_8.id_modele = m_1.id_modele)
67. 6,473.682 6,488.726 ↓ 2.6 427,157 4

Nested Loop (cost=411.43..171,612.95 rows=167,110 width=701) (actual time=2.969..6,488.726 rows=427,157 loops=4)

68. 6.635 14.408 ↑ 1.3 9,489 4

Hash Left Join (cost=411.00..12,954.10 rows=12,156 width=29) (actual time=2.644..14.408 rows=9,489 loops=4)

  • Hash Cond: (g_1.id_pays = pays_1.id_pays)
69. 5.465 7.709 ↑ 1.3 9,489 4

Parallel Bitmap Heap Scan on geographie g_1 (cost=406.88..12,782.84 rows=12,156 width=17) (actual time=2.516..7.709 rows=9,489 loops=4)

  • Recheck Cond: (id_pays = 'FR'::bpchar)
  • Heap Blocks: exact=387
70. 2.244 2.244 ↓ 1.0 37,955 1

Bitmap Index Scan on geographie_id_pays_id_geographie_idx (cost=0.00..397.46 rows=37,685 width=0) (actual time=2.243..2.244 rows=37,955 loops=1)

  • Index Cond: (id_pays = 'FR'::bpchar)
71. 0.004 0.064 ↑ 1.0 1 4

Hash (cost=4.10..4.10 rows=1 width=15) (actual time=0.064..0.064 rows=1 loops=4)

  • Buckets: 1024 Batches: 1 Memory Usage: 9kB
72. 0.004 0.060 ↑ 1.0 1 4

Nested Loop Left Join (cost=0.14..4.10 rows=1 width=15) (actual time=0.057..0.060 rows=1 loops=4)

73. 0.029 0.029 ↑ 1.0 1 4

Seq Scan on pays pays_1 (cost=0.00..1.71 rows=1 width=7) (actual time=0.026..0.029 rows=1 loops=4)

  • Filter: (id_pays = 'FR'::bpchar)
  • Rows Removed by Filter: 56
74. 0.027 0.027 ↑ 1.0 1 4

Index Scan using devise_pkey on devise devise_1 (cost=0.14..2.36 rows=1 width=12) (actual time=0.027..0.027 rows=1 loops=4)

  • Index Cond: (pays_1.id_devise = id_devise)
75. 0.636 0.636 ↓ 4.1 45 37,955

Index Scan using refresh_vo_all on annonce a_8 (cost=0.43..12.94 rows=11 width=680) (actual time=0.019..0.636 rows=45 loops=37,955)

  • Index Cond: (id_geographie = g_1.id_geographie)
76. 0.532 1.008 ↑ 1.0 2,919 4

Hash (cost=51.19..51.19 rows=2,919 width=17) (actual time=1.008..1.008 rows=2,919 loops=4)

  • Buckets: 4096 Batches: 1 Memory Usage: 178kB
77. 0.476 0.476 ↑ 1.0 2,919 4

Seq Scan on modele m_1 (cost=0.00..51.19 rows=2,919 width=17) (actual time=0.014..0.476 rows=2,919 loops=4)

78. 0.697 1.601 ↑ 1.0 4,098 4

Hash (cost=90.98..90.98 rows=4,098 width=11) (actual time=1.601..1.601 rows=4,098 loops=4)

  • Buckets: 8192 Batches: 1 Memory Usage: 245kB
79. 0.904 0.904 ↑ 1.0 4,098 4

Seq Scan on smodele sm_1 (cost=0.00..90.98 rows=4,098 width=11) (actual time=0.025..0.904 rows=4,098 loops=4)

80. 3.538 7.570 ↑ 1.0 20,862 4

Hash (cost=382.62..382.62 rows=20,862 width=6) (actual time=7.569..7.570 rows=20,862 loops=4)

  • Buckets: 32768 Batches: 1 Memory Usage: 1072kB
81. 4.032 4.032 ↑ 1.0 20,862 4

Seq Scan on generation ge_1 (cost=0.00..382.62 rows=20,862 width=6) (actual time=0.015..4.032 rows=20,862 loops=4)

82. 3.965 8.264 ↑ 1.0 25,319 4

Hash (cost=431.19..431.19 rows=25,319 width=8) (actual time=8.263..8.264 rows=25,319 loops=4)

  • Buckets: 32768 Batches: 1 Memory Usage: 1246kB
83. 4.299 4.299 ↑ 1.0 25,319 4

Seq Scan on phase p_1 (cost=0.00..431.19 rows=25,319 width=8) (actual time=0.017..4.299 rows=25,319 loops=4)

84. 0.007 0.028 ↑ 1.0 19 4

Hash (cost=1.19..1.19 rows=19 width=12) (actual time=0.027..0.028 rows=19 loops=4)

  • Buckets: 1024 Batches: 1 Memory Usage: 9kB
85. 0.021 0.021 ↑ 1.0 19 4

Seq Scan on carrosserie c_1 (cost=0.00..1.19 rows=19 width=12) (actual time=0.019..0.021 rows=19 loops=4)

86. 0.004 0.016 ↑ 1.0 6 4

Hash (cost=1.06..1.06 rows=6 width=11) (actual time=0.016..0.016 rows=6 loops=4)

  • Buckets: 1024 Batches: 1 Memory Usage: 9kB
87. 0.012 0.012 ↑ 1.0 6 4

Seq Scan on energie e_1 (cost=0.00..1.06 rows=6 width=11) (actual time=0.011..0.012 rows=6 loops=4)

88. 0.002 0.012 ↑ 1.0 2 4

Hash (cost=1.02..1.02 rows=2 width=14) (actual time=0.012..0.012 rows=2 loops=4)

  • Buckets: 1024 Batches: 1 Memory Usage: 9kB
89. 0.010 0.010 ↑ 1.0 2 4

Seq Scan on boite b_1 (cost=0.00..1.02 rows=2 width=14) (actual time=0.009..0.010 rows=2 loops=4)

90. 87.510 283.951 ↑ 1.3 178,139 4

Parallel Hash (cost=20,821.57..20,821.57 rows=229,857 width=62) (actual time=283.951..283.951 rows=178,139 loops=4)

  • Buckets: 1048576 Batches: 1 Memory Usage: 72672kB
91. 196.441 196.441 ↑ 1.3 178,139 4

Parallel Seq Scan on vehicule ve_1 (cost=0.00..20,821.57 rows=229,857 width=62) (actual time=0.083..196.441 rows=178,139 loops=4)

92. 104.581 306.458 ↑ 1.0 382,292 4

Parallel Hash (cost=32,360.25..32,360.25 rows=400,625 width=15) (actual time=306.458..306.458 rows=382,292 loops=4)

  • Buckets: 2097152 Batches: 1 Memory Usage: 76448kB
93. 201.877 201.877 ↑ 1.0 382,292 4

Parallel Seq Scan on vendeur v_1 (cost=0.00..32,360.25 rows=400,625 width=15) (actual time=0.069..201.877 rows=382,292 loops=4)

94. 1.114 9.995 ↑ 1.0 4,817 4

Hash (cost=338.17..338.17 rows=4,817 width=35) (actual time=9.995..9.995 rows=4,817 loops=4)

  • Buckets: 8192 Batches: 1 Memory Usage: 381kB
95. 8.881 8.881 ↑ 1.0 4,817 4

Seq Scan on dealer r_1 (cost=0.00..338.17 rows=4,817 width=35) (actual time=0.144..8.881 rows=4,817 loops=4)

96.          

SubPlan (forGather)

97. 0.000 0.000 ↓ 0.0 0

Result (cost=0.00..2.52 rows=100 width=8) (never executed)

98. 0.000 0.000 ↓ 0.0 0

ProjectSet (cost=0.00..0.52 rows=100 width=4) (never executed)

99. 0.000 0.000 ↓ 0.0 0

Result (cost=0.00..0.01 rows=1 width=0) (never executed)

Planning time : 22.755 ms