explain.depesz.com

PostgreSQL's explain analyze made readable

Result: 1IY

Settings
# exclusive inclusive rows x rows loops node
1. 76.105 7,334.602 ↑ 1.0 69,750 1

HashAggregate (cost=2,206,026.00..2,207,300.00 rows=72,800 width=156) (actual time=7,315.408..7,334.602 rows=69,750 loops=1)

2.          

CTE tempo_mes

3. 0.007 0.201 ↑ 1.0 6 1

HashAggregate (cost=19.56..19.62 rows=6 width=13) (actual time=0.199..0.201 rows=6 loops=1)

4. 0.194 0.194 ↑ 1.0 6 1

Seq Scan on dim_tempo_mes dtd (cost=0.00..19.53 rows=6 width=13) (actual time=0.029..0.194 rows=6 loops=1)

  • Filter: ((mes >= 1) AND (mes <= 6) AND (ano = 2018))
  • Rows Removed by Filter: 653
5.          

CTE tempo_dia

6. 0.204 0.431 ↑ 1.0 181 1

HashAggregate (cost=277.47..279.29 rows=182 width=13) (actual time=0.304..0.431 rows=181 loops=1)

7. 0.174 0.227 ↑ 1.0 181 1

Bitmap Heap Scan on dim_tempo_dia dtd (cost=6.57..276.56 rows=182 width=13) (actual time=0.069..0.227 rows=181 loops=1)

  • Recheck Cond: ((ano = 2018) AND (mes >= 1) AND (mes <= 6))
8. 0.053 0.053 ↑ 1.0 181 1

Bitmap Index Scan on dim_tempo_dia_idx (cost=0.00..6.53 rows=182 width=0) (actual time=0.053..0.053 rows=181 loops=1)

  • Index Cond: ((ano = 2018) AND (mes >= 1) AND (mes <= 6))
9.          

CTE vendas

10. 54.394 2,812.411 ↑ 5.4 9,480 1

HashAggregate (cost=202,293.25..202,804.64 rows=51,139 width=44) (actual time=2,808.282..2,812.411 rows=9,480 loops=1)

11. 62.544 2,758.017 ↓ 2.1 107,814 1

Hash Join (cost=5,242.94..201,909.71 rows=51,139 width=44) (actual time=1,528.954..2,758.017 rows=107,814 loops=1)

  • Hash Cond: (fato.sk_cliente = dc.sk_cliente)
12. 124.954 2,644.446 ↓ 2.1 107,814 1

Hash Join (cost=331.44..196,039.36 rows=51,139 width=12) (actual time=1,477.879..2,644.446 rows=107,814 loops=1)

  • Hash Cond: (fato.sk_vendedor = dco.sk_vendedor)
13. 1,535.158 2,510.178 ↓ 16.5 1,030,296 1

Hash Join (cost=17.39..193,104.44 rows=62,503 width=16) (actual time=1,468.511..2,510.178 rows=1,030,296 loops=1)

  • Hash Cond: (fato.sk_tempo_mes = dtd.sk_tempo_mes)
14. 974.515 974.515 ↑ 1.0 6,864,862 1

Seq Scan on fato_vendas_mes_familia fato (cost=0.00..166,718.74 rows=6,864,874 width=20) (actual time=0.005..974.515 rows=6,864,862 loops=1)

15. 0.003 0.505 ↑ 1.0 6 1

Hash (cost=17.32..17.32 rows=6 width=8) (actual time=0.505..0.505 rows=6 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 1kB
16. 0.153 0.502 ↑ 1.0 6 1

Hash Join (cost=0.20..17.32 rows=6 width=8) (actual time=0.254..0.502 rows=6 loops=1)

  • Hash Cond: (dtd.sk_tempo_mes = m.sk_tempo_mes)
17. 0.138 0.138 ↑ 1.0 659 1

Seq Scan on dim_tempo_mes dtd (cost=0.00..14.59 rows=659 width=4) (actual time=0.004..0.138 rows=659 loops=1)

18. 0.003 0.211 ↑ 1.0 6 1

Hash (cost=0.12..0.12 rows=6 width=4) (actual time=0.211..0.211 rows=6 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 1kB
19. 0.208 0.208 ↑ 1.0 6 1

CTE Scan on tempo_mes m (cost=0.00..0.12 rows=6 width=4) (actual time=0.201..0.208 rows=6 loops=1)

20. 0.083 9.314 ↑ 1.0 251 1

Hash (cost=310.90..310.90 rows=252 width=4) (actual time=9.314..9.314 rows=251 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 9kB
21. 9.231 9.231 ↑ 1.0 252 1

Seq Scan on dim_consultor dco (cost=0.00..310.90 rows=252 width=4) (actual time=0.676..9.231 rows=252 loops=1)

  • Filter: ((cod_consultor)::text = ANY ('{65,66,67,78,240,69,70,861,74,535,689,77,79,782,81,80,82,84,290,781,88,287,540,860,91,784,94,95,97,678,853,101,313,354,108,111,537,113,112,114,115,116,119,118,884,889,123,243,266,817,799,126,127,490,797,842,130,129,134,785,808,102,136,314,142,297,143,577,848,146,254,267,276,153,154,282,553,552,155,270,157,291,786,160,164,293,427,556,740,776,167,168,447,857,172,170,815,846,175,675,727,855,380,741,180,551,750,245,706,847,258,273,353,536,446,762,790,636,522,195,769,682,718,722,199,201,401,300,309,317,426,721,205,402,703,208,359,360,734,831,835,864,212,418,422,851,262,720,845,216,219,289,686,839,221,227,429,687,777,284,789,792,888,234,233,417,544,816,236,235,351,361,788,838,239,547,344,534,711,858,328,362,833,832,304,859,322,363,388,311,330,378,327,335,813,567,683,487,566,760,356,560,542,332,334,517,374,373,555,573,370,369,341,420,814,384,807,862,368,365,558,541,850,820,779,488,538,732,844,886,491,492,539,583,849,383,867,104,791,150,105,510,819,854,895,809,810,811,852,778,759,513}'::text[]))
  • Rows Removed by Filter: 560
22. 23.400 51.027 ↑ 1.0 84,200 1

Hash (cost=3,859.00..3,859.00 rows=84,200 width=36) (actual time=51.027..51.027 rows=84,200 loops=1)

  • Buckets: 16384 Batches: 1 Memory Usage: 5658kB
23. 27.627 27.627 ↑ 1.0 84,200 1

Seq Scan on dim_cliente dc (cost=0.00..3,859.00 rows=84,200 width=36) (actual time=0.004..27.627 rows=84,200 loops=1)

24.          

CTE potencial

25. 20.493 288.161 ↓ 3.0 9,054 1

HashAggregate (cost=6,739.94..6,770.60 rows=3,066 width=44) (actual time=284.678..288.161 rows=9,054 loops=1)

26. 8.190 267.668 ↓ 5.0 15,332 1

Nested Loop (cost=379.05..6,716.95 rows=3,066 width=44) (actual time=14.823..267.668 rows=15,332 loops=1)

27. 20.976 198.150 ↓ 5.0 15,332 1

Nested Loop (cost=379.05..5,776.51 rows=3,066 width=48) (actual time=14.805..198.150 rows=15,332 loops=1)

28. 16.408 115.846 ↓ 5.0 15,332 1

Hash Join (cost=379.05..4,592.58 rows=3,066 width=16) (actual time=14.775..115.846 rows=15,332 loops=1)

  • Hash Cond: (fato.sk_consultor = dco.sk_consultor)
29. 56.611 85.376 ↓ 4.3 42,473 1

Hash Join (cost=65.00..4,210.83 rows=9,878 width=20) (actual time=0.690..85.376 rows=42,473 loops=1)

  • Hash Cond: (fato.sk_tempo_safra = dts.sk_tempo_safra)
30. 28.111 28.111 ↑ 1.0 134,258 1

Seq Scan on fato_potencial_consultor_safra_dia_evento fato (cost=0.00..3,543.58 rows=134,258 width=24) (actual time=0.008..28.111 rows=134,258 loops=1)

31. 0.056 0.654 ↑ 1.0 159 1

Hash (cost=63.01..63.01 rows=159 width=4) (actual time=0.654..0.654 rows=159 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 6kB
32. 0.598 0.598 ↑ 1.0 159 1

Seq Scan on dim_tempo_safra dts (cost=0.00..63.01 rows=159 width=4) (actual time=0.013..0.598 rows=159 loops=1)

  • Filter: (ano_inicio = 2018)
  • Rows Removed by Filter: 2002
33. 0.111 14.062 ↑ 1.0 252 1

Hash (cost=310.90..310.90 rows=252 width=4) (actual time=14.062..14.062 rows=252 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 9kB
34. 13.951 13.951 ↑ 1.0 252 1

Seq Scan on dim_consultor dco (cost=0.00..310.90 rows=252 width=4) (actual time=0.996..13.951 rows=252 loops=1)

  • Filter: ((cod_consultor)::text = ANY ('{65,66,67,78,240,69,70,861,74,535,689,77,79,782,81,80,82,84,290,781,88,287,540,860,91,784,94,95,97,678,853,101,313,354,108,111,537,113,112,114,115,116,119,118,884,889,123,243,266,817,799,126,127,490,797,842,130,129,134,785,808,102,136,314,142,297,143,577,848,146,254,267,276,153,154,282,553,552,155,270,157,291,786,160,164,293,427,556,740,776,167,168,447,857,172,170,815,846,175,675,727,855,380,741,180,551,750,245,706,847,258,273,353,536,446,762,790,636,522,195,769,682,718,722,199,201,401,300,309,317,426,721,205,402,703,208,359,360,734,831,835,864,212,418,422,851,262,720,845,216,219,289,686,839,221,227,429,687,777,284,789,792,888,234,233,417,544,816,236,235,351,361,788,838,239,547,344,534,711,858,328,362,833,832,304,859,322,363,388,311,330,378,327,335,813,567,683,487,566,760,356,560,542,332,334,517,374,373,555,573,370,369,341,420,814,384,807,862,368,365,558,541,850,820,779,488,538,732,844,886,491,492,539,583,849,383,867,104,791,150,105,510,819,854,895,809,810,811,852,778,759,513}'::text[]))
  • Rows Removed by Filter: 560
35. 61.328 61.328 ↑ 1.0 1 15,332

Index Scan using dim_cliente_pkey on dim_cliente dc (cost=0.00..0.38 rows=1 width=36) (actual time=0.004..0.004 rows=1 loops=15,332)

  • Index Cond: (sk_cliente = fato.sk_cliente)
36. 61.328 61.328 ↑ 1.0 1 15,332

Index Only Scan using dim_propriedade_pkey on dim_propriedade dp (cost=0.00..0.30 rows=1 width=4) (actual time=0.003..0.004 rows=1 loops=15,332)

  • Index Cond: (sk_propriedade = fato.sk_propriedade)
  • Heap Fetches: 0
37.          

CTE atendimentos

38. 95.351 1,204.690 ↓ 9.2 40,684 1

GroupAggregate (cost=45,489.83..45,610.83 rows=4,400 width=53) (actual time=1,083.978..1,204.690 rows=40,684 loops=1)

39. 205.490 1,109.339 ↓ 39.6 174,110 1

Sort (cost=45,489.83..45,500.83 rows=4,400 width=53) (actual time=1,083.948..1,109.339 rows=174,110 loops=1)

  • Sort Key: dc.sk_cliente, dc.cliente, dtd.numero_mes_abrev, dtd.mes, dtd.ano
  • Sort Method: quicksort Memory: 27973kB
40. 22.987 903.849 ↓ 39.6 174,110 1

Nested Loop (cost=889.31..45,223.56 rows=4,400 width=53) (actual time=11.824..903.849 rows=174,110 loops=1)

41. 40.301 532.642 ↓ 39.6 174,110 1

Hash Join (cost=889.31..39,944.92 rows=4,400 width=21) (actual time=11.802..532.642 rows=174,110 loops=1)

  • Hash Cond: (fato.sk_tipo_de_atendimento = dta.sk_tipo_de_atendimento)
42. 57.049 492.316 ↓ 35.6 174,110 1

Hash Join (cost=888.07..39,881.35 rows=4,889 width=25) (actual time=11.761..492.316 rows=174,110 loops=1)

  • Hash Cond: (fato.sk_consultor = dco.sk_consultor)
43. 240.958 428.231 ↓ 15.8 248,298 1

Hash Join (cost=574.02..39,459.33 rows=15,753 width=29) (actual time=4.705..428.231 rows=248,298 loops=1)

  • Hash Cond: (fato.sk_tempo_dia = dtd.sk_tempo_dia)
44. 182.594 182.594 ↑ 1.0 1,696,458 1

Seq Scan on fato_atendimento_dia_area_negocio fato (cost=0.00..32,235.84 rows=1,731,184 width=20) (actual time=0.007..182.594 rows=1,696,458 loops=1)

45. 0.048 4.679 ↑ 1.0 181 1

Hash (cost=571.75..571.75 rows=182 width=21) (actual time=4.679..4.679 rows=181 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 10kB
46. 2.470 4.631 ↑ 1.0 181 1

Hash Join (cost=5.92..571.75 rows=182 width=21) (actual time=0.626..4.631 rows=181 loops=1)

  • Hash Cond: (dtd.sk_tempo_dia = d.sk_tempo_dia)
47. 2.093 2.093 ↑ 1.0 20,001 1

Seq Scan on dim_tempo_dia dtd (cost=0.00..489.01 rows=20,001 width=17) (actual time=0.003..2.093 rows=20,001 loops=1)

48. 0.045 0.068 ↑ 1.0 181 1

Hash (cost=3.64..3.64 rows=182 width=4) (actual time=0.068..0.068 rows=181 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 7kB
49. 0.023 0.023 ↑ 1.0 181 1

CTE Scan on tempo_dia d (cost=0.00..3.64 rows=182 width=4) (actual time=0.001..0.023 rows=181 loops=1)

50. 0.064 7.036 ↑ 1.0 252 1

Hash (cost=310.90..310.90 rows=252 width=4) (actual time=7.036..7.036 rows=252 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 9kB
51. 6.972 6.972 ↑ 1.0 252 1

Seq Scan on dim_consultor dco (cost=0.00..310.90 rows=252 width=4) (actual time=0.515..6.972 rows=252 loops=1)

  • Filter: ((cod_consultor)::text = ANY ('{65,66,67,78,240,69,70,861,74,535,689,77,79,782,81,80,82,84,290,781,88,287,540,860,91,784,94,95,97,678,853,101,313,354,108,111,537,113,112,114,115,116,119,118,884,889,123,243,266,817,799,126,127,490,797,842,130,129,134,785,808,102,136,314,142,297,143,577,848,146,254,267,276,153,154,282,553,552,155,270,157,291,786,160,164,293,427,556,740,776,167,168,447,857,172,170,815,846,175,675,727,855,380,741,180,551,750,245,706,847,258,273,353,536,446,762,790,636,522,195,769,682,718,722,199,201,401,300,309,317,426,721,205,402,703,208,359,360,734,831,835,864,212,418,422,851,262,720,845,216,219,289,686,839,221,227,429,687,777,284,789,792,888,234,233,417,544,816,236,235,351,361,788,838,239,547,344,534,711,858,328,362,833,832,304,859,322,363,388,311,330,378,327,335,813,567,683,487,566,760,356,560,542,332,334,517,374,373,555,573,370,369,341,420,814,384,807,862,368,365,558,541,850,820,779,488,538,732,844,886,491,492,539,583,849,383,867,104,791,150,105,510,819,854,895,809,810,811,852,778,759,513}'::text[]))
  • Rows Removed by Filter: 560
52. 0.005 0.025 ↓ 1.1 10 1

Hash (cost=1.12..1.12 rows=9 width=4) (actual time=0.025..0.025 rows=10 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 1kB
53. 0.020 0.020 ↓ 1.1 10 1

Seq Scan on dim_tipo_de_atendimento dta (cost=0.00..1.12 rows=9 width=4) (actual time=0.018..0.020 rows=10 loops=1)

  • Filter: ((tipo_de_atendimento)::text <> '9999988888'::text)
54. 348.220 348.220 ↑ 1.0 1 174,110

Index Scan using dim_cliente_pkey on dim_cliente dc (cost=0.00..1.19 rows=1 width=36) (actual time=0.002..0.002 rows=1 loops=174,110)

  • Index Cond: (sk_cliente = fato.sk_cliente)
55.          

CTE nao_mensais

56. 17.733 3,182.431 ↓ 58.1 11,625 1

HashAggregate (cost=1,377.13..1,379.13 rows=200 width=20) (actual time=3,178.450..3,182.431 rows=11,625 loops=1)

57. 20.344 3,164.698 ↓ 46.3 18,534 1

HashAggregate (cost=1,366.13..1,370.13 rows=400 width=12) (actual time=3,158.330..3,164.698 rows=18,534 loops=1)

58. 4.693 3,144.354 ↓ 46.3 18,534 1

Append (cost=1,278.48..1,363.13 rows=400 width=12) (actual time=2,825.737..3,144.354 rows=18,534 loops=1)

59. 3.575 2,832.337 ↓ 47.4 9,480 1

Subquery Scan on *SELECT* 1 (cost=1,278.48..1,282.48 rows=200 width=12) (actual time=2,825.737..2,832.337 rows=9,480 loops=1)

60. 9.487 2,828.762 ↓ 47.4 9,480 1

HashAggregate (cost=1,278.48..1,280.48 rows=200 width=12) (actual time=2,825.721..2,828.762 rows=9,480 loops=1)

61. 2,819.275 2,819.275 ↑ 5.4 9,480 1

CTE Scan on vendas v (cost=0.00..1,022.78 rows=51,139 width=12) (actual time=2,808.288..2,819.275 rows=9,480 loops=1)

62. 3.370 307.324 ↓ 45.3 9,054 1

Subquery Scan on *SELECT* 2 (cost=76.65..80.65 rows=200 width=12) (actual time=300.953..307.324 rows=9,054 loops=1)

63. 9.194 303.954 ↓ 45.3 9,054 1

HashAggregate (cost=76.65..78.65 rows=200 width=12) (actual time=300.939..303.954 rows=9,054 loops=1)

64. 294.760 294.760 ↓ 3.0 9,054 1

CTE Scan on potencial p (cost=0.00..61.32 rows=3,066 width=12) (actual time=284.684..294.760 rows=9,054 loops=1)

65.          

CTE distribuicao_mes

66. 1,193.800 5,170.410 ↓ 1.9 69,750 1

HashAggregate (cost=1,097.64..1,461.64 rows=36,400 width=58) (actual time=5,140.892..5,170.410 rows=69,750 loops=1)

67. 456.196 3,976.610 ↓ 57.8 2,104,125 1

Nested Loop (cost=0.00..733.64 rows=36,400 width=58) (actual time=3,178.762..3,976.610 rows=2,104,125 loops=1)

68. 0.688 0.688 ↑ 1.0 181 1

CTE Scan on tempo_dia dtd (cost=0.00..3.64 rows=182 width=38) (actual time=0.305..0.688 rows=181 loops=1)

69. 3,519.726 3,519.726 ↓ 58.1 11,625 181

CTE Scan on nao_mensais nm (cost=0.00..4.00 rows=200 width=20) (actual time=17.561..19.446 rows=11,625 loops=181)

70.          

CTE atendimentos_mes

71. 35.677 1,504.793 ↓ 363.9 72,774 1

GroupAggregate (cost=1,155.77..1,210.09 rows=200 width=50) (actual time=1,426.435..1,504.793 rows=72,774 loops=1)

72.          

CTE clientes_atend

73. 80.117 1,359.205 ↓ 60.6 72,774 1

HashAggregate (cost=704.12..716.12 rows=1,200 width=42) (actual time=1,346.129..1,359.205 rows=72,774 loops=1)

74. 32.931 1,279.088 ↓ 9.2 244,104 1

Nested Loop (cost=0.00..572.12 rows=26,400 width=42) (actual time=1,083.984..1,279.088 rows=244,104 loops=1)

75. 0.005 0.005 ↑ 1.0 6 1

CTE Scan on tempo_mes dtm (cost=0.00..0.12 rows=6 width=38) (actual time=0.001..0.005 rows=6 loops=1)

76. 1,246.152 1,246.152 ↓ 9.2 40,684 6

CTE Scan on atendimentos a (cost=0.00..88.00 rows=4,400 width=4) (actual time=180.664..207.692 rows=40,684 loops=6)

77. 29.625 1,469.116 ↓ 60.6 72,774 1

Merge Left Join (cost=439.65..482.97 rows=1,200 width=50) (actual time=1,426.423..1,469.116 rows=72,774 loops=1)

  • Merge Cond: ((ca.sk_cliente = at.sk_cliente) AND ((ca.numero_mes_abrev)::text = (at.numero_mes_abrev)::text))
78. 42.329 1,421.439 ↓ 60.6 72,774 1

Sort (cost=85.37..88.37 rows=1,200 width=42) (actual time=1,411.975..1,421.439 rows=72,774 loops=1)

  • Sort Key: ca.sk_cliente, ca.numero_mes_abrev
  • Sort Method: quicksort Memory: 6484kB
79. 1,379.110 1,379.110 ↓ 60.6 72,774 1

CTE Scan on clientes_atend ca (cost=0.00..24.00 rows=1,200 width=42) (actual time=1,346.130..1,379.110 rows=72,774 loops=1)

80. 11.539 18.052 ↓ 9.2 40,684 1

Sort (cost=354.27..365.27 rows=4,400 width=50) (actual time=14.442..18.052 rows=40,684 loops=1)

  • Sort Key: at.sk_cliente, at.numero_mes_abrev
  • Sort Method: quicksort Memory: 4715kB
81. 6.513 6.513 ↓ 9.2 40,684 1

CTE Scan on atendimentos at (cost=0.00..88.00 rows=4,400 width=50) (actual time=0.002..6.513 rows=40,684 loops=1)

82. 54.367 7,258.497 ↑ 1.0 69,750 1

Nested Loop Left Join (cost=52.46..1,945,398.16 rows=72,800 width=156) (actual time=6,687.754..7,258.497 rows=69,750 loops=1)

83. 181.213 6,925.130 ↓ 1.9 69,750 1

Hash Left Join (cost=7.00..2,738.82 rows=36,400 width=132) (actual time=6,687.708..6,925.130 rows=69,750 loops=1)

  • Hash Cond: ((dm.sk_cliente = a.sk_cliente) AND ((dm.numero_mes_abrev)::text = (a.numero_mes_abrev)::text))
84. 5,197.147 5,197.147 ↓ 1.9 69,750 1

CTE Scan on distribuicao_mes dm (cost=0.00..728.00 rows=36,400 width=58) (actual time=5,140.896..5,197.147 rows=69,750 loops=1)

85. 16.654 1,546.770 ↓ 363.9 72,774 1

Hash (cost=4.00..4.00 rows=200 width=74) (actual time=1,546.770..1,546.770 rows=72,774 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 3571kB
86. 1,530.116 1,530.116 ↓ 363.9 72,774 1

CTE Scan on atendimentos_mes a (cost=0.00..4.00 rows=200 width=74) (actual time=1,426.436..1,530.116 rows=72,774 loops=1)

87. 139.500 279.000 ↑ 2.0 1 69,750

Bitmap Heap Scan on dim_cliente dc (cost=45.46..53.33 rows=2 width=36) (actual time=0.004..0.004 rows=1 loops=69,750)

  • Recheck Cond: ((sk_cliente = dm.sk_cliente) OR (sk_cliente = a.sk_cliente))
88. 0.000 139.500 ↓ 0.0 0 69,750

BitmapOr (cost=45.46..45.46 rows=2 width=0) (actual time=0.002..0.002 rows=0 loops=69,750)

89. 69.750 69.750 ↑ 1.0 1 69,750

Bitmap Index Scan on dim_cliente_pkey (cost=0.00..0.29 rows=1 width=0) (actual time=0.001..0.001 rows=1 loops=69,750)

  • Index Cond: (sk_cliente = dm.sk_cliente)
90. 69.750 69.750 ↑ 1.0 1 69,750

Bitmap Index Scan on dim_cliente_pkey (cost=0.00..3.07 rows=1 width=0) (actual time=0.001..0.001 rows=1 loops=69,750)

  • Index Cond: (sk_cliente = a.sk_cliente)
Total runtime : 7,345.704 ms