explain.depesz.com

PostgreSQL's explain analyze made readable

Result: S6KL

Settings
# exclusive inclusive rows x rows loops node
1. 40,986.506 40,986.506 ↓ 60.1 32,042,397 1

CTE Scan on metricas (cost=76,186.32..86,852.88 rows=533,328 width=128) (actual time=1,615.713..40,986.506 rows=32,042,397 loops=1)

2.          

CTE info_dataset

3. 229.025 231.034 ↓ 4.9 257 1

Nested Loop (cost=0.84..45.48 rows=52 width=754) (actual time=1.425..231.034 rows=257 loops=1)

4. 0.065 1.752 ↓ 4.9 257 1

Nested Loop (cost=0.56..22.05 rows=52 width=436) (actual time=0.598..1.752 rows=257 loops=1)

5. 0.127 0.127 ↑ 1.0 1 1

Index Scan using eo_fk_cc_idx on evento_operacao eo (cost=0.28..1.40 rows=1 width=595) (actual time=0.016..0.127 rows=1 loops=1)

  • Index Cond: (cd_cliente = 2)
  • Filter: (cd_id = 17,660)
  • Rows Removed by Filter: 142
6. 0.040 1.560 ↓ 85.7 257 1

Append (cost=0.28..20.62 rows=3 width=444) (actual time=0.579..1.560 rows=257 loops=1)

7. 1.520 1.520 ↓ 85.7 257 1

Index Scan using ddn_trecho2_pkey on ddn_trecho2 tot (cost=0.28..20.61 rows=3 width=444) (actual time=0.572..1.520 rows=257 loops=1)

  • Index Cond: ((cd_cliente = 2) AND (cd_id = ANY (eo.vl_trechos_arr)))
  • Filter: ((dt_created)::date >= '2020-07-02'::date)
  • Rows Removed by Filter: 242
8. 0.257 0.257 ↑ 1.0 1 257

Index Scan using cdt_equipamento_pkey on cdt_equipamento eq (cost=0.28..0.35 rows=1 width=11) (actual time=0.001..0.001 rows=1 loops=257)

  • Index Cond: (cd_id = tot.cd_id_equipamento)
9.          

CTE metricas

10. 2,291.203 31,362.273 ↓ 60.1 32,042,397 1

Append (cost=5.81..76,140.84 rows=533,328 width=128) (actual time=1,615.710..31,362.273 rows=32,042,397 loops=1)

11. 642.203 2,666.069 ↓ 77.9 2,596,463 1

Subquery Scan on a (cost=5.81..4,258.81 rows=33,333 width=128) (actual time=1,615.709..2,666.069 rows=2,596,463 loops=1)

  • Filter: (a.campo <= (a.avg_campo * '3'::numeric))
  • Rows Removed by Filter: 332
12. 1,159.117 2,023.866 ↓ 26.0 2,596,795 1

WindowAgg (cost=5.81..2,758.81 rows=100,000 width=128) (actual time=1,615.705..2,023.866 rows=2,596,795 loops=1)

13. 229.695 864.749 ↓ 26.0 2,596,795 1

Subquery Scan on d (cost=5.81..1,508.81 rows=100,000 width=96) (actual time=238.429..864.749 rows=2,596,795 loops=1)

14. 396.565 635.054 ↓ 26.0 2,596,795 1

ProjectSet (cost=5.81..508.81 rows=100,000 width=128) (actual time=238.428..635.054 rows=2,596,795 loops=1)

15. 1.480 238.489 ↑ 1.1 95 1

HashAggregate (cost=5.81..7.06 rows=100 width=64) (actual time=238.424..238.489 rows=95 loops=1)

  • Group Key: (((jsonb_each_text(ds.massa))).key)::numeric
16. 1.847 237.009 ↓ 51.2 5,123 1

Result (cost=0.00..4.31 rows=100 width=64) (actual time=1.454..237.009 rows=5,123 loops=1)

17. 3.402 235.162 ↓ 51.2 5,123 1

ProjectSet (cost=0.00..1.81 rows=100 width=32) (actual time=1.452..235.162 rows=5,123 loops=1)

18. 231.760 231.760 ↓ 255.0 255 1

CTE Scan on info_dataset ds (cost=0.00..1.30 rows=1 width=32) (actual time=1.430..231.760 rows=255 loops=1)

  • Filter: ((massa IS NOT NULL) AND (jsonb_typeof(massa) = 'object'::text))
  • Rows Removed by Filter: 2
19. 1,106.913 4,186.643 ↓ 134.3 4,475,214 1

Subquery Scan on a_1 (cost=5.81..4,258.81 rows=33,333 width=128) (actual time=2,381.268..4,186.643 rows=4,475,214 loops=1)

  • Filter: (a_1.campo <= (a_1.avg_campo * '3'::numeric))
  • Rows Removed by Filter: 198
20. 2,000.421 3,079.730 ↓ 44.8 4,475,412 1

WindowAgg (cost=5.81..2,758.81 rows=100,000 width=128) (actual time=2,381.265..3,079.730 rows=4,475,412 loops=1)

21. 392.900 1,079.309 ↓ 44.8 4,475,412 1

Subquery Scan on d_1 (cost=5.81..1,508.81 rows=100,000 width=96) (actual time=3.516..1,079.309 rows=4,475,412 loops=1)

22. 682.855 686.409 ↓ 44.8 4,475,412 1

ProjectSet (cost=5.81..508.81 rows=100,000 width=128) (actual time=3.515..686.409 rows=4,475,412 loops=1)

23. 0.657 3.554 ↑ 2.1 48 1

HashAggregate (cost=5.81..7.06 rows=100 width=64) (actual time=3.510..3.554 rows=48 loops=1)

  • Group Key: (((jsonb_each_text(ds_1.rpm))).key)::numeric
24. 0.927 2.897 ↓ 27.2 2,716 1

Result (cost=0.00..4.31 rows=100 width=64) (actual time=0.050..2.897 rows=2,716 loops=1)

25. 1.821 1.970 ↓ 27.2 2,716 1

ProjectSet (cost=0.00..1.81 rows=100 width=32) (actual time=0.046..1.970 rows=2,716 loops=1)

26. 0.149 0.149 ↓ 257.0 257 1

CTE Scan on info_dataset ds_1 (cost=0.00..1.30 rows=1 width=32) (actual time=0.005..0.149 rows=257 loops=1)

  • Filter: ((rpm IS NOT NULL) AND (jsonb_typeof(rpm) = 'object'::text))
27. 965.794 4,679.935 ↓ 162.1 5,403,228 1

Subquery Scan on a_2 (cost=5.81..4,258.81 rows=33,333 width=128) (actual time=2,861.579..4,679.935 rows=5,403,228 loops=1)

  • Filter: (a_2.campo <= (a_2.avg_campo * '3'::numeric))
28. 2,411.408 3,714.141 ↓ 54.0 5,403,228 1

WindowAgg (cost=5.81..2,758.81 rows=100,000 width=128) (actual time=2,861.576..3,714.141 rows=5,403,228 loops=1)

29. 475.005 1,302.733 ↓ 54.0 5,403,228 1

Subquery Scan on d_2 (cost=5.81..1,508.81 rows=100,000 width=96) (actual time=0.921..1,302.733 rows=5,403,228 loops=1)

30. 826.810 827.728 ↓ 54.0 5,403,228 1

ProjectSet (cost=5.81..508.81 rows=100,000 width=128) (actual time=0.920..827.728 rows=5,403,228 loops=1)

31. 0.078 0.918 ↑ 100.0 1 1

HashAggregate (cost=5.81..7.06 rows=100 width=64) (actual time=0.917..0.918 rows=1 loops=1)

  • Group Key: (((jsonb_each_text(ds_2.umidade))).key)::numeric
32. 0.095 0.840 ↓ 2.6 257 1

Result (cost=0.00..4.31 rows=100 width=64) (actual time=0.060..0.840 rows=257 loops=1)

33. 0.599 0.745 ↓ 2.6 257 1

ProjectSet (cost=0.00..1.81 rows=100 width=32) (actual time=0.054..0.745 rows=257 loops=1)

34. 0.146 0.146 ↓ 257.0 257 1

CTE Scan on info_dataset ds_2 (cost=0.00..1.30 rows=1 width=32) (actual time=0.006..0.146 rows=257 loops=1)

  • Filter: ((umidade IS NOT NULL) AND (jsonb_typeof(umidade) = 'object'::text))
35. 0.000 0.158 ↓ 0.0 0 1

Subquery Scan on a_3 (cost=5.81..4,258.81 rows=33,333 width=128) (actual time=0.158..0.158 rows=0 loops=1)

  • Filter: (a_3.campo <= (a_3.avg_campo * '3'::numeric))
36. 0.002 0.158 ↓ 0.0 0 1

WindowAgg (cost=5.81..2,758.81 rows=100,000 width=128) (actual time=0.158..0.158 rows=0 loops=1)

37. 0.001 0.156 ↓ 0.0 0 1

Subquery Scan on d_3 (cost=5.81..1,508.81 rows=100,000 width=96) (actual time=0.156..0.156 rows=0 loops=1)

38. 0.000 0.155 ↓ 0.0 0 1

ProjectSet (cost=5.81..508.81 rows=100,000 width=128) (actual time=0.155..0.155 rows=0 loops=1)

39. 0.002 0.155 ↓ 0.0 0 1

HashAggregate (cost=5.81..7.06 rows=100 width=64) (actual time=0.155..0.155 rows=0 loops=1)

  • Group Key: (((jsonb_each_text(ds_3.vazao_ha))).key)::numeric
40. 0.001 0.153 ↓ 0.0 0 1

Result (cost=0.00..4.31 rows=100 width=64) (actual time=0.153..0.153 rows=0 loops=1)

41. 0.001 0.152 ↓ 0.0 0 1

ProjectSet (cost=0.00..1.81 rows=100 width=32) (actual time=0.152..0.152 rows=0 loops=1)

42. 0.151 0.151 ↓ 0.0 0 1

CTE Scan on info_dataset ds_3 (cost=0.00..1.30 rows=1 width=32) (actual time=0.151..0.151 rows=0 loops=1)

  • Filter: ((vazao_ha IS NOT NULL) AND (jsonb_typeof(vazao_ha) = 'object'::text))
  • Rows Removed by Filter: 257
43. 0.000 0.072 ↓ 0.0 0 1

Subquery Scan on a_4 (cost=5.81..4,258.81 rows=33,333 width=128) (actual time=0.072..0.072 rows=0 loops=1)

  • Filter: (a_4.campo <= (a_4.avg_campo * '3'::numeric))
44. 0.001 0.072 ↓ 0.0 0 1

WindowAgg (cost=5.81..2,758.81 rows=100,000 width=128) (actual time=0.072..0.072 rows=0 loops=1)

45. 0.001 0.071 ↓ 0.0 0 1

Subquery Scan on d_4 (cost=5.81..1,508.81 rows=100,000 width=96) (actual time=0.071..0.071 rows=0 loops=1)

46. 0.000 0.070 ↓ 0.0 0 1

ProjectSet (cost=5.81..508.81 rows=100,000 width=128) (actual time=0.070..0.070 rows=0 loops=1)

47. 0.001 0.070 ↓ 0.0 0 1

HashAggregate (cost=5.81..7.06 rows=100 width=64) (actual time=0.070..0.070 rows=0 loops=1)

  • Group Key: (((jsonb_each_text(ds_4.vazao_min))).key)::numeric
48. 0.001 0.069 ↓ 0.0 0 1

Result (cost=0.00..4.31 rows=100 width=64) (actual time=0.069..0.069 rows=0 loops=1)

49. 0.001 0.068 ↓ 0.0 0 1

ProjectSet (cost=0.00..1.81 rows=100 width=32) (actual time=0.068..0.068 rows=0 loops=1)

50. 0.067 0.067 ↓ 0.0 0 1

CTE Scan on info_dataset ds_4 (cost=0.00..1.30 rows=1 width=32) (actual time=0.067..0.067 rows=0 loops=1)

  • Filter: ((vazao_min IS NOT NULL) AND (jsonb_typeof(vazao_min) = 'object'::text))
  • Rows Removed by Filter: 257
51. 839.556 3,243.194 ↓ 100.7 3,357,808 1

Subquery Scan on a_5 (cost=5.81..4,258.81 rows=33,333 width=128) (actual time=1,832.230..3,243.194 rows=3,357,808 loops=1)

  • Filter: (a_5.campo <= (a_5.avg_campo * '3'::numeric))
52. 1,567.556 2,403.638 ↓ 33.6 3,357,808 1

WindowAgg (cost=5.81..2,758.81 rows=100,000 width=128) (actual time=1,832.226..2,403.638 rows=3,357,808 loops=1)

53. 318.800 836.082 ↓ 33.6 3,357,808 1

Subquery Scan on d_5 (cost=5.81..1,508.81 rows=100,000 width=96) (actual time=2.912..836.082 rows=3,357,808 loops=1)

54. 514.345 517.282 ↓ 33.6 3,357,808 1

ProjectSet (cost=5.81..508.81 rows=100,000 width=128) (actual time=2.911..517.282 rows=3,357,808 loops=1)

55. 0.540 2.937 ↑ 9.1 11 1

HashAggregate (cost=5.81..7.06 rows=100 width=64) (actual time=2.908..2.937 rows=11 loops=1)

  • Group Key: (((jsonb_each_text(ds_5.velocidade))).key)::numeric
56. 0.746 2.397 ↓ 22.0 2,202 1

Result (cost=0.00..4.31 rows=100 width=64) (actual time=0.058..2.397 rows=2,202 loops=1)

57. 1.555 1.651 ↓ 22.0 2,202 1

ProjectSet (cost=0.00..1.81 rows=100 width=32) (actual time=0.054..1.651 rows=2,202 loops=1)

58. 0.096 0.096 ↓ 257.0 257 1

CTE Scan on info_dataset ds_5 (cost=0.00..1.30 rows=1 width=32) (actual time=0.002..0.096 rows=257 loops=1)

  • Filter: ((velocidade IS NOT NULL) AND (jsonb_typeof(velocidade) = 'object'::text))
59. 965.415 4,778.952 ↓ 162.1 5,403,228 1

Subquery Scan on a_6 (cost=5.81..4,258.81 rows=33,333 width=128) (actual time=2,933.922..4,778.952 rows=5,403,228 loops=1)

  • Filter: (a_6.campo <= (a_6.avg_campo * '3'::numeric))
60. 2,461.254 3,813.537 ↓ 54.0 5,403,228 1

WindowAgg (cost=5.81..2,758.81 rows=100,000 width=128) (actual time=2,933.918..3,813.537 rows=5,403,228 loops=1)

61. 521.990 1,352.283 ↓ 54.0 5,403,228 1

Subquery Scan on d_6 (cost=5.81..1,508.81 rows=100,000 width=96) (actual time=0.941..1,352.283 rows=5,403,228 loops=1)

62. 829.354 830.293 ↓ 54.0 5,403,228 1

ProjectSet (cost=5.81..508.81 rows=100,000 width=128) (actual time=0.940..830.293 rows=5,403,228 loops=1)

63. 0.072 0.939 ↑ 100.0 1 1

HashAggregate (cost=5.81..7.06 rows=100 width=64) (actual time=0.938..0.939 rows=1 loops=1)

  • Group Key: (((jsonb_each_text(ds_6.temperatura))).key)::numeric
64. 0.099 0.867 ↓ 2.6 257 1

Result (cost=0.00..4.31 rows=100 width=64) (actual time=0.070..0.867 rows=257 loops=1)

65. 0.613 0.768 ↓ 2.6 257 1

ProjectSet (cost=0.00..1.81 rows=100 width=32) (actual time=0.064..0.768 rows=257 loops=1)

66. 0.155 0.155 ↓ 257.0 257 1

CTE Scan on info_dataset ds_6 (cost=0.00..1.30 rows=1 width=32) (actual time=0.008..0.155 rows=257 loops=1)

  • Filter: ((temperatura IS NOT NULL) AND (jsonb_typeof(temperatura) = 'object'::text))
67. 966.270 4,759.501 ↓ 162.1 5,403,228 1

Subquery Scan on a_7 (cost=5.81..4,258.81 rows=33,333 width=128) (actual time=2,928.419..4,759.501 rows=5,403,228 loops=1)

  • Filter: (a_7.campo <= (a_7.avg_campo * '3'::numeric))
68. 2,473.169 3,793.231 ↓ 54.0 5,403,228 1

WindowAgg (cost=5.81..2,758.81 rows=100,000 width=128) (actual time=2,928.415..3,793.231 rows=5,403,228 loops=1)

69. 491.369 1,320.062 ↓ 54.0 5,403,228 1

Subquery Scan on d_7 (cost=5.81..1,508.81 rows=100,000 width=96) (actual time=0.969..1,320.062 rows=5,403,228 loops=1)

70. 827.727 828.693 ↓ 54.0 5,403,228 1

ProjectSet (cost=5.81..508.81 rows=100,000 width=128) (actual time=0.968..828.693 rows=5,403,228 loops=1)

71. 0.078 0.966 ↑ 100.0 1 1

HashAggregate (cost=5.81..7.06 rows=100 width=64) (actual time=0.964..0.966 rows=1 loops=1)

  • Group Key: (((jsonb_each_text(ds_7.ponto_orvalho))).key)::numeric
72. 0.100 0.888 ↓ 2.6 257 1

Result (cost=0.00..4.31 rows=100 width=64) (actual time=0.072..0.888 rows=257 loops=1)

73. 0.626 0.788 ↓ 2.6 257 1

ProjectSet (cost=0.00..1.81 rows=100 width=32) (actual time=0.066..0.788 rows=257 loops=1)

74. 0.162 0.162 ↓ 257.0 257 1

CTE Scan on info_dataset ds_7 (cost=0.00..1.30 rows=1 width=32) (actual time=0.008..0.162 rows=257 loops=1)

  • Filter: ((ponto_orvalho IS NOT NULL) AND (jsonb_typeof(ponto_orvalho) = 'object'::text))
75. 0.001 0.183 ↓ 0.0 0 1

Subquery Scan on a_8 (cost=5.81..4,258.81 rows=33,333 width=128) (actual time=0.183..0.183 rows=0 loops=1)

  • Filter: (a_8.campo <= (a_8.avg_campo * '3'::numeric))
76. 0.001 0.182 ↓ 0.0 0 1

WindowAgg (cost=5.81..2,758.81 rows=100,000 width=128) (actual time=0.182..0.182 rows=0 loops=1)

77. 0.001 0.181 ↓ 0.0 0 1

Subquery Scan on d_8 (cost=5.81..1,508.81 rows=100,000 width=96) (actual time=0.181..0.181 rows=0 loops=1)

78. 0.000 0.180 ↓ 0.0 0 1

ProjectSet (cost=5.81..508.81 rows=100,000 width=128) (actual time=0.180..0.180 rows=0 loops=1)

79. 0.003 0.180 ↓ 0.0 0 1

HashAggregate (cost=5.81..7.06 rows=100 width=64) (actual time=0.179..0.180 rows=0 loops=1)

  • Group Key: (((jsonb_each_text(ds_8.consumo_inst))).key)::numeric
80. 0.001 0.177 ↓ 0.0 0 1

Result (cost=0.00..4.31 rows=100 width=64) (actual time=0.177..0.177 rows=0 loops=1)

81. 0.000 0.176 ↓ 0.0 0 1

ProjectSet (cost=0.00..1.81 rows=100 width=32) (actual time=0.176..0.176 rows=0 loops=1)

82. 0.176 0.176 ↓ 0.0 0 1

CTE Scan on info_dataset ds_8 (cost=0.00..1.30 rows=1 width=32) (actual time=0.176..0.176 rows=0 loops=1)

  • Filter: ((consumo_inst IS NOT NULL) AND (jsonb_typeof(consumo_inst) = 'object'::text))
  • Rows Removed by Filter: 257
83. 0.000 0.081 ↓ 0.0 0 1

Subquery Scan on a_9 (cost=5.81..4,258.81 rows=33,333 width=128) (actual time=0.081..0.081 rows=0 loops=1)

  • Filter: (a_9.campo <= (a_9.avg_campo * '3'::numeric))
84. 0.001 0.081 ↓ 0.0 0 1

WindowAgg (cost=5.81..2,758.81 rows=100,000 width=128) (actual time=0.081..0.081 rows=0 loops=1)

85. 0.000 0.080 ↓ 0.0 0 1

Subquery Scan on d_9 (cost=5.81..1,508.81 rows=100,000 width=96) (actual time=0.080..0.080 rows=0 loops=1)

86. 0.001 0.080 ↓ 0.0 0 1

ProjectSet (cost=5.81..508.81 rows=100,000 width=128) (actual time=0.080..0.080 rows=0 loops=1)

87. 0.001 0.079 ↓ 0.0 0 1

HashAggregate (cost=5.81..7.06 rows=100 width=64) (actual time=0.079..0.079 rows=0 loops=1)

  • Group Key: (((jsonb_each_text(ds_9.pressao_bomba))).key)::numeric
88. 0.001 0.078 ↓ 0.0 0 1

Result (cost=0.00..4.31 rows=100 width=64) (actual time=0.078..0.078 rows=0 loops=1)

89. 0.000 0.077 ↓ 0.0 0 1

ProjectSet (cost=0.00..1.81 rows=100 width=32) (actual time=0.077..0.077 rows=0 loops=1)

90. 0.077 0.077 ↓ 0.0 0 1

CTE Scan on info_dataset ds_9 (cost=0.00..1.30 rows=1 width=32) (actual time=0.077..0.077 rows=0 loops=1)

  • Filter: ((pressao_bomba IS NOT NULL) AND (jsonb_typeof(pressao_bomba) = 'object'::text))
  • Rows Removed by Filter: 257
91. 971.279 4,755.783 ↓ 162.1 5,403,228 1

Subquery Scan on a_10 (cost=5.81..4,258.81 rows=33,333 width=128) (actual time=2,917.061..4,755.783 rows=5,403,228 loops=1)

  • Filter: (a_10.campo <= (a_10.avg_campo * '3'::numeric))
92. 2,483.001 3,784.504 ↓ 54.0 5,403,228 1

WindowAgg (cost=5.81..2,758.81 rows=100,000 width=128) (actual time=2,917.059..3,784.504 rows=5,403,228 loops=1)

93. 474.576 1,301.503 ↓ 54.0 5,403,228 1

Subquery Scan on d_10 (cost=5.81..1,508.81 rows=100,000 width=96) (actual time=0.869..1,301.503 rows=5,403,228 loops=1)

94. 826.060 826.927 ↓ 54.0 5,403,228 1

ProjectSet (cost=5.81..508.81 rows=100,000 width=128) (actual time=0.869..826.927 rows=5,403,228 loops=1)

95. 0.076 0.867 ↑ 100.0 1 1

HashAggregate (cost=5.81..7.06 rows=100 width=64) (actual time=0.866..0.867 rows=1 loops=1)

  • Group Key: (((jsonb_each_text(ds_10.umidade_graos))).key)::numeric
96. 0.094 0.791 ↓ 2.6 257 1

Result (cost=0.00..4.31 rows=100 width=64) (actual time=0.056..0.791 rows=257 loops=1)

97. 0.605 0.697 ↓ 2.6 257 1

ProjectSet (cost=0.00..1.81 rows=100 width=32) (actual time=0.050..0.697 rows=257 loops=1)

98. 0.092 0.092 ↓ 257.0 257 1

CTE Scan on info_dataset ds_10 (cost=0.00..1.30 rows=1 width=32) (actual time=0.002..0.092 rows=257 loops=1)

  • Filter: ((umidade_graos IS NOT NULL) AND (jsonb_typeof(umidade_graos) = 'object'::text))
99. 0.001 0.186 ↓ 0.0 0 1

Subquery Scan on a_11 (cost=5.81..4,258.81 rows=33,333 width=128) (actual time=0.186..0.186 rows=0 loops=1)

  • Filter: (a_11.campo <= (a_11.avg_campo * '3'::numeric))
100. 0.002 0.185 ↓ 0.0 0 1

WindowAgg (cost=5.81..2,758.81 rows=100,000 width=128) (actual time=0.185..0.185 rows=0 loops=1)

101. 0.001 0.183 ↓ 0.0 0 1

Subquery Scan on d_11 (cost=5.81..1,508.81 rows=100,000 width=96) (actual time=0.183..0.183 rows=0 loops=1)

102. 0.000 0.182 ↓ 0.0 0 1

ProjectSet (cost=5.81..508.81 rows=100,000 width=128) (actual time=0.182..0.182 rows=0 loops=1)

103. 0.002 0.182 ↓ 0.0 0 1

HashAggregate (cost=5.81..7.06 rows=100 width=64) (actual time=0.182..0.182 rows=0 loops=1)

  • Group Key: (((jsonb_each_text(ds_11.rendimento_oper))).key)::numeric
104. 0.001 0.180 ↓ 0.0 0 1

Result (cost=0.00..4.31 rows=100 width=64) (actual time=0.179..0.180 rows=0 loops=1)

105. 0.001 0.179 ↓ 0.0 0 1

ProjectSet (cost=0.00..1.81 rows=100 width=32) (actual time=0.179..0.179 rows=0 loops=1)

106. 0.178 0.178 ↓ 0.0 0 1

CTE Scan on info_dataset ds_11 (cost=0.00..1.30 rows=1 width=32) (actual time=0.178..0.178 rows=0 loops=1)

  • Filter: ((rendimento_oper IS NOT NULL) AND (jsonb_typeof(rendimento_oper) = 'object'::text))
  • Rows Removed by Filter: 257
107. 0.000 0.087 ↓ 0.0 0 1

Subquery Scan on a_12 (cost=5.81..4,258.81 rows=33,333 width=128) (actual time=0.087..0.087 rows=0 loops=1)

  • Filter: (a_12.campo <= (a_12.avg_campo * '3'::numeric))
108. 0.001 0.087 ↓ 0.0 0 1

WindowAgg (cost=5.81..2,758.81 rows=100,000 width=128) (actual time=0.087..0.087 rows=0 loops=1)

109. 0.001 0.086 ↓ 0.0 0 1

Subquery Scan on d_12 (cost=5.81..1,508.81 rows=100,000 width=96) (actual time=0.086..0.086 rows=0 loops=1)

110. 0.000 0.085 ↓ 0.0 0 1

ProjectSet (cost=5.81..508.81 rows=100,000 width=128) (actual time=0.085..0.085 rows=0 loops=1)

111. 0.001 0.085 ↓ 0.0 0 1

HashAggregate (cost=5.81..7.06 rows=100 width=64) (actual time=0.085..0.085 rows=0 loops=1)

  • Group Key: (((jsonb_each_text(ds_12.velocidade_vento))).key)::numeric
112. 0.000 0.084 ↓ 0.0 0 1

Result (cost=0.00..4.31 rows=100 width=64) (actual time=0.084..0.084 rows=0 loops=1)

113. 0.001 0.084 ↓ 0.0 0 1

ProjectSet (cost=0.00..1.81 rows=100 width=32) (actual time=0.084..0.084 rows=0 loops=1)

114. 0.083 0.083 ↓ 0.0 0 1

CTE Scan on info_dataset ds_12 (cost=0.00..1.30 rows=1 width=32) (actual time=0.083..0.083 rows=0 loops=1)

  • Filter: ((velocidade_vento IS NOT NULL) AND (jsonb_typeof(velocidade_vento) = 'object'::text))
  • Rows Removed by Filter: 257
115. 0.000 0.074 ↓ 0.0 0 1

Subquery Scan on a_13 (cost=5.81..4,258.81 rows=33,333 width=128) (actual time=0.074..0.074 rows=0 loops=1)

  • Filter: (a_13.campo <= (a_13.avg_campo * '3'::numeric))
116. 0.001 0.074 ↓ 0.0 0 1

WindowAgg (cost=5.81..2,758.81 rows=100,000 width=128) (actual time=0.074..0.074 rows=0 loops=1)

117. 0.000 0.073 ↓ 0.0 0 1

Subquery Scan on d_13 (cost=5.81..1,508.81 rows=100,000 width=96) (actual time=0.073..0.073 rows=0 loops=1)

118. 0.001 0.073 ↓ 0.0 0 1

ProjectSet (cost=5.81..508.81 rows=100,000 width=128) (actual time=0.073..0.073 rows=0 loops=1)

119. 0.001 0.072 ↓ 0.0 0 1

HashAggregate (cost=5.81..7.06 rows=100 width=64) (actual time=0.072..0.072 rows=0 loops=1)

  • Group Key: (((jsonb_each_text(ds_13.temperatura_motor))).key)::numeric
120. 0.000 0.071 ↓ 0.0 0 1

Result (cost=0.00..4.31 rows=100 width=64) (actual time=0.071..0.071 rows=0 loops=1)

121. 0.001 0.071 ↓ 0.0 0 1

ProjectSet (cost=0.00..1.81 rows=100 width=32) (actual time=0.071..0.071 rows=0 loops=1)

122. 0.070 0.070 ↓ 0.0 0 1

CTE Scan on info_dataset ds_13 (cost=0.00..1.30 rows=1 width=32) (actual time=0.070..0.070 rows=0 loops=1)

  • Filter: ((temperatura_motor IS NOT NULL) AND (jsonb_typeof(temperatura_motor) = 'object'::text))
  • Rows Removed by Filter: 257
123. 0.001 0.076 ↓ 0.0 0 1

Subquery Scan on a_14 (cost=5.81..4,258.81 rows=33,333 width=128) (actual time=0.076..0.076 rows=0 loops=1)

  • Filter: (a_14.campo <= (a_14.avg_campo * '3'::numeric))
124. 0.001 0.075 ↓ 0.0 0 1

WindowAgg (cost=5.81..2,758.81 rows=100,000 width=128) (actual time=0.075..0.075 rows=0 loops=1)

125. 0.000 0.074 ↓ 0.0 0 1

Subquery Scan on d_14 (cost=5.81..1,508.81 rows=100,000 width=96) (actual time=0.074..0.074 rows=0 loops=1)

126. 0.000 0.074 ↓ 0.0 0 1

ProjectSet (cost=5.81..508.81 rows=100,000 width=128) (actual time=0.074..0.074 rows=0 loops=1)

127. 0.002 0.074 ↓ 0.0 0 1

HashAggregate (cost=5.81..7.06 rows=100 width=64) (actual time=0.074..0.074 rows=0 loops=1)

  • Group Key: (((jsonb_each_text(ds_14.qtde_sementes_metros))).key)::numeric
128. 0.000 0.072 ↓ 0.0 0 1

Result (cost=0.00..4.31 rows=100 width=64) (actual time=0.072..0.072 rows=0 loops=1)

129. 0.001 0.072 ↓ 0.0 0 1

ProjectSet (cost=0.00..1.81 rows=100 width=32) (actual time=0.072..0.072 rows=0 loops=1)

130. 0.071 0.071 ↓ 0.0 0 1

CTE Scan on info_dataset ds_14 (cost=0.00..1.30 rows=1 width=32) (actual time=0.071..0.071 rows=0 loops=1)

  • Filter: ((qtde_sementes_metros IS NOT NULL) AND (jsonb_typeof(qtde_sementes_metros) = 'object'::text))
  • Rows Removed by Filter: 257
131. 0.000 0.076 ↓ 0.0 0 1

Subquery Scan on a_15 (cost=5.81..4,258.81 rows=33,333 width=128) (actual time=0.076..0.076 rows=0 loops=1)

  • Filter: (a_15.campo <= (a_15.avg_campo * '3'::numeric))
132. 0.001 0.076 ↓ 0.0 0 1

WindowAgg (cost=5.81..2,758.81 rows=100,000 width=128) (actual time=0.076..0.076 rows=0 loops=1)

133. 0.000 0.075 ↓ 0.0 0 1

Subquery Scan on d_15 (cost=5.81..1,508.81 rows=100,000 width=96) (actual time=0.075..0.075 rows=0 loops=1)

134. 0.001 0.075 ↓ 0.0 0 1

ProjectSet (cost=5.81..508.81 rows=100,000 width=128) (actual time=0.075..0.075 rows=0 loops=1)

135. 0.074 0.074 ↓ 0.0 0 1

HashAggregate (cost=5.81..7.06 rows=100 width=64) (actual time=0.074..0.074 rows=0 loops=1)

  • Group Key: (((jsonb_each_text(ds_15.qtde_sementes_ha))).key)::numeric