explain.depesz.com

PostgreSQL's explain analyze made readable

Result: 6Krq

Settings
# exclusive inclusive rows x rows loops node
1. 170.716 8,743.012 ↓ 1.1 58,964 1

Append (cost=3,149.63..228,745.67 rows=53,043 width=579) (actual time=526.634..8,743.012 rows=58,964 loops=1)

2. 460.117 7,104.168 ↓ 1.0 52,351 1

Hash Left Join (cost=3,149.63..221,742.09 rows=52,277 width=461) (actual time=526.633..7,104.168 rows=52,351 loops=1)

  • Hash Cond: (empresas_sucursales.id_tipo_inscripcion = empresas_tipo_inscripcion.id_tipo_inscripcion)
3. 232.805 2,822.414 ↓ 1.0 52,351 1

Hash Left Join (cost=3,148.56..10,900.24 rows=52,277 width=441) (actual time=526.496..2,822.414 rows=52,351 loops=1)

  • Hash Cond: (empresas_sucursales.type = empresas_sucursales_types.id)
4. 240.114 2,589.594 ↓ 1.0 52,351 1

Hash Left Join (cost=3,147.51..10,180.38 rows=52,277 width=434) (actual time=526.443..2,589.594 rows=52,351 loops=1)

  • Hash Cond: (empresas_sucursales.id_activ_econ = actividad_econ.id_activ_econ)
5. 214.416 2,341.534 ↓ 1.0 52,351 1

Hash Left Join (cost=3,122.09..9,436.14 rows=52,277 width=380) (actual time=518.464..2,341.534 rows=52,351 loops=1)

  • Hash Cond: (empresas_sucursales.distrito_id = distritos.id)
6. 198.720 2,127.072 ↓ 1.0 52,351 1

Hash Left Join (cost=3,120.68..8,716.33 rows=52,277 width=369) (actual time=518.391..2,127.072 rows=52,351 loops=1)

  • Hash Cond: (empresas_sucursales.city_id = city.id)
7. 181.815 1,927.754 ↓ 1.0 52,351 1

Hash Left Join (cost=3,107.97..7,987.11 rows=52,277 width=333) (actual time=517.768..1,927.754 rows=52,351 loops=1)

  • Hash Cond: (emp.id_categoria_empresa = empresas_categorias.id_categoria_empresa)
8. 168.626 1,745.924 ↓ 1.0 52,351 1

Hash Left Join (cost=3,106.88..7,759.82 rows=52,277 width=319) (actual time=517.731..1,745.924 rows=52,351 loops=1)

  • Hash Cond: (emp.type = empresas_types.id)
9. 447.765 1,577.226 ↓ 1.0 52,351 1

Hash Join (cost=3,105.20..7,039.34 rows=52,277 width=304) (actual time=517.628..1,577.226 rows=52,351 loops=1)

  • Hash Cond: (empresas_sucursales.empresa_id = emp.id)
10. 611.881 611.881 ↓ 1.0 52,353 1

Seq Scan on empresas_sucursales (cost=0.00..2,888.56 rows=52,280 width=239) (actual time=0.019..611.881 rows=52,353 loops=1)

  • Filter: (type = 1)
  • Rows Removed by Filter: 6652
11. 169.080 517.580 ↓ 1.0 52,210 1

Hash (cost=2,452.64..2,452.64 rows=52,205 width=81) (actual time=517.580..517.580 rows=52,210 loops=1)

  • Buckets: 8192 Batches: 1 Memory Usage: 5856kB
12. 348.500 348.500 ↓ 1.0 52,210 1

Seq Scan on empresas emp (cost=0.00..2,452.64 rows=52,205 width=81) (actual time=0.018..348.500 rows=52,210 loops=1)

  • Filter: (((document)::text <> '0'::text) AND ((document)::text <> ''::text) AND ((document)::text <> '-'::text))
  • Rows Removed by Filter: 2
13. 0.041 0.072 ↑ 1.0 30 1

Hash (cost=1.30..1.30 rows=30 width=23) (actual time=0.072..0.072 rows=30 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 2kB
14. 0.031 0.031 ↑ 1.0 30 1

Seq Scan on empresas_types (cost=0.00..1.30 rows=30 width=23) (actual time=0.006..0.031 rows=30 loops=1)

15. 0.006 0.015 ↑ 1.0 4 1

Hash (cost=1.04..1.04 rows=4 width=18) (actual time=0.015..0.015 rows=4 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 1kB
16. 0.009 0.009 ↑ 1.0 4 1

Seq Scan on empresas_categorias (cost=0.00..1.04 rows=4 width=18) (actual time=0.004..0.009 rows=4 loops=1)

17. 0.309 0.598 ↑ 1.0 254 1

Hash (cost=9.54..9.54 rows=254 width=40) (actual time=0.598..0.598 rows=254 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 19kB
18. 0.289 0.289 ↑ 1.0 254 1

Seq Scan on city (cost=0.00..9.54 rows=254 width=40) (actual time=0.006..0.289 rows=254 loops=1)

19. 0.024 0.046 ↑ 1.0 18 1

Hash (cost=1.18..1.18 rows=18 width=19) (actual time=0.046..0.046 rows=18 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 1kB
20. 0.022 0.022 ↑ 1.0 18 1

Seq Scan on distritos (cost=0.00..1.18 rows=18 width=19) (actual time=0.006..0.022 rows=18 loops=1)

21. 0.900 7.946 ↑ 1.0 730 1

Hash (cost=16.30..16.30 rows=730 width=58) (actual time=7.946..7.946 rows=730 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 66kB
22. 7.046 7.046 ↑ 1.0 730 1

Seq Scan on actividad_econ (cost=0.00..16.30 rows=730 width=58) (actual time=0.006..7.046 rows=730 loops=1)

23. 0.004 0.015 ↑ 1.0 1 1

Hash (cost=1.04..1.04 rows=1 width=23) (actual time=0.015..0.015 rows=1 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 1kB
24. 0.011 0.011 ↑ 1.0 1 1

Seq Scan on empresas_sucursales_types (cost=0.00..1.04 rows=1 width=23) (actual time=0.009..0.011 rows=1 loops=1)

  • Filter: (id = 1)
  • Rows Removed by Filter: 2
25. 0.004 0.014 ↑ 1.0 3 1

Hash (cost=1.03..1.03 rows=3 width=24) (actual time=0.014..0.014 rows=3 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 1kB
26. 0.010 0.010 ↑ 1.0 3 1

Seq Scan on empresas_tipo_inscripcion (cost=0.00..1.03 rows=3 width=24) (actual time=0.006..0.010 rows=3 loops=1)

27.          

SubPlan (forHash Left Join)

28. 209.404 3,821.623 ↑ 1.0 1 52,351

Limit (cost=0.42..4.02 rows=1 width=27) (actual time=0.072..0.073 rows=1 loops=52,351)

29. 601.000 3,612.219 ↑ 2.0 1 52,351

Nested Loop (cost=0.42..7.62 rows=2 width=27) (actual time=0.069..0.069 rows=1 loops=52,351)

  • Join Filter: (cambio_situacion_empresa.situacion_act = situacion_emp.id_situacion)
  • Rows Removed by Join Filter: 3
30. 2,617.550 2,617.550 ↑ 2.0 1 52,351

Index Scan using cambio_situacion_empresa_nro_patronal_fecha_creacion_id_cam_idx on cambio_situacion_empresa (cost=0.42..6.32 rows=2 width=20) (actual time=0.050..0.050 rows=1 loops=52,351)

  • Index Cond: (nro_patronal = empresas_sucursales.nro_patronal)
31. 393.654 393.669 ↑ 1.4 5 43,741

Materialize (cost=0.00..1.10 rows=7 width=15) (actual time=0.002..0.009 rows=5 loops=43,741)

32. 0.015 0.015 ↑ 1.0 7 1

Seq Scan on situacion_emp (cost=0.00..1.07 rows=7 width=15) (actual time=0.005..0.015 rows=7 loops=1)

33. 92.686 1,468.128 ↓ 8.6 6,613 1

Nested Loop Left Join (cost=14.82..6,473.15 rows=766 width=463) (actual time=3.414..1,468.128 rows=6,613 loops=1)

34. 65.423 853.015 ↓ 8.6 6,613 1

Nested Loop Left Join (cost=14.82..3,363.23 rows=766 width=443) (actual time=3.322..853.015 rows=6,613 loops=1)

35. 24.212 661.945 ↓ 8.6 6,613 1

Hash Left Join (cost=14.82..3,312.16 rows=766 width=389) (actual time=3.291..661.945 rows=6,613 loops=1)

  • Hash Cond: (empresas_sucursales_1.distrito_id = distritos_1.id)
36. 48.247 637.690 ↓ 8.6 6,613 1

Nested Loop Left Join (cost=13.42..3,300.23 rows=766 width=378) (actual time=3.214..637.690 rows=6,613 loops=1)

37. 59.209 543.152 ↓ 8.6 6,613 1

Nested Loop Left Join (cost=13.42..3,279.02 rows=766 width=364) (actual time=3.208..543.152 rows=6,613 loops=1)

38. 34.679 371.522 ↓ 8.6 6,613 1

Nested Loop (cost=13.42..3,257.81 rows=766 width=349) (actual time=3.171..371.522 rows=6,613 loops=1)

39. 49.788 110.675 ↓ 8.7 6,652 1

Hash Left Join (cost=13.13..1,488.95 rows=766 width=345) (actual time=3.124..110.675 rows=6,652 loops=1)

  • Hash Cond: (empresas_sucursales_1.city_id = city_1.id)
40. 25.753 57.858 ↓ 8.7 6,652 1

Merge Right Join (cost=0.41..1,465.73 rows=766 width=309) (actual time=0.058..57.858 rows=6,652 loops=1)

  • Merge Cond: (empresas_sucursales_types_1.id = empresas_sucursales_1.type)
41. 0.012 0.012 ↑ 1.0 3 1

Index Scan using empresas_sucursales_types_pkey on empresas_sucursales_types empresas_sucursales_types_1 (cost=0.13..6.17 rows=3 width=23) (actual time=0.007..0.012 rows=3 loops=1)

42. 32.093 32.093 ↓ 8.7 6,652 1

Index Scan using empresas_sucursales_type_idx on empresas_sucursales empresas_sucursales_1 (cost=0.28..1,449.97 rows=766 width=302) (actual time=0.040..32.093 rows=6,652 loops=1)

43. 0.262 3.029 ↑ 1.0 254 1

Hash (cost=9.54..9.54 rows=254 width=40) (actual time=3.029..3.029 rows=254 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 19kB
44. 2.767 2.767 ↑ 1.0 254 1

Seq Scan on city city_1 (cost=0.00..9.54 rows=254 width=40) (actual time=0.007..2.767 rows=254 loops=1)

45. 226.168 226.168 ↑ 1.0 1 6,652

Index Scan using empresas_pk on empresas emp_1 (cost=0.29..2.30 rows=1 width=20) (actual time=0.030..0.034 rows=1 loops=6,652)

  • Index Cond: (id = empresas_sucursales_1.empresa_id)
  • Filter: (((document)::text <> '0'::text) AND ((document)::text <> ''::text) AND ((document)::text <> '-'::text))
  • Rows Removed by Filter: 0
46. 112.421 112.421 ↑ 1.0 1 6,613

Index Scan using empresas_types_id_idx on empresas_types empresas_types_1 (cost=0.00..0.02 rows=1 width=23) (actual time=0.016..0.017 rows=1 loops=6,613)

  • Index Cond: (emp_1.type = id)
47. 46.291 46.291 ↓ 0.0 0 6,613

Index Scan using empresas_categorias_id_categoria_empresa_idx on empresas_categorias empresas_categorias_1 (cost=0.00..0.02 rows=1 width=18) (actual time=0.007..0.007 rows=0 loops=6,613)

  • Index Cond: (emp_1.id_categoria_empresa = id_categoria_empresa)
48. 0.023 0.043 ↑ 1.0 18 1

Hash (cost=1.18..1.18 rows=18 width=19) (actual time=0.043..0.043 rows=18 loops=1)

  • Buckets: 1024 Batches: 1 Memory Usage: 1kB
49. 0.020 0.020 ↑ 1.0 18 1

Seq Scan on distritos distritos_1 (cost=0.00..1.18 rows=18 width=19) (actual time=0.006..0.020 rows=18 loops=1)

50. 125.647 125.647 ↑ 1.0 1 6,613

Index Scan using actividad_econ_id_activ_econ_idx on actividad_econ actividad_econ_1 (cost=0.00..0.06 rows=1 width=58) (actual time=0.017..0.019 rows=1 loops=6,613)

  • Index Cond: (empresas_sucursales_1.id_activ_econ = id_activ_econ)
51. 92.582 92.582 ↑ 1.0 1 6,613

Index Scan using empresas_tipo_inscripcion_id_tipo_inscripcion_idx on empresas_tipo_inscripcion empresas_tipo_inscripcion_1 (cost=0.00..0.03 rows=1 width=24) (actual time=0.011..0.014 rows=1 loops=6,613)

  • Index Cond: (empresas_sucursales_1.id_tipo_inscripcion = id_tipo_inscripcion)
52.          

SubPlan (forNested Loop Left Join)

53. 33.065 429.845 ↑ 1.0 1 6,613

Limit (cost=0.42..4.02 rows=1 width=27) (actual time=0.063..0.065 rows=1 loops=6,613)

54. 69.026 396.780 ↑ 2.0 1 6,613

Nested Loop (cost=0.42..7.62 rows=2 width=27) (actual time=0.060..0.060 rows=1 loops=6,613)

  • Join Filter: (cambio_situacion_empresa_1.situacion_act = situacion_emp_1.id_situacion)
  • Rows Removed by Join Filter: 4
55. 277.746 277.746 ↑ 2.0 1 6,613

Index Scan using cambio_situacion_empresa_nro_patronal_fecha_creacion_id_cam_idx on cambio_situacion_empresa cambio_situacion_empresa_1 (cost=0.42..6.32 rows=2 width=20) (actual time=0.042..0.042 rows=1 loops=6,613)

  • Index Cond: (nro_patronal = empresas_sucursales_1.nro_patronal)
56. 49.997 50.008 ↑ 1.4 5 6,251

Materialize (cost=0.00..1.10 rows=7 width=15) (actual time=0.003..0.008 rows=5 loops=6,251)

57. 0.011 0.011 ↑ 1.2 6 1

Seq Scan on situacion_emp situacion_emp_1 (cost=0.00..1.07 rows=7 width=15) (actual time=0.004..0.011 rows=6 loops=1)