explain.depesz.com

PostgreSQL's explain analyze made readable

Result: EKoe

Settings
# exclusive inclusive rows x rows loops node
1. 0.076 32.027 ↓ 15.9 571 1

Limit (cost=51.86..419.10 rows=36 width=1,230) (actual time=0.832..32.027 rows=571 loops=1)

2. 24.326 31.951 ↓ 15.9 571 1

Nested Loop Left Join (cost=51.86..419.10 rows=36 width=1,230) (actual time=0.831..31.951 rows=571 loops=1)

  • Join Filter: (function_levels.id = jobs."functionLevelId")
  • Rows Removed by Join Filter: 3,148
3. 0.617 7.625 ↓ 15.9 571 1

Nested Loop Left Join (cost=51.86..413.89 rows=36 width=2,677) (actual time=0.702..7.625 rows=571 loops=1)

  • Join Filter: (contract_types.id = jobs."contractTypeId")
  • Rows Removed by Join Filter: 1,994
4. 0.268 7.008 ↓ 15.9 571 1

Hash Left Join (cost=51.86..410.68 rows=36 width=2,473) (actual time=0.676..7.008 rows=571 loops=1)

  • Hash Cond: (jobs."industryId" = industries.id)
5. 0.674 6.667 ↓ 15.9 571 1

Nested Loop Left Join (cost=50.14..408.54 rows=36 width=2,340) (actual time=0.549..6.667 rows=571 loops=1)

  • Join Filter: (years_of_experiences.id = jobs."yearsOfExperienceId")
  • Rows Removed by Join Filter: 2,570
6. 0.276 5.993 ↓ 15.9 571 1

Hash Left Join (cost=50.14..404.78 rows=36 width=2,132) (actual time=0.526..5.993 rows=571 loops=1)

  • Hash Cond: (jobs."categoryId" = categories.id)
7. 0.342 5.682 ↓ 15.9 571 1

Nested Loop Left Join (cost=48.57..402.72 rows=36 width=1,997) (actual time=0.471..5.682 rows=571 loops=1)

  • Join Filter: (educations.id = jobs."educationId")
  • Rows Removed by Join Filter: 4,185
8. 0.893 4.769 ↓ 15.9 571 1

Nested Loop Left Join (cost=48.57..397.85 rows=36 width=1,789) (actual time=0.436..4.769 rows=571 loops=1)

  • Join Filter: (employment_types.id = jobs."employmentTypeId")
  • Rows Removed by Join Filter: 3,426
9. 0.551 3.876 ↓ 15.9 571 1

Nested Loop Left Join (cost=48.57..393.54 rows=36 width=1,549) (actual time=0.408..3.876 rows=571 loops=1)

10. 0.250 2.754 ↓ 15.9 571 1

Hash Left Join (cost=48.30..381.08 rows=36 width=1,459) (actual time=0.389..2.754 rows=571 loops=1)

  • Hash Cond: (jobs."languageId" = languages.id)
11. 0.307 2.456 ↓ 15.9 571 1

Hash Join (cost=46.97..379.25 rows=36 width=1,318) (actual time=0.316..2.456 rows=571 loops=1)

  • Hash Cond: (jobs."companyId" = companies.id)
12. 1.888 1.888 ↓ 1.2 1,120 1

Seq Scan on jobs (cost=0.00..328.52 rows=905 width=910) (actual time=0.022..1.888 rows=1,120 loops=1)

  • Filter: (("closedAt" IS NULL) AND ((deadline IS NULL) OR (deadline > now())))
  • Rows Removed by Filter: 515
13. 0.056 0.261 ↓ 1.0 52 1

Hash (cost=46.33..46.33 rows=51 width=412) (actual time=0.261..0.261 rows=52 loops=1)

  • Buckets: 1,024 Batches: 1 Memory Usage: 25kB
14. 0.205 0.205 ↓ 1.0 52 1

Index Scan using "IDX_6b72cfe59def08e1ce41550229" on companies (cost=0.28..46.33 rows=51 width=412) (actual time=0.117..0.205 rows=52 loops=1)

  • Index Cond: ((state)::text = 'APPROVED'::text)
  • Filter: (offline IS FALSE)
  • Rows Removed by Filter: 1
15. 0.014 0.048 ↑ 1.0 15 1

Hash (cost=1.15..1.15 rows=15 width=145) (actual time=0.048..0.048 rows=15 loops=1)

  • Buckets: 1,024 Batches: 1 Memory Usage: 10kB
16. 0.034 0.034 ↑ 1.0 15 1

Seq Scan on languages (cost=0.00..1.15 rows=15 width=145) (actual time=0.033..0.034 rows=15 loops=1)

17. 0.571 0.571 ↑ 1.0 1 571

Index Scan using countries_pkey on countries (cost=0.27..0.34 rows=1 width=94) (actual time=0.001..0.001 rows=1 loops=571)

  • Index Cond: (id = jobs."countryId")
18. 0.000 0.000 ↓ 1.2 7 571

Materialize (cost=0.00..1.09 rows=6 width=244) (actual time=0.000..0.000 rows=7 loops=571)

19. 0.018 0.018 ↓ 1.2 7 1

Seq Scan on employment_types (cost=0.00..1.06 rows=6 width=244) (actual time=0.016..0.018 rows=7 loops=1)

20. 0.551 0.571 ↓ 1.1 8 571

Materialize (cost=0.00..1.10 rows=7 width=212) (actual time=0.000..0.001 rows=8 loops=571)

21. 0.020 0.020 ↓ 1.1 8 1

Seq Scan on educations (cost=0.00..1.07 rows=7 width=212) (actual time=0.020..0.020 rows=8 loops=1)

22. 0.014 0.035 ↓ 1.3 33 1

Hash (cost=1.25..1.25 rows=25 width=139) (actual time=0.035..0.035 rows=33 loops=1)

  • Buckets: 1,024 Batches: 1 Memory Usage: 14kB
23. 0.021 0.021 ↓ 1.3 33 1

Seq Scan on categories (cost=0.00..1.25 rows=25 width=139) (actual time=0.015..0.021 rows=33 loops=1)

24. 0.000 0.000 ↑ 1.0 5 571

Materialize (cost=0.00..1.07 rows=5 width=212) (actual time=0.000..0.000 rows=5 loops=571)

25. 0.017 0.017 ↑ 1.0 5 1

Seq Scan on years_of_experiences (cost=0.00..1.05 rows=5 width=212) (actual time=0.015..0.017 rows=5 loops=1)

26. 0.014 0.073 ↑ 1.0 32 1

Hash (cost=1.32..1.32 rows=32 width=137) (actual time=0.073..0.073 rows=32 loops=1)

  • Buckets: 1,024 Batches: 1 Memory Usage: 14kB
27. 0.059 0.059 ↑ 1.0 32 1

Seq Scan on industries (cost=0.00..1.32 rows=32 width=137) (actual time=0.052..0.059 rows=32 loops=1)

28. 0.000 0.000 ↑ 1.0 4 571

Materialize (cost=0.00..1.06 rows=4 width=208) (actual time=0.000..0.000 rows=4 loops=571)

29. 0.015 0.015 ↑ 1.0 4 1

Seq Scan on contract_types (cost=0.00..1.04 rows=4 width=208) (actual time=0.013..0.015 rows=4 loops=1)

30. 0.000 0.000 ↑ 1.0 6 571

Materialize (cost=0.00..1.09 rows=6 width=212) (actual time=0.000..0.000 rows=6 loops=571)

31. 0.029 0.029 ↑ 1.0 6 1

Seq Scan on function_levels (cost=0.00..1.06 rows=6 width=212) (actual time=0.028..0.029 rows=6 loops=1)

Planning time : 12.721 ms
Execution time : 32.723 ms