explain.depesz.com

PostgreSQL's explain analyze made readable

Result: s0cZ : Optimization for: Optimization for: Optimization for: plan #ONPT; plan #ApaB; plan #taM4

Settings

Optimization path:

Optimization(s) for this plan:

# exclusive inclusive rows x rows loops node
1. 0.002 0.202 ↑ 1.0 1 1

Nested Loop Left Join (cost=8.35..41.59 rows=1 width=20) (actual time=0.200..0.202 rows=1 loops=1)

2. 0.000 0.194 ↑ 1.0 1 1

Nested Loop Left Join (cost=8.22..41.42 rows=1 width=24) (actual time=0.192..0.194 rows=1 loops=1)

3. 0.001 0.185 ↑ 1.0 1 1

Nested Loop Left Join (cost=7.94..38.93 rows=1 width=24) (actual time=0.183..0.185 rows=1 loops=1)

  • Join Filter: (invoice.position_candidate_id = applicatio0_.id)
4. 0.001 0.099 ↑ 1.0 1 1

Nested Loop Left Join (cost=7.94..19.95 rows=1 width=20) (actual time=0.097..0.099 rows=1 loops=1)

  • Join Filter: (applicatio0_.id = positionca24_.position_candidate_id)
5. 0.001 0.094 ↑ 1.0 1 1

Nested Loop Left Join (cost=7.94..18.79 rows=1 width=20) (actual time=0.093..0.094 rows=1 loops=1)

6. 0.001 0.081 ↑ 1.0 1 1

Nested Loop Left Join (cost=7.81..18.63 rows=1 width=24) (actual time=0.080..0.081 rows=1 loops=1)

7. 0.017 0.070 ↑ 1.0 1 1

Hash Right Join (cost=7.53..18.23 rows=1 width=28) (actual time=0.069..0.070 rows=1 loops=1)

  • Hash Cond: (screenings18_.position_description_id = positionen16_.id)
8. 0.008 0.008 ↑ 25.0 2 1

Seq Scan on position_description_screening_question screenings18_ (cost=0.00..10.50 rows=50 width=4) (actual time=0.007..0.008 rows=2 loops=1)

9. 0.004 0.045 ↑ 1.0 1 1

Hash (cost=7.52..7.52 rows=1 width=28) (actual time=0.045..0.045 rows=1 loops=1)

  • Buckets: 1,024 Batches: 1 Memory Usage: 9kB
10. 0.000 0.041 ↑ 1.0 1 1

Nested Loop (cost=0.86..7.52 rows=1 width=28) (actual time=0.039..0.041 rows=1 loops=1)

11. 0.003 0.030 ↑ 1.0 1 1

Nested Loop (cost=0.58..5.02 rows=1 width=24) (actual time=0.028..0.030 rows=1 loops=1)

12. 0.016 0.016 ↑ 1.0 1 1

Index Scan using position_candidate_pkey on position_candidate applicatio0_ (cost=0.29..2.51 rows=1 width=40) (actual time=0.015..0.016 rows=1 loops=1)

  • Index Cond: (id = 112,250)
13. 0.011 0.011 ↑ 1.0 1 1

Index Scan using candidate_pkey on candidate candidatee1_ (cost=0.29..2.51 rows=1 width=63) (actual time=0.011..0.011 rows=1 loops=1)

  • Index Cond: (id = applicatio0_.candidate_id)
14. 0.011 0.011 ↑ 1.0 1 1

Index Scan using position_description_pkey on position_description positionen16_ (cost=0.28..2.50 rows=1 width=16) (actual time=0.011..0.011 rows=1 loops=1)

  • Index Cond: (id = applicatio0_.position_description_id)
15. 0.010 0.010 ↑ 1.0 1 1

Index Scan using compensation_position_id__uidx on compensation compensati20_ (cost=0.28..0.40 rows=1 width=8) (actual time=0.010..0.010 rows=1 loops=1)

  • Index Cond: (positionen16_.id = position_id)
16. 0.012 0.012 ↓ 0.0 0 1

Index Only Scan using compensation_fee_model_compensation_id__fkey on compensation_fee_model compensati21_ (cost=0.13..0.15 rows=1 width=4) (actual time=0.012..0.012 rows=0 loops=1)

  • Index Cond: (compensation_id = compensati20_.id)
  • Heap Fetches: 0
17. 0.004 0.004 ↓ 0.0 0 1

Seq Scan on position_candidate_company_response positionca24_ (cost=0.00..1.15 rows=1 width=8) (actual time=0.004..0.004 rows=0 loops=1)

  • Filter: (position_candidate_id = 112,250)
  • Rows Removed by Filter: 15
18. 0.085 0.085 ↓ 0.0 0 1

Seq Scan on invoice (cost=0.00..18.96 rows=1 width=8) (actual time=0.085..0.085 rows=0 loops=1)

  • Filter: (position_candidate_id = 112,250)
  • Rows Removed by Filter: 804
19. 0.009 0.009 ↓ 0.0 0 1

Index Scan using offer_pkey on offer offerentit28_ (cost=0.28..2.49 rows=1 width=8) (actual time=0.009..0.009 rows=0 loops=1)

  • Index Cond: (invoice.offer_id = id)
20. 0.006 0.006 ↓ 0.0 0 1

Index Only Scan using offer_fee_model__offer_id__fkey on offer_fee_model offerfeemo30_ (cost=0.14..0.16 rows=1 width=4) (actual time=0.006..0.006 rows=0 loops=1)

  • Index Cond: (offer_id = offerentit28_.id)
  • Heap Fetches: 0
Planning time : 9.810 ms
Execution time : 0.404 ms