explain.depesz.com

PostgreSQL's explain analyze made readable

Result: FnD7L

Settings
# exclusive inclusive rows x rows loops node
1. 0.994 11.960 ↑ 718.1 431 1

Nested Loop Left Join (cost=0.57..10,111.60 rows=309,500 width=69) (actual time=0.150..11.960 rows=431 loops=1)

2. 0.170 0.416 ↑ 1.1 422 1

Append (cost=0.29..238.40 rows=464 width=36) (actual time=0.066..0.416 rows=422 loops=1)

  • Subplans Removed: 21
3. 0.246 0.246 ↓ 1.0 422 1

Index Scan using vehicles_ts_idx on vehicles (cost=0.29..23.47 rows=408 width=36) (actual time=0.065..0.246 rows=422 loops=1)

  • Index Cond: ((ts >= (now() - '01:00:00'::interval)) AND (ts <= now()))
4. 3.376 10.550 ↑ 57.0 1 422

Append (cost=0.28..20.71 rows=57 width=37) (actual time=0.019..0.025 rows=1 loops=422)

5. 0.422 0.422 ↓ 0.0 0 422

Index Scan using clusters_vehicle_id_idx on clusters (cost=0.28..0.63 rows=1 width=37) (actual time=0.001..0.001 rows=0 loops=422)

  • Index Cond: (vehicles.vehicle_id = vehicle_id)
6. 0.422 0.422 ↓ 0.0 0 422

Index Scan using clusters_vehicle_id_idx on clusters clusters_1 (cost=0.28..0.77 rows=1 width=37) (actual time=0.001..0.001 rows=0 loops=422)

  • Index Cond: (vehicles.vehicle_id = vehicle_id)
7. 0.422 0.422 ↓ 0.0 0 422

Index Scan using clusters_vehicle_id_idx on clusters clusters_2 (cost=0.29..1.50 rows=1 width=37) (actual time=0.001..0.001 rows=0 loops=422)

  • Index Cond: (vehicles.vehicle_id = vehicle_id)
8. 0.422 0.422 ↓ 0.0 0 422

Index Scan using clusters_vehicle_id_idx on clusters clusters_3 (cost=0.29..1.49 rows=1 width=37) (actual time=0.001..0.001 rows=0 loops=422)

  • Index Cond: (vehicles.vehicle_id = vehicle_id)
9. 0.422 0.422 ↓ 0.0 0 422

Index Scan using clusters_vehicle_id_idx on clusters clusters_4 (cost=0.29..1.49 rows=1 width=37) (actual time=0.001..0.001 rows=0 loops=422)

  • Index Cond: (vehicles.vehicle_id = vehicle_id)
10. 0.422 0.422 ↓ 0.0 0 422

Index Scan using clusters_vehicle_id_idx on clusters clusters_5 (cost=0.28..1.07 rows=1 width=37) (actual time=0.001..0.001 rows=0 loops=422)

  • Index Cond: (vehicles.vehicle_id = vehicle_id)
11. 0.422 0.422 ↓ 0.0 0 422

Index Scan using clusters_vehicle_id_idx on clusters clusters_6 (cost=0.28..0.95 rows=1 width=37) (actual time=0.001..0.001 rows=0 loops=422)

  • Index Cond: (vehicles.vehicle_id = vehicle_id)
12. 0.422 0.422 ↓ 0.0 0 422

Index Scan using clusters_vehicle_id_idx on clusters clusters_7 (cost=0.29..1.38 rows=1 width=37) (actual time=0.001..0.001 rows=0 loops=422)

  • Index Cond: (vehicles.vehicle_id = vehicle_id)
13. 0.422 0.422 ↓ 0.0 0 422

Index Scan using clusters_vehicle_id_idx on clusters clusters_8 (cost=0.29..1.47 rows=1 width=37) (actual time=0.001..0.001 rows=0 loops=422)

  • Index Cond: (vehicles.vehicle_id = vehicle_id)
14. 0.422 0.422 ↓ 0.0 0 422

Index Scan using clusters_vehicle_id_idx on clusters clusters_9 (cost=0.29..1.40 rows=1 width=37) (actual time=0.001..0.001 rows=0 loops=422)

  • Index Cond: (vehicles.vehicle_id = vehicle_id)
15. 0.422 0.422 ↓ 0.0 0 422

Index Scan using clusters_vehicle_id_idx on clusters clusters_10 (cost=0.29..1.44 rows=1 width=37) (actual time=0.001..0.001 rows=0 loops=422)

  • Index Cond: (vehicles.vehicle_id = vehicle_id)
16. 0.422 0.422 ↓ 0.0 0 422

Index Scan using clusters_vehicle_id_idx on clusters clusters_11 (cost=0.29..1.51 rows=1 width=37) (actual time=0.001..0.001 rows=0 loops=422)

  • Index Cond: (vehicles.vehicle_id = vehicle_id)
17. 0.422 0.422 ↓ 0.0 0 422

Index Scan using clusters_vehicle_id_idx on clusters clusters_12 (cost=0.28..1.08 rows=1 width=37) (actual time=0.001..0.001 rows=0 loops=422)

  • Index Cond: (vehicles.vehicle_id = vehicle_id)
18. 0.422 0.422 ↓ 0.0 0 422

Index Scan using clusters_vehicle_id_idx on clusters clusters_13 (cost=0.28..0.98 rows=1 width=37) (actual time=0.001..0.001 rows=0 loops=422)

  • Index Cond: (vehicles.vehicle_id = vehicle_id)
19. 0.844 0.844 ↑ 1.0 1 422

Index Scan using clusters_vehicle_id_idx on clusters clusters_14 (cost=0.28..0.81 rows=1 width=37) (actual time=0.001..0.002 rows=1 loops=422)

  • Index Cond: (vehicles.vehicle_id = vehicle_id)
20. 0.422 0.422 ↓ 0.0 0 422

Index Scan using clusters_vehicle_id_idx on clusters clusters_15 (cost=0.15..0.35 rows=6 width=37) (actual time=0.001..0.001 rows=0 loops=422)

  • Index Cond: (vehicles.vehicle_id = vehicle_id)
21. 0.000 0.000 ↓ 0.0 0 422

Index Scan using clusters_vehicle_id_idx on clusters clusters_16 (cost=0.15..0.35 rows=6 width=37) (actual time=0.000..0.000 rows=0 loops=422)

  • Index Cond: (vehicles.vehicle_id = vehicle_id)
22. 0.000 0.000 ↓ 0.0 0 422

Index Scan using clusters_vehicle_id_idx on clusters clusters_17 (cost=0.15..0.35 rows=6 width=37) (actual time=0.000..0.000 rows=0 loops=422)

  • Index Cond: (vehicles.vehicle_id = vehicle_id)
23. 0.000 0.000 ↓ 0.0 0 422

Index Scan using clusters_vehicle_id_idx on clusters clusters_18 (cost=0.15..0.35 rows=6 width=37) (actual time=0.000..0.000 rows=0 loops=422)

  • Index Cond: (vehicles.vehicle_id = vehicle_id)
24. 0.000 0.000 ↓ 0.0 0 422

Index Scan using clusters_vehicle_id_idx on clusters clusters_19 (cost=0.15..0.35 rows=6 width=37) (actual time=0.000..0.000 rows=0 loops=422)

  • Index Cond: (vehicles.vehicle_id = vehicle_id)
25. 0.000 0.000 ↓ 0.0 0 422

Index Scan using clusters_vehicle_id_idx on clusters clusters_20 (cost=0.15..0.35 rows=6 width=37) (actual time=0.000..0.000 rows=0 loops=422)

  • Index Cond: (vehicles.vehicle_id = vehicle_id)
26. 0.000 0.000 ↓ 0.0 0 422

Index Scan using clusters_vehicle_id_idx on clusters clusters_21 (cost=0.15..0.35 rows=6 width=37) (actual time=0.000..0.000 rows=0 loops=422)

  • Index Cond: (vehicles.vehicle_id = vehicle_id)
Planning time : 1.988 ms
Execution time : 12.227 ms