explain.depesz.com

PostgreSQL's explain analyze made readable

Result: Rfh

Settings
# exclusive inclusive rows x rows loops node
1. 0.021 4.929 ↑ 2.4 43 1
→

Limit (cost=1,344.54..1,943.53 rows=105 width=472) (actual time=3.167..4.929 rows=43 loops=1)

2. 1.557 4.908 ↑ 2.4 43 1
→

Hash Join (cost=1,344.54..1,943.53 rows=105 width=472) (actual time=3.166..4.908 rows=43 loops=1)

  • Hash Cond: (d.responsible_org_orgref = resporg.swa_org_ref)
3. 0.041 2.491 ↑ 2.4 43 1
→

Hash Join (cost=1,291.07..1,781.77 rows=105 width=619) (actual time=2.172..2.491 rows=43 loops=1)

  • Hash Cond: (d.publisher_orgref = publisher.swa_org_ref)
4. 0.111 1.635 ↑ 2.4 43 1
→

Nested Loop (cost=1,237.59..1,726.86 rows=105 width=597) (actual time=1.349..1.635 rows=43 loops=1)

  • Join Filter: (d.impact_score = ei.impact_score)
  • Rows Removed by Join Filter: 172
5. 0.070 1.481 ↑ 2.4 43 1
→

Nested Loop Left Join (cost=1,237.59..1,717.92 rows=105 width=509) (actual time=1.342..1.481 rows=43 loops=1)

  • Join Filter: (d.impact_score = ei_lang.impact_score)
6. 0.087 1.411 ↑ 2.4 43 1
→

Bitmap Heap Scan on entity_agg d (cost=1,237.59..1,715.28 rows=105 width=291) (actual time=1.334..1.411 rows=43 loops=1)

  • Recheck Cond: ((location_point_easting >= 300000) AND (location_point_easting <= 350000) AND (location_point_northing >= 500000) AND (location_point_northing <= 550000) AND (start_date <= to_timestamp('31/11/2018 23:59:59'::text, 'DD/MM/YYYY HH24:MI:SS'::text)) AND (end_date >= to_timestamp('01/11/2018 00:00:00'::text, 'DD/MM/YYYY HH24:MI:SS'::text)))
  • Filter: ((entity_type = 1) AND (impact_score = ANY ('{1,2,3,4}'::integer[])))
  • Heap Blocks: exact=34
7. 1.324 1.324 ↑ 2.9 43 1
→

Bitmap Index Scan on idx_entity_agg_coords_dates_impact (cost=0.00..1,237.57 rows=125 width=0) (actual time=1.324..1.324 rows=43 loops=1)

  • Index Cond: ((location_point_easting >= 300000) AND (location_point_easting <= 350000) AND (location_point_northing >= 500000) AND (location_point_northing <= 550000) AND (start_date <= to_timestamp('31/11/2018 23:59:59'::text, 'DD/MM/YYYY HH24:MI:SS'::text)) AND (end_date >= to_timestamp('01/11/2018 00:00:00'::text, 'DD/MM/YYYY HH24:MI:SS'::text)))
8. 0.000 0.000 ↓ 0.0 0 43
→

Materialize (cost=0.00..1.07 rows=1 width=222) (actual time=0.000..0.000 rows=0 loops=43)

9. 0.005 0.005 ↓ 0.0 0 1
→

Seq Scan on eton_impact_lang ei_lang (cost=0.00..1.06 rows=1 width=222) (actual time=0.005..0.005 rows=0 loops=1)

  • Filter: ((code)::text = 'en'::text)
  • Rows Removed by Filter: 5
10. 0.039 0.043 ↑ 1.0 5 43
→

Materialize (cost=0.00..1.07 rows=5 width=92) (actual time=0.000..0.001 rows=5 loops=43)

11. 0.004 0.004 ↑ 1.0 5 1
→

Seq Scan on eton_impact ei (cost=0.00..1.05 rows=5 width=92) (actual time=0.002..0.004 rows=5 loops=1)

12. 0.403 0.815 ↑ 1.0 1,310 1
→

Hash (cost=37.10..37.10 rows=1,310 width=26) (actual time=0.815..0.815 rows=1,310 loops=1)

  • Buckets: 2048 Batches: 1 Memory Usage: 82kB
13. 0.412 0.412 ↑ 1.0 1,310 1
→

Seq Scan on orgref publisher (cost=0.00..37.10 rows=1,310 width=26) (actual time=0.002..0.412 rows=1,310 loops=1)

14. 0.391 0.860 ↑ 1.0 1,310 1
→

Hash (cost=37.10..37.10 rows=1,310 width=26) (actual time=0.860..0.860 rows=1,310 loops=1)

  • Buckets: 2048 Batches: 1 Memory Usage: 82kB
15. 0.469 0.469 ↑ 1.0 1,310 1
→

Seq Scan on orgref resporg (cost=0.00..37.10 rows=1,310 width=26) (actual time=0.004..0.469 rows=1,310 loops=1)