#	exclusive	inclusive	rows x	rows	loops	node
1.	674.309	28381.706	↑ 1.0	100000	1	→ Limit (cost=85847.22..92513.15 rows=100000 width=28) (actual time=25465.332..28381.706 rows=100000 loops=1)
2.	935.439	27707.397	↑ 642.9	100000	1	→ Hash Join (cost=85847.22..4371045.75 rows=64285080 width=28) (actual time=25465.324..27707.397 rows=100000 loops=1) Hash Cond: (i.fk_weather_station_id = ws.id)
3.	772.303	26761.468	↑ 642.9	100000	1	→ Hash Join (cost=85811.37..2683526.55 rows=64285080 width=24) (actual time=25454.798..26761.468 rows=100000 loops=1) Hash Cond: (v.id = i.id)
4.	536.522	536.522	↑ 642.9	100000	1	→ Seq Scan on native_weather_data_value v (cost=0.00..1312013.58 rows=64285080 width=12) (actual time=0.014..536.522 rows=100000 loops=1) Filter: (((value)::text <> '-'::text) AND ((value)::text ~ '^[-0-9.]+$'::text))
5.	12928.874	25452.643	↑ 1.0	2972372	1	→ Hash (cost=48656.72..48656.72 rows=2972372 width=16) (actual time=25452.643..25452.643 rows=2972372 loops=1) Buckets: 524288 Batches: 1 Memory Usage: 139330kB
6.	12523.769	12523.769	↑ 1.0	2972372	1	→ Seq Scan on native_weather_data_index i (cost=0.00..48656.72 rows=2972372 width=16) (actual time=0.012..12523.769 rows=2972372 loops=1)
7.	5.186	10.490	↑ 1.0	1149	1	→ Hash (cost=21.49..21.49 rows=1149 width=4) (actual time=10.490..10.490 rows=1149 loops=1) Buckets: 1024 Batches: 1 Memory Usage: 41kB
8.	5.304	5.304	↑ 1.0	1149	1	→ Seq Scan on weather_station ws (cost=0.00..21.49 rows=1149 width=4) (actual time=0.010..5.304 rows=1149 loops=1)

"Limit  (cost=85847.22..92513.15 rows=100000 width=28) (actual time=25465.332..28381.706 rows=100000 loops=1)"
"  ->  Hash Join  (cost=85847.22..4371045.75 rows=64285080 width=28) (actual time=25465.324..27707.397 rows=100000 loops=1)"
"        Hash Cond: (i.fk_weather_station_id = ws.id)"
"        ->  Hash Join  (cost=85811.37..2683526.55 rows=64285080 width=24) (actual time=25454.798..26761.468 rows=100000 loops=1)"
"              Hash Cond: (v.id = i.id)"
"              ->  Seq Scan on native_weather_data_value v  (cost=0.00..1312013.58 rows=64285080 width=12) (actual time=0.014..536.522 rows=100000 loops=1)"
"                    Filter: (((value)::text <> '-'::text) AND ((value)::text ~ '^[-0-9.]+$'::text))"
"              ->  Hash  (cost=48656.72..48656.72 rows=2972372 width=16) (actual time=25452.643..25452.643 rows=2972372 loops=1)"
"                    Buckets: 524288  Batches: 1  Memory Usage: 139330kB"
"                    ->  Seq Scan on native_weather_data_index i  (cost=0.00..48656.72 rows=2972372 width=16) (actual time=0.012..12523.769 rows=2972372 loops=1)"
"        ->  Hash  (cost=21.49..21.49 rows=1149 width=4) (actual time=10.490..10.490 rows=1149 loops=1)"
"              Buckets: 1024  Batches: 1  Memory Usage: 41kB"
"              ->  Seq Scan on weather_station ws  (cost=0.00..21.49 rows=1149 width=4) (actual time=0.010..5.304 rows=1149 loops=1)"
"Total runtime: 28715.065 ms"

Per node type stats

node type	count	sum of times	% of query
Hash	2	12934.060 ms	45.6 %
Hash Join	2	1707.742 ms	6.0 %
Limit	1	674.309 ms	2.4 %
Seq Scan	3	13065.595 ms	46.0 %

Per table stats

Table name	Scan count	Total time	% of query
scan type	count	sum of times	% of table
native_weather_data_index	1	12523.769 ms	44.1 %
Seq Scan	1	12523.769 ms	100.0 %
native_weather_data_value	1	536.522 ms	1.9 %
Seq Scan	1	536.522 ms	100.0 %
weather_station	1	5.304 ms	0.0 %
Seq Scan	1	5.304 ms	100.0 %

explain.depesz.com

A tool for finding a real cause for slow queries.

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Per node type stats

Per table stats