#	exclusive	inclusive	rows x	rows	loops	node
1.	6799.920	54725.740	↑ 1.0	1000000	1	→ Limit (cost=85847.22..152506.52 rows=1000000 width=28) (actual time=25229.844..54725.740 rows=1000000 loops=1)
2.	9440.504	47925.820	↑ 64.3	1000000	1	→ Hash Join (cost=85847.22..4371045.75 rows=64285080 width=28) (actual time=25229.834..47925.820 rows=1000000 loops=1) Hash Cond: (i.fk_weather_station_id = ws.id)
3.	7782.931	38476.645	↑ 64.3	1000000	1	→ Hash Join (cost=85811.37..2683526.55 rows=64285080 width=24) (actual time=25220.950..38476.645 rows=1000000 loops=1) Hash Cond: (v.id = i.id)
4.	5476.440	5476.440	↑ 64.3	1000000	1	→ Seq Scan on native_weather_data_value v (cost=0.00..1312013.58 rows=64285080 width=12) (actual time=0.023..5476.440 rows=1000000 loops=1) Filter: (((value)::text <> '-'::text) AND ((value)::text ~ '^[-0-9.]+$'::text))
5.	12765.638	25217.274	↑ 1.0	2972372	1	→ Hash (cost=48656.72..48656.72 rows=2972372 width=16) (actual time=25217.274..25217.274 rows=2972372 loops=1) Buckets: 524288 Batches: 1 Memory Usage: 139330kB
6.	12451.636	12451.636	↑ 1.0	2972372	1	→ Seq Scan on native_weather_data_index i (cost=0.00..48656.72 rows=2972372 width=16) (actual time=6.689..12451.636 rows=2972372 loops=1)
7.	4.375	8.671	↑ 1.0	1149	1	→ Hash (cost=21.49..21.49 rows=1149 width=4) (actual time=8.671..8.671 rows=1149 loops=1) Buckets: 1024 Batches: 1 Memory Usage: 41kB
8.	4.296	4.296	↑ 1.0	1149	1	→ Seq Scan on weather_station ws (cost=0.00..21.49 rows=1149 width=4) (actual time=0.010..4.296 rows=1149 loops=1)

"Limit  (cost=85847.22..152506.52 rows=1000000 width=28) (actual time=25229.844..54725.740 rows=1000000 loops=1)"
"  ->  Hash Join  (cost=85847.22..4371045.75 rows=64285080 width=28) (actual time=25229.834..47925.820 rows=1000000 loops=1)"
"        Hash Cond: (i.fk_weather_station_id = ws.id)"
"        ->  Hash Join  (cost=85811.37..2683526.55 rows=64285080 width=24) (actual time=25220.950..38476.645 rows=1000000 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=0.023..5476.440 rows=1000000 loops=1)"
"                    Filter: (((value)::text <> '-'::text) AND ((value)::text ~ '^[-0-9.]+$'::text))"
"              ->  Hash  (cost=48656.72..48656.72 rows=2972372 width=16) (actual time=25217.274..25217.274 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=6.689..12451.636 rows=2972372 loops=1)"
"        ->  Hash  (cost=21.49..21.49 rows=1149 width=4) (actual time=8.671..8.671 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..4.296 rows=1149 loops=1)"
"Total runtime: 58089.846 ms"

Per node type stats

node type	count	sum of times	% of query
Hash	2	12770.013 ms	23.3 %
Hash Join	2	17223.435 ms	31.5 %
Limit	1	6799.920 ms	12.4 %
Seq Scan	3	17932.372 ms	32.8 %

Per table stats

Table name	Scan count	Total time	% of query
scan type	count	sum of times	% of table
native_weather_data_index	1	12451.636 ms	22.8 %
Seq Scan	1	12451.636 ms	100.0 %
native_weather_data_value	1	5476.440 ms	10.0 %
Seq Scan	1	5476.440 ms	100.0 %
weather_station	1	4.296 ms	0.0 %
Seq Scan	1	4.296 ms	100.0 %

explain.depesz.com

A tool for finding a real cause for slow queries.

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Per node type stats

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