#	exclusive	inclusive	rows x	rows	loops	node
1.	0.002	252.838	↓ 0.0	0	1	→ Insert on prices (cost=4,079.94..231,257.41 rows=1 width=44) (actual time=252.838..252.838 rows=0 loops=1)
2.	0.000	252.836	↓ 0.0	0	1	→ Subquery Scan on SELECT (cost=4,079.94..231,257.41 rows=1 width=44) (actual time=252.836..252.836 rows=0 loops=1)
3.	7.667	252.836	↓ 0.0	0	1	→ Hash Join (cost=4,079.94..231,257.40 rows=1 width=36) (actual time=252.835..252.836 rows=0 loops=1) Hash Cond: ((new_prices.advert_id = prices_1.advert_id) AND ((SubPlan 2) = prices_1.created)) Join Filter: ((new_prices.new_price <> prices_1.price) OR (prices_1.price IS NULL)) Rows Removed by Join Filter: 13
4.						CTE new_prices
5.	0.034	0.034	↑ 1.0	13	1	→ Values Scan on VALUES (cost=0.00..0.16 rows=13 width=36) (actual time=0.003..0.034 rows=13 loops=1)
6.	0.064	0.064	↑ 1.0	13	1	→ CTE Scan on new_prices (cost=0.00..0.26 rows=13 width=36) (actual time=0.006..0.064 rows=13 loops=1)
7.	34.522	46.283	↑ 1.0	96,430	1	→ Hash (cost=1,932.11..1,932.11 rows=98,311 width=28) (actual time=46.283..46.283 rows=96,430 loops=1) Buckets: 65,536 Batches: 2 Memory Usage: 3,341kB
8.	11.761	11.761	↑ 1.0	96,430	1	→ Seq Scan on prices prices_1 (cost=0.00..1,932.11 rows=98,311 width=28) (actual time=0.038..11.761 rows=96,430 loops=1)
9.						SubPlan (for Hash Join)
10.	0.130	198.822	↑ 1.0	1	26	→ Aggregate (cost=2,177.89..2,177.90 rows=1 width=8) (actual time=7.647..7.647 rows=1 loops=26)
11.	198.692	198.692	↑ 1.0	1	26	→ Seq Scan on prices prices_2 (cost=0.00..2,177.89 rows=1 width=8) (actual time=2.486..7.642 rows=1 loops=26) Filter: (advert_id = new_prices.advert_id) Rows Removed by Filter: 96,429

Planning time	:	4.181 ms
Execution time	:	257.109 ms

QUERY PLAN
Insert on prices  (cost=4079.94..231257.41 rows=1 width=44) (actual time=252.838..252.838 rows=0 loops=1)
  ->  Subquery Scan on *SELECT*  (cost=4079.94..231257.41 rows=1 width=44) (actual time=252.836..252.836 rows=0 loops=1)
        ->  Hash Join  (cost=4079.94..231257.40 rows=1 width=36) (actual time=252.835..252.836 rows=0 loops=1)
              Hash Cond: ((new_prices.advert_id = prices_1.advert_id) AND ((SubPlan 2) = prices_1.created))
              Join Filter: ((new_prices.new_price <> prices_1.price) OR (prices_1.price IS NULL))
              Rows Removed by Join Filter: 13
              CTE new_prices
                ->  Values Scan on *VALUES*  (cost=0.00..0.16 rows=13 width=36) (actual time=0.003..0.034 rows=13 loops=1)
              ->  CTE Scan on new_prices  (cost=0.00..0.26 rows=13 width=36) (actual time=0.006..0.064 rows=13 loops=1)
              ->  Hash  (cost=1932.11..1932.11 rows=98311 width=28) (actual time=46.283..46.283 rows=96430 loops=1)
                    Buckets: 65536  Batches: 2  Memory Usage: 3341kB
                    ->  Seq Scan on prices prices_1  (cost=0.00..1932.11 rows=98311 width=28) (actual time=0.038..11.761 rows=96430 loops=1)
              SubPlan 2
                ->  Aggregate  (cost=2177.89..2177.90 rows=1 width=8) (actual time=7.647..7.647 rows=1 loops=26)
                      ->  Seq Scan on prices prices_2  (cost=0.00..2177.89 rows=1 width=8) (actual time=2.486..7.642 rows=1 loops=26)
                            Filter: (advert_id = new_prices.advert_id)
                            Rows Removed by Filter: 96429
Planning Time: 4.181 ms
Execution Time: 257.109 ms

Per node type stats

node type	count	sum of times	% of query
Aggregate	1	0.130 ms	0.1 %
CTE Scan	1	0.064 ms	0.0 %
Hash	1	34.522 ms	13.7 %
Hash Join	1	7.667 ms	3.0 %
Insert	1	0.002 ms	0.0 %
Seq Scan	2	210.453 ms	83.2 %
Subquery Scan	1	0.000 ms	0.0 %
Values Scan on VALUES	1	0.034 ms	0.0 %

Per table stats

Table name	Scan count	Total time	% of query
scan type	count	sum of times	% of table
prices	3	210.455 ms	83.2 %
Insert	1	0.002 ms	0.0 %
Seq Scan	2	210.453 ms	100.0 %

explain.depesz.com

PostgreSQL's explain analyze made readable

Result: 9LMX

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Per node type stats

Per table stats