#	exclusive	inclusive	rows x	rows	loops	node
1.	159,898.642	245,991.816	↓ 0.0	0	1	→ Insert on live_delivery (cost=51.12..2,329,285.47 rows=58,433,505 width=179) (actual time=245,991.816..245,991.816 rows=0 loops=1)
2.	39,798.315	86,093.174	↑ 1.0	58,110,390	1	→ Hash Join (cost=51.12..2,329,285.47 rows=58,433,505 width=179) (actual time=35.511..86,093.174 rows=58,110,390 loops=1) Hash Cond: ("VALUES".column1 = (audience_map.audience_genesis_name)::text)
3.	16,751.065	46,293.904	↑ 1.0	58,110,390	1	→ Nested Loop (cost=0.42..1,379,690.32 rows=58,433,505 width=173) (actual time=34.532..46,293.904 rows=58,110,390 loops=1)
4.	10,470.711	10,470.711	↑ 1.0	298,002	1	→ Index Scan using idx_rate_card_type_code on demo_groups d (cost=0.42..64,936.46 rows=299,659 width=1,218) (actual time=34.499..10,470.711 rows=298,002 loops=1) Index Cond: ((rate_card_type_code)::text = 'std'::text)
5.	19,072.128	19,072.128	↑ 1.0	195	298,002	→ Values Scan on "VALUES" (cost=0.00..2.44 rows=195 width=64) (actual time=0.000..0.064 rows=195 loops=298,002)
6.	0.049	0.955	↑ 1.0	195	1	→ Hash (cost=48.26..48.26 rows=195 width=28) (actual time=0.955..0.955 rows=195 loops=1) Buckets: 1024 Batches: 1 Memory Usage: 20kB
7.	0.712	0.906	↑ 1.0	195	1	→ HashAggregate (cost=44.36..46.31 rows=195 width=28) (actual time=0.868..0.906 rows=195 loops=1) Group Key: audience_map.audience_sk, audience_map.audience, audience_map.audience_genesis_name
8.	0.194	0.194	↑ 1.0	1,392	1	→ Seq Scan on audience_map (cost=0.00..33.92 rows=1,392 width=28) (actual time=0.012..0.194 rows=1,392 loops=1)

Insert on live_delivery  (cost=51.12..2329285.47 rows=58433505 width=179) (actual time=245991.816..245991.816 rows=0 loops=1)
  ->  Hash Join  (cost=51.12..2329285.47 rows=58433505 width=179) (actual time=35.511..86093.174 rows=58110390 loops=1)
        Hash Cond: ("*VALUES*".column1 = (audience_map.audience_genesis_name)::text)
        ->  Nested Loop  (cost=0.42..1379690.32 rows=58433505 width=173) (actual time=34.532..46293.904 rows=58110390 loops=1)
              ->  Index Scan using idx_rate_card_type_code on demo_groups d  (cost=0.42..64936.46 rows=299659 width=1218) (actual time=34.499..10470.711 rows=298002 loops=1)
                    Index Cond: ((rate_card_type_code)::text = 'std'::text)
              ->  Values Scan on "*VALUES*"  (cost=0.00..2.44 rows=195 width=64) (actual time=0.000..0.064 rows=195 loops=298002)
        ->  Hash  (cost=48.26..48.26 rows=195 width=28) (actual time=0.955..0.955 rows=195 loops=1)
              Buckets: 1024  Batches: 1  Memory Usage: 20kB
              ->  HashAggregate  (cost=44.36..46.31 rows=195 width=28) (actual time=0.868..0.906 rows=195 loops=1)
                    Group Key: audience_map.audience_sk, audience_map.audience, audience_map.audience_genesis_name
                    ->  Seq Scan on audience_map  (cost=0.00..33.92 rows=1392 width=28) (actual time=0.012..0.194 rows=1392 loops=1)
Planning time: 2.081 ms
Execution time: 246015.741 ms

Per node type stats

node type	count	sum of times	% of query
Hash	1	0.049 ms	0.0 %
Hash Join	1	39,798.315 ms	16.2 %
HashAggregate	1	0.712 ms	0.0 %
Index Scan	1	10,470.711 ms	4.3 %
Insert	1	159,898.642 ms	65.0 %
Nested Loop	1	16,751.065 ms	6.8 %
Seq Scan	1	0.194 ms	0.0 %
Values Scan on "VALUES"	1	19,072.128 ms	7.8 %

Per table stats

Table name	Scan count	Total time	% of query
scan type	count	sum of times	% of table
audience_map	1	0.194 ms	0.0 %
Seq Scan	1	0.194 ms	100.0 %
demo_groups	1	10,470.711 ms	4.3 %
Index Scan	1	10,470.711 ms	100.0 %
live_delivery	1	159,898.642 ms	65.0 %
Insert	1	159,898.642 ms	100.0 %

explain.depesz.com

PostgreSQL's explain analyze made readable

Result: OwxI

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Per node type stats

Per table stats