#	rows x	node
1.	↓ 0.0	→ Update on dag_node (cost=411,159.22..470,388.01 rows=271,730 width=178) (actual rows= loops=)
2.	↓ 0.0	→ Hash Join (cost=411,159.22..470,388.01 rows=271,730 width=178) (actual rows= loops=) Hash Cond: (dag_node.object_id = t.id)
3.	↓ 0.0	→ Seq Scan on dag_node (cost=0.00..50,963.54 rows=1,479,454 width=166) (actual rows= loops=)
4.	↓ 0.0	→ Hash (cost=407,358.16..407,358.16 rows=304,085 width=16) (actual rows= loops=)
5.	↓ 0.0	→ Seq Scan on term t (cost=0.00..407,358.16 rows=304,085 width=16) (actual rows= loops=) Filter: (has_genes OR has_diseases OR has_chems OR has_marrays OR has_phenotypes OR has_comps)

Update on dag_node  (cost=411159.22..470388.01 rows=271730 width=178)
  ->  Hash Join  (cost=411159.22..470388.01 rows=271730 width=178)
        Hash Cond: (dag_node.object_id = t.id)
        ->  Seq Scan on dag_node  (cost=0.00..50963.54 rows=1479454 width=166)
        ->  Hash  (cost=407358.16..407358.16 rows=304085 width=16)
              ->  Seq Scan on term t  (cost=0.00..407358.16 rows=304085 width=16)
                    Filter: (has_genes OR has_diseases OR has_chems OR has_marrays OR has_phenotypes OR has_comps)

Per node type stats

node type	count	sum of times	% of query
Hash	1	0.000 ms	0.0 %
Hash Join	1	0.000 ms	0.0 %
Seq Scan	2	0.000 ms	0.0 %
Update	1	0.000 ms	0.0 %

Per table stats

Table name	Scan count	Total time	% of query
scan type	count	sum of times	% of table
dag_node	2	0.000 ms	0.0 %
Seq Scan	1	0.000 ms	0.0 %
Update	1	0.000 ms	0.0 %
term	1	0.000 ms	0.0 %
Seq Scan	1	0.000 ms	0.0 %

explain.depesz.com

PostgreSQL's explain analyze made readable

Result: XyOg

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Per node type stats

Per table stats