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feed_item_id	pubdate	link	title	summary	author	feed_id	likes	dislikes	love	anger	tags
d737a2b7-fbaf-c695-baa5-3953865d030b	2020-06-19 15:20:54+01	abc1.com	scam title	scam summary		2	2	0	null	null	{pro,more,are,gold,up,but,rise,new,s,for,all,sent,hold,at,time,post}
bc5c8dfe-13a9-d97a-a328-0e5b8990c500	2020-06-19 15:20:00+01	abc2.com	normal title	scam summary		3	3	1	null	null	{for}
fc8b3b90-bead-7213-bcd5-89eb7f23e5b6	2020-06-19 15:10:50+01	abc3.com	scam title	scam summary		4	1	2	1	0	{at,s,face,for,time,now,when,are,more,up,day,today,you,space,like,cash,new,start,way,mt,one,ride,put,her,but,data,block,fork,xt,won,may,good,bull,all,get,post,dec,flash,era,drop,well,fuel,fire,pop,top,safe,real,help,sent,can,view,go,team,anti,deal,deep,sake}

QUERY PLAN
Limit (cost=40.99..41.00 rows=1 width=334) (actual time=0.571..0.571 rows=3 loops=1)
-> Sort (cost=40.99..41.00 rows=1 width=334) (actual time=0.570..0.570 rows=3 loops=1)
Sort Key: fi.pubdate DESC, fi.feed_item_id DESC
Sort Method: top-N heapsort Memory: 27kB
-> Hash Right Join (cost=38.95..40.98 rows=1 width=334) (actual time=0.513..0.537 rows=28 loops=1)
Hash Cond: (bba.feed_item_id = fi.feed_item_id)
-> Seq Scan on feed_item_love_anger_aggregate bba (cost=0.00..1.74 rows=74 width=32) (actual time=0.007..0.013 rows=74 loops=1)
-> Hash (cost=38.94..38.94 rows=1 width=318) (actual time=0.488..0.488 rows=28 loops=1)
Buckets: 1024 Batches: 1 Memory Usage: 17kB
-> Nested Loop (cost=2.38..38.94 rows=1 width=318) (actual time=0.151..0.446 rows=28 loops=1)
Join Filter: (t.feed_item_id = fi.feed_item_id)
-> Hash Join (cost=2.24..36.34 rows=1 width=80) (actual time=0.085..0.163 rows=36 loops=1)
Hash Cond: (t.feed_item_id = lda.feed_item_id)
-> Seq Scan on feed_item_tags t (cost=0.00..34.08 rows=5 width=48) (actual time=0.012..0.069 rows=67 loops=1)
Filter: ('for'::text = ANY ((tags)::text[]))
Rows Removed by Filter: 33
-> Hash (cost=1.93..1.93 rows=25 width=32) (actual time=0.050..0.050 rows=50 loops=1)
Buckets: 1024 Batches: 1 Memory Usage: 12kB
-> Seq Scan on feed_item_likes_dislikes_aggregate lda (cost=0.00..1.93 rows=25 width=32) (actual time=0.008..0.019 rows=50 loops=1)
Filter: (likes > 0)
Rows Removed by Filter: 24
-> Index Scan using feed_items_pkey on feed_items fi (cost=0.14..2.59 rows=1 width=270) (actual time=0.007..0.007 rows=1 loops=36)
Index Cond: (feed_item_id = lda.feed_item_id)
Filter: ((title_vector @@ to_tsquery('scam'::text)) OR (summary_vector @@ to_tsquery('scam'::text)))
Rows Removed by Filter: 0
Planning Time: 0.570 ms
Execution Time: 0.696 ms

feed_item_id	pubdate	link	title	summary	author	feed_id	likes	dislikes	love	anger	tags
fc8b3b90-bead-7213-bcd5-89eb7f23e5b6	2020-06-19 15:10:50+01	abc3.com	scam title	scam summary		4	1	2	1	0	{at,s,face,for,time,now,when,are,more,up,day,today,you,space,like,cash,new,start,way,mt,one,ride,put,her,but,data,block,fork,xt,won,may,good,bull,all,get,post,dec,flash,era,drop,well,fuel,fire,pop,top,safe,real,help,sent,can,view,go,team,anti,deal,deep,sake}
f82317c5-83eb-3868-ed68-44b50f54a17f	2020-06-19 15:07:06+01	abc6.com	normal title	scam summary		7	1	3	2	1	{for}
90f43675-f6a3-3373-e055-08c13fd23806	2020-06-19 14:46:22+01	abc10.com	scam title	scam summary		11	1	0	0	3	{at,post,team,now,steps,safe,like,s,way,for,but,block,can,more,inc,all}

QUERY PLAN
Limit (cost=41.00..41.00 rows=1 width=334) (actual time=0.472..0.473 rows=3 loops=1)
-> Sort (cost=41.00..41.00 rows=1 width=334) (actual time=0.472..0.472 rows=3 loops=1)
Sort Key: fi.pubdate DESC, fi.feed_item_id DESC
Sort Method: top-N heapsort Memory: 27kB
-> Hash Right Join (cost=38.96..40.99 rows=1 width=334) (actual time=0.416..0.437 rows=26 loops=1)
Hash Cond: (bba.feed_item_id = fi.feed_item_id)
-> Seq Scan on feed_item_love_anger_aggregate bba (cost=0.00..1.74 rows=74 width=32) (actual time=0.008..0.013 rows=74 loops=1)
-> Hash (cost=38.95..38.95 rows=1 width=318) (actual time=0.396..0.396 rows=26 loops=1)
Buckets: 1024 Batches: 1 Memory Usage: 16kB
-> Nested Loop (cost=2.38..38.95 rows=1 width=318) (actual time=0.099..0.367 rows=26 loops=1)
Join Filter: (t.feed_item_id = fi.feed_item_id)
-> Hash Join (cost=2.24..36.34 rows=1 width=80) (actual time=0.065..0.139 rows=36 loops=1)
Hash Cond: (t.feed_item_id = lda.feed_item_id)
-> Seq Scan on feed_item_tags t (cost=0.00..34.08 rows=5 width=48) (actual time=0.012..0.067 rows=67 loops=1)
Filter: ('for'::text = ANY ((tags)::text[]))
Rows Removed by Filter: 33
-> Hash (cost=1.93..1.93 rows=25 width=32) (actual time=0.040..0.040 rows=50 loops=1)
Buckets: 1024 Batches: 1 Memory Usage: 12kB
-> Seq Scan on feed_item_likes_dislikes_aggregate lda (cost=0.00..1.93 rows=25 width=32) (actual time=0.008..0.018 rows=50 loops=1)
Filter: (likes > 0)
Rows Removed by Filter: 24
-> Index Scan using feed_items_pkey on feed_items fi (cost=0.14..2.59 rows=1 width=270) (actual time=0.005..0.005 rows=1 loops=36)
Index Cond: (feed_item_id = lda.feed_item_id)
Filter: ((ROW(pubdate, feed_item_id) < ROW('2020-06-19 15:20:00+01'::timestamp with time zone, 'bc5c8dfe-13a9-d97a-a328-0e5b8990c500'::uuid)) AND ((title_vector @@ to_tsquery('scam'::text)) OR (summary_vector @@ to_tsquery('scam'::text))))
Rows Removed by Filter: 0
Planning Time: 0.410 ms
Execution Time: 0.514 ms