#	exclusive	inclusive	rows x	rows	loops	node
1.	4.264	6381.769	↑ 1.9	62	1	→ HashAggregate (cost=184022.03..184023.18 rows=115 width=4) (actual time=6381.707..6381.769 rows=62 loops=1) Total runtime: 6381.903 ms
2.	2495.851	6377.505	↑ 24.4	3453	1	→ Hash Join (cost=103857.48..183600.24 rows=84357 width=4) (actual time=1745.449..6377.505 rows=3453 loops=1) Hash Cond: (crowdbreaks_incomingkeyword.tweet_id = crowdbreaks_tweet.tweet_id)
3.	2136.839	2136.839	↑ 1.0	2252597	1	→ Seq Scan on crowdbreaks_incomingkeyword (cost=0.00..36873.97 rows=2252597 width=12) (actual time=0.008..2136.839 rows=2252597 loops=1)
4.	4.164	1744.815	↑ 26.1	3091	1	→ Hash (cost=102535.68..102535.68 rows=80544 width=8) (actual time=1744.815..1744.815 rows=3091 loops=1) Buckets: 4096 Batches: 4 Memory Usage: 32kB
5.	784.263	1740.651	↑ 26.1	3091	1	→ Hash Left Join (cost=16574.93..102535.68 rows=80544 width=8) (actual time=112.551..1740.651 rows=3091 loops=1) Hash Cond: ((crowdbreaks_tweet.place_id)::text = (crowdbreaks_place.place_id)::text) Filter: ((crowdbreaks_tweet.coordinates @ '0103000020E61000000100000005000000AE47E17A141E5FC00000000000003840AE47E17A141E5FC029ED0DBE30B14840A4703D0AD7A350C029ED0DBE30B14840A4703D0AD7A350C00000000000003840AE47E17A141E5FC00000000000003840'::geometry) OR ((crowdbreaks_place.bounding_box && '0103000020E61000000100000005000000AE47E17A141E5FC00000000000003840AE47E17A141E5FC029ED0DBE30B14840A4703D0AD7A350C029ED0DBE30B14840A4703D0AD7A350C00000000000003840AE47E17A141E5FC00000000000003840'::geometry) AND _st_overlaps(crowdbreaks_place.bounding_box, '0103000020E61000000100000005000000AE47E17A141E5FC00000000000003840AE47E17A141E5FC029ED0DBE30B14840A4703D0AD7A350C029ED0DBE30B14840A4703D0AD7A350C00000000000003840AE47E17A141E5FC00000000000003840'::geometry)))
6.	846.203	940.462	↑ 1.0	736784	1	→ Bitmap Heap Scan on crowdbreaks_tweet (cost=15874.18..67060.28 rows=747873 width=125) (actual time=96.012..940.462 rows=736784 loops=1) Recheck Cond: ((created_at > '2012-04-17 15:46:12.109893+00'::timestamp with time zone) AND (created_at < '2012-04-18 15:46:12.109899+00'::timestamp with time zone))
7.	94.259	94.259	↑ 1.0	736784	1	→ Bitmap Index Scan on crowdbreaks_tweet_crreated_at (cost=0.00..15687.22 rows=747873 width=0) (actual time=94.259..94.259 rows=736784 loops=1) Index Cond: ((created_at > '2012-04-17 15:46:12.109893+00'::timestamp with time zone) AND (created_at < '2012-04-18 15:46:12.109899+00'::timestamp with time zone))
8.	9.018	15.926	↑ 1.0	6611	1	→ Hash (cost=217.11..217.11 rows=6611 width=469) (actual time=15.926..15.926 rows=6611 loops=1) Buckets: 1024 Batches: 4 Memory Usage: 259kB
9.	6.908	6.908	↑ 1.0	6611	1	→ Seq Scan on crowdbreaks_place (cost=0.00..217.11 rows=6611 width=469) (actual time=0.005..6.908 rows=6611 loops=1)

HashAggregate  (cost=184022.03..184023.18 rows=115 width=4) (actual time=6381.707..6381.769 rows=62 loops=1)
   ->  Hash Join  (cost=103857.48..183600.24 rows=84357 width=4) (actual time=1745.449..6377.505 rows=3453 loops=1)
         Hash Cond: (crowdbreaks_incomingkeyword.tweet_id = crowdbreaks_tweet.tweet_id)
         ->  Seq Scan on crowdbreaks_incomingkeyword  (cost=0.00..36873.97 rows=2252597 width=12) (actual time=0.008..2136.839 rows=2252597 loops=1)
         ->  Hash  (cost=102535.68..102535.68 rows=80544 width=8) (actual time=1744.815..1744.815 rows=3091 loops=1)
               Buckets: 4096  Batches: 4  Memory Usage: 32kB
               ->  Hash Left Join  (cost=16574.93..102535.68 rows=80544 width=8) (actual time=112.551..1740.651 rows=3091 loops=1)
                     Hash Cond: ((crowdbreaks_tweet.place_id)::text = (crowdbreaks_place.place_id)::text)
                     Filter: ((crowdbreaks_tweet.coordinates @ '0103000020E61000000100000005000000AE47E17A141E5FC00000000000003840AE47E17A141E5FC029ED0DBE30B14840A4703D0AD7A350C029ED0DBE30B14840A4703D0AD7A350C00000000000003840AE47E17A141E5FC00000000000003840'::geometry) OR ((crowdbreaks_place.bounding_box && '0103000020E61000000100000005000000AE47E17A141E5FC00000000000003840AE47E17A141E5FC029ED0DBE30B14840A4703D0AD7A350C029ED0DBE30B14840A4703D0AD7A350C00000000000003840AE47E17A141E5FC00000000000003840'::geometry) AND _st_overlaps(crowdbreaks_place.bounding_box, '0103000020E61000000100000005000000AE47E17A141E5FC00000000000003840AE47E17A141E5FC029ED0DBE30B14840A4703D0AD7A350C029ED0DBE30B14840A4703D0AD7A350C00000000000003840AE47E17A141E5FC00000000000003840'::geometry)))
                     ->  Bitmap Heap Scan on crowdbreaks_tweet  (cost=15874.18..67060.28 rows=747873 width=125) (actual time=96.012..940.462 rows=736784 loops=1)
                           Recheck Cond: ((created_at > '2012-04-17 15:46:12.109893+00'::timestamp with time zone) AND (created_at < '2012-04-18 15:46:12.109899+00'::timestamp with time zone))
                           ->  Bitmap Index Scan on crowdbreaks_tweet_crreated_at  (cost=0.00..15687.22 rows=747873 width=0) (actual time=94.259..94.259 rows=736784 loops=1)
                                 Index Cond: ((created_at > '2012-04-17 15:46:12.109893+00'::timestamp with time zone) AND (created_at < '2012-04-18 15:46:12.109899+00'::timestamp with time zone))
                     ->  Hash  (cost=217.11..217.11 rows=6611 width=469) (actual time=15.926..15.926 rows=6611 loops=1)
                           Buckets: 1024  Batches: 4  Memory Usage: 259kB
                           ->  Seq Scan on crowdbreaks_place  (cost=0.00..217.11 rows=6611 width=469) (actual time=0.005..6.908 rows=6611 loops=1)
 Total runtime: 6381.903 ms

Per node type stats

node type	count	sum of times	% of query
Bitmap Heap Scan	1	846.203 ms	13.3 %
Bitmap Index Scan	1	94.259 ms	1.5 %
Hash	2	13.182 ms	0.2 %
Hash Join	1	2495.851 ms	39.1 %
Hash Left Join	1	784.263 ms	12.3 %
HashAggregate	1	4.264 ms	0.1 %
Seq Scan	2	2143.747 ms	33.6 %

Per table stats

Table name	Scan count	Total time	% of query
scan type	count	sum of times	% of table
crowdbreaks_incomingkeyword	1	2136.839 ms	33.5 %
Seq Scan	1	2136.839 ms	100.0 %
crowdbreaks_place	1	6.908 ms	0.1 %
Seq Scan	1	6.908 ms	100.0 %
crowdbreaks_tweet	1	846.203 ms	13.3 %
Bitmap Heap Scan	1	846.203 ms	100.0 %

explain.depesz.com

A tool for finding a real cause for slow queries.

Result: EfH

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Per node type stats

Per table stats