Sql 慢速postgres查询
OSX上的Postgres 10.3 为什么在#1和#3中扫描“事件”中的所有行 为什么将DISTINCT添加到#2（下面的视图#4查询）会导致截然不同的（快速）执行和执行计划 我对博士后的知识非常肤浅（一般来说，DB理论还可以），所以我不知道其中的怪癖，索引是下一级的 最初，我使用Node+Sequelize进行joins#3，速度很慢，所以在中尝试过。如果“集合项目”的计数为1，则快速执行 如果可以避免的话，我更喜欢使用Sequelize生成的查询，而不是手工创建的查询 谢谢你的帮助 1-慢进： 分析：Sql 慢速postgres查询,sql,postgresql,indexing,Sql,Postgresql,Indexing,OSX上的Postgres 10.3 为什么在#1和#3中扫描“事件”中的所有行 为什么将DISTINCT添加到#2（下面的视图#4查询）会导致截然不同的（快速）执行和执行计划 我对博士后的知识非常肤浅（一般来说，DB理论还可以），所以我不知道其中的怪癖，索引是下一级的 最初，我使用Node+Sequelize进行joins#3，速度很慢，所以在中尝试过。如果“集合项目”的计数为1，则快速执行 如果可以避免的话，我更喜欢使用Sequelize生成的查询，而不是手工创建的查询 谢谢你的帮助 1-慢
Merge Semi Join (cost=2.08..12.60 rows=4 width=1870) (actual time=0.079..6294.739 rows=7 loops=1) |
Merge Cond: (event.id = collection_item.item_id) |
-> Index Scan using idx_event_id on event (cost=0.43..1066819.90 rows=10000215 width=1870) (actual time=0.012..5525.787 rows=9109726 loops=1) |
-> Sort (cost=1.62..1.64 rows=4 width=4) (actual time=0.028..0.029 rows=7 loops=1) |
Sort Key: collection_item.item_id |
Sort Method: quicksort Memory: 25kB |
-> Seq Scan on collection_item (cost=0.00..1.58 rows=4 width=4) (actual time=0.018..0.022 rows=7 loops=1) |
Filter: ((collection_id = 2) AND ((item_type)::text = 'event'::text)) |
Rows Removed by Filter: 33 |
Planning time: 0.412 ms |
Execution time: 6296.260 ms |
Limit (cost=2.90..16.42 rows=4 width=3676) (actual time=0.100..6207.552 rows=7 loops=1) |
-> Nested Loop Left Join (cost=2.90..16.42 rows=4 width=3676) (actual time=0.100..6207.550 rows=7 loops=1) |
-> Nested Loop Left Join (cost=2.76..15.74 rows=4 width=3664) (actual time=0.094..6207.502 rows=7 loops=1) |
-> Nested Loop Left Join (cost=2.22..13.24 rows=4 width=3359) (actual time=0.092..6207.492 rows=7 loops=1) |
-> Merge Join (cost=2.08..12.60 rows=4 width=1836) (actual time=0.089..6207.012 rows=7 loops=1) |
Merge Cond: (event.id = "collectionItem".item_id) |
-> Index Scan using idx_event_id on event (cost=0.43..1066819.90 rows=10000215 width=1832) (actual time=0.018..5455.081 rows=9109726 loops=1) |
-> Sort (cost=1.62..1.64 rows=4 width=8) (actual time=0.027..0.028 rows=7 loops=1) |
Sort Key: "collectionItem".item_id |
Sort Method: quicksort Memory: 25kB |
-> Seq Scan on collection_item "collectionItem" (cost=0.00..1.58 rows=4 width=8) (actual time=0.014..0.019 rows=7 loops=1) |
Filter: (((item_type)::text = 'event'::text) AND (collection_id = 2)) |
Rows Removed by Filter: 33 |
-> Index Scan using location_pkey on location "primaryLocation" (cost=0.14..0.16 rows=1 width=1523) (actual time=0.067..0.067 rows=0 loops=7) |
Index Cond: (event.primary_location_id = id) |
-> Index Scan using event_date_pkey on event_date "primaryEventDate" (cost=0.55..0.63 rows=1 width=305) (actual time=0.000..0.000 rows=0 loops=7) |
Index Cond: (event.primary_event_date_id = id) |
-> Index Scan using idx_usereventtrack_event on user_event_track "userEventTrack" (cost=0.14..0.16 rows=1 width=16) (actual time=0.005..0.006 rows=1 loops=7) |
Index Cond: (event.id = event_id) |
Filter: (user_id = 1) |
Planning time: 6.875 ms |
Execution time: 6208.728 ms |
Index Scan using idx_event_id on event (cost=2.03..76.86 rows=10 width=1870) (actual time=0.092..0.119 rows=7 loops=1) |
Index Cond: (id = ANY ($1)) |
InitPlan 2 (returns $1) |
-> Result (cost=1.58..1.59 rows=1 width=32) (actual time=0.043..0.043 rows=1 loops=1) |
InitPlan 1 (returns $0) |
-> Seq Scan on collection_item (cost=0.00..1.58 rows=4 width=4) (actual time=0.035..0.039 rows=7 loops=1) |
Filter: ((collection_id = 2) AND ((item_type)::text = 'event'::text)) |
Rows Removed by Filter: 33 |
Planning time: 0.204 ms |
Execution time: 0.147 ms |
Nested Loop (cost=2.03..27.01 rows=3 width=1870) (actual time=0.088..0.143 rows=7 loops=1)
-> HashAggregate (cost=1.59..1.62 rows=3 width=4) (actual time=0.054..0.056 rows=7 loops=1)
Group Key: collection_item.item_id
-> Seq Scan on collection_item (cost=0.00..1.58 rows=4 width=4) (actual time=0.042..0.047 rows=7 loops=1)
Filter: ((collection_id = 2) AND ((item_type)::text = 'event'::text))
Rows Removed by Filter: 33
-> Index Scan using idx_event_id on event (cost=0.43..8.45 rows=1 width=1870) (actual time=0.012..0.012 rows=1 loops=7)
Index Cond: (id = collection_item.item_id)
Planning time: 0.388 ms
Execution time: 0.220 ms
Merge Semi Join (cost=2.08..12.60 rows=4 width=1870) (actual time=0.079..6294.739 rows=7 loops=1) |
Merge Cond: (event.id = collection_item.item_id) |
-> Index Scan using idx_event_id on event (cost=0.43..1066819.90 rows=10000215 width=1870) (actual time=0.012..5525.787 rows=9109726 loops=1) |
-> Sort (cost=1.62..1.64 rows=4 width=4) (actual time=0.028..0.029 rows=7 loops=1) |
Sort Key: collection_item.item_id |
Sort Method: quicksort Memory: 25kB |
-> Seq Scan on collection_item (cost=0.00..1.58 rows=4 width=4) (actual time=0.018..0.022 rows=7 loops=1) |
Filter: ((collection_id = 2) AND ((item_type)::text = 'event'::text)) |
Rows Removed by Filter: 33 |
Planning time: 0.412 ms |
Execution time: 6296.260 ms |
Limit (cost=2.90..16.42 rows=4 width=3676) (actual time=0.100..6207.552 rows=7 loops=1) |
-> Nested Loop Left Join (cost=2.90..16.42 rows=4 width=3676) (actual time=0.100..6207.550 rows=7 loops=1) |
-> Nested Loop Left Join (cost=2.76..15.74 rows=4 width=3664) (actual time=0.094..6207.502 rows=7 loops=1) |
-> Nested Loop Left Join (cost=2.22..13.24 rows=4 width=3359) (actual time=0.092..6207.492 rows=7 loops=1) |
-> Merge Join (cost=2.08..12.60 rows=4 width=1836) (actual time=0.089..6207.012 rows=7 loops=1) |
Merge Cond: (event.id = "collectionItem".item_id) |
-> Index Scan using idx_event_id on event (cost=0.43..1066819.90 rows=10000215 width=1832) (actual time=0.018..5455.081 rows=9109726 loops=1) |
-> Sort (cost=1.62..1.64 rows=4 width=8) (actual time=0.027..0.028 rows=7 loops=1) |
Sort Key: "collectionItem".item_id |
Sort Method: quicksort Memory: 25kB |
-> Seq Scan on collection_item "collectionItem" (cost=0.00..1.58 rows=4 width=8) (actual time=0.014..0.019 rows=7 loops=1) |
Filter: (((item_type)::text = 'event'::text) AND (collection_id = 2)) |
Rows Removed by Filter: 33 |
-> Index Scan using location_pkey on location "primaryLocation" (cost=0.14..0.16 rows=1 width=1523) (actual time=0.067..0.067 rows=0 loops=7) |
Index Cond: (event.primary_location_id = id) |
-> Index Scan using event_date_pkey on event_date "primaryEventDate" (cost=0.55..0.63 rows=1 width=305) (actual time=0.000..0.000 rows=0 loops=7) |
Index Cond: (event.primary_event_date_id = id) |
-> Index Scan using idx_usereventtrack_event on user_event_track "userEventTrack" (cost=0.14..0.16 rows=1 width=16) (actual time=0.005..0.006 rows=1 loops=7) |
Index Cond: (event.id = event_id) |
Filter: (user_id = 1) |
Planning time: 6.875 ms |
Execution time: 6208.728 ms |
Index Scan using idx_event_id on event (cost=2.03..76.86 rows=10 width=1870) (actual time=0.092..0.119 rows=7 loops=1) |
Index Cond: (id = ANY ($1)) |
InitPlan 2 (returns $1) |
-> Result (cost=1.58..1.59 rows=1 width=32) (actual time=0.043..0.043 rows=1 loops=1) |
InitPlan 1 (returns $0) |
-> Seq Scan on collection_item (cost=0.00..1.58 rows=4 width=4) (actual time=0.035..0.039 rows=7 loops=1) |
Filter: ((collection_id = 2) AND ((item_type)::text = 'event'::text)) |
Rows Removed by Filter: 33 |
Planning time: 0.204 ms |
Execution time: 0.147 ms |
Nested Loop (cost=2.03..27.01 rows=3 width=1870) (actual time=0.088..0.143 rows=7 loops=1)
-> HashAggregate (cost=1.59..1.62 rows=3 width=4) (actual time=0.054..0.056 rows=7 loops=1)
Group Key: collection_item.item_id
-> Seq Scan on collection_item (cost=0.00..1.58 rows=4 width=4) (actual time=0.042..0.047 rows=7 loops=1)
Filter: ((collection_id = 2) AND ((item_type)::text = 'event'::text))
Rows Removed by Filter: 33
-> Index Scan using idx_event_id on event (cost=0.43..8.45 rows=1 width=1870) (actual time=0.012..0.012 rows=1 loops=7)
Index Cond: (id = collection_item.item_id)
Planning time: 0.388 ms
Execution time: 0.220 ms
多次执行的结果是否相同？最后一个查询根本没有在中使用
。我在帖子顶部说，最初我在做JOIN（#3），速度很慢。然后我在（#1）中进行了测试，速度也出人意料地慢，因为我希望它首先运行子查询，返回一位数的结果，然后在“event”表上执行join/scan。然后我找到了#2的例子，使用任意+转换作为数组。在我看来，这相当于#1，Postgres应该这样做。执行时间是一致的。您的postgresql配置和postgresql版本是什么？看起来像是计划问题。你有新的索引和清空的表吗？10.3在OS X上。我做了一个后期更新-将DISTINCT添加到#1（显示为query#4）会导致完全不同的执行计划和快速。