在PostgreSQL中的IN语句性能(和一般情况下)
问题描述:
我知道这可能以前曾被问过,但是我找不到它与SO的搜索。在PostgreSQL中的IN语句性能(和一般情况下)
比方说我Table 1和Table,我应该怎么指望一个查询的性能像这样:
SELECT * FROM TABLE1 WHERE id IN SUBQUERY_ON_TABLE2;
下井为行的Table 1和Table成长号和ID是TABLE1上的主键。
是的,我知道使用IN是这样一个n00b错误,但TABLE2有一个通用关系(django通用关系)到多个其他表,所以我想不出另一种方式来过滤数据。在TABLE1和TABLE2中,我应该预计哪些(aproximate)行中的行数会因此而注意到性能问题?根据行数的不同,性能是否会呈线性,指数等降级?
答
当子查询返回的记录数很少,并且由主查询返回的结果行数也很小时,您只需在每个记录上快速查找索引即可。随着返回数据的百分比增加,最终每个人都将切换到使用顺序扫描而不是索引的扫描,以一口吞下整个表格,而不是将其整合在一起。这不是一个简单的性能下降,而是线性或指数性的;随着计划类型的变化,存在主要的不连续性。而这些发生的行数取决于表的大小,所以在那里没有有用的规则。你应该建立一个仿真模型,就像我在下面做的一样,看看你自己的数据集上会发生什么,以了解曲线的样子。
下面是使用PostgreSQL 9.0数据库加载Dell Store 2数据库的工作原理示例。一旦子查询返回1000行,它将对主表进行全表扫描。一旦子查询正在考虑10,000条记录,那么也会变成全表扫描。这些每次运行两次,所以你看到了缓存的性能。如何根据缓存状态和未缓存状态改变性能是一个完整的“另一个话题:
dellstore2=# EXPLAIN ANALYZE SELECT * FROM customers WHERE customerid IN
(SELECT customerid FROM orders WHERE orderid<2);
Nested Loop (cost=8.27..16.56 rows=1 width=268) (actual time=0.051..0.060 rows=1 loops=1)
-> HashAggregate (cost=8.27..8.28 rows=1 width=4) (actual time=0.028..0.030 rows=1 loops=1)
-> Index Scan using orders_pkey on orders (cost=0.00..8.27 rows=1 width=4) (actual time=0.011..0.015 rows=1 loops=1)
Index Cond: (orderid < 2)
-> Index Scan using customers_pkey on customers (cost=0.00..8.27 rows=1 width=268) (actual time=0.013..0.016 rows=1 loops=1)
Index Cond: (customers.customerid = orders.customerid)
Total runtime: 0.191 ms
dellstore2=# EXPLAIN ANALYZE SELECT * FROM customers WHERE customerid IN
(SELECT customerid FROM orders WHERE orderid<100);
Nested Loop (cost=10.25..443.14 rows=100 width=268) (actual time=0.488..2.591 rows=98 loops=1)
-> HashAggregate (cost=10.25..11.00 rows=75 width=4) (actual time=0.464..0.661 rows=98 loops=1)
-> Index Scan using orders_pkey on orders (cost=0.00..10.00 rows=100 width=4) (actual time=0.019..0.218 rows=99 loops=1)
Index Cond: (orderid < 100)
-> Index Scan using customers_pkey on customers (cost=0.00..5.75 rows=1 width=268) (actual time=0.009..0.011 rows=1 loops=98)
Index Cond: (customers.customerid = orders.customerid)
Total runtime: 2.868 ms
dellstore2=# EXPLAIN ANALYZE SELECT * FROM customers WHERE customerid IN
(SELECT customerid FROM orders WHERE orderid<1000);
Hash Semi Join (cost=54.25..800.13 rows=1000 width=268) (actual time=4.574..80.319 rows=978 loops=1)
Hash Cond: (customers.customerid = orders.customerid)
-> Seq Scan on customers (cost=0.00..676.00 rows=20000 width=268) (actual time=0.007..33.665 rows=20000 loops=1)
-> Hash (cost=41.75..41.75 rows=1000 width=4) (actual time=4.502..4.502 rows=999 loops=1)
Buckets: 1024 Batches: 1 Memory Usage: 24kB
-> Index Scan using orders_pkey on orders (cost=0.00..41.75 rows=1000 width=4) (actual time=0.056..2.487 rows=999 loops=1)
Index Cond: (orderid < 1000)
Total runtime: 82.024 ms
dellstore2=# EXPLAIN ANALYZE SELECT * FROM customers WHERE customerid IN
(SELECT customerid FROM orders WHERE orderid<10000);
Hash Join (cost=443.68..1444.68 rows=8996 width=268) (actual time=79.576..157.159 rows=7895 loops=1)
Hash Cond: (customers.customerid = orders.customerid)
-> Seq Scan on customers (cost=0.00..676.00 rows=20000 width=268) (actual time=0.007..27.085 rows=20000 loops=1)
-> Hash (cost=349.97..349.97 rows=7497 width=4) (actual time=79.532..79.532 rows=7895 loops=1)
Buckets: 1024 Batches: 1 Memory Usage: 186kB
-> HashAggregate (cost=275.00..349.97 rows=7497 width=4) (actual time=45.130..62.227 rows=7895 loops=1)
-> Seq Scan on orders (cost=0.00..250.00 rows=10000 width=4) (actual time=0.008..20.979 rows=9999 loops=1)
Filter: (orderid < 10000)
Total runtime: 167.882 ms