知方可补不足~SQL中的count命令的一些优化措施(百万以上数据明显)

2023-05-10,,

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SQL中对于求表记录总数的有count这个聚合命令,这个命令给我们感觉就是快,比一般的查询要快,但是,当你的数据表记录比较多时,如百万条,千万条时,对于count来说,就不是那么快了,我们需要掌握一些技巧,来优化这个count。

有人说:

select count(1) from table

select count(primarykey) from table

比较快,一定不要用

select count(*) from table

可我要说的是,count(*)更快一些,为什么呢,count(*)是什么意思?事实上,它真正的含义是找一个占用空间最小的索引字段,然后对它进行记数,不要一看到*就认为“大”,在count命令中,它指的是“任意一个“。

对于一个大表来说,如果你的字段有bit类型,如性别字段,表示真假关系的字段,我们需要为它加上索引,加上之后,我们的count速度就提交几十倍,真的,呵呵 

首先为我们的bit类型字段加索引IsSync添加聚集索引

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" 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如果数据表太大,我们需要在命令行中去运行

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" 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之后,再去运行count命令,呵呵

SET STATISTICS IO ON  --查看IO开销
SET STATISTICS PROFILE ON --查看分析、编译和执行查询所需的时间
SET STATISTICS TIME ON  --查看语句运行的时间
SELECT COUNT(*) FROM dbo.C_User_Info

结果比没有建立索引时快了10多倍!

回到目录

知方可补不足~SQL中的count命令的一些优化措施(百万以上数据明显)的相关教程结束。

《知方可补不足~SQL中的count命令的一些优化措施(百万以上数据明显).doc》

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