有这样的一个需求:select count(distinct nick) from user_access_xx_xx;
这条sql用于统计用户访问的uv,由于单表的数据量在10G以上,即使在user_access_xx_xx上加上nick的索引,
通过查看执行计划,也为全索引扫描,sql在执行的时候,会对整个服务器带来抖动;
root@db 09:00:12>select count(distinct nick) from user_access; +———————-+ | count(distinct nick) | +———————-+ | 806934 | +———————-+ 1 row in set (52.78 sec)
执行一次sql需要花费52.78s,已经非常的慢了
现在需要换一种思路来解决该问题:
我们知道索引的值是按照索引字段升序的,比如我们对(nick,other_column)两个字段做了索引,那么在索引中的则是按照nick,other_column的升序排列:
我们现在的sql:select count(distinct nick) from user_access;则是直接从nick1开始一条条扫描下来,直到扫描到最后一个nick_n,
那么中间过程会扫描很多重复的nick,如果我们能够跳过中间重复的nick,则性能会优化非常多(在oracle中,这种扫描技术为loose index scan,但在5.1的版本中,mysql中还不能直接支持这种优化技术):
所以需要通过改写sql来达到伪loose index scan:
root@db 09:41:30>select count(*) from ( select distinct(nick) from user_access)t ; | count(*) | +———-+ | 806934 | 1 row in set (5.81 sec)
Sql中先选出不同的nick,最后在外面套一层,就可以得到nick的distinct值总和;
最重要的是在子查询中:select distinct(nick) 实现了上图中的伪loose index scan,优化器在这个时候的执行计划为Using index for group-by ,
需要注意的是mysql把distinct优化为group by,它首先利用索引来分组,然后扫描索引,对需要的nick只扫描一次;
两个sql的执行计划分别为:
优化写法:
root@db 09:41:10>explain select distinct(nick) from user_access-> ; +—-+————-+——————————+——-+—————+————-| id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra | +—-+————-+——————————+——-+—————+————- | 1 | SIMPLE | user_access | range | NULL | ind_user_access_nick | 67 | NULL | 2124695 | Using index for group-by | +—-+————-+——————————+——-+—————+————-
原始写法:
root@db 09:42:55>explain select count(distinct nick) from user_access; +—-+————-+——————————+——-+—————+————- | id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra | +—-+————-+——————————+——-+—————+————- | 1 | SIMPLE | user_access | index | NULL | ind_user_access | 177 | NULL | 19546123 | Using index |

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