mysql的优化基础知识
1.查看各种SQL执行的频率
mysql> show status like 'Com_select';--Com_insert,Com_delete,connections(试图连接mysql服务的次数),uptime(mysql工作时间),slow_queries(慢查询次数)等等 如:
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" 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2.定位执行效率较低的SQL语句
通过慢查询日志,定位查询效率低下的SQL语句,然后分析语句进行优化
3.通过explain或desc分析SQL语句的执行计划,如要查看所访问的分区使用explain partitions
aaarticlea/png;base64,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" alt="" />
其中,type: index //扫描方式,效率由低到最好 all(全表)->index(索引全扫描)->range(索引范围扫描)->ref(非唯一索引)->eq_ref(唯一索引)->const/system->null
4.使用profile分析SQL,profile就是详细地列出SQL语句执行过程
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mysql> set profiling=on --开启 OFF--关闭
aaarticlea/png;base64,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" alt="" />
再次查看状态:
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" alt="" />
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查看某个查询语句执行过程每个状态以及消耗的时间
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" alt="" />
优化表的作用主要是对表空间的碎片进行合并以及回收删除或更新造成浪费的空间
aaarticlea/png;base64,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" alt="" />
8.常用SQL优化
加载大量数据时,关闭非唯一索引,取消唯一性检查,以及取消自动提交以提高插入速度
set unique_checks=0
alter table stu disable keys
set autocommit=0
load load infile........
alter table stu enable keys
set unique_checks=1
set autocommit =1
where条件和order by 字段使用相同的索引,并且order by的顺序和索引顺序相同,还有order by的字段都是降序或者升序。例如:
以下情况会使用索引,前提(key-part1,key_part2)为联合索引
select * from tbl_name order by key_part1,key_part2....;
select * from tbl_name where key_part1=xxx order by key_part1,key_part2....;
select * from tbl_name order by key_part1 asc,key_part2 asc....;
以下情况则不会使用索引,(key1,key2分别建立索引)
select * from tbl_name order by key1,key2....;
select * from tbl_name where key1=xxx order by key2;
select * from tbl_name order by key_part1 asc,key_part2 desc....;
SELECT查询时最好指定具体的字段名,SELECT * 会选择所有字段,会增加排序区的使用,降低SQL性能。
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