Mysql语句查询优化
其实对Mysql查询语句进行优化是一件非常有必要的事情。
如何查看当前sql语句的执行效率呢?
1.建一张学生表
CREATE TABLE `student` (
`stu_id` int(11) NOT NULL AUTO_INCREMENT COMMENT '学号(主键id)',
`stu_name` varchar(255) COLLATE utf8mb4_bin DEFAULT NULL COMMENT '学生姓名',
`stu_age` tinyint(4) DEFAULT NULL COMMENT '学生年龄',
PRIMARY KEY (`stu_id`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_bin COMMENT='学生表';
2.加入几条测试数据
INSERT INTO `test`.`Student` (`stu_id`, `stu_name`, `stu_age`) VALUES ('', '岳云鹏', '');
INSERT INTO `test`.`Student` (`stu_id`, `stu_name`, `stu_age`) VALUES ('', '薛之谦', '');
INSERT INTO `test`.`Student` (`stu_id`, `stu_name`, `stu_age`) VALUES ('', '郭德纲', '');
INSERT INTO `test`.`Student` (`stu_id`, `stu_name`, `stu_age`) VALUES ('', '范冰冰', '');
INSERT INTO `test`.`Student` (`stu_id`, `stu_name`, `stu_age`) VALUES ('', '李晨', '');
3.加入查询语句
EXPLAIN SELECT * FROM student WHERE stu_age=18
我们可以看到结果如下:
aaarticlea/png;base64,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" 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这时,我们发现了EXPLAIN。那么EXPLAIN的作用是干嘛的呢?
EXPLAIN显示了mysql如何使用索引来处理select语句以及连接表。也就是校验sql语句是否使用了索引,以及sql语句的查询效率。
EXPLAIN 列的解释
table:显示这一行的数据是关于哪张表的
type:这是重要的列,显示连接使用了何种类型。从最好到最差的连接类型为const、eq_reg、ref、range、indexhe和all
possible_keys:显示可能应用在这张表中的索引。如果为空,没有可能的索引。可以为相关的域从where语句中选择一个合适的语句
key:
实际使用的索引。如果为null,则没有使用索引。很少的情况下,mysql会选择优化不足的索引。这种情况下,可以在select语句中使用use
index(indexname)来强制使用一个索引或者用ignore index(indexname)来强制mysql忽略索引
key_len:使用的索引的长度。在不损失精确性的情况下,长度越短越好
ref:显示索引的哪一列被使用了,如果可能的话,是一个常数
rows:mysql认为必须检查的用来返回请求数据的行数
extra:关于mysql如何解析查询的额外信息。将在表4.3中讨论,但这里可以看到的坏的例子是using temporary和using filesort,意思mysql根本不能使用索引,结果是检索会很慢
其中有一列需要我们特别关注的,那就是type列
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" alt="" />
Explain的type显示的是访问类型,是较为重要的一个指标,结果值从好到坏依次是:
system > const > eq_ref > ref > fulltext > ref_or_null > index_merge > unique_subquery > index_subquery > range > index > ALL
一般来说,得保证查询至少达到range级别,最好能达到ref,否则就可能会出现性能问题。
type:ALL 表示全表查询,这在sql查询中是杜绝的。那怎么优化type至少达到ref呢?
很简单,加索引
ALTER TABLE student ADD INDEX student_stuAge ( `stu_age`)
加完索引之后,在执行
EXPLAIN SELECT * FROM student WHERE stu_age=18
我们发现:
aaarticlea/png;base64,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" alt="" />
type类型达到ref。这时的sql语句效率就比较高了。
只是针对索引方面的sql优化,希望对你有帮助!也欢迎大家多提提意见
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