内容介绍
单表索引失效案例
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| 0、思考题：如果把100万数据插入MYSQL ，如何提高插入效率 （1）关闭自动提交，只手动提交一次 （2）删除除主键索引外其他索引 （3）拼写mysql可以执行的长sql，批量插入数据 （4）使用java多线程 （5）使用框架，设置属性，实现批量插入 1、计算、函数导致索引失效 CREATE INDEX idx_name ON emp (NAME); EXPLAIN SELECT * FROM emp WHERE emp.name LIKE 'abc%'; EXPLAIN SELECT * FROM emp WHERE LEFT(emp.name,3) = 'abc'; ----索引失效 2 LIKE以%开头索引失效 EXPLAIN SELECT * FROM emp WHERE NAME LIKE '%ab%'; ----索引失效 3、不等于(!= 或者<>)索引失效 EXPLAIN SELECT * FROM emp WHERE emp.name = 'abc' ; EXPLAIN SELECT * FROM emp WHERE emp.name <> 'abc' ; ----索引失效 4、IS NOT NULL 和 IS NULL EXPLAIN SELECT * FROM emp WHERE emp.name IS NULL; EXPLAIN SELECT * FROM emp WHERE emp.name IS NOT NULL; ----索引失效 5、类型转换导致索引失效 EXPLAIN SELECT * FROM emp WHERE NAME='123'; EXPLAIN SELECT * FROM emp WHERE NAME= 123; ----索引失效 6、全值匹配我最爱 EXPLAIN SELECT * FROM emp WHERE emp.age = 30 AND deptid = 4 AND emp.name = 'abcd'; CREATE INDEX idx_age ON emp(age); CREATE INDEX idx_age_deptid ON emp(age,deptid); CREATE INDEX idx_age_deptid_name ON emp(age,deptid,`name`); 7、最佳左前缀法则 EXPLAIN SELECT * FROM emp WHERE emp.age=30 AND emp.name = 'abcd' ; CREATE INDEX idx_age_name ON emp (age,NAME); EXPLAIN SELECT * FROM emp WHERE emp.deptid=1 AND emp.name = 'abcd'; EXPLAIN SELECT * FROM emp WHERE emp.age = 30 AND emp.deptid=1 AND emp.name = 'abcd'; CREATE INDEX idx_age_deptid_name ON emp(age,deptid,`name`); EXPLAIN SELECT * FROM emp WHERE emp.deptid=1 AND emp.name = 'abcd' AND emp.age = 30; 8、索引中范围条件右边的列失效 CREATE INDEX idx_age_deptid_name ON emp(age,deptid,`name`); EXPLAIN SELECT * FROM emp WHERE emp.age=30 AND emp.name = 'abc' AND emp.deptId>1000 ; CREATE INDEX idx_age_name_deptid ON emp(age,`name`,deptid); |
关联查询优化
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| 1、数据准备 -- 分类 CREATE TABLE IF NOT EXISTS class ( id INT(10) UNSIGNED NOT NULL AUTO_INCREMENT, card INT(10) UNSIGNED NOT NULL, PRIMARY KEY (id) ); -- 图书 CREATE TABLE IF NOT EXISTS book ( bookid INT(10) UNSIGNED NOT NULL AUTO_INCREMENT, card INT(10) UNSIGNED NOT NULL, PRIMARY KEY (bookid) ); -- 插入16条记录 INSERT INTO class(card) VALUES(FLOOR(1 + (RAND() * 20))); INSERT INTO class(card) VALUES(FLOOR(1 + (RAND() * 20))); INSERT INTO class(card) VALUES(FLOOR(1 + (RAND() * 20))); INSERT INTO class(card) VALUES(FLOOR(1 + (RAND() * 20))); INSERT INTO class(card) VALUES(FLOOR(1 + (RAND() * 20))); INSERT INTO class(card) VALUES(FLOOR(1 + (RAND() * 20))); INSERT INTO class(card) VALUES(FLOOR(1 + (RAND() * 20))); INSERT INTO class(card) VALUES(FLOOR(1 + (RAND() * 20))); INSERT INTO class(card) VALUES(FLOOR(1 + (RAND() * 20))); INSERT INTO class(card) VALUES(FLOOR(1 + (RAND() * 20))); INSERT INTO class(card) VALUES(FLOOR(1 + (RAND() * 20))); INSERT INTO class(card) VALUES(FLOOR(1 + (RAND() * 20))); INSERT INTO class(card) VALUES(FLOOR(1 + (RAND() * 20))); INSERT INTO class(card) VALUES(FLOOR(1 + (RAND() * 20))); INSERT INTO class(card) VALUES(FLOOR(1 + (RAND() * 20))); INSERT INTO class(card) VALUES(FLOOR(1 + (RAND() * 20))); -- 插入20条记录 INSERT INTO book(card) VALUES(FLOOR(1 + (RAND() * 20))); INSERT INTO book(card) VALUES(FLOOR(1 + (RAND() * 20))); INSERT INTO book(card) VALUES(FLOOR(1 + (RAND() * 20))); INSERT INTO book(card) VALUES(FLOOR(1 + (RAND() * 20))); INSERT INTO book(card) VALUES(FLOOR(1 + (RAND() * 20))); INSERT INTO book(card) VALUES(FLOOR(1 + (RAND() * 20))); INSERT INTO book(card) VALUES(FLOOR(1 + (RAND() * 20))); INSERT INTO book(card) VALUES(FLOOR(1 + (RAND() * 20))); INSERT INTO book(card) VALUES(FLOOR(1 + (RAND() * 20))); INSERT INTO book(card) VALUES(FLOOR(1 + (RAND() * 20))); INSERT INTO book(card) VALUES(FLOOR(1 + (RAND() * 20))); INSERT INTO book(card) VALUES(FLOOR(1 + (RAND() * 20))); INSERT INTO book(card) VALUES(FLOOR(1 + (RAND() * 20))); INSERT INTO book(card) VALUES(FLOOR(1 + (RAND() * 20))); INSERT INTO book(card) VALUES(FLOOR(1 + (RAND() * 20))); INSERT INTO book(card) VALUES(FLOOR(1 + (RAND() * 20))); INSERT INTO book(card) VALUES(FLOOR(1 + (RAND() * 20))); INSERT INTO book(card) VALUES(FLOOR(1 + (RAND() * 20))); INSERT INTO book(card) VALUES(FLOOR(1 + (RAND() * 20))); INSERT INTO book(card) VALUES(FLOOR(1 + (RAND() * 20))); 2、左外连接实例 （1）明确角色 
（2）优化 EXPLAIN SELECT * FROM class LEFT JOIN book ON class.card = book.card; CREATE INDEX idx_class_card ON class(card); CREATE INDEX idx_book_card ON book(card); *使用LEFT JOIN，前面的是驱动表、后面是被驱动表 针对两张表的连接条件涉及的列，索引要创建在被驱动表上，驱动表尽量是小表 * 如果驱动表上没有where过滤条件 * 当驱动表的连接条件没有索引时，驱动表是全表扫描 * 当针对驱动表的连接条件建立索引时，驱动表依然要进行全索引扫描 * 因此，此时建立在驱动表上的连接条件上的索引是没有太大意义的 * 如果驱动表上有where过滤条件，那么针对过滤条件创建的索引是有必要的 3、内连接实例 EXPLAIN SELECT * FROM class INNER JOIN book ON class.card = book.card; CREATE INDEX idx_class_card ON class(card); CREATE INDEX idx_book_card ON book(card); *使用INNER JOIN，驱动表、被驱动表不固定，mysql选择 MySQL优化器也会自动选择驱动表，自动选择驱动表的原则是：索引创建在被驱动表上，驱动表是小表。 4、分析4种查询sql（mysql5） #1 NO3 EXPLAIN SELECT ab.name,c.name ceoname FROM (SELECT a.name,b.CEO FROM emp a LEFT JOIN dept b ON a.deptId=b.id)ab LEFT JOIN emp c ON ab.ceo=c.id; #2 NO4 EXPLAIN SELECT c.name,ab.name ceoname FROM emp c LEFT JOIN (SELECT a.name,b.id FROM emp a INNER JOIN dept b ON b.CEO = a.id)ab ON c.deptId= ab.id; #3 NO1 EXPLAIN SELECT a.name,c.name ceoname FROM emp a LEFT JOIN dept b ON a.deptId= b.id LEFT JOIN emp c ON b.CEO= c.id; #4 NO2 EXPLAIN SELECT a.name,(SELECT c.name FROM emp c WHERE c.id =b.CEO)ceoname FROM emp a LEFT JOIN dept b ON a.deptId=b.id; 5、总结 * 保证被驱动表的JOIN字段已经创建了索引 * 需要JOIN 的字段，数据类型保持绝对一致。 * LEFT JOIN 时，选择小表作为驱动表，大表作为被驱动表 。减少外层循环的次数。 * INNER JOIN 时，MySQL会自动将小结果集的表选为驱动表 。选择相信MySQL优化策略。 * 能够直接多表关联的尽量直接关联，不用子查询。(减少查询的趟数) * 衍生表建不了索引（MySQL5.5） |
其他优化
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| 1、子查询优化 （1）获取非掌门人成员 #获取非掌门人成员 CALL proc_drop_index("atguigudb","emp"); CALL proc_drop_index("atguigudb","dept"); SELECT * FROM t_emp a WHERE a.id NOT IN (SELECT b.ceo FROM t_dept b WHERE b.ceo IS NOT NULL); EXPLAIN SELECT * FROM emp a WHERE a.id NOT IN (SELECT b.ceo FROM dept b WHERE b.ceo IS NOT NULL); #子查询优化NOT IN EXPLAIN SELECT * FROM emp a LEFT JOIN dept b ON a.id = b.ceo WHERE b.id IS NULL; （2）结论 尽量不要使用NOT IN 或者 NOT EXISTS，用LEFT JOIN xxx ON xx = xx WHERE xx IS NULL替代 2、排序优化 （1）实例 CALL proc_drop_index("atguigudb","emp"); CALL proc_drop_index("atguigudb","dept"); CREATE INDEX idx_age_deptid_name ON emp (age,deptid,`name`); #无过滤，不索引 EXPLAIN SELECT * FROM emp ORDER BY age,deptid; EXPLAIN SELECT * FROM emp ORDER BY age,deptid LIMIT 10; EXPLAIN SELECT * FROM emp WHERE age=45 ORDER BY deptid; #顺序错，不索引 EXPLAIN SELECT * FROM emp WHERE age=45 ORDER BY deptid, `name`; EXPLAIN SELECT * FROM emp WHERE age=45 ORDER BY deptid, empno; CREATE INDEX idx_age_deptid_empno ON emp (age,deptid,`empno`); EXPLAIN SELECT * FROM emp WHERE age=45 ORDER BY `name`, deptid; EXPLAIN SELECT * FROM emp WHERE deptid=45 ORDER BY age; #方向反，不索引 EXPLAIN SELECT * FROM emp WHERE age=45 ORDER BY deptid DESC, `name` DESC; EXPLAIN SELECT * FROM emp WHERE age=45 ORDER BY deptid ASC, `name` DESC; 1. 总结 无过滤，不索引 顺序错，不索引 方向反，不索引 3、mysql索引选择 EXPLAIN SELECT * FROM emp WHERE age =30 AND empno <101000 ORDER BY `name`; CREATE INDEX idx_age_empno ON emp (age,`empno`); CREATE INDEX idx_age_name ON emp (age,NAME); 
*当【范围条件】和【group by 或者 order by】的字段出现二选一时，优先观察条件字段的过滤数量，如果过滤的数据足够多，而需要排序的数据并不多时，优先把索引放在范围字段上。反之，亦然。 也可以将选择权交给MySQL：索引同时存在，mysql自动选择最优的方案：（对于这个例子，mysql选择idx_age_empno），但是，随着数据量的变化，选择的索引也会随之变化的。 4、双路排序和单路排序 （1）双路排序（慢） 取一批数据，要对磁盘进行两次扫描。众所周知，IO是很耗时的，所以在mysql4.1之后，出现了第二种改进的算法，就是单路排序。 （2）单路排序（快） 它的效率更快一些，因为只读取一次磁盘，避免了第二次读取数据。并且把随机IO变成了顺序IO。但是它会使用更多的空间， 因为它把每一行都保存在内存中了。 5、分组优化 * group by 使用索引的原则几乎跟order by一致。但是group by 即使没有过滤条件用到索引，也可以直接使用索引（Order By 必须有过滤条件才能使用上索引） * 包含了order by、group by、distinct这些查询的语句，where条件过滤出来的结果集请保持在1000行以内，否则SQL会很慢。 6、覆盖索引优化 总结 * 禁止使用select *，禁止查询与业务无关字段 * 尽量利用覆盖索引 |
慢查询日志
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| 1、如何对系统查询慢做索引优化 （1）找运维人员开启生产数据库慢查询日志 （2）等待1-2周时间，积累慢查询日志 （3）借助工具获取慢查询次数最多和查询时间最长的几个sql进行优化 （4）在生产数据库，使用EXPLAIN进行sql分析，找到瓶颈，创建索引优化 （5）关闭慢查询日志。 2、是什么 一种日志记录，查看哪些SQL超出了我们的最大忍耐时间值。 3、使用 （1）开启slow_query_log SET GLOBAL slow_query_log=1; SHOW VARIABLES LIKE '%slow_query_log%'; （2）修改long_query_time阈值 SHOW VARIABLES LIKE '%long_query_time%'; -- 查看值：默认10秒 SET GLOBAL long_query_time=0.1; -- 设置一个比较短的时间，便于测试 （3）运行sql （4）查看慢查询日志 
（5）使用工具分析慢查询日志 -- 查看mysqldumpslow的帮助信息 mysqldumpslow --help -- 工作常用参考 -- 1.得到返回记录集最多的10个SQL mysqldumpslow -s r -t 10 /var/lib/mysql/atguigu-slow.log -- 2.得到访问次数最多的10个SQL mysqldumpslow -s c -t 10 /var/lib/mysql/atguigu-slow.log -- 3.得到按照时间排序的前10条里面含有左连接的查询语句 mysqldumpslow -s t -t 10 -g "left join" /var/lib/mysql/atguigu-slow.log -- 4.另外建议在使用这些命令时结合 | 和more 使用 ，否则语句过多有可能出现爆屏情况 mysqldumpslow -s r -t 10 /var/lib/mysql/atguigu-slow.log | more |
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| 1、单表索引失效案例 2、关联查询优化 3、其他优化 4、慢查询日志 5、视图 6、高性能架构模式 |