下面的演示基于MySQL5.7.27版本

一、关于MySQL子查询的优化策略介绍:

子查询优化策略

对于不同类型的子查询,优化器会选择不同的策略。
1. 对于 IN、=ANY 子查询,优化器有如下策略选择:
semijoin
Materialization
exists

2. 对于 NOT IN、<>ALL 子查询,优化器有如下策略选择:
Materialization
exists

3. 对于 derived 派生表,优化器有如下策略选择:
derived_merge,将派生表合并到外部查询中(5.7 引入 );
将派生表物化为内部临时表,再用于外部查询。
注意:update 和 delete 语句中子查询不能使用 semijoin、materialization 优化策略

二、创建数据进行模拟演示

为了方便分析问题先建两张表并插入模拟数据:

CREATE TABLE `test02` (
  `id` int(11) NOT NULL,
  `a` int(11) DEFAULT NULL,
  `b` int(11) DEFAULT NULL,
  PRIMARY KEY (`id`),
  KEY `a` (`a`)
) ENGINE=InnoDB;

drop procedure idata;
delimiter ;;
create procedure idata()
begin
  declare i int;
  set i=1;
  while(i<=10000)do
    insert into test02 values(i, i, i);
    set i=i+1;
  end while;
end;;
delimiter ;
call idata();

create table test01 like test02;
insert into test01 (select * from test02 where id<=1000)

三、举例分析SQL实例

子查询示例:

SELECT * FROM test01 WHERE test01.a IN (SELECT test02.b FROM test02 WHERE id < 10)

大部分人可定会简单的认为这个 SQL 会这样执行:
SELECT test02.b FROM test02 WHERE id < 10
结果:1,2,3,4,5,6,7,8,9
SELECT * FROM test01 WHERE test01.a IN (1,2,3,4,5,6,7,8,9);

但实际上 MySQL 并不是这样做的。MySQL 会将相关的外层表压到子查询中,优化器认为这样效率更高。也就是说,优化器会将上面的 SQL 改写成这样:
select * from test01 where exists(select b from test02 where id < 10 and test01.a=test02.b);
提示: 针对mysql5.5以及之前的版本

查看执行计划如下,发现这条SQL对表test01进行了全表扫描1000,效率低下:

root@localhost [dbtest01]>desc select * from test01 where exists(select b from test02 where id < 10 and test01.a=test02.b);
+----+--------------------+--------+------------+-------+---------------+---------+---------+------+--------+----------+-------------+
| id | select_type        | table  | partitions | type  | possible_keys | key     | key_len | ref  | rows   | filtered | Extra       |
+----+--------------------+--------+------------+-------+---------------+---------+---------+------+--------+----------+-------------+
|  1 | PRIMARY            | test01 | NULL       | ALL   | NULL          | NULL    | NULL    | NULL | 1000   |   100.00 | Using where |
|  2 | DEPENDENT SUBQUERY | test02 | NULL       | range | PRIMARY       | PRIMARY | 4       | NULL |      9 |    10.00 | Using where |
+----+--------------------+--------+------------+-------+---------------+---------+---------+------+--------+----------+-------------+
2 rows in set, 2 warnings (0.00 sec)

但是此时实际执行下面的SQL,发现也不慢啊,这不是自相矛盾嘛,别急,咱们继续往下分析:

SELECT * FROM test01 WHERE test01.a IN (SELECT test02.b FROM test02 WHERE id < 10)

查看此条SQL的执行计划如下:


root@localhost [dbtest01]>desc SELECT * FROM test01 WHERE test01.a IN (SELECT test02.b FROM test02 WHERE id < 10);
+----+--------------+-------------+------------+-------+---------------+---------+---------+---------------+------+----------+-------------+
| id | select_type  | table       | partitions | type  | possible_keys | key     | key_len | ref           | rows | filtered | Extra       |
+----+--------------+-------------+------------+-------+---------------+---------+---------+---------------+------+----------+-------------+
|  1 | SIMPLE       | <subquery2> | NULL       | ALL   | NULL          | NULL    | NULL    | NULL          | NULL |   100.00 | Using where |
|  1 | SIMPLE       | test01      | NULL       | ref   | a             | a       | 5       | <subquery2>.b |    1 |   100.00 | NULL        |
|  2 | MATERIALIZED | test02      | NULL       | range | PRIMARY       | PRIMARY | 4       | NULL          |    9 |   100.00 | Using where |
+----+--------------+-------------+------------+-------+---------------+---------+---------+---------------+------+----------+-------------+
3 rows in set, 1 warning (0.00 sec)

发现优化器使用到了策略MATERIALIZED。于是对此策略进行了资料查询和学习。
https://dev.mysql.com/doc/refman/5.6/en/subquery-optimization.html

原因是从MySQL5.6版本之后包括MySQL5.6版本,优化器引入了新的优化策略:materialization=[off|on],semijoin=[off|on],(off代表关闭此策略,on代表开启此策略)
可以采用show variables like 'optimizer_switch'; 来查看MySQL采用的优化器策略。当然这些策略都是可以在线进行动态修改的
set global optimizer_switch='materialization=on,semijoin=on';代表开启优化策略materialization和semijoin

MySQL5.7.27默认的优化器策略:


root@localhost [dbtest01]>show variables like 'optimizer_switch';                                                                                                                              
+------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Variable_name    | Value                                                                                                                                                                                                                                                                                                                                                                                                            |
+------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| optimizer_switch | index_merge=on,index_merge_union=on,index_merge_sort_union=on,index_merge_intersection=on,engine_condition_pushdown=on,index_condition_pushdown=on,mrr=on,mrr_cost_based=on,block_nested_loop=on,batched_key_access=off,materialization=on,semijoin=on,loosescan=on,firstmatch=on,duplicateweedout=on,subquery_materialization_cost_based=on,use_index_extensions=on,condition_fanout_filter=on,derived_merge=on |
+------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

所以在MySQL5.6及以上版本时
执行下面的SQL是不会慢的。因为MySQL的优化器策略materialization和semijoin 对此SQL进行了优化

SELECT * FROM test01 WHERE test01.a IN (SELECT test02.b FROM test02 WHERE id < 10)

然而咱们把mysql的优化器策略materialization和semijoin 关闭掉测试,发现SQL确实对test01进行了全表的扫描(1000):

set global optimizer_switch='materialization=off,semijoin=off';

执行计划如下test01表确实进行了全表扫描:

root@localhost [dbtest01]>desc SELECT * FROM test01 WHERE test01.a IN (SELECT test02.b FROM test02 WHERE id < 10);
+----+--------------------+--------+------------+-------+---------------+---------+---------+------+--------+----------+-------------+
| id | select_type        | table  | partitions | type  | possible_keys | key     | key_len | ref  | rows   | filtered | Extra       |
+----+--------------------+--------+------------+-------+---------------+---------+---------+------+--------+----------+-------------+
|  1 | PRIMARY            | test01 | NULL       | ALL   | NULL          | NULL    | NULL    | NULL | 1000   |   100.00 | Using where |
|  2 | DEPENDENT SUBQUERY | test02 | NULL       | range | PRIMARY       | PRIMARY | 4       | NULL |      9 |    10.00 | Using where |
+----+--------------------+--------+------------+-------+---------------+---------+---------+------+--------+----------+-------------+
2 rows in set, 1 warning (0.00 sec)

下面咱们分析下这个执行计划:
!!!!再次提示:如果是mysql5.5以及之前的版本,或者是mysql5.6以及之后的版本关闭掉优化器策略materialization=off,semijoin=off,得到的SQL执行计划和下面的是相同的


root@localhost [dbtest01]>desc select * from test01 where exists(select b from test02 where id < 10 and test01.a=test02.b);
+----+--------------------+--------+------------+-------+---------------+---------+---------+------+------+----------+-------------+
| id | select_type        | table  | partitions | type  | possible_keys | key     | key_len | ref  | rows | filtered | Extra       |
+----+--------------------+--------+------------+-------+---------------+---------+---------+------+------+----------+-------------+
|  1 | PRIMARY            | test01 | NULL       | ALL   | NULL          | NULL    | NULL    | NULL | 1000 |   100.00 | Using where |
|  2 | DEPENDENT SUBQUERY | test02 | NULL       | range | PRIMARY       | PRIMARY | 4       | NULL |    9 |    10.00 | Using where |
+----+--------------------+--------+------------+-------+---------------+---------+---------+------+------+----------+-------------+
2 rows in set, 2 warnings (0.00 sec)

不相关子查询变成了关联子查询(select_type:DEPENDENT SUBQUERY),子查询需要根据 b 来关联外表 test01,因为需要外表的 test01 字段,所以子查询是没法先执行的。执行流程为:

  1. 扫描 test01,从 test01 取出一行数据 R;
  2. 从数据行 R 中,取出字段 a 执行子查询,如果得到结果为 TRUE,则把这行数据 R 放到结果集;
  3. 重复 1、2 直到结束。
    总的扫描行数为 1000+1000*9=10000(这是理论值,但是实际值比10000还少,怎么来的一直没想明白,看规律是子查询结果集每多一行,总扫描行数就会少几行)。

Semi-join优化器:

这样会有个问题,如果外层表是一个非常大的表,对于外层查询的每一行,子查询都得执行一次,这个查询的性能会非常差。我们很容易想到将其改写成 join 来提升效率:

select test01.* from test01 join test02 on test01.a=test02.b and test02.id<10;

# 查看此SQL的执行计划:


desc select test01.* from test01 join test02 on test01.a=test02.b and test02.id<10;

root@localhost [dbtest01]>EXPLAIN extended select test01.* from test01 join test02 on test01.a=test02.b and test02.id<10;
+----+-------------+--------+------------+-------+---------------+---------+---------+-------------------+------+----------+-------------+
| id | select_type | table  | partitions | type  | possible_keys | key     | key_len | ref               | rows | filtered | Extra       |
+----+-------------+--------+------------+-------+---------------+---------+---------+-------------------+------+----------+-------------+
|  1 | SIMPLE      | test02 | NULL       | range | PRIMARY       | PRIMARY | 4       | NULL              |    9 |   100.00 | Using where |
|  1 | SIMPLE      | test01 | NULL       | ref   | a             | a       | 5       | dbtest01.test02.b |    1 |   100.00 | NULL        |
+----+-------------+--------+------------+-------+---------------+---------+---------+-------------------+------+----------+-------------+
2 rows in set, 2 warnings (0.00 sec)

这样优化可以让 t2 表做驱动表,t1 表关联字段有索引,查找效率非常高

但这里会有个问题,join 是有可能得到重复结果的,而 in(select ...) 子查询语义则不会得到重复值。
而 semijoin 正是解决重复值问题的一种特殊联接。
在子查询中,优化器可以识别出 in 子句中每组只需要返回一个值,在这种情况下,可以使用 semijoin 来优化子查询,提升查询效率。
这是 MySQL 5.6 加入的新特性,MySQL 5.6 以前优化器只有 exists 一种策略来“优化”子查询。
经过 semijoin 优化后的 SQL 和执行计划分为:


root@localhost [dbtest01]>desc SELECT * FROM test01 WHERE test01.a IN (SELECT test02.b FROM test02 WHERE id < 10);
+----+--------------+-------------+------------+-------+---------------+---------+---------+---------------+------+----------+-------------+
| id | select_type  | table       | partitions | type  | possible_keys | key     | key_len | ref           | rows | filtered | Extra       |
+----+--------------+-------------+------------+-------+---------------+---------+---------+---------------+------+----------+-------------+
|  1 | SIMPLE       | <subquery2> | NULL       | ALL   | NULL          | NULL    | NULL    | NULL          | NULL |   100.00 | Using where |
|  1 | SIMPLE       | test01      | NULL       | ref   | a             | a       | 5       | <subquery2>.b |    1 |   100.00 | NULL        |
|  2 | MATERIALIZED | test02      | NULL       | range | PRIMARY       | PRIMARY | 4       | NULL          |    9 |   100.00 | Using where |
+----+--------------+-------------+------------+-------+---------------+---------+---------+---------------+------+----------+-------------+
3 rows in set, 1 warning (0.00 sec)
select 
    `test01`.`id`,`test01`.`a`,`test01`.`b` 
from `test01` semi join `test02` 
where
    ((`test01`.`a` = `<subquery2>`.`b`) 
    and (`test02`.`id` < 10)); 
##注意这是优化器改写的SQL,客户端上是不能用 semi join 语法的 

semijoin 优化实现比较复杂,其中又分 FirstMatch、Materialize 等策略,上面的执行计划中 select_type=MATERIALIZED 就是代表使用了 Materialize 策略来实现的 semijoin

这里 semijoin 优化后的执行流程为:

  1. 先执行子查询,把结果保存到一个临时表中,这个临时表有个主键用来去重;
  2. 从临时表中取出一行数据 R;
  3. 从数据行 R 中,取出字段 b 到被驱动表 t1 中去查找,满足条件则放到结果集;
  4. 重复执行 2、3,直到结束。

这样一来,子查询结果有 9 行,即临时表也有 9 行(这里没有重复值),总的扫描行数为 9+9+9*1=27 行,比原来的 10000 行少了很多。

MySQL 5.6 版本中加入的另一种优化特性 materialization,就是把子查询结果物化成临时表,然后代入到外查询中进行查找,来加快查询的执行速度。内存临时表包含主键(hash 索引),消除重复行,使表更小。
如果子查询结果太大,超过 tmp_table_size 大小,会退化成磁盘临时表。这样子查询只需要执行一次,而不是对于外层查询的每一行都得执行一遍。
不过要注意的是,这样外查询依旧无法通过索引快速查找到符合条件的数据,只能通过全表扫描或者全索引扫描,

semijoin 和 materialization 的开启是通过 optimizer_switch 参数中的 semijoin={on|off}、materialization={on|off} 标志来控制的。
上文中不同的执行计划就是对 semijoin 和 materialization 进行开/关产生的
总的来说对于子查询,先检查是否满足各种优化策略的条件(比如子查询中有 union 则无法使用 semijoin 优化)
然后优化器会按成本进行选择,实在没得选就会用 exists 策略来“优化”子查询,exists 策略是没有参数来开启或者关闭的。

下面举一个delete相关的子查询例子:

把上面的2张测试表分别填充350万数据和50万数据来测试delete语句


root@localhost [dbtest01]>select count(*) from test02;
+----------+
| count(*) |
+----------+
|  3532986 |
+----------+
1 row in set (0.64 sec)
root@localhost [dbtest01]>create table test01 like test02;
Query OK, 0 rows affected (0.01 sec)

root@localhost [dbtest01]>insert into test01 (select * from test02 where id<=500000)

root@localhost [dbtest01]>select count(*) from test01;
+----------+
| count(*) |
+----------+
|   500000 |

执行delete删除语句执行了4s

root@localhost [dbtest01]>delete FROM test01 WHERE test01.a IN (SELECT test02.b FROM test02 WHERE id < 10);
Query OK, 9 rows affected (4.86 sec)

查看 执行计划,对test01表进行了几乎全表扫描:


root@localhost [dbtest01]>desc delete FROM test01 WHERE test01.a IN (SELECT test02.b FROM test02 WHERE id < 10);
+----+--------------------+--------+------------+-------+---------------+---------+---------+------+--------+----------+-------------+
| id | select_type        | table  | partitions | type  | possible_keys | key     | key_len | ref  | rows   | filtered | Extra       |
+----+--------------------+--------+------------+-------+---------------+---------+---------+------+--------+----------+-------------+
|  1 | DELETE             | test01 | NULL       | ALL   | NULL          | NULL    | NULL    | NULL | 499343 |   100.00 | Using where |
|  2 | DEPENDENT SUBQUERY | test02 | NULL       | range | PRIMARY       | PRIMARY | 4       | NULL |      9 |    10.00 | Using where |
+----+--------------------+--------+------------+-------+---------------+---------+---------+------+--------+----------+-------------+
2 rows in set (0.00 sec)

于是修改上面的delete SQL语句伪join语句

root@localhost [dbtest01]>desc delete test01.* from test01 join test02 on test01.a=test02.b and test02.id<10;
+----+-------------+--------+------------+-------+---------------+---------+---------+-------------------+------+----------+-------------+
| id | select_type | table  | partitions | type  | possible_keys | key     | key_len | ref               | rows | filtered | Extra       |
+----+-------------+--------+------------+-------+---------------+---------+---------+-------------------+------+----------+-------------+
|  1 | SIMPLE      | test02 | NULL       | range | PRIMARY       | PRIMARY | 4       | NULL              |    9 |   100.00 | Using where |
|  1 | DELETE      | test01 | NULL       | ref   | a             | a       | 5       | dbtest01.test02.b |    1 |   100.00 | NULL        |
+----+-------------+--------+------------+-------+---------------+---------+---------+-------------------+------+----------+-------------+
2 rows in set (0.01 sec)

执行非常的快
root@localhost [dbtest01]>delete test01.* from test01 join test02 on test01.a=test02.b and test02.id<10;
Query OK, 9 rows affected (0.01 sec)

root@localhost [dbtest01]>select test01.* from test01 join test02 on test01.a=test02.b and test02.id<10;
Empty set (0.00 sec)

下面的这个表执行要全表扫描,非常慢,基本对表test01进行了全表扫描:

root@lcalhost [dbtest01]>desc delete FROM test01 WHERE id IN  (SELECT id FROM test02 WHERE id='350000');
+----+--------------------+--------+------------+-------+---------------+---------+---------+-------+--------+----------+-------------+
| id | select_type        | table  | partitions | type  | possible_keys | key     | key_len | ref   | rows   | filtered | Extra       |
+----+--------------------+--------+------------+-------+---------------+---------+---------+-------+--------+----------+-------------+
|  1 | DELETE             | test01 | NULL       | ALL   | NULL          | NULL    | NULL    | NULL  | 499343 |   100.00 | Using where |
|  2 | DEPENDENT SUBQUERY | test02 | NULL       | const | PRIMARY       | PRIMARY | 4       | const |      1 |   100.00 | Using index |
+----+--------------------+--------+------------+-------+---------------+---------+---------+-------+--------+----------+-------------+
2 rows in set (0.00 sec)

然而采用join的话,效率非常的高:


root@localhost [dbtest01]>desc delete test01.* FROM test01  inner join test02  WHERE  test01.id=test02.id and test02.id=350000 ;
+----+-------------+--------+------------+-------+---------------+---------+---------+-------+------+----------+-------------+
| id | select_type | table  | partitions | type  | possible_keys | key     | key_len | ref   | rows | filtered | Extra       |
+----+-------------+--------+------------+-------+---------------+---------+---------+-------+------+----------+-------------+
|  1 | DELETE      | test01 | NULL       | const | PRIMARY       | PRIMARY | 4       | const |    1 |   100.00 | NULL        |
|  1 | SIMPLE      | test02 | NULL       | const | PRIMARY       | PRIMARY | 4       | const |    1 |   100.00 | Using index |
+----+-------------+--------+------------+-------+---------------+---------+---------+-------+------+----------+-------------+
2 rows in set (0.01 sec)

root@localhost [dbtest01]>  desc delete test01.* from test01 join test02 on test01.a=test02.b and test02.id=350000;
+----+-------------+--------+------------+-------+---------------+---------+---------+-------+------+----------+-------+
| id | select_type | table  | partitions | type  | possible_keys | key     | key_len | ref   | rows | filtered | Extra |
+----+-------------+--------+------------+-------+---------------+---------+---------+-------+------+----------+-------+
|  1 | SIMPLE      | test02 | NULL       | const | PRIMARY       | PRIMARY | 4       | const |    1 |   100.00 | NULL  |
|  1 | DELETE      | test01 | NULL       | ref   | a             | a       | 5       | const |    1 |   100.00 | NULL  |
+----+-------------+--------+------------+-------+---------------+---------+---------+-------+------+----------+-------+
2 rows in set (0.00 sec)

参考文档:
https://www.cnblogs.com/zhengyun_ustc/p/slowquery1.html
https://www.jianshu.com/p/3989222f7084
https://dev.mysql.com/doc/refman/5.6/en/subquery-optimization.html