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下面的演示基于MySQL5.7.27版本
一、關于MySQL子查詢的優化策略介紹:
子查詢優化策略
對于不同類型的子查詢，優化器會選擇不同的策略。
1. 對于 IN、=ANY 子查詢，優化器有如下策略選擇：
2. 對于 NOT IN、<>ALL 子查詢，優化器有如下策略選擇：
3. 對于 derived 派生表，優化器有如下策略選擇：
derived_merge，將派生表合并到外部查詢中（5.7 引入 ）；
將派生表物化為內部臨時表，再用于外部查詢。
注意：update 和 delete 語句中子查詢不能使用 semijoin、materialization 優化策略
二、創建數據進行模擬演示
為了方便分析問題先建兩張表并插入模擬數據：
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、舉例分析SQL實例
子查詢示例：
SELECT * FROM test01 WHERE test01.a IN (SELECT test02.b FROM test02 WHERE id < 10)
大部分人可定會簡單的認為這個 SQL 會這樣執行：
SELECT test02.b FROM test02 WHERE id < 10
結果: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);
但實際上 MySQL 并不是這樣做的。MySQL 會將相關的外層表壓到子查詢中，優化器認為這樣效率更高。也就是說，優化器會將上面的 SQL 改寫成這樣：
select * from test01 where exists(select b from test02 where id < 10 and test01.a=test02.b);
提示： 針對mysql5.5以及之前的版本
查看執行計劃如下，發現這條SQL對表test01進行了全表掃描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)
但是此時實際執行下面的SQL，發現也不慢啊,這不是自相矛盾嘛，別急，咱們繼續往下分析:
SELECT * FROM test01 WHERE test01.a IN (SELECT test02.b FROM test02 WHERE id < 10)
查看此條SQL的執行計劃如下：
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)
發現優化器使用到了策略MATERIALIZED。于是對此策略進行了資料查詢和學習。
https://dev.mysql.com/doc/refman/5.6/en/subquery-optimization.html
原因是從MySQL5.6版本之后包括MySQL5.6版本，優化器引入了新的優化策略:materialization=[off|on],semijoin=[off|on],(off代表關閉此策略，on代表開啟此策略)
可以采用show variables like 'optimizer_switch'; 來查看MySQL采用的優化器策略。當然這些策略都是可以在線進行動態修改的
set global optimizer_switch='materialization=on,semijoin=on';代表開啟優化策略materialization和semijoin
MySQL5.7.27默認的優化器策略：
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及以上版本時
執行下面的SQL是不會慢的。因為MySQL的優化器策略materialization和semijoin 對此SQL進行了優化
SELECT * FROM test01 WHERE test01.a IN (SELECT test02.b FROM test02 WHERE id < 10)
然而咱們把mysql的優化器策略materialization和semijoin 關閉掉測試，發現SQL確實對test01進行了全表的掃描(1000):
set global optimizer_switch='materialization=off,semijoin=off';
執行計劃如下test01表確實進行了全表掃描:
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)
下面咱們分析下這個執行計劃:
！！！！再次提示:如果是mysql5.5以及之前的版本，或者是mysql5.6以及之后的版本關閉掉優化器策略materialization=off,semijoin=off，得到的SQL執行計劃和下面的是相同的
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)
不相關子查詢變成了關聯子查詢（select_type:DEPENDENT SUBQUERY），子查詢需要根據 b 來關聯外表 test01，因為需要外表的 test01 字段，所以子查詢是沒法先執行的。執行流程為：
總的掃描行數為 1000+1000*9=10000（這是理論值，但是實際值比10000還少，怎么來的一直沒想明白，看規律是子查詢結果集每多一行，總掃描行數就會少幾行）。
Semi-join優化器:
這樣會有個問題，如果外層表是一個非常大的表，對于外層查詢的每一行，子查詢都得執行一次，這個查詢的性能會非常差。我們很容易想到將其改寫成 join 來提升效率：
select test01.* from test01 join test02 on test01.a=test02.b and test02.id<10;
# 查看此SQL的執行計劃:
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)
這樣優化可以讓 t2 表做驅動表，t1 表關聯字段有索引，查找效率非常高。
但這里會有個問題，join 是有可能得到重復結果的，而 in(select ...) 子查詢語義則不會得到重復值。
而 semijoin 正是解決重復值問題的一種特殊聯接。
在子查詢中，優化器可以識別出 in 子句中每組只需要返回一個值，在這種情況下，可以使用 semijoin 來優化子查詢，提升查詢效率。
這是 MySQL 5.6 加入的新特性，MySQL 5.6 以前優化器只有 exists 一種策略來“優化”子查詢。
經過 semijoin 優化后的 SQL 和執行計劃分為：
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));
##注意這是優化器改寫的SQL，客戶端上是不能用 semi join 語法的
semijoin 優化實現比較復雜，其中又分 FirstMatch、Materialize 等策略,上面的執行計劃中 select_type=MATERIALIZED 就是代表使用了 Materialize 策略來實現的 semijoin
這里 semijoin 優化后的執行流程為：
先執行子查詢，把結果保存到一個臨時表中，這個臨時表有個主鍵用來去重；
從臨時表中取出一行數據 R；
從數據行 R 中，取出字段 b 到被驅動表 t1 中去查找，滿足條件則放到結果集；
重復執行 2、3，直到結束。
這樣一來，子查詢結果有 9 行，即臨時表也有 9 行（這里沒有重復值），總的掃描行數為 9+9+9*1=27 行，比原來的 10000 行少了很多。
MySQL 5.6 版本中加入的另一種優化特性 materialization，就是把子查詢結果物化成臨時表，然后代入到外查詢中進行查找，來加快查詢的執行速度。內存臨時表包含主鍵（hash 索引），消除重復行，使表更小。
如果子查詢結果太大，超過 tmp_table_size 大小，會退化成磁盤臨時表。這樣子查詢只需要執行一次，而不是對于外層查詢的每一行都得執行一遍。
不過要注意的是，這樣外查詢依舊無法通過索引快速查找到符合條件的數據，只能通過全表掃描或者全索引掃描，
semijoin 和 materialization 的開啟是通過 optimizer_switch 參數中的 semijoin={on|off}、materialization={on|off} 標志來控制的。
上文中不同的執行計劃就是對 semijoin 和 materialization 進行開/關產生的
總的來說對于子查詢，先檢查是否滿足各種優化策略的條件（比如子查詢中有 union 則無法使用 semijoin 優化）
然后優化器會按成本進行選擇，實在沒得選就會用 exists 策略來“優化”子查詢，exists 策略是沒有參數來開啟或者關閉的。
下面舉一個delete相關的子查詢例子：
把上面的2張測試表分別填充350萬數據和50萬數據來測試delete語句
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 |
執行delete刪除語句執行了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 執行計劃，對test01表進行了幾乎全表掃描:
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語句偽join語句
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) 執行非常的快 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)
下面的這個表執行要全表掃描，非常慢，基本對表test01進行了全表掃描:
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的話，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)
參考文檔:
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
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