[MySQL优化案例]系列—RAND()优化_MySQL

[yejr@imysql]> show create table t_innodb_random/G*************************** 1. row ***************************Table: t_innodb_randomCreate Table: CREATE TABLE `t_innodb_random` (`id` int(10) unsigned NOT NULL,`user` varchar(64) NOT NULL DEFAULT '',KEY `idx_id` (`id`)) ENGINE=InnoDB DEFAULT CHARSET=latin1

往这个表里灌入一些测试数据，至少10万以上， id 字段也是乱序的。

[yejr@imysql]> select count(*) from t_innodb_random/G*************************** 1. row ***************************count(*): 393216

1、常量等值检索：

[yejr@imysql]> explain select id from t_innodb_random where id = 13412/G*************************** 1. row ***************************id: 1select_type: SIMPLEtable: t_innodb_randomtype: refpossible_keys: idx_idkey: idx_idkey_len: 4ref: constrows: 1Extra: Using index[yejr@imysql]> select id from t_innodb_random where id = 13412;1 row in set (0.00 sec)

可以看到执行计划很不错，是常量等值查询，速度非常快。

2、使用RAND()函数乘以常量，求得随机数后检索：

[yejr@imysql]> explain select id from t_innodb_random where id = round(rand()*13241324)/G*************************** 1. row ***************************id: 1select_type: SIMPLEtable: t_innodb_randomtype: indexpossible_keys: NULLkey: idx_idkey_len: 4ref: NULLrows: 393345Extra: Using where; Using index[yejr@imysql]> select id from t_innodb_random where id = round(rand()*13241324)/GEmpty set (0.26 sec)

可以看到执行计划很糟糕，虽然是只扫描索引，但是做了全索引扫描，效率非常差。因为WHERE条件中包含了RAND()，使得MySQL把它当做变量来处理，无法用常量等值的方式查询，效率很低。

我们把常量改成取t_innodb_random表的最大id值，再乘以RAND()求得随机数后检索看看什么情况：

[yejr@imysql]> explain select id from t_innodb_random where id = round(rand()*(select max(id) from t_innodb_random))/G*************************** 1. row ***************************id: 1select_type: PRIMARYtable: t_innodb_randomtype: indexpossible_keys: NULLkey: idx_idkey_len: 4ref: NULLrows: 393345Extra: Using where; Using index*************************** 2. row ***************************id: 2select_type: SUBQUERYtable: NULLtype: NULLpossible_keys: NULLkey: NULLkey_len: NULLref: NULLrows: NULLExtra: Select tables optimized away[yejr@imysql]> select id from t_innodb_random where id = round(rand()*(select max(id) from t_innodb_random))/GEmpty set (0.27 sec)

可以看到，执行计划依然是全索引扫描，执行耗时也基本相当。

3、改造成普通子查询模式 ，这里有两次子查询

[yejr@imysql]> explain select id from t_innodb_random where id = (select round(rand()*(select max(id) from t_innodb_random)) as nid)/G*************************** 1. row ***************************id: 1select_type: PRIMARYtable: t_innodb_randomtype: indexpossible_keys: NULLkey: idx_idkey_len: 4ref: NULLrows: 393345Extra: Using where; Using index*************************** 2. row ***************************id: 3select_type: SUBQUERYtable: NULLtype: NULLpossible_keys: NULLkey: NULLkey_len: NULLref: NULLrows: NULLExtra: Select tables optimized away[yejr@imysql]> select id from t_innodb_random where id = (select round(rand()*(select max(id) from t_innodb_random)) as nid)/GEmpty set (0.27 sec)

可以看到，执行计划也不好，执行耗时较慢。

4、改造成JOIN关联查询，不过最大值还是用常量表示

[yejr@imysql]> explain select id from t_innodb_random t1 join (select round(rand()*13241324) as id2) as t2 where t1.id = t2.id2/G*************************** 1. row ***************************id: 1select_type: PRIMARYtable: type: systempossible_keys: NULLkey: NULLkey_len: NULLref: NULLrows: 1Extra:*************************** 2. row ***************************id: 1select_type: PRIMARYtable: t1type: refpossible_keys: idx_idkey: idx_idkey_len: 4ref: constrows: 1Extra: Using where; Using index*************************** 3. row ***************************id: 2select_type: DERIVEDtable: NULLtype: NULLpossible_keys: NULLkey: NULLkey_len: NULLref: NULLrows: NULLExtra: No tables used[yejr@imysql]> select id from t_innodb_random t1 join (select round(rand()*13241324) as id2) as t2 where t1.id = t2.id2/GEmpty set (0.00 sec)

这时候执行计划就非常完美了，和最开始的常量等值查询是一样的了，执行耗时也非常之快。这种方法虽然很好，但是有可能查询不到记录，改造范围查找，但结果LIMIT 1就可以了：

[yejr@imysql]> explain select id from t_innodb_random where id > (select round(rand()*(select max(id) from t_innodb_random)) as nid) limit 1/G*************************** 1. row ***************************id: 1select_type: PRIMARYtable: t_innodb_randomtype: indexpossible_keys: NULLkey: idx_idkey_len: 4ref: NULLrows: 393345Extra: Using where; Using index*************************** 2. row ***************************id: 3select_type: SUBQUERYtable: NULLtype: NULLpossible_keys: NULLkey: NULLkey_len: NULLref: NULLrows: NULLExtra: Select tables optimized away[yejr@imysql]> select id from t_innodb_random where id > (select round(rand()*(select max(id) from t_innodb_random)) as nid) limit 1/G*************************** 1. row ***************************id: 13011 row in set (0.00 sec)

可以看到，虽然执行计划也是全索引扫描，但是因为有了LIMIT 1，只需要找到一条记录，即可终止扫描，所以效率还是很快的。

小结：从数据库中随机取一条记录时，可以把RAND()生成随机数放在JOIN子查询中以提高效率。

5、再来看看用ORDRR BY RAND()方式一次取得多个随机值的方式：

[yejr@imysql]> explain select id from t_innodb_random order by rand() limit 1000/G*************************** 1. row ***************************id: 1select_type: SIMPLEtable: t_innodb_randomtype: indexpossible_keys: NULLkey: idx_idkey_len: 4ref: NULLrows: 393345Extra: Using index; Using temporary; Using filesort[yejr@imysql]> select id from t_innodb_random order by rand() limit 1000;1000 rows in set (0.41 sec)

全索引扫描，生成排序临时表，太差太慢了。

6、把随机数放在子查询里看看：

[yejr@imysql]> explain select id from t_innodb_random where id > (select rand() * (select max(id) from t_innodb_random) as nid) limit 1000/G*************************** 1. row ***************************id: 1select_type: PRIMARYtable: t_innodb_randomtype: indexpossible_keys: NULLkey: idx_idkey_len: 4ref: NULLrows: 393345Extra: Using where; Using index*************************** 2. row ***************************id: 3select_type: SUBQUERYtable: NULLtype: NULLpossible_keys: NULLkey: NULLkey_len: NULLref: NULLrows: NULLExtra: Select tables optimized away[yejr@imysql]> select id from t_innodb_random where id > (select rand() * (select max(id) from t_innodb_random) as nid) limit 1000/G1000 rows in set (0.04 sec)

嗯，提速了不少，这个看起来还不赖：）

7、仿照上面的方法，改成JOIN和随机数子查询关联

[yejr@imysql]> explain select id from t_innodb_random t1 join (select rand() * (select max(id) from t_innodb_random) as nid) t2 on t1.id > t2.nid limit 1000/G*************************** 1. row ***************************id: 1select_type: PRIMARYtable: type: systempossible_keys: NULLkey: NULLkey_len: NULLref: NULLrows: 1Extra:*************************** 2. row ***************************id: 1select_type: PRIMARYtable: t1type: rangepossible_keys: idx_idkey: idx_idkey_len: 4ref: NULLrows: 196672Extra: Using where; Using index*************************** 3. row ***************************id: 2select_type: DERIVEDtable: NULLtype: NULLpossible_keys: NULLkey: NULLkey_len: NULLref: NULLrows: NULLExtra: No tables used*************************** 4. row ***************************id: 3select_type: SUBQUERYtable: NULLtype: NULLpossible_keys: NULLkey: NULLkey_len: NULLref: NULLrows: NULLExtra: Select tables optimized away[yejr@imysql]> select id from t_innodb_random t1 join (select rand() * (select max(id) from t_innodb_random) as nid) t2 on t1.id > t2.nid limit 1000/G1000 rows in set (0.00 sec)

可以看到，全索引检索，发现符合记录的条件后，直接取得1000行，这个方法是最快的。

综上，想从MySQL数据库中随机取一条或者N条记录时，最好把RAND()生成随机数放在JOIN子查询中以提高效率。上面说了那么多的废话，最后简单说下，就是把下面这个SQL：

SELECT id FROM table ORDER BY RAND() LIMIT n;

改造成下面这个：

SELECT id FROM table t1, JOIN (SELECT RAND() * (SELECT MAX(id) FROM table) AS nid) t2 ON t1.id > t2.nid LIMIT n;

就可以享受在SQL中直接取得随机数了，不用再在程序中构造一串随机数去检索了。