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 程式師世界 >> 數據庫知識 >> MYSQL數據庫 >> MySQL綜合教程 >> MySQL下的RAND()優化案例剖析

MySQL下的RAND()優化案例剖析

編輯:MySQL綜合教程

MySQL下的RAND()優化案例剖析。本站提示廣大學習愛好者:(MySQL下的RAND()優化案例剖析)文章只能為提供參考,不一定能成為您想要的結果。以下是MySQL下的RAND()優化案例剖析正文


盡人皆知,在MySQL中,假如直接 ORDER BY RAND() 的話,效力異常差,由於會屢次履行。現實上,假如等值查詢也是用 RAND() 的話也如斯,我們先來看看上面這幾個SQL的分歧履行籌劃和履行耗時。
起首,看下建表DDL,這是一個沒有顯式自增主鍵的InnoDB表:

[yejr@imysql]> show create table t_innodb_random\G
*************************** 1. row ***************************
Table: t_innodb_random
Create 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

往這個內外灌入一些測試數據,至多10萬以上, id 字段也是亂序的。

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

1、常量等值檢索:

[yejr@imysql]> explain select id from t_innodb_random where id = 13412\G
*************************** 1. row ***************************
id: 1
select_type: SIMPLE
table: t_innodb_random
type: ref
possible_keys: idx_id
key: idx_id
key_len: 4
ref: const
rows: 1
Extra: Using index

[yejr@imysql]> select id from t_innodb_random where id = 13412;
1 row in set (0.00 sec)

可以看到履行籌劃很不錯,是常量等值查詢,速度異常快。

2、應用RAND()函數乘以常量,求得隨機數後檢索:

[yejr@imysql]> explain select id from t_innodb_random where id = round(rand()*13241324)\G
*************************** 1. row ***************************
id: 1
select_type: SIMPLE
table: t_innodb_random
type: index
possible_keys: NULL
key: idx_id
key_len: 4
ref: NULL
rows: 393345
Extra: Using where; Using index

[yejr@imysql]> select id from t_innodb_random where id = round(rand()*13241324)\G
Empty set (0.26 sec)

可以看到履行籌劃很蹩腳,固然是只掃描索引,然則做了全索引掃描,效力異常差。由於WHERE前提中包括了RAND(),使得MySQL把它當作變量來處置,沒法用常量等值的方法查詢,效力很低。

我們把常量改成取t_innodb_random表的最年夜id值,再乘以RAND()求得隨機數後檢索看看甚麼情形:

[yejr@imysql]> explain select id from t_innodb_random where id = round(rand()*(select max(id) from t_innodb_random))\G
*************************** 1. row ***************************
id: 1
select_type: PRIMARY
table: t_innodb_random
type: index
possible_keys: NULL
key: idx_id
key_len: 4
ref: NULL
rows: 393345
Extra: Using where; Using index
*************************** 2. row ***************************
id: 2
select_type: SUBQUERY
table: NULL
type: NULL
possible_keys: NULL
key: NULL
key_len: NULL
ref: NULL
rows: NULL
Extra: Select tables optimized away

[yejr@imysql]> select id from t_innodb_random where id = round(rand()*(select max(id) from t_innodb_random))\G
Empty set (0.27 sec)

可以看到,履行籌劃仍然是全索引掃描,履行耗時也根本相當。

3、改革成通俗子查詢形式 ,這裡有兩次子查詢

[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: 1
select_type: PRIMARY
table: t_innodb_random
type: index
possible_keys: NULL
key: idx_id
key_len: 4
ref: NULL
rows: 393345
Extra: Using where; Using index
*************************** 2. row ***************************
id: 3
select_type: SUBQUERY
table: NULL
type: NULL
possible_keys: NULL
key: NULL
key_len: NULL
ref: NULL
rows: NULL
Extra: 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)\G
Empty set (0.27 sec)

可以看到,履行籌劃也欠好,履行耗時較慢。

4、改革成JOIN聯系關系查詢,不外最年夜值照樣用常量表現

[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: 1
select_type: PRIMARY
table: <derived2>
type: system
possible_keys: NULL
key: NULL
key_len: NULL
ref: NULL
rows: 1
Extra:
*************************** 2. row ***************************
id: 1
select_type: PRIMARY
table: t1
type: ref
possible_keys: idx_id
key: idx_id
key_len: 4
ref: const
rows: 1
Extra: Using where; Using index
*************************** 3. row ***************************
id: 2
select_type: DERIVED
table: NULL
type: NULL
possible_keys: NULL
key: NULL
key_len: NULL
ref: NULL
rows: NULL
Extra: 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\G
Empty set (0.00 sec)

這時候候履行籌劃就異常完善了,和最開端的常量等值查詢是一樣的了,履行耗時也異常之快。
這類辦法固然很好,然則有能夠查詢不到記載,改革規模查找,但成果LIMIT 1便可以了:

[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: 1
select_type: PRIMARY
table: t_innodb_random
type: index
possible_keys: NULL
key: idx_id
key_len: 4
ref: NULL
rows: 393345
Extra: Using where; Using index
*************************** 2. row ***************************
id: 3
select_type: SUBQUERY
table: NULL
type: NULL
possible_keys: NULL
key: NULL
key_len: NULL
ref: NULL
rows: NULL
Extra: 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: 1301
1 row in set (0.00 sec)

可以看到,固然履行籌劃也是全索引掃描,然則由於有了LIMIT 1,只須要找到一筆記錄,便可終止掃描,所以效力照樣很快的。

小結:
從數據庫中隨機取一筆記錄時,可以把RAND()生成隨機數放在JOIN子查詢中以進步效力。

5、再來看看用ORDRR BY RAND()方法一次獲得多個隨機值的方法:

[yejr@imysql]> explain select id from t_innodb_random order by rand() limit 1000\G
*************************** 1. row ***************************
id: 1
select_type: SIMPLE
table: t_innodb_random
type: index
possible_keys: NULL
key: idx_id
key_len: 4
ref: NULL
rows: 393345
Extra: 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)

全索引掃描,生成排序暫時表,太差太慢了。

6、把隨機數放在子查詢裡看看:

[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: 1
select_type: PRIMARY
table: t_innodb_random
type: index
possible_keys: NULL
key: idx_id
key_len: 4
ref: NULL
rows: 393345
Extra: Using where; Using index
*************************** 2. row ***************************
id: 3
select_type: SUBQUERY
table: NULL
type: NULL
possible_keys: NULL
key: NULL
key_len: NULL
ref: NULL
rows: NULL
Extra: 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\G
1000 rows in set (0.04 sec)

嗯,提速了很多,這個看起來還不賴:)

7、模仿下面的辦法,改成JOIN和隨機數子查詢聯系關系

[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: 1
select_type: PRIMARY
table: <derived2>
type: system
possible_keys: NULL
key: NULL
key_len: NULL
ref: NULL
rows: 1
Extra:
*************************** 2. row ***************************
id: 1
select_type: PRIMARY
table: t1
type: range
possible_keys: idx_id
key: idx_id
key_len: 4
ref: NULL
rows: 196672
Extra: Using where; Using index
*************************** 3. row ***************************
id: 2
select_type: DERIVED
table: NULL
type: NULL
possible_keys: NULL
key: NULL
key_len: NULL
ref: NULL
rows: NULL
Extra: No tables used
*************************** 4. row ***************************
id: 3
select_type: SUBQUERY
table: NULL
type: NULL
possible_keys: NULL
key: NULL
key_len: NULL
ref: NULL
rows: NULL
Extra: 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\G
1000 rows in set (0.00 sec)

可以看到,全索引檢索,發明相符記載的前提後,直接獲得1000行,這個辦法是最快的。

綜上,想從MySQL數據庫中隨機取一條或許N筆記錄時,最好把RAND()生成隨機數放在JOIN子查詢中以進步效力。
下面說了那末多的空話,最初簡略說下,就是把上面這個SQL:

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

改革成上面這個:

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

假如想要到達完整隨機,還可以改成上面這類寫法:

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

便可以享用在SQL中直接獲得隨機數了,不消再在法式中結構一串隨機數去檢索了。

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