mysql 5.1分区表之正确设置按日期分区

 

错误的按日期分区例子

最直观的方法，就是直接用年月日这种日期格式来进行常规的分区：

CODE:

1. mysql>  create table rms (d date)
2.     ->  partition by range (d)
3.     -> (partition p0 values less than ('1995-01-01'),
4.     ->  partition p1 VALUES LESS THAN ('2010-01-01'));

上面的例子中，就是直接用"Y-m-d"的格式来对一个table进行分区，可惜想当然往往不能奏效，会得到一个错误信息:

ERROR 1064 (42000): VALUES value must be of same type as partition function near '),
partition p1 VALUES LESS THAN ('2010-01-01'))' at line 3

上述分区方式没有成功，而且明显的不经济，老练的DBA会用整型数值来进行分区：

CODE:

1. mysql> CREATE TABLE part_date1
2.     ->      (  c1 int default NULL,
3.     ->  c2 varchar(30) default NULL,
4.     ->  c3 date default NULL) engine=myisam
5.     ->      partition by range (cast(date_format(c3,'%Y%m%d') as signed))
6.     -> (PARTITION p0 VALUES LESS THAN (19950101),
7.     -> PARTITION p1 VALUES LESS THAN (19960101) ,
8.     -> PARTITION p2 VALUES LESS THAN (19970101) ,
9.     -> PARTITION p3 VALUES LESS THAN (19980101) ,
10.     -> PARTITION p4 VALUES LESS THAN (19990101) ,
11.     -> PARTITION p5 VALUES LESS THAN (20000101) ,
12.     -> PARTITION p6 VALUES LESS THAN (20010101) ,
13.     -> PARTITION p7 VALUES LESS THAN (20020101) ,
14.     -> PARTITION p8 VALUES LESS THAN (20030101) ,
15.     -> PARTITION p9 VALUES LESS THAN (20040101) ,
16.     -> PARTITION p10 VALUES LESS THAN (20100101),
17.     -> PARTITION p11 VALUES LESS THAN MAXVALUE );
18. Query OK, 0 rows affected (0.01 sec)

搞定？接着往下分析

CODE:

1. mysql> explain partitions
2.     -> select count(*) from part_date1 where
3.     ->      c3> date '1995-01-01' and c3 '1995-12-31'\G
4. *************************** 1. row ***************************
5.            id: 1
6.   select_type: SIMPLE
7.         table: part_date1
8.    partitions: p0,p1,p2,p3,p4,p5,p6,p7,p8,p9,p10,p11
9.          type: ALL
10. possible_keys: NULL
11.           key: NULL
12.       key_len: NULL
13.           ref: NULL
14.          rows: 8100000
15.         Extra: Using where
16. 1 row in set (0.00 sec)

万恶的mysql居然对上面的sql使用全表扫描，而不是按照我们的日期分区分块查询。原文中解释到的优化器并不认这种日期形式的分区，花了大量的篇幅来引诱俺走上歧路，过分。

正确的日期分区例子

mysql优化器支持以下两种内置的日期函数进行分区：

• TO_DAYS()
• YEAR()

看个例子：

CODE:

1. mysql> CREATE TABLE part_date3
2.     ->      (  c1 int default NULL,
3.     ->  c2 varchar(30) default NULL,
4.     ->  c3 date default NULL) engine=myisam
5.     ->      partition by range (to_days(c3))
6.     -> (PARTITION p0 VALUES LESS THAN (to_days('1995-01-01')),
7.     -> PARTITION p1 VALUES LESS THAN (to_days('1996-01-01')) ,
8.     -> PARTITION p2 VALUES LESS THAN (to_days('1997-01-01')) ,
9.     -> PARTITION p3 VALUES LESS THAN (to_days('1998-01-01')) ,
10.     -> PARTITION p4 VALUES LESS THAN (to_days('1999-01-01')) ,
11.     -> PARTITION p5 VALUES LESS THAN (to_days('2000-01-01')) ,
12.     -> PARTITION p6 VALUES LESS THAN (to_days('2001-01-01')) ,
13.     -> PARTITION p7 VALUES LESS THAN (to_days('2002-01-01')) ,
14.     -> PARTITION p8 VALUES LESS THAN (to_days('2003-01-01')) ,
15.     -> PARTITION p9 VALUES LESS THAN (to_days('2004-01-01')) ,
16.     -> PARTITION p10 VALUES LESS THAN (to_days('2010-01-01')),
17.     -> PARTITION p11 VALUES LESS THAN MAXVALUE );
18. Query OK, 0 rows affected (0.00 sec)

以to_days()函数分区成功，我们分析一下看看：

CODE:

1. mysql> explain partitions
2.     -> select count(*) from part_date3 where
3.     ->      c3> date '1995-01-01' and c3 '1995-12-31'\G
4. *************************** 1. row ***************************
5.            id: 1
6.   select_type: SIMPLE
7.         table: part_date3
8.    partitions: p1
9.          type: ALL
10. possible_keys: NULL
11.           key: NULL
12.       key_len: NULL
13.           ref: NULL
14.          rows: 808431
15.         Extra: Using where
16. 1 row in set (0.00 sec)

可以看到，优化器这次不负众望，仅仅在p1分区进行查询。在这种情况下查询，真的能够带来提升查询效率么？下面分别对这次建立的part_date3和之前分区失败的part_date1做一个查询对比：

CODE:

1. mysql> select count(*) from part_date3 where
2.     ->      c3> date '1995-01-01' and c3 '1995-12-31';
3. +----------+
4. | count(*) |
5. +----------+
6. |   805114 |
7. +----------+
8. 1 row in set (4.11 sec)
9.  
10. mysql> select count(*) from part_date1 where
11.     ->      c3> date '1995-01-01' and c3 '1995-12-31';
12. +----------+
13. | count(*) |
14. +----------+
15. |   805114 |
16. +----------+
17. 1 row in set (40.33 sec)

可以看到，分区正确的话query花费时间为4秒，而分区错误则花费时间40秒（相当于没有分区），效率有90％的提升！所以我们千万要正确的使用分区功能，分区后务必用explain验证，这样才能获得真正的性能提升。

热切期待msyql 5.1稳定版发布!

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