錯誤的按日期分區例子
最直觀的方法，就是直接用年月日這種日期格式來進行常規的分區：
CODE:
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mysql> create table rms (d date)
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-> partition by range (d)
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-> (partition p0 values less than ('1995-01-01'),
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-> partition p1 VALUES LESS THAN ('2010-01-01'));
上面的例子中，就是直接用"Y-m-d"的格式來對一個table進行分區，可惜想當然往往不能奏效，會得到一個錯誤信息:
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
上述分區方式沒有成功，而且明顯的不經濟，老練的DBA會用整型數值來進行分區：
CODE:
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mysql> CREATE TABLE part_date1
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-> ( c1 int default NULL,
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-> c2 varchar(30) default NULL,
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-> c3 date default NULL) engine=myisam
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-> partition by range (cast(date_format(c3,'%Y%m%d') as signed))
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-> (PARTITION p0 VALUES LESS THAN (19950101),
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-> PARTITION p1 VALUES LESS THAN (19960101) ,
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-> PARTITION p2 VALUES LESS THAN (19970101) ,
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-> PARTITION p3 VALUES LESS THAN (19980101) ,
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-> PARTITION p4 VALUES LESS THAN (19990101) ,
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-> PARTITION p5 VALUES LESS THAN (20000101) ,
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-> PARTITION p6 VALUES LESS THAN (20010101) ,
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-> PARTITION p7 VALUES LESS THAN (20020101) ,
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-> PARTITION p8 VALUES LESS THAN (20030101) ,
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-> PARTITION p9 VALUES LESS THAN (20040101) ,
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-> PARTITION p10 VALUES LESS THAN (20100101),
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-> PARTITION p11 VALUES LESS THAN MAXVALUE );
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Query OK, 0 rows affected (0.01 sec)
搞定？接著往下分析
CODE:
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mysql> explain partitions
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-> select count(*) from part_date1 where
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-> c3> date '1995-01-01' and c3 <date '1995-12-31'\G
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*************************** 1. row ***************************
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id: 1
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select_type: SIMPLE
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table: part_date1
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partitions: p0,p1,p2,p3,p4,p5,p6,p7,p8,p9,p10,p11
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type: ALL
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possible_keys: NULL
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key: NULL
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key_len: NULL
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ref: NULL
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rows: 8100000
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Extra: Using where
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1 row in set (0.00 sec)
萬惡的mysql居然對上面的sql使用全表掃描，而不是按照我們的日期分區分塊查詢。原文中解釋到MYSQL的優化器并不認這種日期形式的分區，花了大量的篇幅來引誘俺走上歧路，過分。
正確的日期分區例子
mysql優化器支持以下兩種內置的日期函數進行分區：
看個例子：
CODE:
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mysql> CREATE TABLE part_date3
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-> ( c1 int default NULL,
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-> c2 varchar(30) default NULL,
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-> c3 date default NULL) engine=myisam
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-> partition by range (to_days(c3))
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-> (PARTITION p0 VALUES LESS THAN (to_days('1995-01-01')),
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-> PARTITION p1 VALUES LESS THAN (to_days('1996-01-01')) ,
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-> PARTITION p2 VALUES LESS THAN (to_days('1997-01-01')) ,
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-> PARTITION p3 VALUES LESS THAN (to_days('1998-01-01')) ,
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-> PARTITION p4 VALUES LESS THAN (to_days('1999-01-01')) ,
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-> PARTITION p5 VALUES LESS THAN (to_days('2000-01-01')) ,
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-> PARTITION p6 VALUES LESS THAN (to_days('2001-01-01')) ,
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-> PARTITION p7 VALUES LESS THAN (to_days('2002-01-01')) ,
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-> PARTITION p8 VALUES LESS THAN (to_days('2003-01-01')) ,
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-> PARTITION p9 VALUES LESS THAN (to_days('2004-01-01')) ,
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-> PARTITION p10 VALUES LESS THAN (to_days('2010-01-01')),
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-> PARTITION p11 VALUES LESS THAN MAXVALUE );
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Query OK, 0 rows affected (0.00 sec)
以to_days()函數分區成功，我們分析一下看看：
CODE:
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mysql> explain partitions
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-> select count(*) from part_date3 where
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-> c3> date '1995-01-01' and c3 <date '1995-12-31'\G
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*************************** 1. row ***************************
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id: 1
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select_type: SIMPLE
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table: part_date3
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partitions: p1
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type: ALL
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possible_keys: NULL
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key: NULL
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key_len: NULL
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ref: NULL
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rows: 808431
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Extra: Using where
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1 row in set (0.00 sec)
可以看到，mysql優化器這次不負眾望，僅僅在p1分區進行查詢。在這種情況下查詢，真的能夠帶來提升查詢效率么？下面分別對這次建立的part_date3和之前分區失敗的part_date1做一個查詢對比：
CODE:
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mysql> select count(*) from part_date3 where
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-> c3> date '1995-01-01' and c3 <date '1995-12-31';
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+----------+
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| count(*) |
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+----------+
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| 805114 |
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+----------+
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1 row in set (4.11 sec)
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mysql> select count(*) from part_date1 where
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-> c3> date '1995-01-01' and c3 <date '1995-12-31';
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+----------+
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| count(*) |
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+----------+
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| 805114 |
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+----------+
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1 row in set (40.33 sec)
可以看到，分區正確的話query花費時間為4秒，而分區錯誤則花費時間40秒（相當于沒有分區），效率有90％的提升！所以我們千萬要正確的使用分區功能，分區后務必用explain驗證，這樣才能獲得真正的性能提升。