MYSQL之表分区----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：
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CODE:

mysql>  create table rms (d date)
    ->  partition by range (d)
    -> (partition p0 values less than ('1995-01-01'),
    ->  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：
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CODE:

mysql> CREATE TABLE part_date1
    ->      (  c1 int default NULL,
    ->  c2 varchar(30) default NULL,
    ->  c3 date default NULL) engine=myisam
    ->      partition by range (cast(date_format(c3,'%Y%m%d') as signed))
    -> (PARTITION p0 VALUES LESS THAN (19950101),
    -> PARTITION p1 VALUES LESS THAN (19960101) ,
    -> PARTITION p2 VALUES LESS THAN (19970101) ,
    -> PARTITION p3 VALUES LESS THAN (19980101) ,
    -> PARTITION p4 VALUES LESS THAN (19990101) ,
    -> PARTITION p5 VALUES LESS THAN (20000101) ,
    -> PARTITION p6 VALUES LESS THAN (20010101) ,
    -> PARTITION p7 VALUES LESS THAN (20020101) ,
    -> PARTITION p8 VALUES LESS THAN (20030101) ,
    -> PARTITION p9 VALUES LESS THAN (20040101) ,
    -> PARTITION p10 VALUES LESS THAN (20100101),
    -> PARTITION p11 VALUES LESS THAN MAXVALUE );

Query OK, 0 rows affected (0.01 sec)
 
搞定？接着往下分析
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CODE:

mysql> explain partitions
    -> select count(*) from part_date1 where
    ->      c3> '1995-01-01' and c3 <'1995-12-31'\G
*************************** 1. row ***************************
           id: 1
  select_type: SIMPLE
        table: part_date1
   partitions: p0,p1,p2,p3,p4,p5,p6,p7,p8,p9,p10,p11
         type: ALL
possible_keys: NULL
          key: NULL
      key_len: NULL
          ref: NULL
         rows: 8100000
        Extra: Using where
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MYSQL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：
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CODE:

mysql> CREATE TABLE part_date3
    ->      (  c1 int default NULL,
    ->  c2 varchar(30) default NULL,
    ->  c3 date default NULL) engine=myisam
    ->      partition by range (to_days(c3))
    -> (PARTITION p0 VALUES LESS THAN (to_days('1995-01-01')),
    -> PARTITION p1 VALUES LESS THAN (to_days('1996-01-01')) ,
    -> PARTITION p2 VALUES LESS THAN (to_days('1997-01-01')) ,
    -> PARTITION p3 VALUES LESS THAN (to_days('1998-01-01')) ,
    -> PARTITION p4 VALUES LESS THAN (to_days('1999-01-01')) ,
    -> PARTITION p5 VALUES LESS THAN (to_days('2000-01-01')) ,
    -> PARTITION p6 VALUES LESS THAN (to_days('2001-01-01')) ,
    -> PARTITION p7 VALUES LESS THAN (to_days('2002-01-01')) ,
    -> PARTITION p8 VALUES LESS THAN (to_days('2003-01-01')) ,
    -> PARTITION p9 VALUES LESS THAN (to_days('2004-01-01')) ,
    -> PARTITION p10 VALUES LESS THAN (to_days('2010-01-01')),
    -> PARTITION p11 VALUES LESS THAN MAXVALUE );

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：
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CODE:

mysql> explain partitions
    -> select count(*) from part_date3 where
    ->      c3> date '1995-01-01' and c3 

 
可以看到，mysql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：
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CODE:

mysql> select count(*) from part_date3 where
    ->      c3> date '1995-01-01' and c3  select count(*) from part_date1 where
    ->      c3> date '1995-01-01' and c3 

 
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。


注意：
在mysql5.1中建立分区表的语句中，只能包含下列函数：

ABS()
CEILING() and FLOOR() （在使用这2个函数的建立分区表的前提是使用函数的分区键是INT类型），例如
mysql> CREATE TABLE t (c FLOAT) PARTITION BY LIST( FLOOR(c) )( -> PARTITION p0 VALUES IN (1,3,5), -> PARTITION p1 VALUES IN (2,4,6) -> );; ERROR 1491 (HY000): The PARTITION function returns the wrong type   mysql> CREATE TABLE t (c int) PARTITION BY LIST( FLOOR(c) )( -> PARTITION p0 VALUES IN (1,3,5), -> PARTITION p1 VALUES IN (2,4,6) -> ); Query OK, 0 rows affected (0.01 sec)
DAY()
DAYOFMONTH()
DAYOFWEEK()
DAYOFYEAR()
DATEDIFF()
EXTRACT()
HOUR()
MICROSECOND()
MINUTE()
MOD()
MONTH()
QUARTER()
SECOND()
TIME_TO_SEC()
TO_DAYS()
WEEKDAY()
YEAR()
YEARWEEK()