所谓按天，不过是日期精确到天而已。
创新互联专注于做网站、成都网站制作、网页设计、网站制作、网站开发。公司秉持“客户至上,用心服务”的宗旨,从客户的利益和观点出发,让客户在网络营销中找到自己的驻足之地。尊重和关怀每一位客户，用严谨的态度对待客户，用专业的服务创造价值，成为客户值得信赖的朋友，为客户解除后顾之忧。
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：
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：
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
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：
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：
mysql explain partitions
- select count(*) from part_date3 where
- c3 date '1995-01-01' and c3 date '1995-12-31'\G
*************************** 1. row ***************************
id: 1
select_type: SIMPLE
table: part_date3
partitions: p1
type: ALL
possible_keys: NULL
key: NULL
key_len: NULL
ref: NULL
rows: 808431
Extra: Using where
1 row in set (0.00 sec)
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：
mysql select count(*) from part_date3 where
- c3 date '1995-01-01' and c3 date '1995-12-31';
+----------+
| count(*) |
+----------+
| 805114 |
+----------+
1 row in set (4.11 sec)
mysql select count(*) from part_date1 where
- c3 date '1995-01-01' and c3 date '1995-12-31';
+----------+
| count(*) |
+----------+
| 805114 |
+----------+
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。
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()
以下是创建一张测试表TEST并且按照时间CREATE_TIME创建RANGE分区，并使用ID创建hash分区，组成复合分区。
CREATE TABLE TEST (
CREATE_TIME DATETIME DEFAULT NULL, ID BIGINT(15) DEFAULT NULL
) ENGINE=INNODB DEFAULT CHARSET=utf8
PARTITION BY RANGE(TO_DAYS(CREATE_TIME)) PARTITIONS 7 SUBPARTITION BY HASH(ID) SUBPARTITIONS 16
(PARTITION P1710 VALUES LESS THAN (TO_DAYS ('2017-10-01'))
(SUBPARTITION P1710sp0 ,SUBPARTITION P1710sp1 ,
SUBPARTITION P1710sp2 ,SUBPARTITION P1710sp3 ,
SUBPARTITION P1710sp4 ,SUBPARTITION P1710sp5 ,
SUBPARTITION P1710sp6 ,SUBPARTITION P1710sp7 ,
SUBPARTITION P1710sp8 ,SUBPARTITION P1710sp9 ,
SUBPARTITION P1710sp10 ,SUBPARTITION P1710sp11 ,
SUBPARTITION P1710sp12 ,SUBPARTITION P1710sp13 ,
SUBPARTITION P1710sp14 ,SUBPARTITION P1710sp15 ),
PARTITION P1711 VALUES LESS THAN (TO_DAYS ('2017-11-01'))
(SUBPARTITION P1711sp0 ,SUBPARTITION P1711sp1 ,
SUBPARTITION P1711sp2 , SUBPARTITION P1711sp3 ,
SUBPARTITION P1711sp4 , SUBPARTITION P1711sp5 ,
SUBPARTITION P1711sp6 , SUBPARTITION P1711sp7 ,
SUBPARTITION P1711sp8 , SUBPARTITION P1711sp9 ,
SUBPARTITION P1711sp10 , SUBPARTITION P1711sp11 ,
SUBPARTITION P1711sp12 , SUBPARTITION P1711sp13 ,
SUBPARTITION P1711sp14 , SUBPARTITION P1711sp15 ),
用mysql的表分区功能（逻辑上还是一个表，对程序来说是透明的），通过分区函数可实现自动分表。如果想实现根据数据每月或每周动态的再分区，可以写一个存储过程实现分区调整逻辑，最后写一个mysql event（自动化作业）按周期调用这个存储过程就行了。
重庆分公司
重庆分公司
