MySQL支持的分区类型一共有四种：RANGE，LIST，HASH，KEY。其中，RANGE又可分为原生RANGE和RANGE COLUMNS，LIST分为原生LIST和LIST COLUMNS，HASH分为原生HASH和LINEAR HASH，KEY包含原生KEY和LINEAR HASH。关于这些分区之间的差别，改日另写文章进行阐述。
最近，碰到一个需求，要对表的时间字段（类型：datetime）基于天进行分区。于是遍历MySQL官方文档分区章节，总结如下：
实现方式
主要是以下几种：
1. 基于RANGE
2. 基于RANGE COLUMNS
3. 基于HASH
测试数据
为了测试以上三种方案，特构造了100万的测试数据，放在test表中，test表只有两列：id和hiredate，其中hiredate只包含10天的数据，从2015-12-01到2015-12-10。具体信息如下：
mysql> show create table test\G*************************** 1. row *************************** Table: testCreate Table: CREATE TABLE `test` ( `id` int(11) DEFAULT NULL, `hiredate` datetime DEFAULT NULL) ENGINE=InnoDB DEFAULT CHARSET=latin11 row in set (0.00 sec)mysql> select min(hiredate),max(hiredate) from test;+---------------------+---------------------+| min(hiredate) | max(hiredate) |+---------------------+---------------------+| 2015-12-01 00:00:00 | 2015-12-10 23:59:56 |+---------------------+---------------------+1 row in set (0.44 sec)mysql> select date(hiredate),count(*) from test group by date(hiredate);+----------------+----------+| date(hiredate) | count(*) |+----------------+----------+| 2015-12-01 | 99963 || 2015-12-02 | 100032 || 2015-12-03 | 100150 || 2015-12-04 | 99989 || 2015-12-05 | 99908 || 2015-12-06 | 99897 || 2015-12-07 | 100137 || 2015-12-08 | 100171 || 2015-12-09 | 99851 || 2015-12-10 | 99902 |+----------------+----------+10 rows in set (0.98 sec)
测试的维度
测试的维度主要从两个方面进行，
一、分区剪裁
针对特定的查询，是否能进行分区剪裁（即只查询相关的分区，而不是所有分区）
二、查询时间
鉴于该批测试数据是静止的（即没有并发进行的insert，update和delete操作），数据量也不太大，从这个维度来考量貌似意义也不是很大。
因此，重点测试第一个维度。
基于RANGE的分区方案
在这里，选用了TO_DAYS函数
CREATE TABLE range_datetime( id INT, hiredate DATETIME)PARTITION BY RANGE (TO_DAYS(hiredate) ) ( PARTITION p1 VALUES LESS THAN ( TO_DAYS('20151202') ), PARTITION p2 VALUES LESS THAN ( TO_DAYS('20151203') ), PARTITION p3 VALUES LESS THAN ( TO_DAYS('20151204') ), PARTITION p4 VALUES LESS THAN ( TO_DAYS('20151205') ), PARTITION p5 VALUES LESS THAN ( TO_DAYS('20151206') ), PARTITION p6 VALUES LESS THAN ( TO_DAYS('20151207') ), PARTITION p7 VALUES LESS THAN ( TO_DAYS('20151208') ), PARTITION p8 VALUES LESS THAN ( TO_DAYS('20151209') ), PARTITION p9 VALUES LESS THAN ( TO_DAYS('20151210') ), PARTITION p10 VALUES LESS THAN ( TO_DAYS('20151211') ));
插入数据并查看特定查询的执行计划
mysql> insert into range_datetime select * from test; Query OK, 1000000 rows affected (8.15 sec)Records: 1000000 Duplicates: 0 Warnings: 0mysql> explain partitions select * from range_datetime where hiredate >= '20151207124503' and hiredate<='20151210111230'; +----+-------------+----------------+--------------+------+---------------+------+---------+------+--------+-------------+| id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | Extra |+----+-------------+----------------+--------------+------+---------------+------+---------+------+--------+-------------+| 1 | SIMPLE | range_datetime | p7,p8,p9,p10 | ALL | NULL | NULL | NULL | NULL | 400061 | Using where |+----+-------------+----------------+--------------+------+---------------+------+---------+------+--------+-------------+1 row in set (0.03 sec)
注意执行计划中的partitions的内容，只查询了p7，p8，p9，p10三个分区，由此来看，使用to_days函数确实可以实现分区裁剪。
基于RANGE COLUMNS的分区方案
RANGE COLUMNS可以直接基于列，而无需像上述RANGE那种，分区的对象只能为整数。
创表语句如下：
CREATE TABLE range_columns ( id INT, hiredate DATETIME)PARTITION BY RANGE COLUMNS(hiredate) ( PARTITION p1 VALUES LESS THAN ( '20151202' ), PARTITION p2 VALUES LESS THAN ( '20151203' ), PARTITION p3 VALUES LESS THAN ( '20151204' ), PARTITION p4 VALUES LESS THAN ( '20151205' ), PARTITION p5 VALUES LESS THAN ( '20151206' ), PARTITION p6 VALUES LESS THAN ( '20151207' ), PARTITION p7 VALUES LESS THAN ( '20151208' ), PARTITION p8 VALUES LESS THAN ( '20151209' ), PARTITION p9 VALUES LESS THAN ( '20151210' ), PARTITION p10 VALUES LESS THAN ('20151211' ));
插入数据并查看上述查询的执行计划
mysql> insert into range_columns select * from test; Query OK, 1000000 rows affected (9.20 sec)Records: 1000000 Duplicates: 0 Warnings: 0mysql> explain partitions select * from range_columns where hiredate >= '20151207124503' and hiredate<='20151210111230'; +----+-------------+---------------+--------------+------+---------------+------+---------+------+--------+-------------+| id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | Extra |+----+-------------+---------------+--------------+------+---------------+------+---------+------+--------+-------------+| 1 | SIMPLE | range_columns | p7,p8,p9,p10 | ALL | NULL | NULL | NULL | NULL | 400210 | Using where |+----+-------------+---------------+--------------+------+---------------+------+---------+------+--------+-------------+1 row in set (0.11 sec)
同样，使用该分区方案也实现了分区剪裁。
基于HASH的分区方案
因HASH分区对象同样只能为整数，所以我们无法像上述RANGE COLUMNS那种直接引用列，在这里，同样用了TO_DAYS函数进行转换。
创表语句如下：
CREATE TABLE hash_datetime ( id INT, hiredate DATETIME)PARTITION BY HASH( TO_DAYS(hiredate) )PARTITIONS 10;
插入数据并查看上述查询的执行计划
mysql> insert into hash_datetime select * from test;Query OK, 1000000 rows affected (9.43 sec)Records: 1000000 Duplicates: 0 Warnings: 0mysql> explain partitions select * from hash_datetime where hiredate >= '20151207124503' and hiredate<='20151210111230';+----+-------------+---------------+-------------------------------+------+---------------+------+---------+------+---------+-------------+| id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | Extra |+----+-------------+---------------+-------------------------------+------+---------------+------+---------+------+---------+-------------+| 1 | SIMPLE | hash_datetime | p0,p1,p2,p3,p4,p5,p6,p7,p8,p9 | ALL | NULL | NULL | NULL | NULL | 1000500 | Using where |+----+-------------+---------------+-------------------------------+------+---------------+------+---------+------+---------+-------------+1 row in set (0.00 sec)
不难看出，使用hash分区并不能有效的实现分区裁剪，至少在本例，基于天的需求中如此。
以上三种方案都是基于datetime的，那么，对于timestamp类型，又该如何选择呢？
事实上，MySQL提供了一种基于UNIX_TIMESTAMP函数的RANGE分区方案，而且，只能使用UNIX_TIMESTAMP函数，如果使用其它函数，譬如to_days，会报如下错误：“ERROR 1486 (HY000): Constant, random or timezone-dependent expressions in (sub)partitioning function are not allowed”。
而且官方文档中也提到“Any other expressions involving TIMESTAMP values are not permitted. (See Bug #42849.)”。
下面来测试一下基于UNIX_TIMESTAMP函数的RANGE分区方案，看其能否实现分区裁剪。
针对TIMESTAMP的分区方案
创表语句如下：
CREATE TABLE range_timestamp ( id INT, hiredate TIMESTAMP)PARTITION BY RANGE ( UNIX_TIMESTAMP(hiredate) ) ( PARTITION p1 VALUES LESS THAN ( UNIX_TIMESTAMP('2015-12-02 00:00:00') ), PARTITION p2 VALUES LESS THAN ( UNIX_TIMESTAMP('2015-12-03 00:00:00') ), PARTITION p3 VALUES LESS THAN ( UNIX_TIMESTAMP('2015-12-04 00:00:00') ), PARTITION p4 VALUES LESS THAN ( UNIX_TIMESTAMP('2015-12-05 00:00:00') ), PARTITION p5 VALUES LESS THAN ( UNIX_TIMESTAMP('2015-12-06 00:00:00') ), PARTITION p6 VALUES LESS THAN ( UNIX_TIMESTAMP('2015-12-07 00:00:00') ), PARTITION p7 VALUES LESS THAN ( UNIX_TIMESTAMP('2015-12-08 00:00:00') ), PARTITION p8 VALUES LESS THAN ( UNIX_TIMESTAMP('2015-12-09 00:00:00') ), PARTITION p9 VALUES LESS THAN ( UNIX_TIMESTAMP('2015-12-10 00:00:00') ), PARTITION p10 VALUES LESS THAN (UNIX_TIMESTAMP('2015-12-11 00:00:00') ));
插入数据并查看上述查询的执行计划
mysql> insert into range_timestamp select * from test;Query OK, 1000000 rows affected (13.25 sec)Records: 1000000 Duplicates: 0 Warnings: 0mysql> explain partitions select * from range_timestamp where hiredate >= '20151207124503' and hiredate<='20151210111230';+----+-------------+-----------------+--------------+------+---------------+------+---------+------+--------+-------------+| id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | Extra |+----+-------------+-----------------+--------------+------+---------------+------+---------+------+--------+-------------+| 1 | SIMPLE | range_timestamp | p7,p8,p9,p10 | ALL | NULL | NULL | NULL | NULL | 400448 | Using where |+----+-------------+-----------------+--------------+------+---------------+------+---------+------+--------+-------------+1 row in set (0.00 sec)
同样也能实现分区裁剪。
总结：
1. 经过对比，个人倾向于第二种方案，即基于RANGE COLUMNS的分区实现。
2. 在5.7版本之前，对于DATA和DATETIME类型的列，如果要实现分区裁剪，只能使用YEAR() 和TO_DAYS()函数，在5.7版本中，又新增了TO_SECONDS()函数。
3. 其实LIST也能实现基于天的分区方案，但在这个需求上，相比于RANGE，还是显得很鸡肋。
4. TIMESTAMP类型的列，只能基于UNIX_TIMESTAMP函数进行分区，切记！
参考：
http://dev.mysql.com/doc/refman/5.7/en/partitioning.html