http://blog.csdn.net/tjcyjd/article/details/11194489
自5.1开始对分区(Partition)有支持
= 水平分区（根据列属性按行分）= 举个简单例子：一个包含十年发票记录的表可以被分区为十个不同的分区，每个分区包含的是其中一年的记录。 === 水平分区的几种模式：=== * Range（范围） – 这种模式允许DBA将数据划分不同范围。例如DBA可以将一个表通过年份划分成三个分区，80年代（1980's）的数据，90年代（1990's）的数据以及任何在2000年（包括2000年）后的数据。 * Hash（哈希） – 这中模式允许DBA通过对表的一个或多个列的Hash Key进行计算，最后通过这个Hash码不同数值对应的数据区域进行分区，。例如DBA可以建立一个对表主键进行分区的表。 * Key（键值） – 上面Hash模式的一种延伸，这里的Hash Key是MySQL系统产生的。 * List（预定义列表） – 这种模式允许系统通过DBA定义的列表的值所对应的行数据进行分割。例如：DBA建立了一个横跨三个分区的表，分别根据2004年2005年和2006年值所对应的数据。 * Composite（复合模式） - 很神秘吧，哈哈，其实是以上模式的组合使用而已，就不解释了。举例：在初始化已经进行了Range范围分区的表上，我们可以对其中一个分区再进行hash哈希分区。 = 垂直分区（按列分）= 举个简单例子：一个包含了大text和BLOB列的表，这些text和BLOB列又不经常被访问，这时候就要把这些不经常使用的text和BLOB了划分到另一个分区，在保证它们数据相关性的同时还能提高访问速度。 [分区表和未分区表试验过程] *创建分区表,按日期的年份拆分 [sql] view plain copy mysql> CREATE TABLE part_tab ( c1 int default NULL, c2 varchar(30) default NULL, c3 date default NULL) engine=myisam PARTITION BY RANGE (year(c3)) (PARTITION p0 VALUES LESS THAN (1995), PARTITION p1 VALUES LESS THAN (1996) , PARTITION p2 VALUES LESS THAN (1997) , PARTITION p3 VALUES LESS THAN (1998) , PARTITION p4 VALUES LESS THAN (1999) , PARTITION p5 VALUES LESS THAN (2000) , PARTITION p6 VALUES LESS THAN (2001) , PARTITION p7 VALUES LESS THAN (2002) , PARTITION p8 VALUES LESS THAN (2003) , PARTITION p9 VALUES LESS THAN (2004) , PARTITION p10 VALUES LESS THAN (2010), PARTITION p11 VALUES LESS THAN MAXVALUE ); 注意最后一行，考虑到可能的最大值 *创建未分区表 [sql] view plain copy mysql> create table no_part_tab (c1 int(11) default NULL,c2 varchar(30) default NULL,c3 date default NULL) engine=myisam; *通过存储过程灌入800万条测试数据 mysql> set sql_mode=''; /* 如果创建存储过程失败，则先需设置此变量, bug? */MySQL> delimiter // /* 设定语句终结符为 //，因存储过程语句用;结束 */
[sql] view plain copy mysql> CREATE PROCEDURE load_part_tab() begin declare v int default 0; while v < 8000000 do insert into part_tab values (v,'testing partitions',adddate('1995-01-01',(rand(v)*36520) mod 3652)); set v = v + 1; end while; end // mysql> delimiter ; mysql> call load_part_tab(); Query OK, 1 row affected (8 min 17.75 sec) [sql] view plain copy mysql> insert into no_part_tab select * from part_tab; Query OK, 8000000 rows affected (51.59 sec) Records: 8000000 Duplicates: 0 Warnings: 0
* 测试SQL性能
[sql] view plain copy mysql> select count(*) from part_tab where c3 > date '1995-01-01' and c3 < date '1995-12-31';
+----------+ | count(*) | +----------+ | 795181 | +----------+
1 row in set (0.55 sec)
[sql] view plain copy mysql> select count(*) from no_part_tab where c3 > date '1995-01-01' and c3 < date '1995-12-31'; +----------+ | count(*) | +----------+ | 795181 | +----------+ 1 row in set (4.69 sec) 结果表明分区表比未分区表的执行时间少90%。 * 通过explain语句来分析执行情况 [sql] view plain copy mysql > explain select count(*) from no_part_tab where c3 > date '1995-01-01' and c3 < date '1995-12-31'\G /* 结尾的\G使得mysql的输出改为列模式 */ *************************** 1. row *************************** id: 1 select_type: SIMPLE table: no_part_tab type: ALL possible_keys: NULL key: NULL key_len: NULL ref: NULL rows: 8000000 Extra: Using where 1 row in set (0.00 sec)
[sql] view plain copy mysql> explain select count(*) from part_tab where c3 > date '1995-01-01' and c3 < date '1995-12-31'\G
*************************** 1. row *************************** id: 1 select_type: SIMPLE table: part_tab type: ALL possible_keys: NULL key: NULL key_len: NULL ref: NULL rows: 798458 Extra: Using where 1 row in set (0.00 sec) explain语句显示了SQL查询要处理的记录数目 * 试验创建索引后情况 [sql] view plain copy mysql> create index idx_of_c3 on no_part_tab (c3); Query OK, 8000000 rows affected (1 min 18.08 sec) Records: 8000000 Duplicates: 0 Warnings: 0 [sql] view plain copy mysql> create index idx_of_c3 on part_tab (c3); Query OK, 8000000 rows affected (1 min 19.19 sec) Records: 8000000 Duplicates: 0 Warnings: 0 创建索引后的数据库文件大小列表： 2008-05-24 09:23 8,608 no_part_tab.frm 2008-05-24 09:24 255,999,996 no_part_tab.MYD 2008-05-24 09:24 81,611,776 no_part_tab.MYI 2008-05-24 09:25 0 part_tab#P#p0.MYD 2008-05-24 09:26 1,024 part_tab#P#p0.MYI 2008-05-24 09:26 25,550,656 part_tab#P#p1.MYD 2008-05-24 09:26 8,148,992 part_tab#P#p1.MYI 2008-05-24 09:26 25,620,192 part_tab#P#p10.MYD 2008-05-24 09:26 8,170,496 part_tab#P#p10.MYI 2008-05-24 09:25 0 part_tab#P#p11.MYD 2008-05-24 09:26 1,024 part_tab#P#p11.MYI 2008-05-24 09:26 25,656,512 part_tab#P#p2.MYD 2008-05-24 09:26 8,181,760 part_tab#P#p2.MYI 2008-05-24 09:26 25,586,880 part_tab#P#p3.MYD 2008-05-24 09:26 8,160,256 part_tab#P#p3.MYI 2008-05-24 09:26 25,585,696 part_tab#P#p4.MYD 2008-05-24 09:26 8,159,232 part_tab#P#p4.MYI 2008-05-24 09:26 25,585,216 part_tab#P#p5.MYD 2008-05-24 09:26 8,159,232 part_tab#P#p5.MYI 2008-05-24 09:26 25,655,740 part_tab#P#p6.MYD 2008-05-24 09:26 8,181,760 part_tab#P#p6.MYI 2008-05-24 09:26 25,586,528 part_tab#P#p7.MYD 2008-05-24 09:26 8,160,256 part_tab#P#p7.MYI 2008-05-24 09:26 25,586,752 part_tab#P#p8.MYD 2008-05-24 09:26 8,160,256 part_tab#P#p8.MYI 2008-05-24 09:26 25,585,824 part_tab#P#p9.MYD 2008-05-24 09:26 8,159,232 part_tab#P#p9.MYI 2008-05-24 09:25 8,608 part_tab.frm 2008-05-24 09:25 68 part_tab.par
* 再次测试SQL性能
[sql] view plain copy mysql> select count(*) from no_part_tab where c3 > date '1995-01-01' and c3 < date '1995-12-31';
+----------+ | count(*) | +----------+ | 795181 | +----------+
1 row in set (2.42 sec) /* 为原来4.69 sec 的51%*/
重启mysql ( net stop mysql, net start mysql)后，查询时间降为0.89 sec,几乎与分区表相同。 [sql] view plain copy mysql> select count(*) from part_tab where c3 > date '1995-01-01' and c3 < date '1995-12-31'; +----------+ | count(*) | +----------+ | 795181 | +----------+ 1 row in set (0.86 sec) * 更进一步的试验 ** 增加日期范围 [sql] view plain copy mysql> select count(*) from no_part_tab where c3 > date '1995-01-01' and c3 < date '1997-12-31'; +----------+ | count(*) | +----------+ | 2396524 | +----------+ 1 row in set (5.42 sec) [sql] view plain copy mysql> select count(*) from part_tab where c3 > date '1995-01-01' and c3 < date '1997-12-31'; +----------+ | count(*) | +----------+ | 2396524 | +----------+
1 row in set (2.63 sec)
** 增加未索引字段查询 [sql] view plain copy mysql> select count(*) from part_tab where c3 > date '1995-01-01' and c3 < date '1996-12-31' and c2='hello'; +----------+ | count(*) | +----------+ | 0 | +----------+ 1 row in set (0.75 sec) [sql] view plain copy mysql> select count(*) from no_part_tab where c3 > date '1995-01-01' and c3 < date '1996-12-31' and c2='hello'; +----------+ | count(*) | +----------+ | 0 | +----------+ 1 row in set (11.52 sec) = 初步结论 = * 分区和未分区占用文件空间大致相同 （数据和索引文件） * 如果查询语句中有未建立索引字段，分区时间远远优于未分区时间 * 如果查询语句中字段建立了索引，分区和未分区的差别缩小，分区略优于未分区。 = 最终结论 = * 对于大数据量，建议使用分区功能。 * 去除不必要的字段 * 根据手册， 增加myisam_max_sort_file_size 会增加分区性能 [分区命令详解] = 分区例子 = * RANGE 类型 [sql] view plain copy CREATE TABLE users ( uid INT UNSIGNED NOT NULL AUTO_INCREMENT PRIMARY KEY, name VARCHAR(30) NOT NULL DEFAULT '', email VARCHAR(30) NOT NULL DEFAULT '' ) PARTITION BY RANGE (uid) ( PARTITION p0 VALUES LESS THAN (3000000) DATA DIRECTORY = '/data0/data' INDEX DIRECTORY = '/data1/idx', PARTITION p1 VALUES LESS THAN (6000000) DATA DIRECTORY = '/data2/data' INDEX DIRECTORY = '/data3/idx', PARTITION p2 VALUES LESS THAN (9000000) DATA DIRECTORY = '/data4/data' INDEX DIRECTORY = '/data5/idx', PARTITION p3 VALUES LESS THAN MAXVALUE DATA DIRECTORY = '/data6/data' INDEX DIRECTORY = '/data7/idx' ); 在这里，将用户表分成4个分区，以每300万条记录为界限，每个分区都有自己独立的数据、索引文件的存放目录，与此同时，这些目录所在的物理磁盘分区可能也都是完全独立的，可以提高磁盘IO吞吐量。 * LIST 类型 [sql] view plain copy CREATE TABLE category ( cid INT UNSIGNED NOT NULL AUTO_INCREMENT PRIMARY KEY, name VARCHAR(30) NOT NULL DEFAULT '' ) PARTITION BY LIST (cid) ( PARTITION p0 VALUES IN (0,4,8,12) DATA DIRECTORY = '/data0/data' INDEX DIRECTORY = '/data1/idx', PARTITION p1 VALUES IN (1,5,9,13) DATA DIRECTORY = '/data2/data' INDEX DIRECTORY = '/data3/idx', PARTITION p2 VALUES IN (2,6,10,14) DATA DIRECTORY = '/data4/data' INDEX DIRECTORY = '/data5/idx', PARTITION p3 VALUES IN (3,7,11,15) DATA DIRECTORY = '/data6/data' INDEX DIRECTORY = '/data7/idx' ); 分成4个区，数据文件和索引文件单独存放。 * HASH 类型 [sql] view plain copy CREATE TABLE users ( uid INT UNSIGNED NOT NULL AUTO_INCREMENT PRIMARY KEY, name VARCHAR(30) NOT NULL DEFAULT '', email VARCHAR(30) NOT NULL DEFAULT '' ) PARTITION BY HASH (uid) PARTITIONS 4 ( PARTITION p0 DATA DIRECTORY = '/data0/data' INDEX DIRECTORY = '/data1/idx', PARTITION p1 DATA DIRECTORY = '/data2/data' INDEX DIRECTORY = '/data3/idx', PARTITION p2 DATA DIRECTORY = '/data4/data' INDEX DIRECTORY = '/data5/idx', PARTITION p3 DATA DIRECTORY = '/data6/data' INDEX DIRECTORY = '/data7/idx' ); 分成4个区，数据文件和索引文件单独存放。 例子： [sql] view plain copy CREATE TABLE ti2 (id INT, amount DECIMAL(7,2), tr_date DATE) ENGINE=myisam PARTITION BY HASH( MONTH(tr_date) ) PARTITIONS 6; CREATE PROCEDURE load_ti2() begin declare v int default 0; while v < 80000 do insert into ti2 values (v,'3.14',adddate('1995-01-01',(rand(v)*3652) mod 365)); set v = v + 1; end while; end //
* KEY 类型 [sql] view plain copy CREATE TABLE users ( uid INT UNSIGNED NOT NULL AUTO_INCREMENT PRIMARY KEY, name VARCHAR(30) NOT NULL DEFAULT '', email VARCHAR(30) NOT NULL DEFAULT '' ) PARTITION BY KEY (uid) PARTITIONS 4 ( PARTITION p0 DATA DIRECTORY = '/data0/data' INDEX DIRECTORY = '/data1/idx', PARTITION p1 DATA DIRECTORY = '/data2/data' INDEX DIRECTORY = '/data3/idx', PARTITION p2 DATA DIRECTORY = '/data4/data' INDEX DIRECTORY = '/data5/idx', PARTITION p3 DATA DIRECTORY = '/data6/data' INDEX DIRECTORY = '/data7/idx' ); 分成4个区，数据文件和索引文件单独存放。 * 子分区 子分区是针对 RANGE/LIST 类型的分区表中每个分区的再次分割。再次分割可以是 HASH/KEY 等类型。例如： [sql] view plain copy CREATE TABLE users ( uid INT UNSIGNED NOT NULL AUTO_INCREMENT PRIMARY KEY, name VARCHAR(30) NOT NULL DEFAULT '', email VARCHAR(30) NOT NULL DEFAULT '' ) PARTITION BY RANGE (uid) SUBPARTITION BY HASH (uid % 4) SUBPARTITIONS 2( PARTITION p0 VALUES LESS THAN (3000000) DATA DIRECTORY = '/data0/data' INDEX DIRECTORY = '/data1/idx', PARTITION p1 VALUES LESS THAN (6000000) DATA DIRECTORY = '/data2/data' INDEX DIRECTORY = '/data3/idx' ); 对 RANGE 分区再次进行子分区划分，子分区采用 HASH 类型。 或者 [sql] view plain copy CREATE TABLE users ( uid INT UNSIGNED NOT NULL AUTO_INCREMENT PRIMARY KEY, name VARCHAR(30) NOT NULL DEFAULT '', email VARCHAR(30) NOT NULL DEFAULT '' ) PARTITION BY RANGE (uid) SUBPARTITION BY KEY(uid) SUBPARTITIONS 2( PARTITION p0 VALUES LESS THAN (3000000) DATA DIRECTORY = '/data0/data' INDEX DIRECTORY = '/data1/idx', PARTITION p1 VALUES LESS THAN (6000000) DATA DIRECTORY = '/data2/data' INDEX DIRECTORY = '/data3/idx' ); 对 RANGE 分区再次进行子分区划分，子分区采用 KEY 类型。 = 分区管理 = * 删除分区 [sql] view plain copy ALERT TABLE users DROP PARTITION p0;
删除分区 p0。
* 重建分区 o RANGE 分区重建 [sql] view plain copy ALTER TABLE users REORGANIZE PARTITION p0,p1 INTO (PARTITION p0 VALUES LESS THAN (6000000)); 将原来的 p0,p1 分区合并起来，放到新的 p0 分区中。 o LIST 分区重建 [sql] view plain copy ALTER TABLE users REORGANIZE PARTITION p0,p1 INTO (PARTITION p0 VALUES IN(0,1,4,5,8,9,12,13)); 将原来的 p0,p1 分区合并起来，放到新的 p0 分区中。 o HASH/KEY 分区重建 [sql] view plain copy ALTER TABLE users REORGANIZE PARTITION COALESCE PARTITION 2; 用 REORGANIZE 方式重建分区的数量变成2，在这里数量只能减少不能增加。想要增加可以用 ADD PARTITION 方法。 * 新增分区 o 新增 RANGE 分区 [sql] view plain copy ALTER TABLE category ADD PARTITION (PARTITION p4 VALUES IN (16,17,18,19) DATA DIRECTORY = '/data8/data' INDEX DIRECTORY = '/data9/idx'); 新增一个RANGE分区。 o 新增 HASH/KEY 分区 [sql] view plain copy ALTER TABLE users ADD PARTITION PARTITIONS 8; 将分区总数扩展到8个。 [ 给已有的表加上分区 ] [sql] view plain copy alter table results partition by RANGE (month(ttime)) (PARTITION p0 VALUES LESS THAN (1), PARTITION p1 VALUES LESS THAN (2) , PARTITION p2 VALUES LESS THAN (3) , PARTITION p3 VALUES LESS THAN (4) , PARTITION p4 VALUES LESS THAN (5) , PARTITION p5 VALUES LESS THAN (6) , PARTITION p6 VALUES LESS THAN (7) , PARTITION p7 VALUES LESS THAN (8) , PARTITION p8 VALUES LESS THAN (9) , PARTITION p9 VALUES LESS THAN (10) , PARTITION p10 VALUES LESS THAN (11), PARTITION p11 VALUES LESS THAN (12), PARTITION P12 VALUES LESS THAN (13) ); 默认分区限制分区字段必须是主键（PRIMARY KEY)的一部分，为了去除此 限制： [方法1] 使用ID [sql] view plain copy mysql> ALTER TABLE np_pk -> PARTITION BY HASH( TO_DAYS(added) ) -> PARTITIONS 4; ERROR 1503 (HY000): A PRIMARY KEY must include all columns in the table's partitioning function However, this statement using the id column for the partitioning column is valid, as shown here: [sql] view plain copy mysql> ALTER TABLE np_pk -> PARTITION BY HASH(id) -> PARTITIONS 4; Query OK, 0 rows affected (0.11 sec) Records: 0 Duplicates: 0 Warnings: 0 [方法2] 将原有PK去掉生成新PK [sql] view plain copy mysql> alter table results drop PRIMARY KEY; Query OK, 5374850 rows affected (7 min 4.05 sec) Records: 5374850 Duplicates: 0 Warnings: 0 [sql] view plain copy mysql> alter table results add PRIMARY KEY(id, ttime); Query OK, 5374850 rows affected (6 min 14.86 sec)
Records: 5374850 Duplicates: 0 Warnings: 0
