MySQL分区（Partition）功能

时间 2019-12-11

原文原文链接

引用地址：http://blog.csdn.net/tjcyjd/article/details/11194489mysql

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能够被分区为十个不一样的分区，每一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） – 这种模式容许DBA将数据划分不一样范围。例如DBA能够将一个表经过年份划分红三个分区，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） – 这中模式容许DBA经过对表的一个或多个列的Hash Key进行计算，最后经过这个Hash码不一样数值对应的数据区域进行分区，。例如DBA能够创建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（预约义列表） – 这种模式容许系统经过DBA定义的列表的值所对应的行数据进行分割。例如：DBA创建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，实际上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，咱们能够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列又不常常被访问，这时候就要把这些不常常使用的text和BLOB了划分到另外一个分区，在保证它们数据相关性的同时还能提升访问速度。

[分区表和未分区表试验过程]

*建立分区表,按日期的年份拆分
sql

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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

*建立未分区表
数据库

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mysql> create table no_part_tab (c1 int(11) default NULL,c2 varchar(30) default NULL,c3 date default NULL) engine=myisam;

*经过存储过程灌入800万条测试数据

mysql> set sql_mode=''; /* 若是建立存储过程失败，则先需设置此变量, bug? */
性能

mysql> delimiter // /* 设定语句终结符为 //，因存储过程语句用;结束 */测试

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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)
大数据

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mysql> insert into no_part_tab select * from part_tab;

Query OK, 8000000 rows affected (51.59 sec)
Records: 8000000 Duplicates: 0 Warnings: 0

ui

* 测试SQL性能this

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mysql> select count(*) from part_tab where c3 > date '1995-01-01' and c3 < date '1995-12-31';

+----------+
| count(*) |
+----------+
| 795181 |
+----------+
spa

1 row in set (0.55 sec).net

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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)
结果代表分区表比未分区表的执行时间少90%。

* 经过explain语句来分析执行状况

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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的输出改成列模式 */
*************************** 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)

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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

* 试验建立索引后状况

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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

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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
建立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
** 增长日期范围

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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)

** 增长未索引字段查询

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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)

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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体相同（数据和索引文件）
* 若是查询语句中有未创建索引字段，分区时间远远优于未分区时间
* 若是查询语句中字段创建了索引，分区和未分区的差异缩小，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。
* 去除没必要要的字段
* 根据手册，增长myisam_max_sort_file_size 会增长分区性能

[分区命令详解]

= 分区例子 =
* RANGE 类型

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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，将用户表分红4个分区，以每300万条记录为界限，每一个分区都有本身独立的数据、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彻底独立的，能够提升磁盘IO吞吐量。

* LIST 类型

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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'
);

分红4个区，数据文件和索引文件单独存放。

* HASH 类型

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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'
);

分红4个区，数据文件和索引文件单独存放。

例子：

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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

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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'
);

分红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 类型的分区表中每一个分区的再次分割。再次分割能够是 HASH/KEY 等类型。例如：

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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

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ALERT TABLE users DROP PARTITION p0;

删除分区 p0。

* 重建分区
o RANGE 分区重建

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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

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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

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ALTER TABLE users REORGANIZE PARTITION COALESCE PARTITION 2;

            用 REORGANIZE 方式重建分区的数量变成2，在这里数量只能减小不能增长。想要增长能够用 ADD PARTITION 方法。
    * 新增分区
          o 新增 RANGE 分区

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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

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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 ]

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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

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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

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mysql> alter table results drop PRIMARY KEY;

Query OK, 5374850 rows affected (7 min 4.05 sec)
Records: 5374850 Duplicates: 0 Warnings: 0

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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