数据切分——Mysql分区表的建立及性能分析

朱雀 2022-07-27 10:46 201阅读 0赞

Mysql的安装方法可以参考：

[http://blog.csdn.net/jhq0113/article/details/43812895][http_blog.csdn.net_jhq0113_article_details_43812895]

Mysql分区表的介绍可以参考：

[http://blog.csdn.net/jhq0113/article/details/44592865][http_blog.csdn.net_jhq0113_article_details_44592865]

1.检查你的Mysql是否支持分区

mysql> SHOW VARIABLES LIKE '%partition%';

若结果如下，表示你的Mysql支持表分区:

\+-----------------------+-------+

| Variable_name         | Value |

+-----------------------+-------+

| have_partition_engine | YES   |

+-----------------------+-------+

1 row in set (0.00 sec)

RANGE分区表创建方式：

DROP TABLE IF EXISTS `my_orders`;
    CREATE TABLE `my_orders` (
      `id` int(10) unsigned NOT NULL AUTO_INCREMENT COMMENT '表主键',
      `pid` int(10) unsigned NOT NULL COMMENT '产品ID',
      `price` decimal(15,2) NOT NULL COMMENT '单价',
      `num` int(11) NOT NULL COMMENT '购买数量',
      `uid` int(10) unsigned NOT NULL COMMENT '客户ID',
      `atime` datetime NOT NULL COMMENT '下单时间',
      `utime` int(10) unsigned NOT NULL DEFAULT 0 COMMENT '修改时间',
      `isdel` tinyint(4) NOT NULL DEFAULT '0' COMMENT '软删除标识',
      PRIMARY KEY (`id`,`atime`)
    ) ENGINE=InnoDB DEFAULT CHARSET=utf8
    
    /*********分区信息**************/
    PARTITION BY RANGE (YEAR(atime))
    (
       PARTITION p0 VALUES LESS THAN (2016),
       PARTITION p1 VALUES LESS THAN (2017),
       PARTITION p2 VALUES LESS THAN MAXVALUE
    );

以上是一个简单的订单表，分区字段是atime,根据RANGE分区，这样当你向该表中插入数据的时候，Mysql会根据YEAR(atime)的值进行分区存储。

检查分区是否创建成功，执行查询语句：

EXPLAIN PARTITIONS SELECT \* FROM \`my\_orders\`

若成功，结果如下：

![Center][]

性能分析：

1）.创建同样表结构，但没有进行分区的表

DROP TABLE IF EXISTS `my_order`;
    CREATE TABLE `my_order` (
      `id` int(10) unsigned NOT NULL AUTO_INCREMENT COMMENT '表主键',
      `pid` int(10) unsigned NOT NULL COMMENT '产品ID',
      `price` decimal(15,2) NOT NULL COMMENT '单价',
      `num` int(11) NOT NULL COMMENT '购买数量',
      `uid` int(10) unsigned NOT NULL COMMENT '客户ID',
      `atime` datetime NOT NULL COMMENT '下单时间',
      `utime` int(10) unsigned NOT NULL DEFAULT 0 COMMENT '修改时间',
      `isdel` tinyint(4) NOT NULL DEFAULT '0' COMMENT '软删除标识',
      PRIMARY KEY (`id`,`atime`)
    ) ENGINE=InnoDB DEFAULT CHARSET=utf8;

2）.向两张表中插入相同的数据

/**************************向分区表插入数据****************************/
    INSERT INTO my_orders(`pid`,`price`,`num`,`uid`,`atime`) VALUES(1,12.23,1,89757,CURRENT_TIMESTAMP());
    INSERT INTO my_orders(`pid`,`price`,`num`,`uid`,`atime`) VALUES(1,12.23,1,89757,'2016-05-01 00:00:00');
    INSERT INTO my_orders(`pid`,`price`,`num`,`uid`,`atime`) VALUES(1,12.23,1,89757,'2017-05-01 00:00:00');
    INSERT INTO my_orders(`pid`,`price`,`num`,`uid`,`atime`) VALUES(1,12.23,1,89757,'2018-05-01 00:00:00');
    INSERT INTO my_orders(`pid`,`price`,`num`,`uid`,`atime`) VALUES(1,12.23,1,89756,'2015-05-01 00:00:00');
    INSERT INTO my_orders(`pid`,`price`,`num`,`uid`,`atime`) VALUES(1,12.23,1,89756,'2016-05-01 00:00:00');
    INSERT INTO my_orders(`pid`,`price`,`num`,`uid`,`atime`) VALUES(1,12.23,1,89756,'2017-05-01 00:00:00');
    INSERT INTO my_orders(`pid`,`price`,`num`,`uid`,`atime`) VALUES(1,12.23,1,89756,'2018-05-01 00:00:00');
    
    /**************************向未分区表插入数据****************************/
    INSERT INTO my_order(`pid`,`price`,`num`,`uid`,`atime`) VALUES(1,12.23,1,89757,CURRENT_TIMESTAMP());
    INSERT INTO my_order(`pid`,`price`,`num`,`uid`,`atime`) VALUES(1,12.23,1,89757,'2016-05-01 00:00:00');
    INSERT INTO my_order(`pid`,`price`,`num`,`uid`,`atime`) VALUES(1,12.23,1,89757,'2017-05-01 00:00:00');
    INSERT INTO my_order(`pid`,`price`,`num`,`uid`,`atime`) VALUES(1,12.23,1,89757,'2018-05-01 00:00:00');
    INSERT INTO my_order(`pid`,`price`,`num`,`uid`,`atime`) VALUES(1,12.23,1,89756,'2015-05-01 00:00:00');
    INSERT INTO my_order(`pid`,`price`,`num`,`uid`,`atime`) VALUES(1,12.23,1,89756,'2016-05-01 00:00:00');
    INSERT INTO my_order(`pid`,`price`,`num`,`uid`,`atime`) VALUES(1,12.23,1,89756,'2017-05-01 00:00:00');
    INSERT INTO my_order(`pid`,`price`,`num`,`uid`,`atime`) VALUES(1,12.23,1,89756,'2018-05-01 00:00:00');

3）.主从复制，大约20万条左右（主从复制的数据和真实环境有差距，但是能体现出表分区查询的性能优劣）

/**********************************主从复制大量数据******************************/
    INSERT INTO `my_orders`(`pid`,`price`,`num`,`uid`,`atime`) SELECT `pid`,`price`,`num`,`uid`,`atime` FROM `my_orders`;
    INSERT INTO `my_order`(`pid`,`price`,`num`,`uid`,`atime`) SELECT `pid`,`price`,`num`,`uid`,`atime` FROM `my_order`;

4）.查询测试

/***************************查询性能分析**************************************/
    SELECT * FROM `my_orders` WHERE `uid`=89757 AND `atime`< CURRENT_TIMESTAMP();
    /****用时0.084s****/
    
    SELECT * FROM `my_order` WHERE `uid`=89757 AND `atime`< CURRENT_TIMESTAMP();
    /****用时0.284s****/

通过以上查询可以明显看出进行表分区的查询性能更好，查询所花费的时间更短。

分析查询过程：

EXPLAIN PARTITIONS SELECT \* FROM \`my\_orders\` WHERE \`uid\`=89757 AND \`atime\`< CURRENT\_TIMESTAMP();

![Center 1][]

EXPLAIN PARTITIONS SELECT \* FROM \`my\_order\` WHERE \`uid\`=89757 AND \`atime\`< CURRENT\_TIMESTAMP();

![Center 2][]

通过以上结果可以看出，my\_orders表查询直接经过p0分区，只扫描了49386行，而my\_order表没有进行分区，扫描了196983行，这也是性能得到提升的关键所在。

当然，表的分区并不是分的越多越好，当表的分区太多时找分区又是一个性能的瓶颈了，建议在200个分区以内。

LIST分区表创建方式：

/*****************创建分区表*********************/
    CREATE TABLE `products` (
    `id`  bigint UNSIGNED NOT NULL AUTO_INCREMENT COMMENT '表主键' ,
    `name`  varchar(64) CHARACTER SET utf8 COLLATE utf8_general_ci NOT NULL COMMENT '产品名称' ,
    `metrial`  tinyint UNSIGNED NOT NULL COMMENT '材质' ,
    `weight`  double UNSIGNED NOT NULL DEFAULT 0 COMMENT '重量' ,
    `vol`  double UNSIGNED NOT NULL DEFAULT 0 COMMENT '容积' ,
    `c_id`  tinyint UNSIGNED NOT NULL COMMENT '供货公司ID' ,
    PRIMARY KEY (`id`,`c_id`)
    )ENGINE=InnoDB DEFAULT CHARSET=utf8
    
    /*********分区信息**************/
    PARTITION BY LIST(c_id)
    (
        PARTITION pA VALUES IN (1,3,11,13),
        PARTITION pB VALUES IN (2,4,12,14),
        PARTITION pC VALUES IN (5,7,15,17),
        PARTITION pD VALUES IN (6,8,16,18),
        PARTITION pE VALUES IN (9,10,19,20)
    );

可以看出，LIST分区和RANGE分区很类似，这里就不做性能分析了，和RANGE很类似。

HASH分区表的创建方式：

/*****************分区表*****************/
    CREATE TABLE `msgs` (
      `id` bigint(20) unsigned NOT NULL AUTO_INCREMENT COMMENT '表主键',
      `sender` int(10) unsigned NOT NULL COMMENT '发送者ID',
      `reciver` int(10) unsigned NOT NULL COMMENT '接收者ID',
      `msg_type` tinyint(3) unsigned NOT NULL COMMENT '消息类型',
      `msg` varchar(225) NOT NULL COMMENT '消息内容',
      `atime` int(10) unsigned NOT NULL COMMENT '发送时间',
      `sub_id` tinyint(3) unsigned NOT NULL COMMENT '部门ID',
      PRIMARY KEY (`id`,`sub_id`)
    ) ENGINE=InnoDB DEFAULT CHARSET=utf8
    /*********分区信息**************/
    PARTITION BY HASH(sub_id)
    PARTITIONS 10;

以上语句代表，msgs表按照sub\_id进行HASH分区，一共分了十个区。

Key分区和HASH分区很类似，不再介绍，若想了解可以参考Mysql官方文档进行详细了解。

子分区的创建方式:

CREATE TABLE `msgss` (
      `id` bigint(20) unsigned NOT NULL AUTO_INCREMENT COMMENT '表主键',
      `sender` int(10) unsigned NOT NULL COMMENT '发送者ID',
      `reciver` int(10) unsigned NOT NULL COMMENT '接收者ID',
      `msg_type` tinyint(3) unsigned NOT NULL COMMENT '消息类型',
      `msg` varchar(225) NOT NULL COMMENT '消息内容',
      `atime` int(10) unsigned NOT NULL COMMENT '发送时间',
      `sub_id` tinyint(3) unsigned NOT NULL COMMENT '部门ID',
      PRIMARY KEY (`id`,`atime`,`sub_id`)
    ) ENGINE=InnoDB DEFAULT CHARSET=utf8
    /*********分区信息**************/
    PARTITION BY RANGE (atime) SUBPARTITION BY HASH (sub_id) 
    (
    		PARTITION t0 VALUES LESS THAN(1451577600)
    		(
    			SUBPARTITION s0,
    			SUBPARTITION s1,
    			SUBPARTITION s2,
    			SUBPARTITION s3,
    			SUBPARTITION s4,
    			SUBPARTITION s5
    		),
    		PARTITION t1 VALUES LESS THAN(1483200000)
    		(
    			SUBPARTITION s6,
    			SUBPARTITION s7,
    			SUBPARTITION s8,
    			SUBPARTITION s9,
    			SUBPARTITION s10,
    			SUBPARTITION s11
    		),
    		PARTITION t2 VALUES LESS THAN MAXVALUE
    		(
    			SUBPARTITION s12,
    			SUBPARTITION s13,
    			SUBPARTITION s14,
    			SUBPARTITION s15,
    			SUBPARTITION s16,
    			SUBPARTITION s17
    		)
    );

检查子分区是否创建成功：

EXPLAIN PARTITIONS SELECT \* FROM msgss;

结果如下图：

![Center 3][]

[http_blog.csdn.net_jhq0113_article_details_43812895]: http://blog.csdn.net/jhq0113/article/details/43812895
[http_blog.csdn.net_jhq0113_article_details_44592865]: http://blog.csdn.net/jhq0113/article/details/44592865
[Center]: /images/20220724/733a9dc34ad94c2e9ecc40f921103ba4.png
[Center 1]: /images/20220724/e0b0665ef1da4f8bac7dac205a8d361c.png
[Center 2]: /images/20220724/ff09f1bc797a4fadbf8a722433cc2cbf.png
[Center 3]: /images/20220724/571c1955898948698f3e74b7472ac815.png