数据切分——Mysql分区表的建立及性能分析

Mysql的安装方法可以参考:

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

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:

数据切分——Mysql分区表的建立及性能分析

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();

数据切分——Mysql分区表的建立及性能分析

EXPLAIN PARTITIONS SELECT * FROM `my_order` WHERE `uid`=89757 AND `atime`< CURRENT_TIMESTAMP();

数据切分——Mysql分区表的建立及性能分析

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:

数据切分——Mysql分区表的建立及性能分析

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