Detailed code explanation of the four partition types of MySQL tables

I have only implemented the following four database partitions: RANGE (range) partitions. There are three more that I will try to implement later. Friends who have implemented them can share what they think is a good write-up. Article

MySQLFour partition types of tables

1. What is table partitioning
In layman terms, table partitioning is a large table. The conditions are divided into several small tables. Mysql5.1 begins to support data table partitioning. For example, if a user table has more than 6 million records, the table can be partitioned according to the date of entry into the database, or the table can be partitioned according to the location. Of course, partitioning can also be based on other conditions.

2. Why partition the table
In order to improve the scalability, manageability and database efficiency of large tables and tables with various access modes.

Some advantages of partitioning include:

Can store more data than a single disk or file system partition.
For data that has lost its meaning, you can usually delete the data easily by deleting the partitions related to those data. On the contrary, in some cases, the process of adding new data can be easily implemented by adding a new partition specifically for those new data. Other advantages commonly associated with partitioning include those listed below. These features in MySQL partitioning are not yet implemented, but are high on our priority list; we hope to include them in the production version of 5.1.
Some queries can be greatly optimized, mainly by means that the data that satisfies a given WHERE statement can be stored in only one or more partitions, so that there is no need to search for other remaining partitions when searching. Because partitioning can be modified after the partitioned table is created, you can reorganize the data to improve the efficiency of commonly used queries if you have not done so when you first configure the partitioning scheme.
Queries involving aggregation functions such as SUM() and COUNT() can be easily processed in parallel. A simple example of such a query is "SELECT salesperson_id, COUNT (orders) as order_total FROM sales GROUP BY salesperson_id;". By "parallel", this means that the query can be run on each partition simultaneously, and the final result is simply the sum of the results obtained by all partitions.
Get greater query throughput by spreading data queries across multiple disks.
3. Partition type
RANGE partitioning: Allocate multiple rows to partitions based on column values ​​belonging to a given continuous interval.

LIST partitioning: Similar to partitioning by RANGE, the difference is that LIST partitioning is selected based on the column value matching a certain value in a discrete value set.

HASH partitioning: A partition selected based on the return value of a user-defined expression that is calculated using the column values ​​of the rows to be inserted into the table. This function can contain any valid expression in MySQL that produces a non-negative integer value.

KEY partitioning: Similar to HASH partitioning, the difference is that KEY partitioning only supports calculation of one or more columns, and the MySQL server provides its own hash function. There must be one or more columns containing integer values.

RANGE Partitioning

Assigns multiple rows to partitions based on column values ​​belonging to a given contiguous range.

These intervals must be continuous and cannot overlap each other, use VALUES LESS THANoperator to define. Below are examples.

Sql code:

CREATE TABLE employees (
id INT NOT NULL,
fname VARCHAR(30),
lname VARCHAR(30),
hired DATE NOT NULL DEFAULT '1970-01-01',
separated DATE NOT NULL DEFAULT '9999-12-31',
job_code INT NOT NULL,
store_id INT NOT NULL
)

partition BY RANGE (store_id) (
partition p0 VALUES LESS THAN (6),
partition p1 VALUES LESS THAN (11),
partition p2 VALUES LESS THAN (16),
partition p3 VALUES LESS THAN (21)
);

According to this partitioning scheme, all rows corresponding to employees working in stores 1 to 5 are saved in partition P0, and those of stores 6 to 10 Employees are saved in P1, and so on. Note that each partition is defined sequentially, from lowest to highest. This is a requirement of the PARTITION BY RANGE syntax; in this regard, it is similar to a "switch ... case" statement in C or Java. For a new row containing the data (72, 'Michael', 'Widenius', '1998-06-25', NULL, 13), it is easy to determine that it will be inserted into the p2 partition, but if a number is added What will happen to the 21st store? In this scenario, since there is no rule to include stores with a store_id greater than 20, the server will not know where to save the row, which will result in an error. This error can be avoided by using a "catchall" VALUES LESS THAN clause in the CREATE TABLE statement that provides all values ​​greater than the explicitly specified highest value:

Sql code:

CREATE TABLE employees (
id INT NOT NULL,
fname VARCHAR(30),
lname VARCHAR(30),
hired DATE NOT NULL DEFAULT '1970-01-01',
separated DATE NOT NULL DEFAULT '9999-12-31',
job_code INT NOT NULL,
store_id INT NOT NULL
)

PARTITION BY RANGE (store_id) (
PARTITION p0 VALUES LESS THAN (6),
PARTITION p1 VALUES LESS THAN (11),
PARTITION p2 VALUES LESS THAN (16),
PARTITION p3 VALUES LESS THAN MAXVALUE
);

MAXVALUE 表示最大的可能的整数值。现在，store_id 列值大于或等于16（定义了的最高值）的所有行都将保存在分区p3中。在将来的某个时候，当商店数已经增长到25, 30, 或更多 ，可以使用ALTER TABLE语句为商店21-25, 26-30,等等增加新的分区。在几乎一样的结构中，你还可以基于雇员的工作代码来分割表，也就是说，基于job_code 列值的连续区间。例如——假定2位数字的工作代码用来表示普通（店内的）工人，三个数字代码表示办公室和支持人员，四个数字代码表示管理层，你可以使用下面的语句创建该分区表：

Sql代码：

CREATE TABLE employees (
id INT NOT NULL,
fname VARCHAR(30),
lname VARCHAR(30),
hired DATE NOT NULL DEFAULT '1970-01-01',
separated DATE NOT NULL DEFAULT '9999-12-31',
job_code INT NOT NULL,
store_id INT NOT NULL
)

PARTITION BY RANGE (job_code) (
PARTITION p0 VALUES LESS THAN (100),
PARTITION p1 VALUES LESS THAN (1000),
PARTITION p2 VALUES LESS THAN (10000)
);

在这个例子中, 店内工人相关的所有行将保存在分区p0中，办公室和支持人员相关的所有行保存在分区p1中，管理层相关的所有行保存在分区p2中。在VALUES LESS THAN 子句中使用一个表达式也是可能的。这里最值得注意的限制是MySQL 必须能够计算表达式的返回值作为LESS THAN (

Sql代码：

CREATE TABLE employees (
id INT NOT NULL,
fname VARCHAR(30),
lname VARCHAR(30),
hired DATE NOT NULL DEFAULT '1970-01-01',
separated DATE NOT NULL DEFAULT '9999-12-31',
job_code INT,
store_id INT
)

PARTITION BY RANGE (YEAR(separated)) (
PARTITION p0 VALUES LESS THAN (1991),
PARTITION p1 VALUES LESS THAN (1996),
PARTITION p2 VALUES LESS THAN (2001),
PARTITION p3 VALUES LESS THAN MAXVALUE
);

在这个方案中，在1991年前雇佣的所有雇员的记录保存在分区p0中，1991年到1995年期间雇佣的所有雇员的记录保存在分区p1中， 1996年到2000年期间雇佣的所有雇员的记录保存在分区p2中，2000年后雇佣的所有工人的信息保存在p3中。

RANGE分区在如下场合特别有用：1）、当需要删除一个分区上的“旧的”数据时,只删除分区即可。如果你使用上面最近的那个例子给出的分区方案，你只需简单地使用”ALTER TABLE employees DROP PARTITION p0;”来删除所有在1991年前就已经停止工作的雇员相对应的所有行。对于有大量行的表，这比运行一个如”DELETE FROM employees WHERE YEAR (separated)

注释：这种优化还没有在MySQL 5.1源程序中启用，但是，有关工作正在进行中。

LIST分区

类似于按RANGE分区，区别在于LIST分区是基于列值匹配一个离散值集合中的某个值来进行选择。

LIST分区通过使用“PARTITION BY LIST(expr)”来实现，其中“expr”是某列值或一个基于某个列值、并返回一个整数值的表达式，然后通过“VALUES IN (value_list)”的方式来定义每个分区，其中“value_list”是一个通过逗号分隔的整数列表。 注释：在MySQL 5.1中，当使用LIST分区时，有可能只能匹配整数列表。

Sql代码：

CREATE TABLE employees (
id INT NOT NULL,
fname VARCHAR(30),
lname VARCHAR(30),
hired DATE NOT NULL DEFAULT '1970-01-01',
separated DATE NOT NULL DEFAULT '9999-12-31',
job_code INT,
store_id INT
)；

假定有20个音像店，分布在4个有经销权的地区，如下表所示：

====================
地区      商店ID 号
北区      3, 5, 6, 9, 17
东区      1, 2, 10, 11, 19, 20
西区      4, 12, 13, 14, 18
中心区   7, 8, 15, 16
====================

要按照属于同一个地区商店的行保存在同一个分区中的方式来分割表，可以使用下面的“CREATE TABLE”语句：

Sql代码：

CREATE TABLE employees (
id INT NOT NULL,
fname VARCHAR(30),
lname VARCHAR(30),
hired DATE NOT NULL DEFAULT '1970-01-01',
separated DATE NOT NULL DEFAULT '9999-12-31',
job_code INT,
store_id INT
)

PARTITION BY LIST(store_id)
PARTITION pNorth VALUES IN (3,5,6,9,17),
PARTITION pEast VALUES IN (1,2,10,11,19,20),
PARTITION pWest VALUES IN (4,12,13,14,18),
PARTITION pCentral VALUES IN (7,8,15,16)
);

这使得在表中增加或删除指定地区的雇员记录变得容易起来。例如，假定西区的所有音像店都卖给了其他公司。那么与在西区音像店工作雇员相关的所有记录（行）可以使用查询“ALTER TABLE employees DROP PARTITION pWest；”来进行删除，它与具有同样作用的DELETE（删除）查询“DELETE query DELETE FROM employees WHERE store_id IN (4,12,13,14,18)；”比起来，要有效得多。【要点】：如果试图插入列值（或分区表达式的返回值）不在分区值列表中的一行时，那么“INSERT”查询将失败并报错。例如，假定LIST分区的采用上面的方案，下面的查询将失败：

Sql代码：

INSERT INTO employees VALUES(224, 'Linus', 'Torvalds', '2002-05-01', '2004-10-12', 42, 21);

这是因为“store_id”列值21不能在用于定义分区pNorth, pEast, pWest,或pCentral的值列表中找到。要重点注意的是，LIST分区没有类似如“VALUES LESS THAN MAXVALUE”这样的包含其他值在内的定义。将要匹配的任何值都必须在值列表中找到。
LIST分区除了能和RANGE分区结合起来生成一个复合的子分区，与HASH和KEY分区结合起来生成复合的子分区也是可能的。

HASH分区

基于用户定义的表达式的返回值来进行选择的分区，该表达式使用将要插入到表中的这些行的列值进行计算。这个函数可以包含MySQL 中有效的、产生非负整数值的任何表达式。

要使用HASH分区来分割一个表，要在CREATE TABLE 语句上添加一个“PARTITION BY HASH (expr)”子句，其中“expr”是一个返回一个整数的表达式。它可以仅仅是字段类型为MySQL整型的一列的名字。此外，你很可能需要在后面再添加一个“PARTITIONS num”子句，其中num是一个非负的整数，它表示表将要被分割成分区的数量。

Sql代码：

CREATE TABLE employees (
id INT NOT NULL,
fname VARCHAR(30),
lname VARCHAR(30),
hired DATE NOT NULL DEFAULT '1970-01-01',
separated DATE NOT NULL DEFAULT '9999-12-31',
job_code INT,
store_id INT
)
PARTITION BY HASH(store_id)
PARTITIONS 4;

如果没有包括一个PARTITIONS子句，那么分区的数量将默认为1。例外：对于NDB Cluster（簇）表，默认的分区数量将与簇数据节点的数量相同，这种修正可能是考虑任何MAX_ROWS设置，以便确保所有的行都能合适地插入到分区中。

LINER HASH
MySQL还支持线性哈希功能，它与常规哈希的区别在于，线性哈希功能使用的一个线性的2的幂（powers-of-two）运算法则，而常规哈希使用的是求哈希函数值的模数。线性哈希分区和常规哈希分区在语法上的唯一区别在于，在“PARTITION BY”子句中添加“LINEAR”关键字。

Sql代码：

CREATE TABLE employees (
id INT NOT NULL,
fname VARCHAR(30),
lname VARCHAR(30),
hired DATE NOT NULL DEFAULT '1970-01-01',
separated DATE NOT NULL DEFAULT '9999-12-31',
job_code INT,
store_id INT
)
PARTITION BY LINEAR HASH(YEAR(hired))
PARTITIONS 4;

假设一个表达式expr，当使用线性哈希功能时，记录将要保存到的分区是num 个分区中的分区N，其中N是根据下面的算法得到： 1. 找到下一个大于num.的、2的幂，我们把这个值称为V ，它可以通过下面的公式得到： 2. V = POWER(2, CEILING(LOG(2, num))) （例如，假定num是13。那么LOG(2,13)就是3.7004397181411。 CEILING(3.7004397181411)就是4，则V = POWER(2,4), 即等于16）。 3. 设置 N = F(column_list) & (V – 1). 4.    当 N >= num: ·  设置 V = CEIL(V / 2) ·  设置 N = N & (V – 1) 例如，假设表t1，使用线性哈希分区且有4个分区，是通过下面的语句创建的： CREATE TABLE t1 (col1 INT, col2 CHAR(5), col3 DATE) PARTITION BY LINEAR HASH( YEAR(col3) ) PARTITIONS 6; 现在假设要插入两行记录到表t1中，其中一条记录col3列值为’2003-04-14′，另一条记录col3列值为’1998-10-19′。第一条记录将要保存到的分区确定如下： V = POWER(2, CEILING(LOG(2,7))) = 8 N = YEAR(’2003-04-14′) & (8 – 1)    = 2003 & 7    = 3 (3 >= 6 为假（FALSE）: 记录将被保存到#3号分区中) 第二条记录将要保存到的分区序号计算如下： V = 8 N = YEAR(’1998-10-19′) & (8-1)   = 1998 & 7   = 6 (6 >= 4 为真（TRUE）: 还需要附加的步骤) N = 6 & CEILING(5 / 2)   = 6 & 3   = 2   (2 >= 4 为假（FALSE）: 记录将被保存到#2分区中) 按照线性哈希分区的优点在于增加、删除、合并和拆分分区将变得更加快捷，有利于处理含有极其大量（1000吉）数据的表。它的缺点在于，与使用常规HASH分区得到的数据分布相比，各个分区间数据的分布不大可能均衡。

KSY分区

类似于按HASH分区，区别在于KEY分区只支持计算一列或多列，且MySQL服务器提供其自身的哈希函数。必须有一列或多列包含整数值。

Sql代码：

CREATE TABLE tk (
col1 INT NOT NULL,
col2 CHAR(5),
col3 DATE
)
PARTITION BY LINEAR KEY (col1)
PARTITIONS 3;

在KEY分区中使用关键字LINEAR和在HASH分区中使用具有同样的作用，分区的编号是通过2的幂（powers-of-two）算法得到，而不是通过模数算法。

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