MySQL table query

Table splitting is a database partitioning technology that is used to split a large table into multiple small tables to improve database performance and manageability. In MySQL, you can use multiple methods to partition tables, such as based on range, hash or list. The following will introduce in detail how MySQL divides tables and how to perform data query after dividing tables.

Hash-based sharding

Hash-based sharding is a database sharding strategy that spreads data into multiple sub-tables. This method determines in which subtable the data should be stored by calculating a hash value of the data. Hash-based sharding can help distribute data evenly, improve query performance, and reduce the load on individual tables. The following are the steps that detail how to split tables based on hashing:

Step 1: Create subtable

First, you need to create multiple subtables, each of which will store a portion of the data. Usually, the number of subtables is a fixed value, such as 10 or 100, depending on your needs. The name of the subtable can be generated using certain rules so that it can be easily identified during subsequent queries.

Example subtable creation:

	CREATE TABLE orders_0 (
	order_id INT PRIMARY KEY,
	customer_id INT,
	order_date DATE,
	...
	);
	CREATE TABLE orders_1 (
	order_id INT PRIMARY KEY,
	customer_id INT,
	order_date DATE,
	...
	);
	-- 创建更多的子表...

Step 2: Data Hashing

When inserting data, you need to calculate the hash value of the data, and then insert the data into the subtable corresponding to the hash value. Typically, you would select a column as the hash column, and the value of that column will be used to calculate the hash value.

Example inserting data:

	-- 计算数据的哈希值（示例使用MySQL的MD5哈希函数）
	SET @hash = MD5(CONCAT(customer_id, order_date));
	-- 根据哈希值决定插入到哪个子表中
	SET @table_number = ABS(CAST(CONV(SUBSTRING(@hash, 1, 6), 16, 10) AS SIGNED)) % 10; -- 10是子表数量
	-- 插入数据到对应的子表
	INSERT INTO orders_@table_number (order_id, customer_id, order_date, ...)
	VALUES (@order_id, @customer_id, @order_date, ...);

In this example, we used the MD5 hash function to calculate customer_idthe order_datehash value of the sum and then inserted the data into a subtable determined by a portion of the hash value.

Step 3: Query the hash

When querying, you need to calculate the hash value of the query conditions and route the query to the corresponding sub-table. The hash value calculation method for query conditions should be consistent with the method used when inserting data.

Example query data:

	-- 计算查询条件的哈希值
	SET @hash = MD5(CONCAT(@customer_id, @start_date));
	-- 根据哈希值决定查询哪个子表
	SET @table_number = ABS(CAST(CONV(SUBSTRING(@hash, 1, 6), 16, 10) AS SIGNED)) % 10; -- 10是子表数量
	-- 查询对应的子表
	SELECT *
	FROM orders_@table_number
	WHERE customer_id = @customer_id AND order_date >= @start_date;

In this example, we use the same hash function and hash value calculation method used to insert the data to determine which subtable to query. Then, execute the query operation in the corresponding sub-table.

Performance optimization and considerations

• Hash function selection:  Choose an appropriate hash function to ensure even distribution of data. In general, a hash function should distribute data as evenly as possible to avoid overloading certain subtables.
• Number of subtables:  The number of subtables should be large enough to distribute data evenly, but not too many to avoid increased management complexity.
• Query performance:  Hash-based sharding is usually suitable for specific query modes, such as range queries or specific condition queries. Other queries may require merging results from multiple subtables, which may increase query complexity and performance overhead.
• Maintenance:  Hash-based sharding requires careful maintenance, including regular checks on hash distribution and data migration to ensure even distribution of data and prevent overloading of subtables.

Range-based sub-table

Range-based sharding is a database sharding strategy that splits data into different sub-tables based on the range conditions of the data. This method is suitable for queries based on time, geographical area, or other ordered ranges. The following are the steps that detail how to partition tables based on range:

Step 1: Create subtable

First, you need to create multiple subtables, each of which will store a portion of the data. Each subtable should contain the same structure as the original table, but only contain data within a specific range. Usually, you can use the prefix or suffix of the table to identify the subtable so that it can be easily identified in subsequent queries.

Example to create a subtable:

	CREATE TABLE orders_2023 (
	order_id INT PRIMARY KEY,
	customer_id INT,
	order_date DATE,
	...
	);
	CREATE TABLE orders_2024 (
	order_id INT PRIMARY KEY,
	customer_id INT,
	order_date DATE,
	...
	);
	-- 创建更多的子表...

In the above example, we created a subtable for each year, such as orders_2023and orders_2024.

Step 2: Data Routing

When inserting data, the data needs to be inserted into the corresponding subtable according to the range conditions of the data. You can decide which subtable the data should be inserted into based on the value of a certain column, such as date range, geographical area, etc.

Example inserting data:

	-- 插入数据到特定子表（示例基于订单日期范围）
	INSERT INTO orders_2023 (order_id, customer_id, order_date, ...)
	VALUES (@order_id, @customer_id, @order_date, ...);
	INSERT INTO orders_2024 (order_id, customer_id, order_date, ...)
	VALUES (@order_id, @customer_id, @order_date, ...);

In this example, we insert data into the corresponding subtable based on the order date range.

Step 3: Query routing

When querying, the query needs to be routed to the corresponding sub-table based on the scope of the query conditions. This usually requires deciding which subtable to query based on range conditions in the query criteria.

Example query data:

	-- 查询特定范围内的数据
	SELECT *
	FROM orders_2023
	WHERE order_date BETWEEN @start_date AND @end_date;
	SELECT *
	FROM orders_2024
	WHERE order_date BETWEEN @start_date AND @end_date;

In this example, we decide which subtable to query based on the date range of the query criteria.

Performance optimization and considerations

• Indexes:  Create appropriate indexes on child tables to speed up range query operations. Typically, indexes need to be created on columns based on range conditions.
• Query performance:  Range-based table sharding is suitable for queries based on range conditions. Other queries may need to be executed on multiple subtables and the results merged at the application level.
• 维护： 定期维护子表，包括删除不再需要的数据和创建新的子表以容纳新数据。
• 查询路由算法： 查询路由算法应该与数据分布策略一致，以确保正确路由查询。

基于列表的分表

基于列表的分表是一种数据库分表策略，它根据某个列的值将数据分割到不同的子表中。这种方法适用于按照特定条件或分类进行查询的场景。以下是详细介绍如何基于列表进行分表的步骤：

步骤1：创建子表

首先，你需要创建多个子表，每个子表将存储一部分数据。子表应该包含与原始表相同的结构，但只包含符合特定条件的数据。通常，你可以使用表名的后缀或前缀来标识子表，以便后续查询时能够轻松识别。

示例创建子表：

	CREATE TABLE customers_active (
	customer_id INT PRIMARY KEY,
	name VARCHAR(255),
	...
	);
	CREATE TABLE customers_inactive (
	customer_id INT PRIMARY KEY,
	name VARCHAR(255),
	...
	);
	-- 创建更多的子表...

在上面的示例中，我们创建了两个子表，一个用于存储活跃客户，另一个用于存储不活跃客户。

步骤2：数据路由

在插入数据时，需要根据数据的特定条件将数据插入到对应的子表中。你可以使用某个列的值来决定数据应该插入到哪个子表中，例如客户状态、地理位置等。

示例插入数据：

	-- 插入数据到特定子表（示例基于客户状态）
	INSERT INTO customers_active (customer_id, name, ...)
	VALUES (@customer_id, @name, ...);
	INSERT INTO customers_inactive (customer_id, name, ...)
	VALUES (@customer_id, @name, ...);

在这个示例中，我们根据客户的状态将数据插入到对应的子表中。

步骤3：查询路由

在查询时，需要根据查询条件中的特定条件将查询路由到对应的子表。这通常需要根据查询条件中的列值来决定要查询哪个子表。

示例查询数据：

	-- 查询特定条件下的数据（示例查询活跃客户）
	SELECT *
	FROM customers_active
	WHERE registration_date >= @start_date;
	-- 查询不活跃客户
	SELECT *
	FROM customers_inactive
	WHERE last_activity_date < @cutoff_date;

在这个示例中，我们根据查询条件中的客户状态来决定要查询哪个子表。

性能优化和注意事项

• 索引： 在子表中创建合适的索引以加速查询操作。通常，根据查询条件的列需要创建索引。
• 查询性能： 基于列表的分表适用于按照特定条件进行查询的场景。其他查询可能需要在多个子表上执行，并在应用程序层合并结果。
• 维护： 定期维护子表，包括删除不再需要的数据和创建新的子表以容纳新数据。
• 查询路由算法： 查询路由算法应该与数据分布策略一致，以确保正确路由查询。