How to perform data sub-database, table and horizontal split in MySQL?

How to divide data into databases, tables and horizontal splits in MySQL?

In the era of big data, as the amount of data continues to grow, the database design of a single database and a single table can no longer meet the needs of large-scale data storage and processing. Therefore, sharding databases, sharding tables and horizontal splitting have become a common solution. This article will introduce how to perform data sharding, table splitting and horizontal splitting in MySQL, and provide corresponding code examples.

1. Data sub-database and table

1. Sub-database

In the sub-database operation, the original database is divided into multiple databases according to certain rules. Sub-databases, each sub-database is deployed independently on different physical servers to achieve horizontal expansion of the database. Common database sharding strategies are:

(1) Database sharding according to business: store data from different businesses in different libraries to improve data isolation and concurrent processing capabilities.

(2) Divide the database according to geographical location: Split the data according to geographical location information to improve data locality and access efficiency.

(3) Divide the database according to time: Divide the data according to the time range, such as storing it by year, month, etc., to facilitate data management and query.

2. Table splitting

In the table splitting operation, the original table is divided into multiple sub-tables according to certain rules, and each sub-table independently stores a part of the data, thereby achieving Horizontal expansion of the table. Common table splitting strategies are:

(1) Split tables according to the primary key range: Divide the data into several sub-tables according to the range of the primary key, such as dividing according to the ID range, to facilitate data management and query.

(2) Divide the data according to date: Divide the data according to date, such as storing it according to the year, month and day of the date to facilitate data management and query.

(3) Divide tables according to hash algorithm: Use hash algorithm to partition data to ensure uniform data distribution and improve query performance.

The following is a sample code for sub-database and sub-table:

-- 创建分库
CREATE DATABASE database1;
CREATE DATABASE database2;

-- 切换到database1库
USE database1;

-- 创建分表
CREATE TABLE table1 (
  id INT PRIMARY KEY AUTO_INCREMENT,
  name VARCHAR(20),
  age INT,
  ...
);

-- 切换到database2库
USE database2;

-- 创建分表
CREATE TABLE table1 (
  id INT PRIMARY KEY AUTO_INCREMENT,
  name VARCHAR(20),
  age INT,
  ...
);

2. Horizontal split

Horizontal split refers to splitting the data of a single table into multiple sub-tables Table, each sub-table independently stores a part of the data, thereby achieving horizontal expansion of the table. Common horizontal split strategies are:

(1) Split according to fields: Split according to a field in the table, such as user ID, order ID, etc., to facilitate data management and Inquire.

(2) Split according to geographical location: Split the data according to geographical location information to improve data locality and access efficiency.

(3) Split according to time: Divide the data according to time range, such as storing by year, month, etc., to facilitate data management and query.

The following is a sample code for horizontal splitting:

-- 创建拆分表1
CREATE TABLE table1 (
  id INT PRIMARY KEY AUTO_INCREMENT,
  name VARCHAR(20),
  age INT,
  ...
);

-- 创建拆分表2
CREATE TABLE table2 (
  id INT PRIMARY KEY AUTO_INCREMENT,
  name VARCHAR(20),
  age INT,
  ...
);

Through the above code example, we can see how to perform data database, table and horizontal splitting in MySQL. These technologies can help us improve the scalability and performance of the database and are suitable for large-scale data storage and processing scenarios. Of course, in practical applications, we also need to consider other factors, such as data migration, data consistency and other issues, to ensure the smooth progress of database sharding, table sharding and horizontal splitting.

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