How to design an optimized MySQL table structure to implement data distribution function?

How to design an optimized MySQL table structure to achieve data distribution function?

In the process of developing database applications, we often need to distribute data to different tables to meet the needs of different business scenarios. Designing an optimized MySQL table structure to implement data distribution function can improve data access efficiency and query performance, and ensure data consistency and integrity. This article will introduce how to design an optimized MySQL table structure and provide specific code examples.

1. Principles and requirements analysis of data distribution

Data distribution refers to distributing data in the source table to the target table according to certain rules. Generally, data distribution has the following common requirements:

1.1 Vertical table partitioning: Distribute the fields of one table into multiple tables. This requirement is usually caused by too many records in a single table, which slows down the query.

1.2 Horizontal table sharding: Spread the records of one table into multiple tables. This requirement is usually due to the excessive number of records in a single table, which slows down data insertion and query speeds.

1.3 Splitting databases and tables: Spread the data into multiple databases, and then disperse the data of each database into multiple tables. This requirement is usually due to the inability of a single library to store massive amounts of data, or to improve data access speed and query performance.

When designing an optimized MySQL table structure, we need to analyze the specific needs and choose a suitable data distribution strategy. Below is a specific code example.

2. Vertical Table Implementation Example

Suppose we have a user table user, which contains the user’s basic information, such as name, age, gender, etc. Now we scatter the age field into two tables, user_age_1 and user_age_2. According to the user's age range, we store records with an age less than 30 in the user_age_1 table, and records with an age greater than or equal to 30 in the user_age_2 table.

Code example:

--Create user table
CREATE TABLE user (
id INT(11) NOT NULL AUTO_INCREMENT,
name VARCHAR(100) NOT NULL,
age INT(11) NOT NULL,
gender ENUM('M', 'F') DEFAULT NULL,
PRIMARY KEY (id)
) ENGINE=InnoDB DEFAULT CHARSET=utf8;

-- Create user table (user_age_1)
CREATE TABLE user_age_1 AS SELECT * FROM user WHERE age

-- Create user table (user_age_2)
CREATE TABLE user_age_2 AS SELECT * FROM user WHERE age >= 30;

In practical applications, we need to ensure the consistency and integrity of data through triggers or stored procedures.

3. Horizontal table implementation example

Suppose we have an order table order, which contains the basic information of the order, such as order number, order time, and total amount wait. Now we spread the order records into 12 tables, one table for each month. For example, records with order records for January 2022 are stored in the order_01 table, records with order records for February 2022 are stored in the order_02 table, and so on.

Code example:

--Create order table (order_01)
CREATE TABLE order_01 AS SELECT * FROM order WHERE YEAR(order_time) = 2022 AND MONTH(order_time) = 1;

-- Create order table (order_02)
CREATE TABLE order_02 AS SELECT * FROM order WHERE YEAR(order_time) = 2022 AND MONTH(order_time) = 2;

...

-- Create order table (order_12)
CREATE TABLE order_12 AS SELECT * FROM order WHERE YEAR(order_time) = 2022 AND MONTH(order_time) = 12;

In practical applications, we Scheduled tasks or stored procedures need to be used to automatically create and delete tables, and data must be archived or migrated regularly.

4. Example of implementation of sub-database and sub-table

Suppose we have a product table product, which contains basic information about the product, such as product number, name, price, etc. Now we disperse the product records into two databases, and each database into three tables. According to the range of product numbers, we store records with product numbers less than 100 in the product_01 table of database db1, records with product numbers from 100 to 199 in the product_02 table of database db1, and so on. Similarly, records with product numbers less than 100 are stored in the product_01 table of database db2, and records with product numbers from 100 to 199 are stored in the product_02 table of database db2.

Code example:

-- Create product table (product_01) in database db1
CREATE TABLE product_01 AS SELECT * FROM product WHERE product_id

-- Create a product table (product_02) in database db1
CREATE TABLE product_02 AS SELECT * FROM product WHERE product_id BETWEEN 100 AND 199;

-- Create a product table (product_03) in database db1
CREATE TABLE product_03 AS SELECT * FROM product WHERE product_id >= 200;

-- Create a product table (product_01) in database db2
CREATE TABLE product_01 AS SELECT * FROM product WHERE product_id

-- Create product table (product_02) in database db2
CREATE TABLE product_02 AS SELECT * FROM product WHERE product_id BETWEEN 100 AND 199;

-- Create in database db2 Product table (product_03)
CREATE TABLE product_03 AS SELECT * FROM product WHERE product_id >= 200;

In practical applications, we need to use sharding algorithms to decide which library and table to store data in, and use database middleware to implement data distribution and routing.

Summary:

Designing an optimized MySQL table structure to implement the data distribution function can improve the performance of database applications and meet different business needs. Through the design patterns of vertical table sharding, horizontal sharding and database sharding, data can be flexibly stored in appropriate tables and libraries, and the consistency and integrity of the data can be guaranteed. In practical applications, we need to choose a suitable data distribution strategy based on specific demand analysis, and use triggers, stored procedures, scheduled tasks or database middleware to achieve data distribution and management.

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