How to improve MySQL performance by using Denormalization

MySQL is a commonly used relational database system, and its efficiency is widely used in various Web applications. However, under high load, MySQL's performance sometimes becomes very poor, affecting the user experience of web applications. In this case, using Denormalization is an effective strategy to improve MySQL performance. This article will introduce how to improve MySQL performance by using Denormalization.

1. What is Denormalization

Denormalization, that is, denormalization, refers to when designing a relational database, in order to improve query performance and reduce the joins between tables, in a table Store data in other tables. This approach violates the principle of normalization, but improves query performance and makes query operations faster.

For example, in a web application, it is necessary to display a user's personal information, including his or her name, phone number, address and other information. In the traditional standardized design, this information may be stored in the user table, phone table and address table respectively, and multi-table joins are required to complete the query operation. Through denormalization, this information can be stored in one table, avoiding multi-table join operations, thereby improving query performance.

2. How to use Denormalization to improve MySQL performance

Although Denormalization can improve the performance of MySQL, you need to pay attention to the following aspects:

2.1 Data redundancy

Denormalization means storing data in other tables in one table, which can lead to data redundancy. Although this can improve query performance, it can also lead to data consistency issues. Therefore, when using denormalization, you need to pay attention to the consistency of the data to ensure the correctness and integrity of the data.

2.2 Query Optimization

Denormalization can improve query performance, but query operations also need to be optimized. When using denormalization, you should consider which query operations are most frequently used, and store frequently used fields in one table to avoid multi-table join operations, thereby improving query performance.

2.3 Update operation

When the data stored in one table changes, the data in multiple tables needs to be updated at the same time to ensure data consistency. This may cause performance degradation when performing update operations.

Therefore, when using Denormalization, you need to weigh query performance and update performance. For data with high query frequency and low update frequency, Denormalization can be used to improve query performance; while for data with low query frequency and high update frequency, Denormalization is not suitable.

3. Example

The following uses an example to introduce in detail how to use Denormalization to improve MySQL performance.

Suppose there is a web application, which has a user table user, an article table post, and a comment table comment. The relationship between them is as follows:

user
---------
id (primary key)
name
email

post
---------
id (primary key)
title
author_id (foreign key)

comment
---------
id (primary key)
content
post_id (foreign key)
user_id (foreign key)

At this time, if a one-time Obtaining the comments of an article and its author requires two cross-table association operations.

In order to improve query performance, you can use Denormalization to store data in a table, as shown below:

post_comment_user
------------------
id (primary key)
post_id (foreign key)
comment_id (foreign key)
user_id (foreign key)
post_title
post_author_name
comment_content
comment_user_name

After using Denormalization, you can obtain the comments and comments of an article in one query operation All information about its authors avoids multi-table join operations and improves query performance.

Finally, it should be noted that when using Denormalization, you need to weigh query performance and data consistency. If data consistency is critical to a web application, you would rather sacrifice some query performance to ensure data consistency.

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