MySQL InnoDB engine detailed optimization skills: practical combat from storage structure to index algorithm

MySQL InnoDB engine detailed optimization skills: practical combat from storage structure to index algorithm

Introduction:
MySQL is one of the most widely used relational database management systems currently, and InnoDB is the default for MySQL Storage engine. The InnoDB engine is a high-performance, reliable engine suitable for large-scale data storage and high-concurrency access.

This article will introduce some detailed optimization techniques of the InnoDB engine from storage structure to index algorithm, and provide code examples to help readers better improve database performance.

1. Storage structure optimization
1.1 Use smaller data types
When designing the table structure, rationally selecting appropriate data types can significantly reduce storage space. For example, when storing ages, using TINYINT instead of INT can reduce storage space and thus improve query performance.

Code example:

CREATE TABLE users (
  id INT PRIMARY KEY,
  name VARCHAR(50),
  age TINYINT
);

1.2 Vertically split table
Vertically split table refers to splitting a table by columns to optimize data storage at the storage level. A common vertical split method is to split columns that are frequently queried but have large amounts of data into separate tables.

For example, for the user table (User), we can split the user information and user extended information into two tables to reduce I/O operations and improve query performance.

Code example:

CREATE TABLE users (
  id INT PRIMARY KEY,
  name VARCHAR(50),
  gender ENUM('male', 'female')
);

CREATE TABLE user_details (
  user_id INT PRIMARY KEY,
  age TINYINT,
  address VARCHAR(100),
  job VARCHAR(50)
);

1.3 Query optimization of vertically split tables
When querying vertically split tables, you can use the JOIN operation to jointly query the required data. This can avoid frequent disk I/O operations and improve query efficiency.

Code sample:

SELECT users.id, users.name, user_details.age, user_details.address
FROM users
JOIN user_details ON users.id = user_details.user_id
WHERE users.id = 1;

2. Index optimization
2.1 Use appropriate data types as indexes
When creating an index, choosing the appropriate data type can significantly improve index performance . For example, for long text type fields, you can choose to create a prefix index instead of using a full-text index to reduce the index size.

Code example:

CREATE INDEX idx_title ON articles (title(10));

2.2 Selection of clustered index and auxiliary index
For the InnoDB engine, the primary key is used as the clustered index by default. The clustered index determines the physical storage order of data, so reasonable selection of primary key fields is very important for query performance. At the same time, auxiliary indexes must also be optimized based on actual query needs.

Code example:

ALTER TABLE users
DROP PRIMARY KEY,
ADD PRIMARY KEY (id, name);

2.3 Shorten the index length
The shorter the length of the index, the fewer pages it reads and the faster the reading speed. Therefore, when creating an index, you can shorten the field length to improve index performance.

Code examples:

CREATE INDEX idx_title ON articles (title(100));

3. Summary
This article introduces some detailed optimization techniques of the InnoDB engine from storage structure to index algorithm, and provides corresponding code examples. In practice, readers can adjust and optimize according to specific business needs to improve database performance and response speed.

Through reasonable use of data types, vertical partitioning of tables, optimized indexes and other techniques, we can optimize the performance of the InnoDB engine and improve the overall performance of the database. I hope this article will be helpful to readers in database optimization in practical work.

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