How to implement MySQL underlying optimization: applications and advantages of table partitioning

How to realize MySQL underlying optimization: Application and advantages of table partitioning

With the advent of the big data era, the performance requirements of the database are getting higher and higher. As a commonly used relational database, MySQL provides the function of table partitioning in order to meet the needs of large-scale data storage and high concurrent access. This article will introduce how to implement table partitioning in MySQL's underlying optimization, as well as the applications and advantages of table partitioning, and provide specific code examples.

1. The concept and classification of table partitioning
Table partitioning refers to splitting a large table into multiple sub-tables according to certain rules, and each sub-table stores a part of the data. Typically, table partitions can be classified based on ranges, lists, hashes, and key values ​​of data. Among them, table partitions classified according to data range are called range partitions, table partitions classified according to column values ​​are called list partitions, table partitions classified according to hash values ​​are called hash partitions, and table partitions classified according to user-defined key values Table partitioning for classification is called key-value partitioning.

2. Application scenarios of table partitions

1. Tables with huge amounts of data: When the amount of data in a table reaches tens of millions or billions, query, update, and delete operations may It will become very slow. By partitioning the table, data can be dispersed across multiple sub-tables to improve query efficiency.
2. Highly concurrent reading and writing scenarios: When multiple concurrent requests operate the same table at the same time, table locks or row locks will cause a lot of resource waste and performance bottlenecks. Through table partitioning, concurrent operations can be performed in different sub-tables, reducing lock conflicts and improving concurrency performance.
3. Archiving and access of historical data: For historical data, frequent access is usually not required, but storage is still required. Through table partitioning, historical data can be stored in independent sub-tables to reduce access pressure on the main table.

3. Advantages of table partitioning

1. Improving query performance: by dispersing data in multiple sub-tables, only specific sub-tables need to be accessed during query, which can be greatly improved Query efficiency.
2. Reduce lock conflicts: Performing concurrent operations in different sub-tables can reduce lock conflicts and improve concurrency performance.
3. Quick deletion and archiving: The deletion and archiving of historical data can be completed quickly by operating specific sub-tables, reducing the operation time of the entire table.
4. More granular permission control: Different permissions can be set for different sub-tables to achieve more granular data security control.

4. Specific code examples
Suppose there is a user table user, including fields id, name, age, etc. Partition the table based on age range.

1. Create main table:

   CREATE TABLE user (
    id INT(11) NOT NULL AUTO_INCREMENT,
    name VARCHAR(50) NOT NULL,
    age INT(11) NOT NULL,
    PRIMARY KEY (id, age)
   ) ENGINE=InnoDB;

2. Create sub-table (partitioned table):

   CREATE TABLE user_youth (
    CHECK (age >=0 AND age <= 35)
   ) ENGINE = InnoDB
    PARTITION BY RANGE (age) (
        PARTITION p0 VALUES LESS THAN (18),
        PARTITION p1 VALUES LESS THAN (35)
   );
   
   CREATE TABLE user_middle_age (
    CHECK (age >=36 AND age <= 55)
   ) ENGINE = InnoDB
    PARTITION BY RANGE (age) (
        PARTITION p2 VALUES LESS THAN (45),
        PARTITION p3 VALUES LESS THAN (55)
   );

3. Insert data into the subtable:

   INSERT INTO user_youth SELECT * FROM user WHERE age >= 0 AND age <= 35;
   INSERT INTO user_middle_age SELECT * FROM user WHERE age >= 36 AND age <= 55;

4. Query the subtable data:

   SELECT * FROM user_youth WHERE age >= 0 AND age <= 35;
   SELECT * FROM user_middle_age WHERE age >= 36 AND age <= 55;

Through the above code example, we can see how to pass the table Create and operate partitioned tables using partitioning methods. Of course, specific partitioning strategies can be adjusted and optimized according to actual needs.

Summary:
Table partitioning is an effective way to achieve underlying optimization of MySQL. By partitioning data, you can improve query performance, reduce lock conflicts, quickly delete and archive data, and achieve more granular permission control. In practical applications, different partitioning strategies can be selected according to specific needs and combined with other optimization methods to achieve better performance results.

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