Use MySQL MVCC to optimize database design and improve application performance

Use MySQL MVCC to optimize database design and improve application performance

Abstract: In today's Internet applications, database performance is crucial to the stable operation and response time of the system . As one of the most commonly used relational database management systems, MySQL uses multi-version concurrency control (MVCC) to improve concurrency performance and data consistency when designing the database. This article will introduce the basic principles of MVCC and its implementation in MySQL, and give some examples of optimizing database design.

1. Basic Principles of MVCC
   Multi-version Concurrency Control (MVCC) is a technology used to implement isolation levels of transactions. It achieves concurrency control by saving snapshots between multiple transactions in the database. Each transaction can see a separate snapshot that contains all data that has been committed before the transaction started.

The basic principle of MVCC is to create and manage snapshots by marking each data row as a version chain. When a transaction starts, it creates a new snapshot and associates the current timestamp with the transaction. The transaction can then read and modify the data in the snapshot without interference from other concurrent transactions.

2. MVCC implementation in MySQL
   MySQL uses two important record fields to implement MVCC: rollback pointer (rollpointer) and version number (version). The rollback pointer points to the originally inserted and modified data row, and instant recovery is achieved by recording undo log. The version number is an incrementing counter. Whenever a new transaction modifies data, the version number will increase.

During a read operation, MySQL will determine visibility based on the timestamp of the read transaction. If the version number of the data is greater than or equal to the timestamp of the current transaction, then the data is visible. Otherwise, you need to obtain the old version of data through undo log.

During a write operation, MySQL will create a new version of the data row, write the new version of data to the new version chain, and move the old version of data to the undo log. The advantage of this is that in a concurrent situation, different transactions can read the old version and the new version of the data at the same time without conflict.

3. Instances of optimizing database design
   (1) Use appropriate data types
   Using appropriate data types can reduce storage space usage and improve the efficiency of data reading and writing. Try to choose the simplest, most compact data type and avoid using excessively long characters or unnecessary data types.

For example, if a field only needs to store Boolean values, you can use TINYINT(1) instead of the BOOL type, because TINYINT(1) only takes up 1 byte of storage space.

(2) Reasonable use of indexes
Indexes are an important way to improve query efficiency, but too many or unreasonable indexes will reduce the performance of write operations. When designing an index, you need to select appropriate fields and index types based on actual query requirements and data volume.

For example, for fields that are frequently range-queried, you can consider using multi-column indexes or covering indexes to improve query efficiency.

(3) Batch operation and transaction control
Batch operation can reduce the number of IO operations and greatly improve the efficiency of data processing. For a large number of insert, update and delete operations, you can use batch operation statements (such as INSERT INTO ... VALUES ...) to process multiple pieces of data at one time.

At the same time, reasonable use of transactions can ensure data consistency and integrity. In high-concurrency scenarios, using appropriate transaction isolation levels and reasonable transaction control can avoid data competition and conflicts.

(4) Partitioning and sub-tables
Partitioning and sub-tables are effective means to solve performance problems of large tables. By dividing a large table into multiple small tables, data can be stored on different disks, reducing the amount of data in a single table and improving query efficiency.

For example, for the scenario of querying by time range, the data of one year can be divided into different partition tables by month, and each partition table only contains the data of that month.

Code example:

-- 创建表
CREATE TABLE `user` (
  `id` INT(11) NOT NULL AUTO_INCREMENT,
  `username` VARCHAR(50) NOT NULL,
  `password` VARCHAR(50) NOT NULL,
  `email` VARCHAR(50) NOT NULL,
  PRIMARY KEY (`id`),
  INDEX `idx_username` (`username`),
  INDEX `idx_email` (`email`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_unicode_ci;

-- 插入数据
INSERT INTO `user` (`username`, `password`, `email`) VALUES
('user1', 'password1', 'user1@example.com'),
('user2', 'password2', 'user2@example.com'),
('user3', 'password3', 'user3@example.com');


-- 查询数据
SELECT * FROM `user` WHERE `username` = 'user1';

-- 更新数据
UPDATE `user` SET `password` = 'newpassword' WHERE `username` = 'user1';

-- 删除数据
DELETE FROM `user` WHERE `username` = 'user1';

Conclusion: By using MySQL MVCC, we can optimize the database design and improve application performance. Using appropriate data types, reasonable use of indexes, batch operations and transaction control, partitioning and table subdivision can effectively reduce IO operations, improve query efficiency and reduce concurrency conflicts, thus improving the overall performance and stability of the system.

Reference materials:

1. MySQL 5.7 Reference Manual - 14.2.5 InnoDB row format and MVCC detailed introduction (https://dev.mysql.com/doc/refman/5.7/ en/innodb-row-format-and-mvcc.html)
2. High Performance MySQL, 3rd Edition (https://www.oreilly.com/library/view/high-performance-mysql/9781449332471/)

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