Garbage collection and lock competition optimization of MySQL InnoDB engine: improving performance and concurrency

Garbage collection and lock competition optimization of MySQL InnoDB engine: improving performance and concurrency capabilities

Introduction:
With the development of modern applications, the demand for database performance and concurrency capabilities is also increasing. As a commonly used relational database management system, MySQL's InnoDB engine has become the default storage engine. The biggest feature of the InnoDB engine is that it supports transactions and row-level locks.

However, in a high-concurrency environment, the performance and concurrency capabilities of the InnoDB engine may be affected by garbage collection and lock competition. This article will introduce how to improve the performance and concurrency of the MySQL InnoDB engine by optimizing garbage collection and lock competition.

1. Garbage Collection Optimization
Garbage collection means that after the transaction execution is completed, the InnoDB engine needs to release the data pages that are no longer used so that they can be used by the next transaction. Garbage collection has a great impact on database performance and storage space utilization.

Usually, the InnoDB engine performs garbage collection in two ways: adaptive hash sorting and adaptive hash indexing. Adaptive hash sorting periodically moves data pages that are no longer in use out of the buffer pool and places them on a separate free list. Adaptive hash indexes monitor index usage and reclaim unused index pages when appropriate.

In some cases, garbage collection may cause performance degradation due to the InnoDB engine's internal algorithm not being smart enough. In order to optimize garbage collection, you can improve performance by adjusting the following parameters:

1. innodb_io_capacity: Set the disk IO capacity of the InnoDB engine. You can adjust the value of this parameter according to the actual situation to make full use of the disk. performance.
2. innodb_max_dirty_pages_pct: Set the maximum proportion of dirty pages in the InnoDB engine to reduce the frequency of refreshing dirty pages.
3. innodb_lazy_drop_table: Set whether to enable the lazy drop table function to avoid frequent refresh operations.

The following is an example configuration file for optimizing garbage collection of the InnoDB engine:

[mysqld]
innodb_io_capacity = 200
innodb_max_dirty_pages_pct = 50
innodb_lazy_drop_table = ON

2. Optimization of lock competition
Lock competition means that multiple transactions access the same data object at the same time and try to obtain an exclusive lock on the data object. When multiple transactions compete for the same data object, lock waits and lock conflicts may occur, thereby reducing the concurrency of the system.

In order to optimize lock competition, the following measures can be taken:

1. Use appropriate indexes: By using appropriate indexes, you can reduce lock competition and improve the concurrency of the system. .
2. Reduce the length of the transaction: The longer the length of the transaction, the greater the possibility of lock contention. Therefore, try to split a transaction into multiple shorter transactions to reduce lock contention.
3. Use optimistic locking: Optimistic locking does not lock the data, but checks whether the data has been modified by other transactions when submitting the transaction. If the data has not been modified, the transaction is submitted successfully; if the data is modified, the transaction needs to be re-executed.

The following is an example showing how to use optimistic locking to optimize lock contention:

-- 示例表结构
CREATE TABLE book (
  id INT PRIMARY KEY,
  name VARCHAR(100),
  version INT
);

-- 示例事务
START TRANSACTION;

-- 乐观锁检查
SELECT version INTO @version FROM book WHERE id = 1;

-- 更新数据
UPDATE book SET name = '新书名', version = @version + 1 WHERE id = 1 AND version = @version;

COMMIT;

Conclusion:
The MySQL InnoDB engine can be significantly improved by optimizing garbage collection and lock contention performance and concurrency capabilities. In practical applications, relevant parameters can be adjusted according to the characteristics and needs of the system, and appropriate optimization strategies can be adopted. Only through continuous optimization and improvement can the performance and concurrency capabilities of the database be maximized.

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