MySQL and PostgreSQL: Best practices for handling large-scale concurrent requests

MySQL and PostgreSQL: Best Practices for Handling Large-Scale Concurrent Requests

Abstract: In today's Internet era, the performance and stability of the database system are crucial to the processing of large-scale concurrent requests. This article will explore the best practices for handling large-scale concurrent requests, two popular relational databases, MySQL and PostgreSQL, and provide some code examples to help readers better understand.

Introduction:
With the continuous development and growth of the Internet industry, the database has become the core component that supports large-scale concurrent requests. MySQL and PostgreSQL are two widely used relational databases. How to optimize their configuration and use has become a topic of common concern to database administrators and developers.

1. Concurrency control
   Concurrency control is one of the core concepts in the database system. Its purpose is to ensure that multiple concurrent operations can correctly read and modify the database. MySQL and PostgreSQL have different implementation methods for concurrency control, which are introduced below.

MySQL’s concurrency control is mainly implemented through the lock mechanism. Control concurrent operations by using different lock granularities (table locks, row-level locks, and page-level locks). For large-scale concurrent requests, it is recommended to use row-level locks, which can minimize the probability of lock conflicts. In MySQL, you can use the following code example to set row-level locks:

-- 开启事务
START TRANSACTION;
-- 设置行级锁
SELECT * FROM table_name WHERE id = 1 FOR UPDATE;
-- 执行查询、插入、更新、删除等操作
-- 提交事务
COMMIT;

PostgreSQL uses multi-version concurrency control (MVCC). MVCC implements concurrency control by assigning a unique transaction ID to each transaction and adding a version number to each data row. In PostgreSQL, a transaction can see a snapshot of the data rows that were committed before it started, thus achieving data consistency and concurrency. Compared with MySQL's lock mechanism, the advantage of MVCC is that it reduces lock conflicts and improves concurrent processing capabilities. The following is an example of using MVCC to implement concurrency control:

-- 开启事务
BEGIN;
-- 设置事务隔离级别为可重复读
SET TRANSACTION ISOLATION LEVEL REPEATABLE READ;
-- 执行查询、插入、更新、删除等操作
-- 提交事务
COMMIT;

2. Query Optimization
   For database systems with large-scale concurrent requests, query optimization is an indispensable part. Both MySQL and PostgreSQL provide a range of query optimization tools and techniques. Here are some common optimization suggestions:

• Use appropriate indexes: Creating appropriate indexes on the table can significantly improve Query performance. Both MySQL and PostgreSQL support different types of indexes such as B-trees, hash and full-text indexes. You can select the appropriate index type according to specific business scenarios and optimize it by viewing the query plan using the EXPLAIN command.
• Use appropriate data types: Choosing appropriate data types can reduce storage space and improve query performance. Try to avoid using too large data types, such as using INT instead of BIGINT, avoiding unnecessary string lengths, etc.
• Avoid full table scan: Avoiding full table scan is the key to improving query performance. Full table scans can be avoided by properly designing the data table structure, using indexes, and optimizing query statements.

3. Configuration Optimization
   Database configuration is crucial to handling large-scale concurrent requests. The following are some configuration optimization suggestions for MySQL and PostgreSQL:

• Adjust the memory buffer size: Properly adjusting the memory buffer size of the database can improve query response speed. For example, the innodb_buffer_pool_size parameter can be adjusted in MySQL, while the shared_buffers parameter can be adjusted in PostgreSQL.
• Limit on the number of concurrent connections: In order to avoid excessive occupation of server resources, the number of concurrent connections should be reasonably limited. In MySQL, the number of concurrent connections can be controlled by modifying the max_connections parameter, and in PostgreSQL, this can be achieved by modifying the max_connections parameter and the max_worker_processes parameter.
• Log settings: When processing large-scale concurrent requests, turning on the log function appropriately can help troubleshoot problems and optimize performance. Slow query logs, error logs, transaction logs, etc. can be enabled according to specific needs.
• Regular maintenance and optimization: Regular maintenance and optimization of the database can maintain the normal operation and stable performance of the database. For example, regularly clean useless data, reorganize tables and indexes, collect statistical information, etc.

Conclusion:
MySQL and PostgreSQL are two powerful relational databases. When processing large-scale concurrent requests, performance and stability can be improved through reasonable configuration and optimization. This article introduces best practices in concurrency control, query optimization, and configuration optimization, and provides some code examples to help readers better understand. In practical applications, readers should choose appropriate optimization strategies to improve database performance and stability based on specific needs and scenarios.

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