MySQL high concurrency techniques for data implementation

MySQL is one of the most widely used relational database management systems at present. Its powerful data processing and storage capabilities have become the first choice of many enterprises and developers. With the increasing popularity of Internet applications and the expansion of user scale, MySQL needs to use specific techniques to achieve high concurrent data processing to meet the needs of a large number of users.

This article will introduce how MySQL achieves high concurrency processing from several aspects such as an overview of MySQL, the sources of high concurrency problems, and MySQL's techniques for achieving high concurrency.

1. Overview of MySQL

MySQL is a relational database management system based on SQL (Structured Query Language). It was developed by the Swedish company MySQL AB. MySQL is commonly used in web applications, such as WordPress, Joomla, Drupal, etc. Some commercial software such as Movable Type, Zimbra, etc. also use MySQL. In addition, MySQL is often used in daily embedded devices, such as mobile phones, PDAs, etc.

As a relational database system, MySQL’s basic data structure is a relational table. A table consists of several columns, and each column has its own independent data type. MySQL provides SQL language to operate these tables and perform operations such as additions, deletions, modifications, and queries.

2. Sources of high concurrency problems

With the popularity of Internet applications, many websites and applications face high concurrency data processing problems when using MySQL databases. This situation comes from the following aspects:

1. The data table design is unreasonable.

If the table design of the MySQL database is unreasonable, it will be difficult to satisfy simultaneous access requests for data in high concurrency scenarios. We need to consider how to split the data table into several sub-data tables. These sub-data tables can store data separately while achieving load balancing of access.

2.Mysql cache does not work or is invalid.

MySQL cache exists to reduce the number of interactive operations and improve system performance. But if the cache fails or cannot play a role, you need to consider other solutions, such as using partition tables, vertical splitting and horizontal splitting of data, and other techniques.

3. There are too many concurrent connections.

MySQL's default maximum number of concurrent connections is 100. Under high concurrency conditions, this number may be quickly exhausted, causing the database to become unresponsive. In order to avoid this situation from happening, we need to adjust the parameter settings in a targeted manner and increase the limit on the maximum number of concurrent connections.

4. Multiple queries are executed simultaneously.

When multiple queries are executed at the same time, it will occupy a large amount of MySQL resources, causing other queries to be unable to respond. In order to solve this problem, you can use indexes for optimization and improve query efficiency.

3. MySQL techniques for achieving high concurrency

1. Analyze data tables

When designing the MySQL table structure, you can use the techniques of analyzing the table structure to propose various solutions , find the solution that best meets business needs, thereby optimizing the data table. You can use the techniques of horizontal splitting of data tables and vertical splitting of data tables to split a large table into multiple small tables or split the fields of the original table into different tables so that different requests can target different tables. for a visit.

2. Use indexes for optimization

Indexes are one of the important optimization methods for MySQL. Indexes can help MySQL quickly locate and retrieve specified data, thereby improving the system's response speed. However, it should also be noted that too many indexes will increase the storage space and maintenance costs of the system, so the application should be based on the actual situation.

3. Cache query results

MySQL caching mechanism can cache query results when needed to reduce the number of database queries. Caching query results can be achieved by using caching systems such as Memcached and Redis. However, it should be noted that the implementation of the cache mechanism needs to pay attention to issues such as expiration rules, expiration time, cache updates, etc. Otherwise, the cache will become invalid and even cause dirty data.

4. Separate read and write requests

For the MySQL database, its read operations and write operations will occupy different system resources. Therefore, read requests and write requests can be processed in a separate manner and using different servers. For example, place write requests and read requests in different MySQL servers, or perform read and write partitions on the same MySQL server, etc.

5. Increase the buffer size

MySQL's default cache size is 8M, but in a high-concurrency environment, this value may be quickly exhausted. Therefore, a larger buffer size needs to be set for MySQL. When configuring the MyISAM asynchronous IO mode, you can use the default value of the buffer pool, so that different IO requests from MyISAM will directly enter the buffer pool, thus improving the data access speed.

4. Set a reasonable maximum number of connections

In a high-concurrency environment, it is necessary to avoid too many query requests, which will cause serious consumption of hardware resources. Therefore, the maximum number of connections can be reasonably set based on the hardware performance of MySQL to avoid the avalanche effect.

Summary

The high concurrency processing problem of MySQL is the focus of many enterprises and developers. By using various techniques such as data table splitting, index optimization, caching query results, separating read and write requests, increasing the buffer size, and setting a reasonable maximum number of connections, MySQL's data access efficiency can be effectively improved, hardware resources can be saved, and High concurrency processing of MySQL.

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