How does query caching work in MySQL?

The working principle of MySQL query cache is to store the results of SELECT query, and when the same query is executed again, the cached results are directly returned. 1) Query cache improves database reading performance and finds cached results through hash values. 2) Simple configuration, set query_cache_type and query_cache_size in MySQL configuration file. 3) Use the SQL_NO_CACHE keyword to disable the cache of specific queries. 4) In high-frequency update environments, query cache may cause performance bottlenecks and needs to be optimized for use through monitoring and adjustment of parameters.

introduction

Have you ever wondered how query caching in MySQL works? When we talk about database performance optimization, query cache is undoubtedly one of the powerful tools to improve response speed. Today we will explore in-depth how MySQL query caching works and share some practical experience to help you better understand and utilize this feature. Read this article and you will learn how to configure and use query caches, understand their pros and cons, and master some tips to avoid common pitfalls.

Review of basic knowledge

MySQL query cache is a storage mechanism that saves the results of SELECT queries so that when the same query is executed again, the cached results are directly returned, thereby avoiding repeated query operations. To understand query cache, you need to be familiar with the query execution process in MySQL, including parsing SQL statements, optimizing query plans, and executing queries.

Core concept or function analysis

Definition and function of query cache

The core function of query cache is to improve the read performance of the database. When a SELECT query is executed, MySQL checks whether the cached result of the query already exists. If it exists, MySQL will return the cached data directly without performing the query again. This mechanism is particularly effective in scenarios where high frequency repeated query is performed.

To give a simple example, if your application frequently executes SELECT * FROM users WHERE id = 1 , query cache can significantly reduce the load on the database.

How it works

When a query is executed, MySQL generates a hash value of a query and looks for this hash value in the query cache. If a matching hash is found, MySQL returns cached results. Otherwise, MySQL executes the query and stores the results in the cache for next use.

 -- Suppose we have a simple query SELECT * FROM users WHERE id = 1;

MySQL generates a hash for this query and then checks the cache. If this hash is not present in the cache, MySQL executes the query and stores the results in the cache.

However, the validity of the query cache depends on the stability of the table data. Any update operation to the table (such as INSERT, UPDATE, DELETE) will cause the cache of related queries to be invalidated. This means that in environments with high frequency updates, the effect of query cache may not be obvious.

Example of usage

Basic usage

Configuring query cache is very simple, just set relevant parameters in the MySQL configuration file. For example:

 [mysqld]
query_cache_type = 1
query_cache_size = 16M

This sets the query cache's turn-on status and cache size. Through SHOW VARIABLES LIKE 'query_cache%' command, you can view the current query cache configuration.

Advanced Usage

In some complex scenarios, you may want to disable cache for certain queries. This can be achieved by using the SQL_NO_CACHE keyword in the query:

 SELECT SQL_NO_CACHE * FROM users WHERE id = 1;

This approach is very useful when testing query performance, because it ensures that every query is read new data from the database.

Common Errors and Debugging Tips

A common misconception is that query caches can always improve performance. In environments with high concurrency and frequent updates, query caches may become a performance bottleneck, because each update will cause a large number of cache failures.

When debugging query cache issues, you can use the SHOW STATUS LIKE 'Qcache%' command to view the usage of query cache. For example:

 SHOW STATUS LIKE 'Qcache%';

This returns a series of statistics related to query caches to help you diagnose and optimize cache usage.

Performance optimization and best practices

In practical applications, optimizing query cache requires weighing its performance improvements and possible negative impacts. Here are some suggestions:

• Choose the right cache size : too large cache will waste memory, and too small cache will not be effectively utilized. Find the best balance point by monitoring and adjusting the query_cache_size parameter.
• Monitor cache hit rate : Use statistics such as Qcache_hits and Qcache_inserts to evaluate the effectiveness of query cache. If the hit rate is low, you may need to rethink whether to use query cache.
• Avoid frequently updated tables using query cache : For frequently updated data tables, query cache may do more harm than good. The cache behavior can be controlled by SQL_NO_CACHE by disabling the cache for specific queries, or adjusting query_cache_type parameter.

In my actual project, I once encountered a case where the database table update frequency is too high, the query cache is frequently invalidated, which in turn increases the load of the database. Through analysis and tuning, we finally decided to turn off query cache and improve performance through other means such as index optimization and read-write separation.

In short, MySQL query caching is a powerful tool, but it needs to decide whether to use and how to optimize according to the specific application scenario. I hope this article can help you better understand and apply query cache, thereby improving database performance.

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