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Sharing optimization methods for developing mutual attention functions in PHP

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2023-09-11 14:39:221172browse

Sharing optimization methods for developing mutual attention functions in PHP

Sharing optimization methods for developing mutual follow function in PHP

The mutual follow function is an important part of modern social media platforms. By following each other, users can track and follow the activities of other users and build a social network. However, as the number of users increases, the follow each other feature also faces some performance challenges. In this article, I will share some methods to optimize the mutual follow function to improve the performance and user experience of the system.

  1. Use cache

In the mutual follow function, the user's follow relationship is stored in the database. A large number of database queries and write operations can reduce system performance. In order to solve this problem, cache can be used to store the user's attention relationship. When a user follows or unfollows another user, the following relationship is saved to the cache, and the cached data is written to the database regularly. This can reduce the number of database operations and improve system response speed.

  1. Asynchronous processing

In the mutual follow function, when a user follows another user, the system usually sends a notification to the followed user. If notifications are sent synchronously, users may experience delays when following. To avoid this problem, notification sending can be handled asynchronously. When the user performs a follow operation, the notification request is placed in the message queue, and the background task processes and sends the notification. This allows for faster response to user operations.

  1. Data fragmentation

In the mutual follow function, the user's follow relationship is stored in the database. When the number of users increases, a single database may not be able to carry more user relationship data. In order to solve this problem, you can consider sharding the user relationship data. User relationship data can be stored in different databases based on user ID or other sharding rules. This can improve the read and write performance of the database and reduce the load pressure on a single database.

  1. Batch operations

In the mutual follow function, users may perform batch follow or unfollow operations. If a separate database query and write is performed for each operation, the system overhead will be increased. To optimize performance, consider batching follow and unfollow operations. Put users' attention requests into a queue and process these requests regularly to reduce the number of database operations and improve system performance.

  1. Database Index Optimization

In the mutual follow function, user follow relationships are frequently queried data. In order to improve query performance, you can add appropriate indexes to the interest relationship tables in the database. According to the query requirements, you can choose to create a unique index, a combined index or a full-text index. The use of indexes can speed up queries and improve system performance.

  1. Regular data cleaning

In the mutual follow function, as users use it, the follow relationship data may become huge. In order to maintain system performance, regular cleanup of inactive interest relationship data is necessary. You can set a time threshold, and when users do not follow each other for a long time, their following relationship data will be deleted from the database. This can reduce unnecessary data storage and query overhead and improve system performance.

By using the above optimization methods, the performance and user experience of the mutual follow function can be improved. These methods are helpful whether for the optimization of existing systems or the design of new systems. The mutual following function is crucial to the success of social media platforms, so optimizing the mutual following function is also a very important part. I hope that the sharing of this article will inspire PHP developers to optimize the mutual follow function.

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