Home Database Redis Redis application example sharing: article like function design

Redis application example sharing: article like function design

Jun 20, 2023 am 09:30 AM
redis design like

In the Internet age, article reading and sharing have become an essential part of people's daily lives. However, for the likes and collection functions of articles, user experience is very critical. As a high-performance key-value storage database, Redis has great advantages in realizing article likes and collection functions. This article will share an article like function design based on Redis.

  1. Functional design

In the design process of the article like function, many factors need to be taken into consideration. First, the like interface needs to be exposed to users. Users can like or cancel likes at any time, and the data should be updated in real time.

Secondly, in the case of high concurrency, it is necessary to ensure system stability and avoid data inconsistency.

Finally, because the data of the like function is very important, it needs to be persisted to ensure data reliability.

In this case, we will use Redis to store the like data of the article, and at the same time set a certain caching strategy in the application to reduce the access pressure on the database.

  1. Data structure design

In Redis, we can use the Hash data structure to store the data of article likes, as shown below:

hash (article:1, vote:user1, 1)
hash(article:1, vote:user2, 1)
hash(article:1, vote:user3, 0)

The above structural design Among them, "article:1" is the Key of the Hash, "vote:user1", "vote:user2", and "vote:user3" are the Fields of the Hash respectively. "1" and "0" represent the like and cancel points respectively. Thumbs up.

  1. Function Implementation

Using the Spring Boot framework to build a Java application, in which RedisTemplate is used to implement access and operation of the Redis data structure, which is mainly divided into likes and cancels Like, get the total number of likes, get likes and other functions.

1) Implementation of the like function

The implementation of the like function mainly requires the following operations:

1.1) Determine whether the user has liked the article.

1.2) If the user does not like it, update the number of likes and store the like status in Redis.

2) Implementation of canceling the like function

The implementation of canceling the like function mainly requires the following operations:

2.1) Determine whether the user has liked the article.

2.2) If the user has liked it, update the number of likes and store the like status in Redis.

3) Implementation of the function of obtaining the total number of likes

The implementation of obtaining the total number of likes mainly requires the use of Redis fuzzy query to traverse all article Hash data in Redis and count the number of likes.

4) Implementation of the function of getting likes or not

The implementation of getting likes or not mainly requires querying Hash data through Redis to determine whether there is a like record for the user.

  1. Cache processing

In order to reduce the access pressure on the system and reduce access to the database, we need to cache the data in Redis. There are two commonly used caching strategies:

1) First read the data from the cache. If there is no data in the cache, obtain the data from the database and store the data in Redis.

2) When a modification operation occurs, the data in Redis is updated first, and then the data is synchronized to the database to ensure data consistency.

Here, we use the first caching strategy to reduce the access pressure on the database and improve system performance.

  1. Performance Optimization

In the actual production environment, certain configurations of Redis are required to optimize performance.

5.1) Set the maximum memory limit

In Redis, the maximum memory usage should be set to avoid memory overflow problems and service exceptions.

5.2) Persistent data

In order to ensure data persistence, we can use AOF or RDB to write data to disk.

5.3) Using Redis Cluster

When the number of visits is high, we can use Redis Cluster to distribute the load and improve the concurrency capability of the system.

  1. Summary

In the design of the article like function, Redis, as a high-performance key-value storage database, has the advantages of flexibility and efficiency, and improves system performance and stability. Sexuality plays an important role. Through the sharing of this article, I believe that everyone will have a deeper understanding and understanding of the application of Redis in application development.

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