Redis Spring Cluster Configuration Process

Redis Spring Configuration Cluster

In distributed systems, Redis is a commonly used in-memory database that can be used to store various data and provide high-performance read and write capabilities.

When we need to improve the availability and fault tolerance of Redis, we can configure the Redis cluster to enable sharded storage of data and node failure recovery.

In this article, we describe how to use the Spring framework to configure a Redis cluster. Spring provides convenient components to interact with Redis clusters, allowing us to simplify configuration and operations.

Preparation

Before we start configuring the Redis cluster, we need to make sure that the Redis and Spring frameworks are installed and a Spring project has been created.

Configuration dependencies

First, we need to add Redis and Spring dependencies to the project.

Add the following to the file:

&lt;dependencies&gt;
    &lt;!-- Redisrely --&gt;
    &lt;dependency&gt;
        &lt;groupId&gt;&lt;/groupId&gt;
        &lt;artifactId&gt;spring-boot-starter-data-redis&lt;/artifactId&gt;
    &lt;/dependency&gt;
    &lt;!-- Springrely --&gt;
    &lt;!-- 其他rely --&gt;
&lt;/dependencies&gt;

After adding these dependencies, the Spring framework will automatically configure Redis-related components.

Configure cluster nodes

Next, we need to specify the node of the Redis cluster in Spring's configuration file.

In the (or) file, add the following configuration:

=redis://localhost:6379,redis://localhost:6380,redis://localhost:6381

In the above configuration, we specify three nodes in the Redis cluster, running on local ports 6379, 6380 and 6381 respectively.

Configure RedisTemplate

Finally, we need to configure RedisTemplate to use Redis cluster in Spring.

Add the following to the Java configuration class:

@Configuration
public class RedisConfig {
    @Value("${}")
    private String clusterNodes;
    @Bean
    public RedisConnectionFactory redisConnectionFactory() {
        RedisClusterConfiguration clusterConfiguration = new RedisClusterConfiguration(((",")));
        return new JedisConnectionFactory(clusterConfiguration);
    }
    @Bean
    public RedisTemplate<String, Object> redisTemplate() {
        RedisTemplate<String, Object> template = new RedisTemplate<>();
        (redisConnectionFactory());
        return template;
    }
}

In the above configuration, we use RedisClusterConfiguration to specify the node of the Redis cluster, and then create the Redis connection factory through the JedisConnectionFactory. Finally, we set up the connection factory in RedisTemplate.

Using Redis Cluster

Now that we have completed the configuration of the Redis cluster, we can use Redis in Spring.

@Service
public class ExampleService {
    @Autowired
    private RedisTemplate&lt;String, Object&gt; redisTemplate;
    public void exampleMethod() {
        //Storage data to Redis        ().set("key", "value");
        // Get data from Redis        Object value = ().get("key");
        (value);
    }
}

In the example above, we inject RedisTemplate through the @Autowired annotation and use it to store and get the data.

For e-commerce websites, Redis is required to cache product information and set expiration time to improve system performance. In this scenario, we can use the Redis cluster to store product information and manage and operate the Redis cluster through the Spring framework.

First, we need to create a product service class that is used to obtain and cache product information:

@Service
public class ProductService {
    @Autowired
    private RedisTemplate&lt;String, Object&gt; redisTemplate;
    
    public Product getProductById(String productId) {
        // Get product information from the Redis cache first        Product product = (Product) ().get(productId);
        if (product == null) {
            // If the Redis cache does not exist, get product information from the database            product = getProductFromDatabase(productId);
            if (product != null) {
                //Storage product information into Redis cache and set expiration time                ().set(productId, product, (30));
            }
        }
        return product;
    }
    
    // Simulate the method of obtaining product information from the database    private Product getProductFromDatabase(String productId) {
        // Actual database operation...        // Return to product information    }
}

In the above example code, we use RedisTemplate to operate the Redis cluster. existgetProductByIdIn the method, first try to obtain product information from the Redis cache. If the cache does not exist, it will be obtained from the database and store the obtained product information in the Redis cache, and set the expiration time to 30 minutes. Next, we can call product services in the controller to obtain product information:

@RestController
public class ProductController {
    @Autowired
    private ProductService productService;
    
    @GetMapping("/products/{id}")
    public ResponseEntity&lt;Product&gt; getProductById(@PathVariable String id) {
        Product product = (id);
        if (product != null) {
            return (product);
        } else {
            return ().build();
        }
    }
    
    // Other controller methods...}

In the above example code, we define an interface for GET requests/products/{id}, by calling goods and servicesgetProductByIdMethod to obtain product information. If the product exists, return 200 and product information; if the product does not exist, return 404. Through such sample code, we can use Redis clusters in actual applications to cache and manage product information, improving system performance and user experience.

An online learning platform requires the use of a recommendation system to recommend suitable courses for users. In this scenario, we can use collaborative filtering algorithms to implement course recommendations, and use the scikit-learn library in Python to train models and make predictions.

First, we need to prepare a user-course rating dataset for model training and recommendation:

import pandas as pd
from  import cosine_similarity
# Read user-course rating datasetratings = pd.read_csv('')
# Build a user-course scoring matrixratings_matrix = ratings.pivot_table(index='user_id', columns='course_id', values='rating')
# Calculate similarity matrix between coursessimilarity_matrix = cosine_similarity(ratings_matrix.fillna(0), dense_output=True)
# Output similarity matrix between coursesprint(similarity_matrix)

In the example code above, we construct a user-course scoring matrix by reading the user-course scoring dataset and calculate the similarity matrix between courses using cosine similarity. The output similarity matrix can be used as the basis for the recommendation system. Next, we can write a recommendation service class to recommend courses for users:

class RecommenderService:
    def __init__(self, ratings_matrix, similarity_matrix):
        self.ratings_matrix = ratings_matrix
        self.similarity_matrix = similarity_matrix
    
    def recommend_courses(self, user_id, top_n=5):
        # Get users' ratings for courses        user_ratings = self.ratings_matrix.loc[user_id]
        
        # Calculate user similarity to other courses        user_similarity = self.similarity_matrix[user_ratings.index]
        
        # Calculate user ratings for recommended courses        user_scores = user_similarity.dot(user_ratings)
        
        # Get the top rated top_n courses        top_courses = user_scores.nlargest(top_n).index
        
        return top_courses

In the above example code, we define a recommended service classRecommenderService, it accepts the scoring matrix and the similarity matrix as parameters.recommend_coursesMethods: Based on the user's rating and the similarity of courses, the user's rating for the recommended courses is calculated, and the top _n courses with the highest rating are returned. Finally, we can call the recommendation service in the controller to recommend courses for users:

from flask import Flask, jsonify
app = Flask(__name__)
@('/users/&lt;int:user_id&gt;/recommendations', methods=['GET'])
def recommend_courses(user_id):
    # Create a recommended service instance    recommender_service = RecommenderService(ratings_matrix, similarity_matrix)
    
    # Call the recommendation service to obtain recommended courses    recommended_courses = recommender_service.recommend_courses(user_id)
    
    # Return to the recommended course list    return jsonify({'recommended_courses': recommended_courses.tolist()})
if __name__ == '__main__':
    ()

In the above example code, we use the Flask framework to create an HTTP interface when the user accesses/users/<user_id>/recommendationsWhen calling the recommendation service to obtain the recommended course and return to the recommended course list in JSON format. Through such sample code, we can implement course recommendation functions in practical applications and provide a personalized learning experience.

Summarize

Through this blog post, we learned how to use the Spring framework to configure a Redis cluster. By configuring dependencies, specifying cluster nodes, configuring RedisTemplate, and using RedisTemplate, we can easily use Redis clusters in Spring projects to improve system availability and fault tolerance.

The above is personal experience. I hope you can give you a reference and I hope you can support me more.