Ever hit a performance wall in your Spring Boot applications? I recently faced one that made me rethink database interactions. When our user base grew, response times spiked during peak hours. That’s when I turned to distributed caching with Redis. Today, I’ll show you how to implement this powerful combination to transform your application’s speed. Follow along as we optimize performance together.

To start, let’s set up our project. Add these critical dependencies to your pom.xml:

<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-data-redis</artifactId>
</dependency>
<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-cache</artifactId>
</dependency>
<dependency>
    <groupId>org.apache.commons</groupId>
    <artifactId>commons-pool2</artifactId>
</dependency>

Run Redis locally using this Docker Compose snippet:

services:
  redis:
    image: redis:7-alpine
    ports:
      - "6379:6379"
    command: redis-server --appendonly yes

Now, how do we connect Spring Boot to Redis? Here’s the core configuration:

@Configuration
@EnableCaching
public class RedisConfig {
    
    @Bean
    public RedisTemplate<String, Object> redisTemplate() {
        RedisTemplate<String, Object> template = new RedisTemplate<>();
        template.setConnectionFactory(redisConnectionFactory());
        template.setKeySerializer(new StringRedisSerializer());
        template.setValueSerializer(new GenericJackson2JsonRedisSerializer());
        return template;
    }
    
    @Bean
    public CacheManager cacheManager() {
        return RedisCacheManager.builder(redisConnectionFactory())
            .cacheDefaults(RedisCacheConfiguration.defaultCacheConfig()
                .entryTtl(Duration.ofMinutes(10)))
            .build();
    }
}

Add these properties to application.yml:

spring:
  data:
    redis:
      host: localhost
      port: 6379
  cache:
    type: redis

For our data model, consider this User entity:

@Entity
@Data
public class User {
    @Id
    @GeneratedValue(strategy = GenerationType.IDENTITY)
    private Long id;
    private String email;
    private String firstName;
    private String lastName;
}

Now the exciting part: implementing caching. Ever wondered how much faster your queries could run? Use Spring’s @Cacheable annotation:

@Repository
public interface UserRepository extends JpaRepository<User, Long> {
    
    @Cacheable(value = "users", key = "#id")
    Optional<User> findById(Long id);
}

But what happens when data changes? We need cache invalidation:

@Service
public class UserService {
    
    @CacheEvict(value = "users", key = "#user.id")
    public User updateUser(User user) {
        // Database update logic
    }
}

For advanced scenarios, let’s implement patterns. The cache-aside pattern looks like this:

public User getUser(Long id) {
    User user = cache.get(id);
    if (user == null) {
        user = database.load(id);
        cache.put(id, user);
    }
    return user;
}

Security is crucial for production. Always enable Redis TLS and authentication:

spring:
  data:
    redis:
      password: your-strong-password
      ssl: true

Monitoring cache performance? Try these Redis commands:

redis-cli info stats | grep keyspace
redis-cli --latency

When scaling horizontally, Redis Cluster handles sharding automatically. Add this configuration:

@Bean
public RedisConnectionFactory redisConnectionFactory() {
    RedisClusterConfiguration config = new RedisClusterConfiguration();
    config.addClusterNode(new RedisNode("redis-node1", 6379));
    config.addClusterNode(new RedisNode("redis-node2", 6380));
    return new LettuceConnectionFactory(config);
}

Notice how cache misses affect latency? That’s why choosing the right eviction policy matters. Redis supports LRU, LFU, and time-based expiration. Test different approaches for your workload.

I’ve seen applications reduce database load by 70% using these techniques. The key is consistent cache invalidation and monitoring. What performance gains could you achieve?

Implementing distributed caching transformed our application from sluggish to responsive. Give these methods a try in your next project. If you found this helpful, share it with your team or leave a comment about your caching experiences. What performance challenges are you facing? Let’s discuss solutions together.