I’ve been thinking about distributed caching a lot lately. Why? Because modern applications demand speed and scalability, and hitting the database for every request just doesn’t cut it anymore. When our user base grew beyond 500,000, I saw firsthand how database bottlenecks can cripple performance. That’s when Redis with Spring Boot became our go-to solution. Let me show you how we implemented it.

First, why Redis? It’s not just another cache. Redis handles data structures in memory with optional persistence, supports clustering, and offers sub-millisecond response times. When integrated with Spring Boot’s caching abstraction, it becomes a powerhouse.

Here’s a fundamental question: How do you prevent cache misses from overwhelming your database? The cache-aside pattern solves this. When data is requested, we first check Redis. If missing, we fetch from the database and populate the cache. Here’s how we implemented it for user data:

@Cacheable(value = "users", key = "#id", unless = "#result == null")
public Optional<User> findById(Long id) {
    log.info("Fetching user {} from database", id);
    return userRepository.findById(id);
}

Notice the @Cacheable annotation? It automatically handles the “check cache first” logic. The unless parameter prevents caching null results. But what happens when data changes? We need cache invalidation:

@CacheEvict(value = "users", key = "#user.id")
public User updateUser(User user) {
    return userRepository.save(user);
}

Now, let’s talk about write-through caching. Why use it? When data consistency is critical, this pattern writes to both cache and database simultaneously. Imagine an e-commerce inventory system - you can’t risk overselling. Here’s our approach:

public void saveProductWithWriteThrough(Product product) {
    // Write to DB
    productRepository.save(product);  
    
    // Write to cache
    redisTemplate.opsForValue().set(
        "product:" + product.getId(), 
        product,
        2, TimeUnit.HOURS
    );
}

But caching isn’t just about patterns. Serialization matters. Have you ever seen java.io.NotSerializableException? We use Jackson for JSON serialization to avoid this:

@Bean
public RedisTemplate<String, Object> redisTemplate() {
    RedisTemplate<String, Object> template = new RedisTemplate<>();
    template.setConnectionFactory(jedisConnectionFactory());
    
    Jackson2JsonRedisSerializer<Object> serializer = 
        new Jackson2JsonRedisSerializer<>(Object.class);
    
    ObjectMapper mapper = new ObjectMapper();
    mapper.configure(DeserializationFeature.FAIL_ON_UNKNOWN_PROPERTIES, false);
    serializer.setObjectMapper(mapper);
    
    template.setValueSerializer(serializer);
    return template;
}

TTL management is another critical aspect. Different data has different lifespans. User sessions? Maybe 30 minutes. Product listings? 24 hours. We configure this in our cache manager:

@Bean
public CacheManager cacheManager() {
    Map<String, RedisCacheConfiguration> configs = Map.of(
        "users", RedisCacheConfiguration.defaultCacheConfig()
            .entryTtl(Duration.ofMinutes(30)),
        "products", RedisCacheConfiguration.defaultCacheConfig()
            .entryTtl(Duration.ofHours(24))
    );
    
    return RedisCacheManager.builder(redisConnectionFactory())
        .withInitialCacheConfigurations(configs)
        .build();
}

For high availability, Redis Sentinel is our safety net. It monitors master and replica instances, automatically handling failover. Configuration in application.yaml:

spring:
  redis:
    sentinel:
      master: redis-master
      nodes: sentinel1:26379,sentinel2:26379,sentinel3:26379

Monitoring is non-negotiable. We use Spring Boot Actuator with Micrometer to track hit rates and latency:

management:
  endpoints:
    web:
      exposure:
        include: health,metrics,redis
  metrics:
    tags:
      application: ${spring.application.name}

Cache warming during startup prevents cold starts. We load frequently accessed data on application init:

@PostConstruct
public void warmUpCache() {
    List<User> activeUsers = userRepository.findByActiveTrue();
    activeUsers.forEach(user -> 
        redisTemplate.opsForValue().set(
            "user:" + user.getId(), 
            user,
            30, TimeUnit.MINUTES
        )
    );
}

What about cache penetration? Malicious requests for non-existent keys can overwhelm your database. We solved this by caching null values with short TTLs:

@Cacheable(value = "users", key = "#id", unless = "#result != null")
public Optional<User> findById(Long id) {
    User user = userRepository.findById(id).orElse(null);
    if(user == null) {
        redisTemplate.opsForValue().set("user:null:"+id, "", 2, TimeUnit.MINUTES);
    }
    return Optional.ofNullable(user);
}

When we implemented these patterns, our API response times dropped from 450ms to 9ms for cached data. Database load decreased by 70%. The results speak for themselves.

This journey with Redis and Spring Boot transformed how we handle scale. I’d love to hear about your caching experiences! What challenges have you faced? Share your thoughts in the comments below, and if this helped you, please like and share with others who might benefit.