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In modern distributed systems, applications often run across multiple instances. Without a shared cache, each instance repeatedly queries the database, creating unnecessary load. Redis solves this problem by acting as a distributed caching layer shared by all services.
This article explains how to design a high-performance distributed caching strategy using Redis and Spring Boot.

1. What Is Distributed Caching

Distributed caching means multiple application instances share the same cache system.
Architecture example: 
Users
   ↓
Load Balancer
   ↓
Spring Boot Instances
   ↓
Redis Distributed Cache
   ↓
Database
Benefits:
  • Faster responses
  • Reduced database load
  • Shared cache across services
  • Improved scalability

2. Cache-Aside Pattern

The cache-aside pattern is the most common distributed caching strategy.
Workflow:
  1. Application checks Redis.
  2. If data exists → return cached value.
  3. If not → query database.
  4. Store result in Redis.
Example implementation: 
@Cacheable(value = "products", key = "#id")
public Product getProduct(Long id) {
    return productRepository.findById(id).orElse(null);
}
This ensures frequently requested data stays in Redis.

3. Write-Through Caching

In write-through caching, updates are written to both the database and the cache.
Example: 
@CachePut(value = "products", key = "#product.id")
public Product updateProduct(Product product) {
    return productRepository.save(product);
}
Benefits:
  • Cache always stays updated
  • Reduces stale data problems

4. Cache Invalidation

When data changes, cached entries must be removed.
Example: 
@CacheEvict(value = "products", key = "#id")
public void deleteProduct(Long id) {
    productRepository.deleteById(id);
}
Without proper invalidation, the application may return outdated data.

5. Cache Expiration (TTL)

TTL automatically removes old cache entries.
Example Redis configuration: 
@Bean
public RedisCacheConfiguration cacheConfiguration() {
    return RedisCacheConfiguration.defaultCacheConfig()
            .entryTtl(Duration.ofMinutes(10));
}
Benefits:
  • Prevents stale data
  • Reduces memory usage
  • Keeps cache fresh

6. Avoiding Cache Stampede

A cache stampede happens when many requests hit the database simultaneously after cache expiration.
Solutions:
Randomized TTL 
long ttl = 600 + new Random().nextInt(120);
Distributed locking
Redis locks ensure only one request rebuilds the cache.

7. Redis Cluster for Scaling

For large applications, Redis can run in cluster mode.
Example cluster architecture: 
Redis Cluster
 ├── Node 1
 ├── Node 2
 ├── Node 3
Benefits:
  • Horizontal scaling
  • High availability
  • Automatic data sharding
Spring Boot can connect to Redis clusters using standard Redis configuration.

8. Monitoring Cache Performance

Important metrics to monitor:
  • Cache hit rate
  • Cache miss rate
  • Memory usage
  • Evicted keys
Enable Spring Boot Actuator: 
management.endpoints.web.exposure.include=health,metrics
These metrics help detect cache inefficiencies early.
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