🗄️ Caching Strategies in Distributed Systems

Welcome back to The Code Hut Distributed Systems series! In this post, we’ll explore caching strategies that improve performance and reduce load on distributed systems.

Why Caching Matters

Caching reduces latency, avoids repeated computations, and decreases database load. Choosing the right caching strategy is crucial for performance and consistency.

1. Local Caching

Local caches store data in the memory of the application instance:

• Very fast, low latency
• Not shared across instances
• Example libraries: Guava Cache, Caffeine

// Using Caffeine for local caching
Cache orderCache = Caffeine.newBuilder()
    .expireAfterWrite(10, TimeUnit.MINUTES)
    .maximumSize(1000)
    .build();

Order order = orderCache.get(orderId, id -> orderService.fetchOrder(id));

2. Distributed Caching

Distributed caches are shared across multiple application instances:

• Data is consistent across nodes
• Supports scaling horizontally
• Example libraries: Redis, Hazelcast, Ehcache (distributed mode)

// Using Redis with Spring Boot
@Autowired
private RedisTemplate redisTemplate;

public Order getOrder(String orderId) {
    Order order = redisTemplate.opsForValue().get(orderId);
    if (order == null) {
        order = orderService.fetchOrder(orderId);
        redisTemplate.opsForValue().set(orderId, order, 10, TimeUnit.MINUTES);
    }
    return order;
}

Choosing a Strategy

Consider:

• Local cache for ultra-low latency and single instance scenarios
• Distributed cache for horizontally scaled applications
• Combining both in a multi-level caching approach

Next in the Series

In the next post, we’ll explore Observability in distributed systems, including logging, metrics, and distributed tracing.

Label for this post: Distributed Systems