🗄️ 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 technologies: 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;
}

3. 🧩 Cache Interaction Patterns

These patterns define how your application, cache, and database interact. Choosing the right one ensures the correct balance between performance, consistency, and freshness.

📦 Cache-Aside (Lazy Loading)

• 🔍 App checks cache first
• 📉 On miss → load from DB → store in cache
• ✔️ Simple and commonly used
• ❗ First request is slower

// Cache-Aside example
public Order getOrder(String orderId) {
    Order order = redisTemplate.opsForValue().get(orderId);
    if (order == null) {
        order = orderRepository.findById(orderId);
        redisTemplate.opsForValue().set(orderId, order);
    }
    return order;
}

📚 Read-Through

• 🧠 Application reads through the cache
• 🔄 Cache loads data from DB if missing
• 📦 Complex but centralizes cache loading logic

✍️ Write-Through

• 📝 Writes go to cache first
• 🔁 Cache synchronously writes to DB
• ✔️ Strong consistency
• 🐌 Write operations are slower

🚀 Write-Back (Write-Behind)

• ⚡ Very fast writes — only write to cache
• 🕒 Cache writes to DB asynchronously
• 📉 Reduced DB load
• ❗ Risk of data loss if cache fails before flushing

🔄 Refresh-Ahead

• ⏳ Cache refreshes items proactively before they expire
• ⚡ Eliminates cache misses for hot data
• 📢 Useful for dashboards, trending items

🤔 Choosing a Strategy

Consider:

• 🏠 Local cache for ultra-low latency and single instance scenarios
• 🌐 Distributed cache for horizontally scaled applications
• 🧩 Cache-Aside for general purpose systems
• ✍️ Write-Through when consistency is critical
• 🚀 Write-Back for high-write workloads
• 🔄 Refresh-Ahead for hot / frequently accessed data
• 🔀 Combining multiple strategies in multi-level caching

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