27-Apr-2025

Caching is a crucial technique for improving performance in any application, especially in distributed systems. Distributed caching takes this a step further by distributing the cache across multiple nodes. This approach offers several advantages, including increased scalability, improved fault tolerance, and reduced latency.

Imagine a scenario where multiple application servers are trying to access frequently used data. If they all rely on a single database, it becomes a bottleneck, slowing down the entire system. A distributed cache solves this by storing copies of the data closer to the application servers.

How it Works:

1. Data Partitioning: The cached data is distributed across multiple cache servers. Various techniques like consistent hashing can determine which server holds a specific piece of data.

2. Cache Invalidation: When data changes in the primary data store (e.g., a database), the cache needs to be updated to reflect these changes. Common strategies include write-through caching (update cache and database simultaneously) and write-back caching (update cache first and database later).

3. Cache Eviction: Cache servers have limited memory. When the cache becomes full, algorithms like Least Recently Used (LRU) or Least Frequently Used (LFU) decide which entries to remove to make space for new ones.

4. Client Libraries: Applications interact with the distributed cache through client libraries. These libraries handle tasks like connecting to cache servers, retrieving and storing data, and managing cache invalidation.

Example using Redis (pseudo-code with JavaScript-like syntax for illustration):

// Assuming a Redis client library is available

// Connect to the Redis cluster
const redisClient = connectToRedisCluster(['server1:6379', 'server2:6379']);


// Set a key-value pair in the cache
await redisClient.set('product:123', JSON.stringify({ name: 'Awesome Gadget', price: 99 }));


// Retrieve the value from the cache
const cachedProduct = await redisClient.get('product:123');

if (cachedProduct) {
  // Cache hit! Use the cached data
  const product = JSON.parse(cachedProduct);
  console.log(product.name); // Output: Awesome Gadget
} else {
  // Cache miss. Fetch data from the database, then store it in the cache
  const product = await fetchProductFromDatabase(123); 
  await redisClient.set('product:123', JSON.stringify(product));
  console.log(product.name);
}


// Invalidate the cache when data changes
await updateProductInDatabase(123, { name: 'Even Awesomer Gadget' });
await redisClient.del('product:123'); // Remove the old cached value

Benefits of Distributed Caching:

• Scalability: Handles increasing load by distributing it across multiple cache servers.
• Fault Tolerance: If one cache server fails, others can continue serving requests.
• Reduced Latency: Data retrieval is faster as data is located closer to the application servers.
• Improved Database Performance: Reduced load on the database, preventing bottlenecks.

Considerations:

• Cache Consistency: Ensuring data consistency across all cache servers can be challenging.
• Network Latency: Communication between application servers and cache servers introduces some network overhead.
• Cache Invalidation Strategy: Choosing the right invalidation strategy is important for maintaining data integrity.

Distributed caching is a powerful technique for improving performance and scalability in distributed systems. By understanding its principles and considering the various trade-offs, developers can leverage distributed caching to create highly performant and resilient applications.