Caching Strategies

A cache is a high-speed data storage layer that stores a subset of data, typically transient in nature, so that future requests for that data are served up faster than is possible by accessing the data's primary storage location. Caching allows you to efficiently reuse previously retrieved or computed data.

The data in a cache is generally stored in fast-access hardware such as RAM and may be used in conjunction with a software component. Caching is a fundamental technique for improving the performance and scalability of any system.

Why Use a Cache?

1. Reduce Latency: Reading data from an in-memory cache is extremely fast compared to reading from a database (which involves disk I/O) or a remote service (which involves network latency). This significantly improves the user experience.
2. Reduce Load on Backend Systems: By serving a significant portion of requests from the cache, you can dramatically reduce the load on your primary database and application servers. This can save costs and prevent your backend from being overwhelmed during traffic spikes.
3. Increase Throughput: A system can handle a much higher number of requests per second if a large percentage of them can be served from a fast, in-memory cache.

Where to Cache: Layers of Caching

Caching is not a single component; it's a strategy that can be applied at multiple layers of your architecture.

1. Client-Side Caching (e.g., Browser Cache)

The cache is located on the client itself, such as in a web browser or a mobile app.

• How it works: The server includes cache control headers (e.g., Cache-Control, Expires) in its HTTP responses. The browser then stores these assets (like images, CSS, JavaScript files) locally. When the user revisits the page, the browser can load these assets from its local cache instead of re-downloading them.
• Pros: The fastest possible cache, as it eliminates network latency entirely.
• Cons: You have limited control over it, and it's only effective for a single user.

2. Content Delivery Network (CDN)

A CDN is a geographically distributed network of proxy servers that cache static and dynamic content close to the end-users.

• How it works: When a user requests a piece of content, the request is routed to the nearest CDN edge location. If the content is in the cache at that location, it's served directly. If not, the CDN retrieves it from your origin server and caches it for future requests.
• Pros: Dramatically reduces latency for users around the world. Offloads a significant amount of traffic from your origin servers.
• Cons: Primarily for publicly accessible, non-personalized content. Can be expensive.

3. Server-Side Caching

This is a cache that you manage and deploy within your own infrastructure.

a) In-Process Cache

The cache lives within the same process as your application server.

• How it works: You use a library (like Guava Cache in Java or a simple dictionary/map in Python) to store frequently accessed data in the application's memory.
• Pros: Extremely fast, as there is no network overhead.
• Cons:
  • The cache is local to each application server. This can lead to inconsistencies and redundant data storage if you have multiple servers.
  • The cache size is limited by the server's memory.
  • When the application restarts, the cache is lost.

b) Distributed Cache (e.g., Redis, Memcached)

The cache is a separate service that runs on its own cluster of servers. Your application servers connect to this distributed cache over the network.

• How it works: You deploy a dedicated caching cluster using a technology like Redis or Memcached. All your application servers talk to this central cache.
• Pros:
  • Centralized: All servers share the same cache, ensuring data consistency.
  • Scalable: You can scale the cache cluster independently of your application servers.
  • Durable: The cache can survive application restarts.
• Cons:
  • Higher Latency: There is network overhead involved in accessing the cache.
  • More Complex: It's another component in your system that you have to deploy, manage, and monitor.

Common Caching Strategies (Cache-Aside)

The most common caching strategy is called Cache-Aside.

How it works:

1. Read Operation:
   • The application first checks if the data is in the cache.
   • Cache Hit: If the data is in the cache, it's returned to the application.
   • Cache Miss: If the data is not in the cache, the application reads the data from the database, stores a copy in the cache, and then returns it.
2. Write Operation:
   • The application writes the data directly to the database.
   • Then, it invalidates (deletes) the corresponding entry in the cache.

Why invalidate instead of update on write? Updating the cache on every write can be inefficient if the data is written frequently but read infrequently. Invalidating the cache is a simpler and often more performant approach. The cache will be populated with the new data the next time a read operation results in a cache miss. This pattern is called "write-around".

Cache Eviction Policies

A cache has a limited size. An eviction policy is the algorithm used to decide which items to remove from the cache when it's full.

• LRU (Least Recently Used): This is the most common policy. It removes the item that has not been accessed for the longest time.
• LFU (Least Frequently Used): Removes the item that has been accessed the fewest times.
• FIFO (First-In, First-Out): Removes the item that has been in the cache the longest, regardless of how often it was accessed.

In a system design interview, caching is almost always part of the solution. Be prepared to discuss where you would add a cache, what data you would store in it, and what trade-offs you are making (e.g., choosing a distributed cache for consistency over an in-process cache for speed).