Explain the role of caching in system design. What are the common caching strategies, and where would you place caches in an architecture?

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Caching stores frequently accessed data in a faster storage layer so that future requests can be served more quickly. It is one of the most effective techniques for improving system performance, reducing latency, and lowering the load on backend databases and services.

Caches can be placed at multiple layers of an architecture. At the edge, a Content Delivery Network caches static assets like images, CSS, and JavaScript at locations geographically close to users, reducing latency dramatically. At the application layer, an in-memory cache like Redis or Memcached stores computed results, database query results, or session data. At the database layer, query result caching and buffer pools reduce disk I/O. Even within the client, browser caching and local storage reduce network requests entirely.

The main caching strategies define how data enters and leaves the cache. Cache-aside, also called lazy loading, is the most common pattern. The application checks the cache first. On a cache miss, it reads from the database, stores the result in the cache, and returns it. This approach loads data only when needed but means the first request for each item is always slow. Write-through caching writes data to the cache and the database simultaneously. This ensures the cache is always up to date but adds latency to write operations. Write-behind caching writes to the cache immediately and asynchronously updates the database later. This provides fast write performance but risks data loss if the cache fails before the database is updated.

Cache invalidation is the hardest part of caching. When the underlying data changes, the cache must be updated or cleared. Time-based expiration using TTL values is the simplest approach: set data to expire after a period, accepting that it might be stale within that window. Event-based invalidation actively removes or updates cached data when the source changes, providing stronger consistency but adding complexity.

When designing caching for a system, consider the read-to-write ratio. Caching benefits read-heavy workloads most. If a system writes frequently and reads infrequently, caching adds complexity without proportional benefit. Also consider data sensitivity: cached data must be protected with the same security controls as the source, and personally identifiable information in caches needs careful handling for compliance.