Design a Distributed Caching System (Memcached)

To design a Memcached-like system for Google-scale traffic, I first clarify that we need low-latency reads with high availability. We will use a client-side library that implements consistent hashing to map keys to specific servers, eliminating the need for a central lookup service. This ensures even distribution as nodes are added or removed. For data placement, we replicate each key across three distinct nodes to handle single-node failures without data loss. Regarding access patterns, I recommend a cache-aside strategy where the application checks the cache first; if missing, it fetches from the database and updates the cache. This prevents thundering herd issues better than read-through, which adds complexity to the cache layer itself. We must also define an eviction policy, such as LRU, to manage memory limits. Finally, we handle network partitions by setting short TTLs and allowing stale reads if necessary, prioritizing availability over strict consistency, which aligns with Google's engineering values of building resilient, large-scale services.