The Traditional Hashing Problem That Breaks at Scale

Most engineers intuitively reach for modular hashing when distributing data across multiple servers: hash(key) % server_count. This approach works beautifully for static clusters, but it harbors a catastrophic flaw that becomes apparent only at scale.

Consider what happens when your traffic grows and you need to add a server to your 3-node cache cluster. Suddenly, hash(key) % 3 becomes hash(key) % 4, causing approximately 75% of your cached data to become unreachable. Every cache miss translates to a database query, potentially triggering a cascading failure that can bring down your entire system.

This isn't just a theoretical concern. Netflix learned this lesson the hard way during their early scaling phase when adding cache nodes during peak traffic hours would effectively invalidate their entire cache layer, causing their backend databases to buckle under the sudden load spike.