In a naive distributed cache with N nodes, you route a key by doing `hash(key) % N`. This works perfectly - until you add a node. Now you have N+1 nodes, and `hash(key) % (N+1)` gives entirely different results for almost every key. Every cached item is now on the wrong node. You've just invalidated ~100% of your cache in one go.

Consistent Hashing solves this by placing both servers and keys on a conceptual ring (a circular number line, e.g., 0 to 2^32). Each server owns an arc of the ring. A key is assigned to the first server clockwise from its position on the ring.

When you add a node, only keys in the arc that the new node takes over need to be moved. On average, only 1/N of the data moves (where N is the number of existing nodes).

When you remove a node, only the keys on that node's arc need to be redistributed to the next node clockwise.

Virtual nodes (vnodes): A real system assigns each physical server many positions on the ring (e.g., 150 virtual nodes per server). This ensures even distribution even when servers have different capacities or arrive/depart at different times. Cassandra uses 256 vnodes per server by default.