How Consistent Hashing Solves Distributed Cache Failures at Scale

A software team encountered severe database overload and stale data issues when a flash sale exposed the limits of their per-node, in-process caching approach. The core problem was treating a distributed system challenge as a local one, with each node maintaining its own isolated cache copy. The team resolved this by adopting consistent hashing — a technique used in systems like Dynamo and Cassandra — which uses a hash ring to assign keys to nodes, minimising reshuffling when nodes are added or removed. They paired this with a leader-replica replication model, where a single leader per shard handles writes while replicas serve reads asynchronously, avoiding both split-brain conflicts and consensus overhead. The resulting architecture delivered even load distribution, horizontal read scaling, and predictable write performance without the latency penalties of full consensus protocols like Raft.