What is consistent hashing and how does Cassandra use it?

Consistent hashing is the technique that lets Cassandra distribute data across nodes so that adding or removing a node only reshuffles a small fraction of the data, instead of the entire dataset.

1. Cassandra arranges a hash space (from 0 to a maximum value) into a logical ring.
2. Each node is assigned one or more positions (tokens) on that ring.
3. A partition key is hashed by the partitioner into a token, and the row is stored on the node whose token range contains that value, walking clockwise around the ring.
4. Replicas for that partition are simply the next N‑1 distinct nodes further around the ring, where N is the replication factor.

Without consistent hashing, a naive hash(key) % number_of_nodes scheme would remap almost every key whenever a node was added or removed, forcing a massive, disruptive data shuffle. Consistent hashing bounds that disruption to only the token ranges actually adjacent to the change, which is what makes elastic scaling practical in Cassandra.

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