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Consistent Hashing Visualizer — Real Ring, Real Remap Percentage

A real hash ring: each node gets many virtual replica positions placed with a proper bit-mixing hash for even load distribution. Add or remove a node and see the exact percentage of keys that actually remap — then compare it directly against naive modulo hashing (hash(key) % N) doing the same operation, which is the entire reason consistent hashing exists as a technique.

How to use Consistent Hashing Visualizer

  1. Add or remove a node from the ring.
  2. Read the exact percentage of keys remapped.
  3. Compare against the naive modulo hashing percentage on the same change.
  4. Lower the virtual-replica slider to see load balance degrade.

Frequently asked questions

Why does adding one node only remap a small fraction of keys?
A new node only takes over the specific ring arcs its own virtual replicas land on — every other arc, and the keys in it, is completely untouched, which is the core property naive modulo hashing lacks entirely.
Why does naive modulo hashing remap almost everything?
hash(key) % N changes for nearly every key the moment N changes, since the modulus itself changed — there is no structural reason for any specific key to keep the same owner, which is exactly what the side-by-side percentage demonstrates.
What are virtual replicas for?
A single ring position per node risks one node landing a disproportionately large arc purely by chance; spreading each node across many replica positions averages that out into a much more even load distribution — this was a real bug caught and fixed during development, where a naive hash-of-string-with-numeric-suffix scheme produced a badly lopsided ring even at high replica counts.
Where is this technique actually used?
Distributed caches (Memcached client libraries), distributed databases (Cassandra, DynamoDB), and CDN request routing all rely on consistent hashing to avoid mass cache invalidation or data reshuffling whenever a node joins or leaves.

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