What is Consistent Hashing
Consistent Hashing — algorithm for distributing keys across nodes where adding/removing a node moves only ~1/N of keys (not all). Basis of distributed caches (Memcached, Redis Cluster), CDN (Akamai routing), DB sharding. Key insight: hash both nodes and keys onto a circle (ring); key goes to the closest node clockwise. Virtual nodes (vnodes) balance load.
Below: details, example, related terms, FAQ.
Details
- Hash both keys and nodes in common space (e.g. SHA-1 → 0..2^160)
- Each key → closest node clockwise on ring
- Adding a node: only keys between previous and new node reshuffle
- Virtual nodes: 1 physical node = 100-200 virtual points on ring → better balance
- Alternative: Rendezvous Hashing (simpler, comparable properties)
Example
// Pseudocode
function nodeForKey(key, ring):
h = hash(key)
for node in ring sorted by hash:
if hash(node) >= h: return node
return ring[0] // wrap aroundRelated Terms
How Consistent Hashing Works
Practical Examples of Consistent Hashing
Benefits of Using Consistent Hashing
Learn more
Frequently Asked Questions
Where is it used?
Redis Cluster (16384 slots), Memcached client-side sharding, DynamoDB partitioning, Cassandra, Akamai CDN, any distributed caches.
Consistent Hashing vs Range Sharding?
Range — sorted, easy range queries. Consistent — better balance, auto-reshuffle. For large-scale — consistent.
Is Rendezvous hashing better?
Rendezvous is simpler, O(n) per lookup vs O(log n). For small N — comparable. For huge clusters — consistent hashing wins.
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