consistent hash ring

It is used in distributed storage systems like Amazon Dynamo, memcached, Project Voldemort and Riak.

how can you find a server in a distributed system to store or retrieve a value identified by a key, while at the same time being able to cope with server failures and network partitions?

Simply finding a server for value is easy; just number your set of s servers from 0 to s – 1. When you want to store or retrieve a value, hash the value’s key modulo s, and that gives you the server.

The problem comes when servers fail or become unreachable through a network partition. At that point, the servers no longer fill the hash space, so the only option is to invalidate the caches on all servers, renumber them, and start again. Given that, in a system with hundreds or thousands of servers, failures are commonplace, this solution is not feasible.


In consistent hashing, the servers, as well as the keys, are hashed, and it is by this hash that they are looked up. The hash space is large, and is treated as if it wraps around to form a circle – hence hash ring. The process of creating a hash for each server is equivalent to placing it at a point on the circumference of this circle. When a key needs to be looked up, it is hashed, which again corresponds to a point on the circle. In order to find its server, one then simply moves round the circle clockwise from this point until the next server is found. If no server is found from that point to end of the hash space, the first server is used – this is the “wrapping round” that makes the hash space circular.


instead of hashing the node only, hash the node and its replicas to construct a big hashtable

add a node, that means add node and its replica to the big hashtable

{ node+replica1 => node, node + replica2 => node, … }

remove a node, remove (node+all replica) from the big hashtable

get a node, iterate the hash, find lowest value greater than or equal to the node; if such node are not found, return the first one.

read the code there

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