https://en.wikipedia.org/wiki/Consistent_hashing .

To Run python3.7 -m test

Simplest Explanation

What is normal hashing ?

Let's say we have to store the following key value pair in a distributed memory store like redis.

Id	Name
Id-1	Smith
Id-2	Jackson
Id-3	Thomas
Id-4	Tom
Id-5	Jerry

Let say we have a hash function f(id) ,that takes above ids and creates hashes from it .
Assume we have 3 servers - (s1 , s2 and s3)
We can do a modulo of hash by the no of servers ie 3 , to map each each key to a server and we are left with following.

Id	Name	Hash f(Id)	Server(Hash % No of servers)
Id-1	Smith	1123	node-1
Id-2	Jackson	1211	node-2
Id-3	Thomas	1600	node-1
Id-4	Tom	1801	node-1
Id-5	Jerry	1788	node-0

We could retreive the value for a key by simple lookup using f(). Say for key Jackson , f("Jackson")%(no of servers) => 1211*3 = 2 (node-2)

This looks perfecto , yea close but not cigar !
But What if a server say node-1 went down ? Applying the same formula ie f(id)%(no of servers) for user Jackson, 1211%2 = 1 ie we got node-1 when the actual key is hashed to node-2 from the above table .

We could do remapping here , What if we have a billion keys ,in that case we have to remap a large no of keys which is tedious :(

This is a major flow in traditional hashing technique.

What is Consistent Hashing ?

In Consistent hashing , we visualize list of all nodes in a circular ring .(Basically a sorted array)

start func
For each node:
 Find f(node) where f is the hash function
 Append each f(node) to a sorted array
For any key
  Compute the hash f(key)
  Find the first f(node)>f(key)
  map it
end func

for example, if we have to hash key smith, we compute the hash value 1123 , find the immediate node having hash value > 1123 ie node 3 with hash value 1500

Now , What if we loose a server , say we loose node-2 , All the keys can be mapped to next server node-3 :)
Yea , we only have to remap the keys of node-2