Consistent Hash Ring

Consistent Hash Ring
Consistent Hashing
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.
The only remaining problem is that in practice hashing algorithms are likely to result in clusters of servers on the ring (or, to be more precise, some servers with a disproportionately large space before them), and this will result in greater load on the first server in the cluster and less on the remainder. This can be ameliorated by adding each server to the ring a number of times in different places. This is achieved by having a replica count, which applies to all servers in the ring, and when adding a server, looping from 0 to the count - 1, and hashing a string made from both the server and the loop variable to produce the position. This has the effect of distributing the servers more evenly over the ring. Note that this has nothing to do with server replication; each of the replicas represents the same physical server, and replication of data between servers is an entirely unrelated issue.
Consistent Hashing
It is interesting to note that it is only the client that needs to implement the consistent hashing algorithm - the memcached server is unchanged. Other systems that employ consistent hashing include Chord, which is a distributed hash table implementation

public class ConsistentHash<T> {

 private final HashFunction hashFunction;
 private final int numberOfReplicas;
 private final SortedMap<Integer, T> circle = new TreeMap<Integer, T>();

 public ConsistentHash(HashFunction hashFunction, int numberOfReplicas,
     Collection<T> nodes) {
   this.hashFunction = hashFunction;
   this.numberOfReplicas = numberOfReplicas;

   for (T node : nodes) {
     add(node);
   }
 }

 public void add(T node) {
   for (int i = 0; i < numberOfReplicas; i++) {
     circle.put(hashFunction.hash(node.toString() + i), node);
   }
 }

 public void remove(T node) {
   for (int i = 0; i < numberOfReplicas; i++) {
     circle.remove(hashFunction.hash(node.toString() + i));
   }
 }

 public T get(Object key) {
   if (circle.isEmpty()) {
     return null;
   }
   int hash = hashFunction.hash(key);
   if (!circle.containsKey(hash)) {
     SortedMap<Integer, T> tailMap = circle.tailMap(hash);
     hash = tailMap.isEmpty() ? circle.firstKey() : tailMap.firstKey();
   }
   return circle.get(hash);
 }

}

The circle is represented as a sorted map of integers, which represent the hash values, to caches (of type T here).
When a ConsistentHash object is created each node is added to the circle map a number of times (controlled by numberOfReplicas). The location of each replica is chosen by hashing the node's name along with a numerical suffix, and the node is stored at each of these points in the map.

To find a node for an object (the get method), the hash value of the object is used to look in the map. Most of the time there will not be a node stored at this hash value (since the hash value space is typically much larger than the number of nodes, even with replicas), so the next node is found by looking for the first key in the tail map. If the tail map is empty then we wrap around the circle by getting the first key in the circle.

http://engineering.ticketbis.com/consistent-hashing-implementation/
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