Distributed computing is an essential component of modern software systems that require the processing of large amounts of data. In a distributed computing environment, the data is spread across multiple nodes, and each node is responsible for processing a portion of the data. Two critical concepts that are commonly used in distributed computing are Distributed Hashtable and Consistent Hashing. In this article, we will explore what Distributed Hashtable and Consistent Hashing are, how they can be combined to create an efficient distributed computing system, and where they can be used.

What is a Distributed Hashtable?

A Distributed Hashtable is a data structure used to store and retrieve key-value pairs in a distributed environment. In a Distributed Hashtable, each node in the system is responsible for storing and retrieving a portion of the data. When a node receives a request to store or retrieve data, it uses a hash function to determine the node responsible for the data. Once the responsible node is identified, the operation is performed on that node’s local hash table. Distributed Hashtable is an effective way to distribute data across multiple nodes, providing scalability and fault tolerance. E.g. Kademlia, BitTorrent, OpenDHT, etc.

What is Consistent Hashing?

Consistent Hashing is a technique used to partition data across multiple nodes in a distributed system, such that the addition or removal of a node only affects a small portion of the data. In Consistent Hashing, a hash function is used to map each node to a point on a ring, and each key is also mapped to a point on the same ring. The key is then assigned to the node whose point is closest to the key’s point in a clockwise direction around the ring. This approach ensures that only a small portion of the data needs to be migrated when a node is added or removed from the system. E.g. used in Apache Cassandra, Amazon DynamoDB, Riak etc.

Let’s consider an example. Suppose we have a distributed system with three nodes, as shown in t