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 the diagram below:
Each node is identified by a unique identifier or hash value, which is obtained by hashing the node’s IP address or another unique identifier. For simplicity, let’s assume that the hash values for the three nodes are as follows:
Node 1: 10
Node 2: 20
Node 3: 30
The next step is to map the nodes to a circular data structure, called a ring. The ring is divided into several partitions, and each partition corresponds to a range of hash values. For example, in the diagram below, the ring is divided into six partitions, each corresponding to a range of 10 hash values:
For example, let’s say we want to store the key “apple” in the distributed system. The hash value for “apple” is 15, which corresponds to the second partition on the ring. Therefore, “apple” is assigned to Node 2, which is the node closest to the second partition in a clockwise direction around the ring.
Let’s say we want to add a new node, Node 7, to the system. To maintain the balance of the data, some of the partitions that were previously assigned to Node 6 need to be reassigned to Node 7.
In a distributed system, there are multiple nodes responsible for storing and processing data. If one node fails, the system should continue to function without losing data or performance. Consistent hashing provides fault tolerance by ensuring that the addition or removal of a node only affects a small portion of the data.
When a node fails, the data stored on that node needs to be redistributed to other nodes in the system. With consistent hashing, only a small portion of the data needs to be redistributed, as the majority of the data remains assigned to its original nodes. This means that the impact of a node failure is minimized, and the system can continue to function without losing data or performance.
In addition, consistent hashing provides load balancing by evenly distributing data across multiple nodes in the system. When new nodes are added to the system, some of the partitions are reassigned to the new nodes, which ensures that the data is evenly distributed among all nodes. This helps prevent overload on any one node, which can lead to degraded performance or system failure.
Combining Distributed Hashtable and Consistent Hashing
Distributed Hashtable and Consistent Hashing are often used together in distributed systems to create an efficient data storage and retrieval system. The combination of Distributed Hashtable and Consistent Hashing ensures that data is distributed across multiple nodes in a balanced way, and the addition or removal of a node only affects a small portion of the data.
To combine Distributed Hashtable and Consistent Hashing, a hash function is used to map each node to a point on a ring, similar to Consistent Hashing. However, instead of each key being mapped to a point on the same ring, it is hashed to a value within a particular range. Each node is then responsible for storing and retrieving keys within its range. This approach ensures that data is distributed evenly across multiple nodes, and the addition or removal of a node only affects the keys within its range.
Where can Distributed Hashtable and Consistent Hashing be used?
Distributed Hashtable and Consistent Hashing can be used in various applications, including distributed caching, distributed computing, content delivery networks, and distributed databases. For example, in distributed computing, Distributed Hashtable and Consistent Hashing can be used to store intermediate results or coordinate tasks between nodes, improving system performance and fault tolerance.
Conclusion
Distributed Hashtable and Consistent Hashing are two fundamental concepts in distributed systems that provide efficient and scalable solutions for storing and retrieving data. By using these concepts in combination, distributed computing systems can improve performance, scalability, and fault tolerance, making them an essential component of modern software systems. Therefore, Distributed Hashtable and Consistent Hashing are a powerful combination for building large-scale distributed systems that need to handle high levels of traffic and data.
References
System Design Interview — An insider’s guide
https://www.cs.princeton.edu/courses/archive/fall18/cos418/docs/L6-dhts.pdf