What is a distributed hash table and how can you use it in distributed systems?

1 What is a DHT?

A DHT is a data structure that allows you to store and retrieve key-value pairs in a distributed manner. Each node in the system is responsible for a subset of the keys, and can use a hashing function to locate and access the values. A DHT can also handle dynamic changes in the network, such as node failures, joins, and departures, by using algorithms that redistribute the keys and values among the remaining nodes.

2 Why use a DHT?

A distributed hash table (DHT) can be a great asset to distributed systems, as it offers scalability, availability, consistency, and performance. It can scale to millions of nodes and billions of key-value pairs without relying on a central authority or fixed structure. Additionally, it is resilient to node failures and network partitions, as it replicates data across multiple nodes and uses redundancy mechanisms. Furthermore, depending on the application requirements and the trade-offs involved, it can provide various levels of consistency guarantees. Finally, it can reduce latency and bandwidth consumption of data access by routing queries to the nearest or most suitable nodes.

3 How to use a DHT?

In order to use a DHT, one must define the key space, the value space, the hashing function, and the routing algorithm. The key space is usually a large finite domain, such as a 160-bit integer space, while the value space can be anything from strings to files or pointers. The hashing function should be uniform, deterministic, and collision-resistant so that it distributes keys evenly and produces the same output for the same input. The routing algorithm should be efficient, robust, and adaptable in order to minimize the number of hops, cope with network changes, and balance the load.

4 Examples of DHTs

There are various types of DHTs, each having its own design and implementation choices. Chord is a DHT that organizes the nodes in a circular ring, assigning each node a unique identifier in the key space. It maintains a finger table and uses it to route queries, while also utilizing a stabilization protocol to update the finger tables periodically. Kademlia is a DHT that utilizes a binary tree for node organization, with each node having its own unique identifier in the key space. It maintains a routing table with buckets of nodes close to it in the key space, and uses a recursive lookup algorithm for node failure and joins. Lastly, Pastry is a DHT that organizes the nodes in a prefix tree, assigning each node an identifier in the key space. It keeps track of leaf sets, neighborhood sets, and routing tables to route queries, while also employing joining protocols to build routing state and maintenance protocols to repair it.