This is a guest post by Srushtika Neelakantam, Developer Advovate for Ably Realtime, a realtime data delivery platform. You can view the original article—How to implement consistent hashing efficiently—on Ably's blog.

Ably’s realtime platform is distributed across more than 14 physical data centres and 100s of nodes. In order for us to ensure both load and data are distributed evenly and consistently across all our nodes, we use consistent hashing algorithms.

In this article, we’ll understand what consistent hashing is all about and why it is an essential tool in scalable distributed system architectures. Further, we’ll look at data structures that can be used to implement this algorithm efficiently at scale. At the end, we’ll also have a look at a working example for the same.

Hashing revisited

Remember the good old naïve Hashing approach that you learnt in college? Using a hash function, we ensured that resources required by computer programs could be stored in memory in an efficient manner, ensuring that in-memory data structures are loaded evenly. We also ensured that this resource storing strategy also made information retrieval more efficient and thus made programs run faster.

The classic hashing approach used a hash function to generate a pseudo-random number, which is then divided by the size of the memory space to transform the random identifier into a position within the available space. Something that looked like the following:

location = hash(key) mod size

So, why can’t we use the same method for handling requests over the network?