til consistent hashing

I stumbled upon a pretty interesting concept for “balancing” nodes, called “Consistent Hashing”. The rough premise is a way to distribute things across a field (like servers, threads, continents, or you name it) — without rebalancing1 the members when targets disappear.

The thing that fascinates me is that it’s purely a hashing function, an object, and a ring. Assigning an object an angle around this ring, then looking for the next target along that ring in a clockwise fashion, for where that data should be.

If a target goes down, only the nodes before it will be rebalanced, as opposed to the entire suite of nodes.

The implications of this are massive. Think of persistent connections and load-balanced origins. If an origin goes offline, only the connections connected to it are moved (obviously), but as soon as the origin comes back online, it naturally balances back out, whereas ordinarily, we’d have to remember which nodes were moved before. Or if we think about a content delivery network, balancing content evenly amongst its edges, as edges go offline and online, it minimizes data movement.

It’s not only about balancing, but also about minimizing data movement when a rebalancing occurs.

What does it look like?


Nodes are the elements or things we want to store, something hashable — ideally something you can deterministically assign an angle or degrees to. The easiest way to achieve this is to generate a hash number (Murmur3 can be good for this), and map that between 0..360 (or some consistent search space, h(t) % M — hash of the target mod search space) as in zero would correspond to an angle of zero, and the maximum would correspond to 360 degrees. Dispersing all other values linearly between them.

So what do we do here? We compute a hash of the node and find where it lies around the edge of our ring.

Targets and Phantoms

Targets are where our stuff is stored, or likely to be stored, and phantoms are pointers to those targets. Much like our nodes, these targets and phantoms are also assigned an angle and placed around the ring.

But why phantoms?

Phantoms reduce the gap between targets around the ring. From the example above, you see that A and B are quite far apart, meaning that A would receive an overwhelming amount of nodes than B would.

To combat this, you spin up a few more targets that point to real servers and randomly place them around the ring as well. Thus reducing the gap.

You can assign a weight to each target, for how many phantoms it can produce — as a big machine might be able to handle more things.

  1. When you remove a target, all nodes need to be rebalanced across the new set of servers. For example, caching across 5 servers evenly and removing 1 server now you need to rebalance across 4 instead. ↩︎