Consistent Hashing in memcache-client

2009-01-14

One of the most important features needed to create a scalable memcached infrastructure is consistent hashing. I recently added consistent hashing to the Ruby memcache-client project and I want to take a moment to explain why this is important.

The Naive Approach

Memcached is simple in principle: just like a hashtable, a unique key maps to a value. This is simple when you have a single memcached server, but what do you do if you have two servers? Which server is the key/value pair stored on?

The naive approach is to hash the key to an integer and do a modulo based on the size of the server set. For a given key, this should return the same, random server from the set every time.

idx = Zlib.crc32(key) % servers.size

This works great – if the server set never changes.

Where It All Goes Wrong

Now our website is doing great and getting tens of hits per day so we find ourselves needing to add a new memcached server in order to handle the surge in traffic. We add the new server, restart our daemons and soon the database disk(s) grinds to a halt. What happened? When you change the server set, the modulo value for most of the key hashes changes. This means that memcache-client goes to the “wrong” server, the lookup misses and that expensive operation you are caching needs to be performed again. You essentially get a storm of recalculation as your cache contents shift from their old server to their new server.

Enter the Continuum

The better algorithm, instead of using modulo, uses a predefined continuum of values which map onto a server. We select N random integers (where N is around 100 or 200) for each server and sort those values into an array of N * server.size values. To look up the server for a key, we find the closest value >= the key hash and use the associated server. The values form a virtual circle; the key hash maps to a point on that circle and then we find the server clockwise from that point.

Results

Assume we have 3 memcached servers and want to add a fourth.

The continuum approach will invalidate 1/4 or 25% of your keys.

The modulo approach will invalidate 3/4 or 75% of your keys.

The more servers you have, the worse modulo performs and the better continuum performs.

The Future

I’ve checked in this change into GitHub but haven’t released a new version yet. I’m waiting on one set of fixes and then I’ll release 1.6.0 to RubyForge.

Tom White has a more in-depth explanation of consistent hashing. Thanks to him for the image above. I based my implementation on the explanation of libketama.