Ryan Smith is pretty good at thinking about distributed systems. Distributed systems, the systems we (sometimes unwittingly) create on a regular basis these days, are a complicated, dense, far-reaching topic. Ryan’s managed to take a few of its problems and concisely introduce them with simple solutions that apply to all but the largest systems.
In The Worker Pattern, he presents a novel solution to a problem you are probably tackling with background or asynchronous job queues. Teaser: do you know what the HTTP 202 status code does?
A web service that requires high throughput will undoubtedly need to ensure low latency while processing requests. In other words, the process that is serving HTTP requests should spend the least amount of time possible to serve the request. Subsequently if the server does not have all of the data necessary to properly respond to the request, it must not wait until the data is found. Instead it must let the client know that it is working on the fulfillment of the request and that the client should check back later.
Coordinating multiple processes that need to process a dataset in bulk is tricky. Large systems usually end up needing some kind of Paxos service like Doozer or ZooKeeper to keep all the worker processes from butting heads or duplicating work. Leader Election shows how, by scoping the problem space to existing tools, it becomes possible to put together a solution that scales down to small and medium-sized systems:
My environment already is dependent on Ruby & PostgreSQL so I want a solution that leverages my existing technologies. Also, I don’t want to create a table other than the one which I need to process.
As applications grow, they tend to maintain more and more state across more and more systems. Incidental state is problematic, especially when you have to maintain several services to keep all of it available. Applying Event Buffering mitigates many of these problems. The core idea of this one is my favorite:
We have seen several examples of how to transfer state from our client to our server. The primary reason that we take these steps to transfer state is to eliminate the number of services in our distributed system that have to maintain state. Keeping a database on a service eventually becomes and operational hazard.
Most of the systems we build on the web today are distributed systems. Ryan’s writings are an excellent introduction to thinking about and building these systems. It certainly helps to comb through research papers on the topic, but these three essays are excellent starters down the path to intentionally building distributed systems.
