Scaling for Latency with Async I/O

I’ve just spent the last month rewriting the core component in a monitoring stack which is responsible for protecting the availability of a billion dollar per year franchise. The purpose of this rewrite was to improve the ability of our engineers to implement new features in a safe, quick and easy way - what we delivered ended up offering a four order of magnitude performance and efficiency improvement over our previous system.

Let’s talk about how that happened, why it was possible and how we achieved that without it being a focal point of the redesign. I’m going to discuss evented input-output, often referred to as async.

Hopefully, by the time you’ve finished reading this article you should have a good grasp of what evented IO is, how it works and some of the situations in which it has a lot to offer - as well as some of the significant advantages it has over alternative approaches when we start talking about large scale production systems.

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Docker’s Logo

Docker is become an incredibly prevalent tool in the development and operations realms in recent months. Its combination of developer friendly configuration and simple operational management make it a very attractive prospect for companies and teams looking to adopt CI and CD practices.

In most cases, you’ll see Docker used to deploy applications in much the same way as a zip file or virtual machine image. This is certainly the most common use case for Docker, but by no means the extent of its functionality.

In this post I’m going to discuss some of the more interesting problems we’ve used Docker to solve and why it serves as a great solution to them.

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Scaling for Latency with Async I/O

Out of the Box Docker