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
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.
What is Async I/O?
TODO: Talk about how async io uses a combination of coroutines, event loops and tasks
TODO: Talk about how
Effects on Scalability
TODO: Talk about how blocking I/O requires you to scale your compute resources to meet the needs of increased latency. Show maths that demonstrates this for a given pipeline.
How a simple application behaves
TODO: Talk about how a basic application performing a single task works and how it needs to be scaled as your request rate and latency increase.
How to scale a simple application
TODO: Show how one could scale the simple application by adding more processes or threads.