Book Image

Mastering Elixir

By : André Albuquerque, Daniel Caixinha
Book Image

Mastering Elixir

By: André Albuquerque, Daniel Caixinha

Overview of this book

Running concurrent, fault-tolerant applications that scale is a very demanding responsibility. After learning the abstractions that Elixir gives us, developers are able to build such applications with inconceivable low effort. There is a big gap between playing around with Elixir and running it in production, serving live requests. This book will help you fll this gap by going into detail on several aspects of how Elixir works and showing concrete examples of how to apply the concepts learned to a fully ?edged application. In this book, you will learn how to build a rock-solid application, beginning by using Mix to create a new project. Then you will learn how the use of Erlang's OTP, along with the Elixir abstractions that run on top of it (such as GenServer and GenStage), that allow you to build applications that are easy to parallelize and distribute. You will also master supervisors (and supervision trees), and comprehend how they are the basis for building fault-tolerant applications. Then you will use Phoenix to create a web interface for your application. Upon fnishing implementation, you will learn how to take your application to the cloud, using Kubernetes to automatically deploy, scale, and manage it. Last, but not least, you will keep your peace of mind by learning how to thoroughly test and then monitor your application.
Table of Contents (18 chapters)
Title Page
Dedication
Packt Upsell
Contributors
Preface
5
Demand-Driven Processing
Index

Task


As we saw earlier, processes are cheap to spawn in Elixir because we are always in the realm of the BEAM. This is the perfect setup for us to spawn a process whenever we need. If you need to do something in parallel, you can spawn a process. If you need to do many things in parallel, you can spawn many processes. The virtual machine won't break a sweat and you will have an army of processes in no time to accomplish whatever you need.

However, process communication is laborious, so if you need results from those parallel computations, you will have to receive the results via message passing and eventually terminate the processes afterwards.

In this situation, we can resort to the Task module, using its Task.async/1 function to spawn tasks, and then use Task.await/1 when you need the results from the tasks you previously spawned.

Imagine that we want to tell our users their rank relative to the disk space they currently use. Fetching the disk space of each and every user could be an expensive...