Book Image

Rust Web Programming - Second Edition

By : Maxwell Flitton
5 (1)
Book Image

Rust Web Programming - Second Edition

5 (1)
By: Maxwell Flitton

Overview of this book

Are safety and high performance a big concern for you while developing web applications? With this practical Rust book, you’ll discover how you can implement Rust on the web to achieve the desired performance and security as you learn techniques and tooling to build fully operational web apps. In this second edition, you’ll get hands-on with implementing emerging Rust web frameworks, including Actix, Rocket, and Hyper. It also features HTTPS configuration on AWS when deploying a web application and introduces you to Terraform for automating the building of web infrastructure on AWS. What’s more, this edition also covers advanced async topics. Built on the Tokio async runtime, this explores TCP and framing, implementing async systems with the actor framework, and queuing tasks on Redis to be consumed by a number of worker nodes. Finally, you’ll go over best practices for packaging Rust servers in distroless Rust Docker images with database drivers, so your servers are a total size of 50Mb each. By the end of this book, you’ll have confidence in your skills to build robust, functional, and scalable web applications from scratch.
Table of Contents (27 chapters)
Free Chapter
1
Part 1:Getting Started with Rust Web Development
4
Part 2:Processing Data and Managing Displays
8
Part 3:Data Persistence
12
Part 4:Testing and Deployment
16
Part 5:Making Our Projects Flexible
19
Part 6:Exploring Protocol Programming and Async Concepts with Low-Level Network Applications

Queuing Tasks with Redis

Receiving requests, performing an action, and then returning a response to the user can solve a lot of problems in web programming. However, there are times when this simple approach will simply not cut it. For instance, when I was working at MonolithAi, we had a functionality where the user would be able to put in data and parameters and then train a machine learning model on that data at a click of a button. However, trying to train a machine learning model before sending a response to the user would simply take too long. The connection would probably time out. To solve this, we had a Redis queue and a pool of workers consuming tasks. The training task would be put into the queue and one of the workers would work on training the model when they got round to it. The HTTP server would accept the request from the user, post the training task to the queue, and respond to the user that the task was posted. When the model was trained, the user would get an update...