Book Image

The Complete Rust Programming Reference Guide

By : Rahul Sharma, Vesa Kaihlavirta, Claus Matzinger
Book Image

The Complete Rust Programming Reference Guide

By: Rahul Sharma, Vesa Kaihlavirta, Claus Matzinger

Overview of this book

Rust is a powerful language with a rare combination of safety, speed, and zero-cost abstractions. This Learning Path is filled with clear and simple explanations of its features along with real-world examples, demonstrating how you can build robust, scalable, and reliable programs. You’ll get started with an introduction to Rust data structures, algorithms, and essential language constructs. Next, you will understand how to store data using linked lists, arrays, stacks, and queues. You’ll also learn to implement sorting and searching algorithms, such as Brute Force algorithms, Greedy algorithms, Dynamic Programming, and Backtracking. As you progress, you’ll pick up on using Rust for systems programming, network programming, and the web. You’ll then move on to discover a variety of techniques, right from writing memory-safe code, to building idiomatic Rust libraries, and even advanced macros. By the end of this Learning Path, you’ll be able to implement Rust for enterprise projects, writing better tests and documentation, designing for performance, and creating idiomatic Rust code. This Learning Path includes content from the following Packt products: • Mastering Rust - Second Edition by Rahul Sharma and Vesa Kaihlavirta • Hands-On Data Structures and Algorithms with Rust by Claus Matzinger
Table of Contents (29 chapters)
Title Page
Copyright
About Packt
Contributors
Preface
Index

Concurrency


The ability of a program to manage more than one thing at a time while giving an illusion of them happening at the same time is called concurrency, and such programs are called concurrent programs. Concurrency allows you to structure your program in a way that it performs faster if you have a problem that can be split into multiple sub-problems. When talking about concurrency, another term called parallelism is often thrown in the discussion, and it is important we know the differences as the usage of these terms often overlap. Parallelism is when each task runs simultaneously on separate CPU cores  with non-overlapping time periods. The following diagram illustrates the difference between concurrency and parallelism:

To put it another way, concurrency is about structuring your program to manage more than one thing at a time, while parallelism is about putting your program on multiple cores to increase the amount of work it does in a period of time. With this definition, it follows...