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

The Big O notation


Physics is not a topic in this book, but its influence is far-reaching and powerful enough to be obeyed everywhere, even by virtual constructs such as algorithms! However great their design, they still are constrained by two important factors: time and space.

Time? Whenever anything needs to be done, a sequence of steps is required. By multiplying the number of steps by the time for each step, the total—absolute—time is easy to calculate. Or so we think. For computers, this is mostly true, but many questions make it very hard to really know, since modern CPUs go way beyond what previous generations were able to achieve. Is that only thanks to higher clock rates? What about the additional cores? SIMD? Simply taking the absolute time won't achieve real comparability between algorithms. Maybe the number of steps is what we should use.

Space (as in memory) has become a commodity in many domains over the last few years, even in the embedded space. While the situation has improved...