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

In the wild


In reality, there are a lot of factors that may influence the choice of space and runtime complexity. Typically, these factors are forms of resource constraints, such as power consumption on embedded devices, clock cycles in a cloud-hosted environment, and so on.

Since it is difficult to find out the complexities of a particular algorithm, it is helpful to know a few, so the choice comes intuitively. Often, the runtime complexity is not the only important aspect, but the absolute execution time counts. Under these conditions, a higher runtime complexity can be preferable if n is sufficiently small.

This is best demonstrated when Vec<T> contains only a few elements, where a linear search is a lot faster than sorting and then running a binary search. The overhead of sorting might just be too much compared to searching right away.

Getting this trade-off and the overall implementation right is hugely beneficial for the entire program and will outweigh any other optimizations....