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

Summary


Search, as a part of the information retrieval (among others) process, is an elementary way of finding something independently of the data structure being used. There are three popular types of algorithm: linear search, jump search, and binary search. Completely different approaches (such as locally-sensitive hashing) have been discussed in an earlier chapter about maps and sets, but they still need a mechanism to compare quickly.

A linear search is the least complex approach: iterate over a collection and compare the items with the element that is to be found. This has also been implemented in Rust's iterator and exhibits O(n) runtime complexity.

Jump searches are superior. By operating on a sorted collection, they can use a step size that is greater than 1 (like a linear search) in order to skip to the required parts faster by checking whether the relevant section has already passed. While faster in absolute terms, the worst-case runtime complexity is still O(n).

The (at the time...