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

Finding the best


The search domain is present on various levels of abstraction: finding a word in a body of text is typically more complex than simply calling the contains() function, and if there are several results, which is the one that was searched for? This entire class of problem is summed up under the umbrella of information retrieval, where problems of ranking, indexing, understanding, storing, and searching are solved in order to retrieve the optimum result (for all definitions). This chapter focuses only on the latter part, where we actually look through a collection of items (for example, an index) in order to find a match.

This means that we will compare items directly (a == b) to determine closeness, rather than using something such as a distance - or locally-sensitive hashing function. These can be found in more specific domains such as a fuzzy search or matching bodies of text, which is a field of its own. To learn more about hashing, please check out Chapter 16, Exploring...