Book Image

Hands-On Data Structures and Algorithms with Rust

By : Claus Matzinger
Book Image

Hands-On Data Structures and Algorithms with Rust

By: Claus Matzinger

Overview of this book

Rust has come a long way and is now utilized in several contexts. Its key strengths are its software infrastructure and resource-constrained applications, including desktop applications, servers, and performance-critical applications, not forgetting its importance in systems' programming. This book will be your guide as it takes you through implementing classic data structures and algorithms in Rust, helping you to get up and running as a confident Rust programmer. The book begins with an introduction to Rust data structures and algorithms, while also covering essential language constructs. You will learn how to store data using linked lists, arrays, stacks, and queues. You will also learn how to implement sorting and searching algorithms. You will learn how to attain high performance by implementing algorithms to string data types and implement hash structures in algorithm design. The book will examine algorithm analysis, including Brute Force algorithms, Greedy algorithms, Divide and Conquer algorithms, Dynamic Programming, and Backtracking. By the end of the book, you will have learned how to build components that are easy to understand, debug, and use in different applications.
Table of Contents (15 chapters)

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...