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


This chapter went deep into trees, starting off with the simplest form: the binary search tree. This tree prepares the inserted data for search by creating a left and a right branch which hold smaller or greater values. A search algorithm can therefore just pick the direction based on the current node and the value coming in, thereby skipping a majority of the other nodes.

The regular binary search tree has a major drawback, however: it can become unbalanced. Red-black trees provide a solution for that: by rotating subtrees, a balanced tree structure is maintained and search performance is guaranteed.

Heaps are a more exotic use of the tree structure. With their primary use as a priority queue, they efficiently produce the lowest or highest number of an array in constant time. The upheap and downheap operations repair the structure upon insert or removal so that the root is again the lowest (min-heap) or highest (max-heap) number.

Another very exotic structure is the trie. They are...