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

Heaps


Since binary trees are the most basic forms of trees, there are several variations designed for a specific purpose. Where the red-black tree is an advanced version of the initial tree, the binary heap is a version of the binary tree that does not facilitate search.

In fact, it has a specified purpose: finding the maximum or minimum value of a node. These heaps (min-heap or max-heap) are built in a way that the root node is always the value with the desired property (min or max) so it can be retrieved in constant time—that is, it always takes the same number of operations to fetch. Once fetched, the tree is restored in a way that the next operation works the same. How is this done though?

Heaps work, irrespective of whether they are min-heaps or max-heaps, because a node's children always have the same property as the entire tree. In a max-heap, this means that the root node is the maximum value of the sequence, so it has to be the greatest value of its children (it's the same with min...