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

Sorting


Sorting is an important feature in user interfaces, but also provides the predictability that's necessary for many algorithms. Whenever there is no way to use an appropriate data structure (such as a tree), a generic sorting algorithm can take care of creating that order. One important question arises regarding equal values: will they end up at the same exact spot every time? When using a stable sorting algorithm, the answer is yes.

Stable sorting

The key to stable sorting is not reordering equal elements, so in [1, 1, 2, 3, 4, 5], 1s never change their positions relative to each other. In Rust, this is actually used when sort() is called on Vec<T>.

 

 

The current (2018 edition) implementation of Vec<T> uses a merge sort variation based on Timsort. Here is the source code:

pub fn sort(&mut self)
    where T: Ord
{
    merge_sort(self, |a, b| a.lt(b));
}

The code is quite verbose, but can be split into smaller parts. The first step is to sort smaller (20 elements or less...