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

Maps and sets


Rust's maps and sets are based largely on two strategies: B-Tree search and hashing. They are very distinct implementations, but achieve the same results: associating a key with a value (map) and providing a fast unique collection based on keys (set).

Hashing in Rust works with a Hasher trait, which is a universal, stateful hasher, to create a hash value from an arbitrary byte stream. By repeatedly calling the appropriate write() function, data can be added to the hasher's internal state and finished up with the finish() function.

Unsurprisingly the B-Tree in Rust is highly optimized. The BTreeMap documentation provides rich details on why the regular implementation (as previously shown) is cache inefficient and not optimized for modern CPU architectures. Hence, they provide a more efficient implementation, which is definitely fascinating, and you should check it out in the source code.

 

HashMap and HashSet

Both HashMap and HashSet use a hashing algorithm to produce the unique...