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


Index operations in arrays are fast, simple, and easy to understand, with one drawback: they only work with integers. Since an array is a continuous portion in memory that can be accessed by dividing it evenly, which makes the jumps between the elements easy, can this work with arbitrary keys as well? Yes! Enter maps.

Maps (also called dictionaries or associative arrays), are data structures that store and manage unique key-value pairs in an efficient way. These structures aim to quickly provide access to the values associated with the keys that are typically stored in one of the following two ways:

  • A hashtable
  • A tree

When key-value pairs are stored in a tree, the result is very similar to what was discussed in the previous chapter: self-balancing trees will provide consistent performance, avoiding the worst-case cost of a hash map.

Since trees have been discussed extensively in the previous chapter, the hash map is the main focus in this section. It uses a hashing function to translate...