Book Image

Hands-On Data Structures and Algorithms with Rust

By : Claus Matzinger
Book Image

Hands-On Data Structures and Algorithms with Rust

By: Claus Matzinger

Overview of this book

Rust has come a long way and is now utilized in several contexts. Its key strengths are its software infrastructure and resource-constrained applications, including desktop applications, servers, and performance-critical applications, not forgetting its importance in systems' programming. This book will be your guide as it takes you through implementing classic data structures and algorithms in Rust, helping you to get up and running as a confident Rust programmer. The book begins with an introduction to Rust data structures and algorithms, while also covering essential language constructs. You will learn how to store data using linked lists, arrays, stacks, and queues. You will also learn how to implement sorting and searching algorithms. You will learn how to attain high performance by implementing algorithms to string data types and implement hash structures in algorithm design. The book will examine algorithm analysis, including Brute Force algorithms, Greedy algorithms, Divide and Conquer algorithms, Dynamic Programming, and Backtracking. By the end of the book, you will have learned how to build components that are easy to understand, debug, and use in different applications.
Table of Contents (15 chapters)


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...