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Hands-On Data Structures and Algorithms with Rust

Hands-On Data Structures and Algorithms with Rust

By : Claus Matzinger
2.7 (3)
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Hands-On Data Structures and Algorithms with Rust

Hands-On Data Structures and Algorithms with Rust

2.7 (3)
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)
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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...

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