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

B-Tree


As you have noticed, restricting the number of children to 2 (like the binary trees earlier) yields a tree that only lets the algorithm decide whether to go left or right, and it's easily hardcoded. Additionally, storing only a single key-value pair in a node can be seen as a waste of space—after all, the pointers can be a lot larger than the actual payload!

B-Trees generally store multiple keys and values per node, which can make them more space-efficient (the payload-to-pointer ratio is higher). As a tree, each of these (key-value) pairs has children, which hold the values between the nodes they are located at. Therefore, a B-Tree stores triples of key, value, and child, with an additional child pointer to cover any "other" values. The following diagram shows a simple B-Tree. Note the additional pointer to a node holding smaller keys:

As depicted here, a B-Tree can have varying amounts of those key-value pairs (only the keys are visible), but they will have a maximum number of children...