Book Image

Swift Data Structure and Algorithms

By : Mario Eguiluz Alebicto
Book Image

Swift Data Structure and Algorithms

By: Mario Eguiluz Alebicto

Overview of this book

Apple’s Swift language has expressive features that are familiar to those working with modern functional languages, but also provides backward support for Objective-C and Apple’s legacy frameworks. These features are attracting many new developers to start creating applications for OS X and iOS using Swift. Designing an application to scale while processing large amounts of data or provide fast and efficient searching can be complex, especially running on mobile devices with limited memory and bandwidth. Learning about best practices and knowing how to select the best data structure and algorithm in Swift is crucial to the success of your application and will help ensure your application is a success. That’s what this book will teach you. Starting at the beginning, this book will cover the basic data structures and Swift types, and introduce asymptotic analysis. You’ll learn about the standard library collections and bridging between Swift and Objective-C collections. You will see how to implement advanced data structures, sort algorithms, work with trees, advanced searching methods, use graphs, and performance and algorithm efficiency. You’ll also see how to choose the perfect algorithm for your problem.
Table of Contents (15 chapters)
Swift Data Structure and Algorithms
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface

B-trees,


B-trees are similar to binary search trees in many ways, but they have two big differences: the number of children per node is not limited to two and the number of keys in the node is also variable (not just 1).

B-trees are self-balanced, rooted, sorted trees. They allow operations such as insert, search, deletion, and access in logarithmic time.

Each internal node has n keys. These keys are like dividing points between child nodes. So, for n keys, the internal node has n+1 child nodes.

This feature makes B-trees suitable for different applications in fields such as databases and external storage. Having more than two children per node and multiple keys allows the B-tree to perform multiple comparisons for each internal node, so it has less tree height and therefore reduces the time complexity to access and search nodes.

As has been said, each internal node in a B-tree has a different number of keys inside. These keys are used to divide the subtrees below them in order. Look at the...