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

Evaluating runtime complexity


Now that you have the big picture in mind, let's look at some real data for the time performance of common Big-O functions so you will definitely understand the magnitude of the different orders. These running times are done in a faster computer where a simple instruction takes just one nanosecond. Take a look at the following table:

Big-O types and performance – running times for different inputs

The grayed section is colored because the times are not practical in real-world applications. Any algorithm with a running time of more than 1 second is going to have an impact in your application. So by looking at the data provided by this table, some conclusions arise:

  • For a very tiny n (n < 10), almost any order function works quick

  • Algorithms that run on log(n) can have a huge amount of data without becoming slow at all

  • Linear and n*log(n) algorithms have a great performance for huge inputs

  • Quadratic functions (n2) start to have bad performance for n>10,000

  • Algorithms...