Book Image

Swift Functional Programming - Second Edition

By : Dr. Fatih Nayebi
Book Image

Swift Functional Programming - Second Edition

By: Dr. Fatih Nayebi

Overview of this book

Swift is a multi-paradigm programming language enabling you to tackle different problems in various ways. Understanding each paradigm and knowing when and how to utilize and combine them can lead to a better code base. Functional programming (FP) is an important paradigm that empowers us with declarative development and makes applications more suitable for testing, as well as performant and elegant. This book aims to simplify the FP paradigms, making them easily understandable and usable, by showing you how to solve many of your day-to-day development problems using Swift FP. It starts with the basics of FP, and you will go through all the core concepts of Swift and the building blocks of FP. You will also go through important aspects, such as function composition and currying, custom operator definition, monads, functors, applicative functors,memoization, lenses, algebraic data types, type erasure, functional data structures, functional reactive programming (FRP), and protocol-oriented programming(POP). You will then learn to combine those techniques to develop a fully functional iOS application from scratch
Table of Contents (19 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Dedication
Preface

Memoization


In previous sections, we talked about functions as building blocks and explained that we can compose our applications with functions. If functions can be building blocks in our programs, then they should be cacheable! But how do we cache them? The answer is memoization.

Memoization is the process of storing the result of functions, given their input, in order to improve the performance of our programs. We can memoize pure functions as pure functions do not rely on external data and do not change anything outside themselves. Pure functions provide the same result for a given input every time. Therefore, we can save or cache the results (in other words, memoize the results) given their inputs and use them in the future without going through the calculation process.

To be able to understand the concept, let's look at the following example in which we will manually memoize the power2 function:

var memo = Dictionary<Int, Int>() 

func memoizedPower2(n: Int) -> Int { 
    if...