Book Image

Scala Functional Programming Patterns

By : Atul S. Khot
Book Image

Scala Functional Programming Patterns

By: Atul S. Khot

Overview of this book

Scala is used to construct elegant class hierarchies for maximum code reuse and extensibility and to implement their behavior using higher-order functions. Its functional programming (FP) features are a boon to help you design “easy to reason about” systems to control the growing software complexities. Knowing how and where to apply the many Scala techniques is challenging. Looking at Scala best practices in the context of what you already know helps you grasp these concepts quickly, and helps you see where and why to use them. This book begins with the rationale behind patterns to help you understand where and why each pattern is applied. You will discover what tail recursion brings to your table and will get an understanding of how to create solutions without mutations. We then explain the concept of memorization and infinite sequences for on-demand computation. Further, the book takes you through Scala’s stackable traits and dependency injection, a popular technique to produce loosely-coupled software systems. You will also explore how to currying favors to your code and how to simplify it by de-construction via pattern matching. We also show you how to do pipeline transformations using higher order functions such as the pipes and filters pattern. Then we guide you through the increasing importance of concurrent programming and the pitfalls of traditional code concurrency. Lastly, the book takes a paradigm shift to show you the different techniques that functional programming brings to your plate. This book is an invaluable source to help you understand and perform functional programming and solve common programming problems using Scala’s programming patterns.
Table of Contents (19 chapters)
Scala Functional Programming Patterns
About the Author
About the Reviewers


The flatMap method takes a function as a parameter, applies it to each element in a container, and flattens the overall result as shown in the following code:

scala> val f1 = (n: Int) => (1 to n).toList map { _ % 2 == 0 }
f1: Int => List[Boolean] = <function1>
scala> :t f1
Int => List[Boolean]
scala> List(2,3) flatMap f1
res4: List[Boolean] = List(false, true, false, true, false)

As earlier, f1 is of the type X => List[Y], where X is bound to Int and Y is bound to Boolean. Applying flatMap to List(2,3), which is List[Int], we get List[Boolean]. So, given the function, X => List[Y] we were able to get a transformation, List[Int] => List[Boolean].

Where does this come in useful? To know this, let's look at the following example:

scala> def f(x: Int) = if (x % 2 == 0) Some(x) else None
f: (x: Int)Option[Int]

The result of evaluating the function is Option, as shown in the following code. However, we wish to do something about this. Let's say, we add...