Book Image

Learning Functional Data Structures and Algorithms

By : Raju Kumar Mishra
Book Image

Learning Functional Data Structures and Algorithms

By: Raju Kumar Mishra

Overview of this book

Functional data structures have the power to improve the codebase of an application and improve efficiency. With the advent of functional programming and with powerful functional languages such as Scala, Clojure and Elixir becoming part of important enterprise applications, functional data structures have gained an important place in the developer toolkit. Immutability is a cornerstone of functional programming. Immutable and persistent data structures are thread safe by definition and hence very appealing for writing robust concurrent programs. How do we express traditional algorithms in functional setting? Won’t we end up copying too much? Do we trade performance for versioned data structures? This book attempts to answer these questions by looking at functional implementations of traditional algorithms. It begins with a refresher and consolidation of what functional programming is all about. Next, you’ll get to know about Lists, the work horse data type for most functional languages. We show what structural sharing means and how it helps to make immutable data structures efficient and practical. Scala is the primary implementation languages for most of the examples. At times, we also present Clojure snippets to illustrate the underlying fundamental theme. While writing code, we use ADTs (abstract data types). Stacks, Queues, Trees and Graphs are all familiar ADTs. You will see how these ADTs are implemented in a functional setting. We look at implementation techniques like amortization and lazy evaluation to ensure efficiency. By the end of the book, you will be able to write efficient functional data structures and algorithms for your applications.
Table of Contents (20 chapters)
Learning Functional Data Structures and Algorithms
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

No boilerplate


Boilerplate code consists sections of the same code written again and again. For example, writing loops is boilerplate, as is writing getters and setters for private class members.

As the preceding code shows, the loop is implicit:

scala> List(1, 2, 3, 4, 5) partition(_ % 2 == 0) 
res3: (List[Int], List[Int]) = (List(2, 4),List(1, 3, 5)) 

We just wish to separate the odd and even numbers. So we just specify the criteria via a function, an anonymous function in this case. This is shown in the following image:

What is boilerplate? It is a for loop, for example. In the imperative world, we code the loop ourselves. We need to tell the system how to iterate over a data structure.

Isn't Scala code just to the point? We tell what we need and the loop is implied for us. No need to write a for loop, no need to invent a name for the loop variable, and so on. We just got rid of the boilerplate.

Here is a Clojure snippet that shows how to multiply each element of a vector by 2:

user=> (map * (repeat 2) [1 2 3 4 5]) 
(2 4 6 8 10) 

The map function hides the loop from us. Then (repeat 2) function call generates an infinite sequence.

So we are just saying this: for the input sequence [1 2 3 4 5], create another lazy sequence of 2's. Then use the map function to multiply these two sequences and output the result. The following figure depicts the flow:

Compare this with an imperative language implementation. We would have needed a loop and a list to collect the result. Instead, we just say what needs to be done.