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

Referential transparency


We appreciate the virtues of caching, but we do this to look at referential transparency, a cornerstone of functional programming.

In the preceding example, note that we are able to cache the results, as the results of the computation are not going to change for the same input. We need not repeat the computations; instead, we could compute the answer once and save and substitute it.

In the FP world, where we can substitute a function by its value, the function is called referentially transparent. Just like we avoid repeated calls in the previous algorithm, repeated calls to such functions could be avoided by caching the result.

Mathematical functions are referentially transparent. For example, the following Clojure functions are referentially transparent:

user=> (* 3 4) 
12 
user=> (apply + [1 2 3 4 5 6]) 
21 

You will always get 21 when you add up 1,2,3,4,5, and 6. Multiplying 3 and 4 will always give the result 12.

Note

Note that the preceding functions are pure...