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

Summary


We looked at another common data structure called a deque. A deque is a double-ended queue, so insertions and deletions are possible at both ends. We looked at pushFront, popFront, pushBack, and popBack operations. We also looked at some applications of deques.

Deques have a simple implementation in the imperative world. A doubly linked list could be used for implementing it. A vector could be used too.

We looked at two important concepts: amortization and lazy evaluation. We took a detailed look at Scala's Streams. We saw what a thunk is and how it helps us with delayed evaluation.

We then used all these concepts to check whether we can create more efficient queues. As we saw in the previous chapter, reversing an in list and appending it to the out list could be costly.

Then we applied the same concepts to look at how to implement persistent and immutable deques. We also saw how balancing a data structure plays a vital role in improving performance.

With all this knowhow, in the next...