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

Understanding priority queues/heaps


Priority queues are queues where each element has a priority. An element with high priority is served before an element with low priority.

For example, consider we have a task queue where tasks are inserted and need to be executed. A high priority task may appear after some tasks are inserted in the queue; however, it would need to be executed prior to tasks with low priority.

There are min-heaps and max-heaps. Min-heaps always have the least element as their root, which would be readily accessible. For max-heaps, the max element will be the root.

Let's look at the min-heap data structure first and then the functional version. Heaps are complete binary trees.

For more on the definition, visit http://web.cecs.pdx.edu/~sheard/course/Cs163/Doc/FullvsComplete.html .

The nodes also have partial ordering such that the root value is always less that its children. You will get this if you've been a keen observer all this while: this is an invariant.

In the imperative...