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

A binomial heap


A heap-ordered binomial tree is one in which every parent value is less than or equal to its children. In other words, a parent value is never greater than its children values.

Here's a diagram illustrating this:

The diagram shows a binomial heap with 13 nodes. All the binomial trees are linked together in increasing order of their ranks. This linked list of roots is the root list.

Let's start shaping up our code now.

Linking up

Our node is defined as a case class:

  case class Node(rank: Int, v: Int, children: List[Node]) 

The node holds the rank, the value v, and a list of children (possibly empty).

Given this definition, let's see how we could link two binomial trees. We always link trees of equal rank:

  def linkUp(t1: Node, t2: Node) = 
    if (t1.v <= t2.v) 
      Node(t1.rank+1, t1.v, t2 :: t1.children) 
    else 
      Node(t1.rank+1, t2.v, t1 :: t2.children) 

Here is the diagram showing linking up two trees of rank 0:

Let's grok this code...