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Khot A. Learning Functional Data Structures and Algorithms (+code files)

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Khot A. Learning Functional Data Structures and Algorithms (+code files)
Packt Publishing, 2017. — 394 p. — ISBN 1785888730.
Key Features
Moving from object-oriented programming to functional programming? This book will help you get started with functional programming.
Easy-to-understand explanations of practical topics will help you get started with functional data structures.
Illustrative diagrams to explain the algorithms in detail.
Get hands-on practice of Scala to get the most out of functional programming.
Book Description
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.
What you will learn
Learn to think in the functional paradigm
Understand common data structures and the associated algorithms, as well as the context in which they are commonly used
Take a look at the runtime and space complexities with the O notation
See how ADTs are implemented in a functional setting
Explore the basic theme of immutability and persistent data structures
Find out how the internal algorithms are redesigned to exploit structural sharing, so that the persistent data structures perform well, avoiding needless copying.
Get to know functional features like lazy evaluation and recursion used to implement efficient algorithms
Gain Scala best practices and idioms
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