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Learning Functional Data Structures and Algorithms

Learning Functional Data Structures and Algorithms

By : S. Khot, Mishra
5 (2)
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Learning Functional Data Structures and Algorithms

Learning Functional Data Structures and Algorithms

5 (2)
By: S. Khot, 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 (14 chapters)
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Preface

This book is about functional algorithms and data structures. Algorithms and data structures are fundamentals of computer programming.

I started my career writing C and C++ code. I always enjoyed designing efficient algorithms. I have experienced many an Aha! moments, when I saw how powerful and creative pointer twiddling could be!

For example, reversing a singly linked list using three node pointers is a well known algorithm. We scan the list once and reverse it by changing the pointer fields of each node. The three pointer variables guide the reversal process. 

I have come across many such pointer tricks and have used them as needed.

I was next initiated into the world of multi-threading! Variables became shared states between threads! My bagful of tricks was still valid; however, changing state needed a lot of care, to stay away from insidious threading bugs.

The real world is never picture perfect and someone forgot to synchronize a data structure.

Thankfully we started using C++, which had another bagful of tricks, to control the state sharing. You could now make objects immutable!

For example, we were able to implement the readers/writer locking pattern effectively. Immutable objects could be shared without worry among thousands of readers!

We slept easier, the code worked as expected, and all was well with the world!

I soon realized the reason it worked well! Immutability was finally helping us better understand the state changes!

The sands of time kept moving and I discovered functional programming.

I could very well see why writing side-effect free code worked! I was hooked and started playing with Scala, Clojure, and Erlang. Immutability was the norm here.

However, I wondered how the traditional algorithms would look like in a functional setting--and started learning about it.

A data structure is never mutated in place. Instead, a new version of the data structure is created. The strategy of copy on write with maximized sharing was an intriguing one! All that careful synchronization is simply not needed!

The languages come equipped with garbage collection. So, if a version is not needed anymore, the runtime would take care of reclaiming the memory.

All in good time though! Reading this book will help you see that we need not sacrifice algorithmic performance while avoiding in-place mutation!

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