Book Image

Learning Functional Data Structures and Algorithms

By : S. Khot, Mishra
Book Image

Learning Functional Data Structures and Algorithms

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)

Concatenating lists


In the imperative world, where we perform mutation as needed, concatenating two linked lists is easy.

Given the two lists a and b, we just traverse the first list a until we reach its last node. Then we change its next pointer to the head of list b:

Note what happened to the original list a. It changed. The original list simply does not exist anymore.

We destroyed list a when we connected its third node to list b. The preceding list mutation is also not thread-safe. As seen in the previous chapter, additional mechanism, such as locking, is needed to make sure the state is synchronized correctly.

We could do the concatenation by keeping the original list intact; we do this by copying list a into another list c and then changing the third node of the new list to point to list b.

Our list a and list b are not touched at all. We copy list a into a new list, namely list c, and change its third node to point to the head of list b.

Note that the new list, that is, list c, and...