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

Functional programming is declarative


When we use SQL, we just express our intent. For example, consider this:

mysql> select count(*) from book where author like '%wodehouse%'; 

We just say what we are looking for. The actual mechanism that gets the answer is hidden from us. The following is a little too simplistic but suitable example to prove the point.

The SQL engine will have to loop over the table and check whether the author column contains the wodehouse string. We really don't need to worry about the search  algorithm. The author table resides on a disk somewhere. The number of table rows that need to be filtered could easily exceed the available memory. The engine handles all such complexities for us though.

We just declare our intent. The following Scala snippet is declarative. It counts the number of even elements in the input list:

scala> val list = List(1, 2, 3, 4, 5, 6) 
list: List[Int] = List(1, 2, 3, 4, 5, 6) 
 
scala> list.count( _ % 2 == 0 ) 
res0: Int = 3 

The code uses a higher order function, namely count. This takes another function, a predicate, as an argument. The line loops over each list element, invokes the argument predicate function, and returns the count.

Here is another example of Clojure code that shows how to generate a combination of values from two lists:

user=> (defn fun1 [list1 list2] 
  #_=>   (for [x list1 y list2] 
  #_=>     (list x y))) 
#'user/fun1 
user=> (fun1 '(1 2 3) '(4 5 6)) 
((1 4) (1 5) (1 6) (2 4) (2 5) (2 6) (3 4) (3 5) (3 6)) 

Note the code used to generate the combination. We use for comprehension to just state what we need done and it would be done for us.