Book Image

Scala and Spark for Big Data Analytics

By : Md. Rezaul Karim, Sridhar Alla
Book Image

Scala and Spark for Big Data Analytics

By: Md. Rezaul Karim, Sridhar Alla

Overview of this book

Scala has been observing wide adoption over the past few years, especially in the field of data science and analytics. Spark, built on Scala, has gained a lot of recognition and is being used widely in productions. Thus, if you want to leverage the power of Scala and Spark to make sense of big data, this book is for you. The first part introduces you to Scala, helping you understand the object-oriented and functional programming concepts needed for Spark application development. It then moves on to Spark to cover the basic abstractions using RDD and DataFrame. This will help you develop scalable and fault-tolerant streaming applications by analyzing structured and unstructured data using SparkSQL, GraphX, and Spark structured streaming. Finally, the book moves on to some advanced topics, such as monitoring, configuration, debugging, testing, and deployment. You will also learn how to develop Spark applications using SparkR and PySpark APIs, interactive data analytics using Zeppelin, and in-memory data processing with Alluxio. By the end of this book, you will have a thorough understanding of Spark, and you will be able to perform full-stack data analytics with a feel that no amount of data is too big.
Table of Contents (19 chapters)

Using higher-order functions

Suppose you work in a restaurant as a chef and one of your colleagues ask you a question: Implement a HOF (higher-order function) that performs currying. Looking for clues? Suppose you have the following two signatures for your HOF:

def curry[X,Y,Z](f:(X,Y) => Z) : X => Y => Z

Similarly, implement a function that performs uncurrying as follows:

def uncurry[X,Y,Z](f:X => Y => Z): (X,Y) => Z

Now, how could you use HOFs to perform the currying operation? Well, you could create a trait that encapsulates the signatures of two HOFs (that is, curry and uncurry) as follows:

trait Curry {
def curry[A, B, C](f: (A, B) => C): A => B => C
def uncurry[A, B, C](f: A => B => C): (A, B) => C
}

Now, you can implement and extend this trait as an object as follows:


object CurryImplement extends Curry {
def uncurry[X, Y, Z](f: X ...