Transforming RDDs with set operation APIs

In this recipe, we explore set operations on RDDs, such as intersection(), union(), subtract(), and distinct() and Cartesian(). Let's implement the usual set operations in a distributed manner.

How to do it...

1. Start a new project in IntelliJ or in an IDE of your choice. Make sure the necessary JAR files are included.

2. Set up the package location where the program will reside

package spark.ml.cookbook.chapter3

3. Import the necessary packages

import breeze.numerics.pow 
import org.apache.spark.sql.SparkSession 
import Array._

4. Import the packages for setting up the logging level for log4j. This step is optional, but we highly recommend it (change the level appropriately as you move through the development cycle).

import org.apache.log4j.Logger 
import org.apache.log4j.Level 

5. Set up the logging level to warning and error to cut down on output. See the previous step for package requirements.

Logger.getLogger("org").setLevel(Level.ERROR) 
Logger.getLogger("akka").setLevel...