Transforming RDDs with Spark 2.0 using the filter() API

In this recipe, we explore the filter() method of which is used to select a subset of the base RDD and return a new filtered RDD. The format is similar to map(), but a lambda selects which members are to be included in the resulting RDD.

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 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...