Book Image

Hands-On Big Data Analytics with PySpark

By : Rudy Lai, Bartłomiej Potaczek
Book Image

Hands-On Big Data Analytics with PySpark

By: Rudy Lai, Bartłomiej Potaczek

Overview of this book

Apache Spark is an open source parallel-processing framework that has been around for quite some time now. One of the many uses of Apache Spark is for data analytics applications across clustered computers. In this book, you will not only learn how to use Spark and the Python API to create high-performance analytics with big data, but also discover techniques for testing, immunizing, and parallelizing Spark jobs. You will learn how to source data from all popular data hosting platforms, including HDFS, Hive, JSON, and S3, and deal with large datasets with PySpark to gain practical big data experience. This book will help you work on prototypes on local machines and subsequently go on to handle messy data in production and at scale. This book covers installing and setting up PySpark, RDD operations, big data cleaning and wrangling, and aggregating and summarizing data into useful reports. You will also learn how to implement some practical and proven techniques to improve certain aspects of programming and administration in Apache Spark. By the end of the book, you will be able to build big data analytical solutions using the various PySpark offerings and also optimize them effectively.
Table of Contents (15 chapters)

Delving into the Spark RDD's parent/child chain

In this section, we will try to implement our own RDD that inherits the parent properties of RDD.

We will go through the following topics:

  • Extending an RDD
  • Chaining a new RDD with the parent
  • Testing our custom RDD

Extending an RDD

This is a simple test that has a lot of hidden complexity. Let's start by creating a list of the record, as shown in the following code block:

class InheritanceRdd extends FunSuite {
val spark: SparkContext = SparkSession
.builder().master("local[2]").getOrCreate().sparkContext

test("use extended RDD") {
//given
val rdd = spark.makeRDD(List(Record(1, "d1")))

The Record is just a case class that...