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)

Testing operations that cause a shuffle in Apache Spark

In this section, we will test the operations that cause a shuffle in Apache Spark. We will cover the following topics:

  • Using join for two DataFrames
  • Using two DataFrames that are partitioned differently
  • Testing a join that causes a shuffle

A join is a specific operation that causes shuffle, and we will use it to join our two DataFrames. We will first check whether it causes shuffle and then we will check how to avoid it. To understand this, we will use two DataFrames that are partitioned differently and check the operation of joining two datasets or DataFrames that are not partitioned or partitioned randomly. It will cause shuffle because there is no way to join two datasets with the same partition key if they are on different physical machines.

Before we join the dataset, we need to send them to the same physical machine...