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)

Avoiding Shuffle and Reducing Operational Expenses

In this chapter, we will learn how to avoid shuffle and reduce the operational expense of our jobs, along with detecting a shuffle in a process. We will then test operations that cause a shuffle in Apache Spark to find out when we should be very careful and which operations we should avoid. Next, we will learn how to change the design of jobs with wide dependencies. After that, we will be using the keyBy() operations to reduce shuffle and, in the last section of this chapter, we'll see how we can use custom partitioning to reduce the shuffle of our data.

In this chapter, we will cover the following topics:

  • Detecting a shuffle in a process
  • Testing operations that cause a shuffle in Apache Spark
  • Changing the design of jobs with wide dependencies
  • Using keyBy() operations to reduce shuffle
  • Using the custom partitioner to reduce...