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)

Immutability in the highly concurrent environment

We saw how immutability affects the creation and design of programs, so now we will understand how it is useful.

In this section, we will cover the following topics:

  • The cons of mutable collections
  • Creating two threads that simultaneously modify a mutable collection
  • Reasoning about a concurrent program

Let's first understand the cause of mutable collections. To do this, we will be creating two threads that simultaneously modify the mutable collection. We will be using this code for our test. First, we will create a ListBuffer that is a mutable list. Then, we can add and delete links without creating another list for any modification. We can then create an Executors service with two threads. We need two threads to start simultaneously to modify the state. Later, we will use a CountDownLatch construct from Java.util.concurrent...