Book Image

PySpark Cookbook

By : Denny Lee, Tomasz Drabas
Book Image

PySpark Cookbook

By: Denny Lee, Tomasz Drabas

Overview of this book

Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. The PySpark Cookbook presents effective and time-saving recipes for leveraging the power of Python and putting it to use in the Spark ecosystem. You’ll start by learning the Apache Spark architecture and how to set up a Python environment for Spark. You’ll then get familiar with the modules available in PySpark and start using them effortlessly. In addition to this, you’ll discover how to abstract data with RDDs and DataFrames, and understand the streaming capabilities of PySpark. You’ll then move on to using ML and MLlib in order to solve any problems related to the machine learning capabilities of PySpark and use GraphFrames to solve graph-processing problems. Finally, you will explore how to deploy your applications to the cloud using the spark-submit command. By the end of this book, you will be able to use the Python API for Apache Spark to solve any problems associated with building data-intensive applications.
Table of Contents (13 chapters)
Title Page
Packt Upsell

Understanding DStreams

Before we dive into structured streaming, let's start by talking about DStreams. DStreams are built on top of RDDs and represent a stream of data divided into small chunks. The following figure represents these data chunks in micro-batches of milliseconds to seconds. In this example, the lines of DStream is micro-batched into seconds where each square represents a micro-batch of events that occurred within that second window:

  • At time interval 1 second, there were five occurrences of the event blue and three occurrences of the event green
  • At time interval 2 seconds, there is a single occurrence of gohawks
  • At time interval 4 seconds, there are two occurrences of the event green


Because DStreams are built on top of RDDs, Apache Spark's core data abstraction, this allows Spark Streaming to easily integrate with other Spark components such as MLlib and Spark SQL.

Getting ready

For these Apache Spark Streaming examples, we will be creating and executing a console application...