Book Image

Learning PySpark

By : Tomasz Drabas, Denny Lee
Book Image

Learning PySpark

By: Tomasz Drabas, Denny Lee

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. This book will show you how to leverage the power of Python and put it to use in the Spark ecosystem. You will start by getting a firm understanding of the Spark 2.0 architecture and how to set up a Python environment for Spark. You will get familiar with the modules available in PySpark. You will learn how to abstract data with RDDs and DataFrames and understand the streaming capabilities of PySpark. Also, you will get a thorough overview of machine learning capabilities of PySpark using ML and MLlib, graph processing using GraphFrames, and polyglot persistence using Blaze. Finally, you will learn how to deploy your applications to the cloud using the spark-submit command. By the end of this book, you will have established a firm understanding of the Spark Python API and how it can be used to build data-intensive applications.
Table of Contents (20 chapters)
Learning PySpark
Credits
Foreword
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Index

A quick primer on global aggregations


As noted in the previous section, so far, our script has performed a point in time streaming word count. The following diagram denotes the lines DStream and its micro-batches as per how our script had executed in the previous section:

At the 1 second mark, our Python Spark Streaming script returned the value of {(blue, 5), (green, 3)}, at the 2 second mark it returned {(gohawks, 1)}, and at the 4 second mark, it returned {(green, 2)}. But what if you had wanted the aggregate word count over a specific time window?

The following figure represents us calculating a stateful aggregation:

In this case, we have a time window between 0-5 seconds. Note, that in our script we have not got the specified time window: each second, we calculate the cumulative sum of the words. Therefore, at the 2 second mark, the output is not just the green and blue from the 1 second mark, but it also includes the gohawks from the 2 second mark: {(blue, 5), (green, 3), (gohawks, 1...