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

Simple streaming application using DStreams


Let's create a simple word count example using Spark Streaming in Python. For this example, we will be working with DStream – the Discretized Stream of small batches that make up the stream of data. The example used for this section of the book can be found in its entirety at: https://github.com/drabastomek/learningPySpark/blob/master/Chapter10/streaming_word_count.py.

This word count example will use the Linux / Unix nc command – it is a simple utility that reads and writes data across network connections. We will use two different bash terminals, one using the nc command to send words to our computer's local port (9999) and one terminal that will run Spark Streaming to receive those words and count them. The initial set of commands for our script are noted here:

1. # Create a local SparkContext and Streaming Contexts
2. from pyspark import SparkContext
3. from pyspark.streaming import StreamingContext
4. 
5. # Create sc with two working threads...