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Learning PySpark

You're reading from   Learning PySpark Build data-intensive applications locally and deploy at scale using the combined powers of Python and Spark 2.0

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Product type Paperback
Published in Feb 2017
Publisher Packt
ISBN-13 9781786463708
Length 274 pages
Edition 1st Edition
Languages
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Authors (2):
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Denny Lee Denny Lee
Author Profile Icon Denny Lee
Denny Lee
Tomasz Drabas Tomasz Drabas
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Tomasz Drabas
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Toc

Table of Contents (13) Chapters Close

Preface 1. Understanding Spark FREE CHAPTER 2. Resilient Distributed Datasets 3. DataFrames 4. Prepare Data for Modeling 5. Introducing MLlib 6. Introducing the ML Package 7. GraphFrames 8. TensorFrames 9. Polyglot Persistence with Blaze 10. Structured Streaming 11. Packaging Spark Applications 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...
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