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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
Author Profile Icon Tomasz Drabas
Tomasz Drabas
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Table of Contents (13) Chapters Close

Preface 1. Understanding Spark 2. Resilient Distributed Datasets FREE CHAPTER 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

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...

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