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Frank Kane's Taming Big Data with Apache Spark and Python

You're reading from   Frank Kane's Taming Big Data with Apache Spark and Python Real-world examples to help you analyze large datasets with Apache Spark

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Product type Paperback
Published in Jun 2017
Publisher Packt
ISBN-13 9781787287945
Length 296 pages
Edition 1st Edition
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Concepts
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Author (1):
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Frank Kane Frank Kane
Author Profile Icon Frank Kane
Frank Kane
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Table of Contents (13) Chapters Close

Title Page
Credits
About the Author
www.PacktPub.com
Customer Feedback
Preface
1. Getting Started with Spark FREE CHAPTER 2. Spark Basics and Spark Examples 3. Advanced Examples of Spark Programs 4. Running Spark on a Cluster 5. SparkSQL, DataFrames, and DataSets 6. Other Spark Technologies and Libraries 7. Where to Go From Here? – Learning More About Spark and Data Science

Sorting the word count results


Okay, let's do one more round of improvements on our word-count script. We need to sort our results of word-count by something useful. Instead of just having a random list of words associated with how many times they appear, what we want is to see the least used words at the beginning of our list and the most used words at the end. This should give us some actually interesting information to look at. To do this, we're going to need to manipulate our results a little bit more directly-we can't just cheat and use countByValue and call it done.

Step 1 - Implement countByValue() the hard way to create a new RDD

So the first thing we're going to do is actually implement what countByValue does by hand, the hard way. This way we can actually play with the results more directly and stick the results in an RDD instead of just getting a Python object that we need to deal with at that point. The way we do that is we take our map of words-words.map-and we use a mapper that...

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