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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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Author (1):
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Frank Kane Frank Kane
Author Profile Icon Frank Kane
Frank Kane
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Table of Contents (8) Chapters Close

Preface 1. Getting Started with Spark 2. Spark Basics and Spark Examples FREE CHAPTER 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

Using broadcast variables to display movie names instead of ID numbers


In this section, we'll figure out how to actually include information about the names of the movies in our MovieLens dataset with our Spark job. We'll include them in such a way that they'll get broadcast out to the entire cluster. I'm going to introduce a concept called broadcast variables to do that. There are a few ways we could go about identifying movie names; the most straightforward method would be to just read in the u.item file to look up what movie ID 50 was, see it means Star Wars, load up a giant table in Python, and reference that when we're printing out the results within our driver program at the end. That'd be fine, but what if our executors actually need access to that information? How do we get that information to the executors? What if one of our mappers, or one of our reduce functions or something, needed access to the movie names? Well, it turns out that Spark will sort of automatically and magically...

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