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...
Frank Kane's Taming Big Data with Apache Spark and Python
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Frank Kane's Taming Big Data with Apache Spark and Python
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Overview of this book
Frank Kane’s Taming Big Data with Apache Spark and Python is your companion to learning Apache Spark in a hands-on manner. Frank will start you off by teaching you how to set up Spark on a single system or on a cluster, and you’ll soon move on to analyzing large data sets using Spark RDD, and developing and running effective Spark jobs quickly using Python.
Apache Spark has emerged as the next big thing in the Big Data domain – quickly rising from an ascending technology to an established superstar in just a matter of years. Spark allows you to quickly extract actionable insights from large amounts of data, on a real-time basis, making it an essential tool in many modern businesses.
Frank has packed this book with over 15 interactive, fun-filled examples relevant to the real world, and he will empower you to understand the Spark ecosystem and implement production-grade real-time Spark projects with ease.
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Current Title:
Frank Kane's Taming Big Data with Apache Spark and Python
Table of Contents (13 chapters)
Title Page
Credits
About the Author
www.PacktPub.com
Customer Feedback
Preface
Free Chapter
Getting Started with Spark
Spark Basics and Spark Examples
Advanced Examples of Spark Programs
Running Spark on a Cluster
SparkSQL, DataFrames, and DataSets
Other Spark Technologies and Libraries
Where to Go From Here? – Learning More About Spark and Data Science
Customer Reviews