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Scala for Data Science

You're reading from   Scala for Data Science Leverage the power of Scala with different tools to build scalable, robust data science applications

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Product type Paperback
Published in Jan 2016
Publisher
ISBN-13 9781785281372
Length 416 pages
Edition 1st Edition
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Author (1):
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Pascal Bugnion Pascal Bugnion
Author Profile Icon Pascal Bugnion
Pascal Bugnion
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Table of Contents (17) Chapters Close

Preface 1. Scala and Data Science FREE CHAPTER 2. Manipulating Data with Breeze 3. Plotting with breeze-viz 4. Parallel Collections and Futures 5. Scala and SQL through JDBC 6. Slick – A Functional Interface for SQL 7. Web APIs 8. Scala and MongoDB 9. Concurrency with Akka 10. Distributed Batch Processing with Spark 11. Spark SQL and DataFrames 12. Distributed Machine Learning with MLlib 13. Web APIs with Play 14. Visualization with D3 and the Play Framework A. Pattern Matching and Extractors Index

Lifting the hood


In the last section of this chapter, we will discuss, very briefly, how Spark works internally. For a more detailed discussion, see the References section at the end of the chapter.

When you open a Spark context, either explicitly or by launching the Spark shell, Spark starts a web UI with details of how the current task and past tasks have executed. Let's see this in action for the example mutual information program we wrote in the last section. To prevent the context from shutting down when the program completes, you can insert a call to readLine as the last line of the main method (after the call to takeOrdered). This expects input from the user, and will therefore pause program execution until you press enter.

To access the UI, point your browser to 127.0.0.1:4040. If you have other instances of the Spark shell running, the port may be 4041, or 4042 and so on.

The first page of the UI tells us that our application contains three jobs. A job occurs as the result of an action...

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