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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

The spark-submit command

The entry point for submitting jobs to Spark (be it locally or on a cluster) is the spark-submit script. The script, however, allows you not only to submit the jobs (although that is its main purpose), but also kill jobs or check their status.

Note

Under the hood, the spark-submit command passes the call to the spark-class script that, in turn, starts a launcher Java application. For those interested, you can check the GitHub repository for Spark: https://github.com/apache/spark/blob/master/bin/sparksubmitt.

The spark-submit command provides a unified API for deploying apps on a variety of Spark supported cluster managers (such as Mesos or Yarn), thus relieving you from configuring your application for each of them separately.

On the general level, the syntax looks as follows:

spark-submit [options] <python file> [app arguments]

We will go through the list of all the options soon. The app arguments are the parameters you want to pass to your application.

Note

You...

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