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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 FREE CHAPTER 2. Resilient Distributed Datasets 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

Deploying the app programmatically

Unlike the Jupyter notebooks, when you use the spark-submit command, you need to prepare the SparkSession yourself and configure it so your application runs properly.

In this section, we will learn how to create and configure the SparkSession as well as how to use modules external to Spark.

Note

If you have not created your free account with either Databricks or Microsoft (or any other provider of Spark) do not worry - we will be still using your local machine as this is easier to get us started. However, if you decide to take your application to the cloud it will literally only require changing the --master parameter when you submit the job.

Configuring your SparkSession

The main difference between using Jupyter and submitting jobs programmatically is the fact that you have to create your Spark context (and Hive, if you plan to use HiveQL), whereas when running Spark with Jupyter the contexts are automatically started for you.

In this section, we will develop...

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