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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

Installing GraphFrames


If you are running your job from a Spark CLI (for example, spark-shell, pyspark, spark-sql, spark-submit), you can use the –-packages command, which will extract, compile, and execute the necessary code for you to use the GraphFrames package.

For example, to use the latest GraphFrames package (version 0.3) with Spark 2.0 and Scala 2.11 with spark-shell, the command is:

> $SPARK_HOME/bin/spark-shell --packages graphframes:graphframes:0.3.0-spark2.0-s_2.11

If you are using a notebook service, you may need to install the package first. For example, the following section shows the steps to install the GraphFrames library within the free Databricks Community Edition (http://databricks.com/try-databricks).

Creating a library

Within Databricks, you can create a library that is comprised of a Scala/Java JAR, Python Egg, or Maven Coordinate (including the Spark package).

To start, go to your Workspace within databricks, right-click the folder you want to create the library in...

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