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Scala Data Analysis Cookbook (new)

You're reading from   Scala Data Analysis Cookbook (new) Navigate the world of data analysis, visualization, and machine learning with over 100 hands-on Scala recipes

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Product type Paperback
Published in Oct 2015
Publisher
ISBN-13 9781784396749
Length 254 pages
Edition 1st Edition
Languages
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Author (1):
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Arun Manivannan Arun Manivannan
Author Profile Icon Arun Manivannan
Arun Manivannan
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Table of Contents (9) Chapters Close

Preface 1. Getting Started with Breeze FREE CHAPTER 2. Getting Started with Apache Spark DataFrames 3. Loading and Preparing Data – DataFrame 4. Data Visualization 5. Learning from Data 6. Scaling Up 7. Going Further Index

Building the Uber JAR


The first step for deploying our Spark application on a cluster is to bundle it into a single Uber JAR, also known as the assembly JAR. In this recipe, we'll be looking at how to use the SBT assembly plugin to generate the assembly JAR. We'll be using this assembly JAR in subsequent recipes when we run Spark in distributed mode. We could alternatively set dependent JARs using the spark.driver.extraClassPath property (https://spark.apache.org/docs/1.3.1/configuration.html#runtime-environment). However, for a large number of dependent JARs, this is inconvenient.

How to do it...

The goal of building the assembly JAR is to build a single, Fat JAR that contains all dependencies and our Spark application. Refer to the following screenshot, which shows the innards of an assembly JAR. You can see not only the application's files in the JAR, but also all the packages and files of the dependent libraries:

The assembly JAR can easily be built in SBT using the SBT assembly plugin...

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