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Programming MapReduce with Scalding

You're reading from   Programming MapReduce with Scalding A practical guide to designing, testing, and implementing complex MapReduce applications in Scala

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Product type Paperback
Published in Jun 2014
Publisher
ISBN-13 9781783287017
Length 148 pages
Edition 1st Edition
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Author (1):
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Antonios Chalkiopoulos Antonios Chalkiopoulos
Author Profile Icon Antonios Chalkiopoulos
Antonios Chalkiopoulos
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Table of Contents (11) Chapters Close

Preface 1. Introduction to MapReduce 2. Get Ready for Scalding FREE CHAPTER 3. Scalding by Example 4. Intermediate Examples 5. Scalding Design Patterns 6. Testing and TDD 7. Running Scalding in Production 8. Using External Data Stores 9. Matrix Calculations and Machine Learning Index

Using slim JAR files


The majority of articles and tutorials recommend developers to package all the dependencies and the application code into a single JAR file. This is known as the fat jar approach and can be achieved using maven or sbt.

A build tool can generate the JAR file with a single command, such as mvn package, once we have all the appropriate plugins, such as maven-assembly-plugin, in place.

This process is awesome, until we have to deal with the compiler or the deployment process more than once a day. Assembling a single distributable archive takes time. The plugin needs to iterate through all the project dependencies, uncompressing every single dependency and aggregating the project output along with its dependencies, modules, and other files.

Scalding applications depend on Hadoop libraries, the Scala library, Cascading libraries, and other utility libraries. The dependency hierarchy means that the resulting JAR files occupy between 60 MB and 100 MB, depending on the amount of...

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