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Hadoop Beginner's Guide

You're reading from   Hadoop Beginner's Guide Get your mountain of data under control with Hadoop. This guide requires no prior knowledge of the software or cloud services – just a willingness to learn the basics from this practical step-by-step tutorial.

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Product type Paperback
Published in Feb 2013
Publisher Packt
ISBN-13 9781849517300
Length 398 pages
Edition 1st Edition
Tools
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Table of Contents (19) Chapters Close

Hadoop Beginner's Guide
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. What It's All About 2. Getting Hadoop Up and Running FREE CHAPTER 3. Understanding MapReduce 4. Developing MapReduce Programs 5. Advanced MapReduce Techniques 6. When Things Break 7. Keeping Things Running 8. A Relational View on Data with Hive 9. Working with Relational Databases 10. Data Collection with Flume 11. Where to Go Next Pop Quiz Answers Index

Using languages other than Java with Hadoop


We have mentioned previously that MapReduce programs don't have to be written in Java. Most programs are written in Java, but there are several reasons why you may want or need to write your map and reduce tasks in another language. Perhaps you have existing code to leverage or need to use third-party binaries—the reasons are varied and valid.

Hadoop provides a number of mechanisms to aid non-Java development, primary amongst these are Hadoop Pipes that provides a native C++ interface to Hadoop and Hadoop Streaming that allows any program that uses standard input and output to be used for map and reduce tasks. We will use Hadoop Streaming heavily in this chapter.

How Hadoop Streaming works

With the MapReduce Java API, both map and reduce tasks provide implementations for methods that contain the task functionality. These methods receive the input to the task as method arguments and then output results via the Context object. This is a clear and...

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