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Learning Hadoop 2

You're reading from   Learning Hadoop 2 Design and implement data processing, lifecycle management, and analytic workflows with the cutting-edge toolbox of Hadoop 2

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Product type Paperback
Published in Feb 2015
Publisher Packt
ISBN-13 9781783285518
Length 382 pages
Edition 1st Edition
Tools
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Toc

Table of Contents (13) Chapters Close

Preface 1. Introduction 2. Storage FREE CHAPTER 3. Processing – MapReduce and Beyond 4. Real-time Computation with Samza 5. Iterative Computation with Spark 6. Data Analysis with Apache Pig 7. Hadoop and SQL 8. Data Lifecycle Management 9. Making Development Easier 10. Running a Hadoop Cluster 11. Where to Go Next Index

Hadoop streaming


We have mentioned previously that MapReduce programs don't have to be written in Java. There are several reasons why you might 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 which are Hadoop pipes that provide a native C++ interface and Hadoop streaming that allows any program that uses standard input and output to be used for map and reduce tasks. 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 type-safe interface, but it is by definition Java-specific.

Hadoop streaming takes a different approach. With streaming, you write a map task that reads its...

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