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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 FREE CHAPTER 2. Storage 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

Java API to MapReduce

The Java API to MapReduce is exposed by the org.apache.hadoop.mapreduce package. Writing a MapReduce program, at its core, is a matter of subclassing Hadoop-provided Mapper and Reducer base classes, and overriding the map() and reduce() methods with our own implementation.

The Mapper class

For our own Mapper implementations, we will subclass the Mapper base class and override the map() method, as follows:

   class Mapper<K1, V1, K2, V2>
   {
         void map(K1 key, V1 value Mapper.Context context)
               throws IOException, InterruptedException
         ...
   }

The class is defined in terms of the key/value input and output types, and then the map method takes an input key/value pair as its parameter. The other parameter is an instance of the Context class that provides various mechanisms to communicate with the Hadoop framework, one of which is to output the results of a map or reduce method.

Notice that the map method only refers to a single instance...

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