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Java Data Analysis

You're reading from   Java Data Analysis Data mining, big data analysis, NoSQL, and data visualization

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781787285651
Length 412 pages
Edition 1st Edition
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Concepts
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Author (1):
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John R. Hubbard John R. Hubbard
Author Profile Icon John R. Hubbard
John R. Hubbard
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Table of Contents (14) Chapters Close

Preface 1. Introduction to Data Analysis 2. Data Preprocessing FREE CHAPTER 3. Data Visualization 4. Statistics 5. Relational Databases 6. Regression Analysis 7. Classification Analysis 8. Cluster Analysis 9. Recommender Systems 10. NoSQL Databases 11. Big Data Analysis with Java A. Java Tools Index

Hadoop MapReduce

After installing Hadoop you can run its version of MapReduce quite easily. As we have seen, this amounts to writing your own versions of the map() and reduce() methods to solve the particular problem. This is done by extending the Mapper and Reducer classes defined in the package org.apache.hadoop.mapreduce.

For example, to implement the WordCount program, you could set your program up like the one shown in Listing 11-5.

Hadoop MapReduce

Listing 11-5. WordCount program in Hadoop

The main class has two nested classes named WordCountMapper and WordCountReducer. These extend the corresponding Hadoop Mapper and Reducer classes, with a few details omitted. The point is that the map() and reduce() methods, that are to be written, are defined in these corresponding classes. This structure is what makes the Hadoop MapReduce framework an actual software framework.

Note that the Text class used in the parameter lists at lines 11 and 17 are defined in the org.apache.hadoop.io package.

This complete example...

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