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Hadoop Real-World Solutions Cookbook- Second Edition

You're reading from   Hadoop Real-World Solutions Cookbook- Second Edition Over 90 hands-on recipes to help you learn and master the intricacies of Apache Hadoop 2.X, YARN, Hive, Pig, Oozie, Flume, Sqoop, Apache Spark, and Mahout

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Product type Paperback
Published in Mar 2016
Publisher
ISBN-13 9781784395506
Length 290 pages
Edition 2nd Edition
Tools
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Author (1):
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Tanmay Deshpande Tanmay Deshpande
Author Profile Icon Tanmay Deshpande
Tanmay Deshpande
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Toc

Table of Contents (12) Chapters Close

Preface 1. Getting Started with Hadoop 2.X FREE CHAPTER 2. Exploring HDFS 3. Mastering Map Reduce Programs 4. Data Analysis Using Hive, Pig, and Hbase 5. Advanced Data Analysis Using Hive 6. Data Import/Export Using Sqoop and Flume 7. Automation of Hadoop Tasks Using Oozie 8. Machine Learning and Predictive Analytics Using Mahout and R 9. Integration with Apache Spark 10. Hadoop Use Cases Index

Executing the Map Reduce program in a Hadoop cluster


In the previous recipe, we took a look at how to write a map reduce program for a page view counter. In this recipe, we will explore how to execute this in a Hadoop cluster.

Getting ready

To perform this recipe, you should already have a running Hadoop cluster as well as an eclipse similar to an IDE.

How to do it

To execute the program, we first need to create a JAR file of it. JAR stands for Java Archive file, which contains compiled class files. To create a JAR file in eclipse, we need to perform the following steps:

  1. Right-click on the project where you've written your Map Reduce Program. Then, click on Export.

  2. Select Java->Jar File and click on the Next button. Browse through the path where you wish to export the JAR file, and provide a proper name to the jar file. Click on Finish to complete the creation of the JAR file.

  3. Now, copy this file to the Hadoop cluster. If you have your Hadoop cluster running in the AWS EC2 instance, you can...

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