Book Image

Hadoop Real-World Solutions Cookbook - Second Edition

By : Tanmay Deshpande
Book Image

Hadoop Real-World Solutions Cookbook - Second Edition

By: Tanmay Deshpande

Overview of this book

Big data is the current requirement. Most organizations produce huge amount of data every day. With the arrival of Hadoop-like tools, it has become easier for everyone to solve big data problems with great efficiency and at minimal cost. Grasping Machine Learning techniques will help you greatly in building predictive models and using this data to make the right decisions for your organization. Hadoop Real World Solutions Cookbook gives readers insights into learning and mastering big data via recipes. The book not only clarifies most big data tools in the market but also provides best practices for using them. The book provides recipes that are based on the latest versions of Apache Hadoop 2.X, YARN, Hive, Pig, Sqoop, Flume, Apache Spark, Mahout and many more such ecosystem tools. This real-world-solution cookbook is packed with handy recipes you can apply to your own everyday issues. Each chapter provides in-depth recipes that can be referenced easily. This book provides detailed practices on the latest technologies such as YARN and Apache Spark. Readers will be able to consider themselves as big data experts on completion of this book. This guide is an invaluable tutorial if you are planning to implement a big data warehouse for your business.
Table of Contents (18 chapters)
Hadoop Real-World Solutions Cookbook Second Edition
Credits
About the Author
Acknowledgements
About the Reviewer
www.PacktPub.com
Preface
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...