Book Image

Hadoop Beginner's Guide

Book Image

Hadoop Beginner's Guide

Overview of this book

Data is arriving faster than you can process it and the overall volumes keep growing at a rate that keeps you awake at night. Hadoop can help you tame the data beast. Effective use of Hadoop however requires a mixture of programming, design, and system administration skills."Hadoop Beginner's Guide" removes the mystery from Hadoop, presenting Hadoop and related technologies with a focus on building working systems and getting the job done, using cloud services to do so when it makes sense. From basic concepts and initial setup through developing applications and keeping the system running as the data grows, the book gives the understanding needed to effectively use Hadoop to solve real world problems.Starting with the basics of installing and configuring Hadoop, the book explains how to develop applications, maintain the system, and how to use additional products to integrate with other systems.While learning different ways to develop applications to run on Hadoop the book also covers tools such as Hive, Sqoop, and Flume that show how Hadoop can be integrated with relational databases and log collection.In addition to examples on Hadoop clusters on Ubuntu uses of cloud services such as Amazon, EC2 and Elastic MapReduce are covered.
Table of Contents (19 chapters)
Hadoop Beginner's Guide
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Time for action – fixing WordCount to work with a combiner


Let's make the necessary modifications to WordCount to correctly use a combiner.

Copy WordCount2.java to a new file called WordCount3.java and change the reduce method as follows:

public void reduce(Text key, Iterable<IntWritable> values,            
Context context) throws IOException, InterruptedException 
{
int total = 0 ;
for (IntWritable val : values))
{
total+= val.get() ;
}
            context.write(key, new IntWritable(total));
}

Remember to also change the class name to WordCount3 and then compile, create the JAR file, and run the job as before.

What just happened?

The output is now as expected. Any map-side invocations of the combiner performs successfully and the reducer correctly produces the overall output value.

Tip

Would this have worked if the original reducer was used as the combiner and the new reduce implementation as the reducer? The answer is no, though our test example would not have demonstrated it. Because...