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 – implementing WordCount using Streaming


Let's flog the dead horse of WordCount one more time and implement it using Streaming by performing the following steps:

  1. Save the following file to wcmapper.rb:

    #/bin/env ruby
    
    while line = gets
        words = line.split("\t")
        words.each{ |word| puts word.strip+"\t1"}}
    end
  2. Make the file executable by executing the following command:

    $ chmod +x wcmapper.rb
    
  3. Save the following file to wcreducer.rb:

    #!/usr/bin/env ruby
    
    current = nil
    count = 0
    
    while line = gets
        word, counter = line.split("\t")
    
        if word == current
            count = count+1
        else
            puts current+"\t"+count.to_s if current
            current = word
            count = 1
        end
    end
    puts current+"\t"+count.to_s
  4. Make the file executable by executing the following command:

    $ chmod +x wcreducer.rb
    
  5. Execute the scripts as a Streaming job using the datafile from the previous chapter:

    $ hadoop jar hadoop/contrib/streaming/hadoop-streaming-1.0.3.jar 
    -file wcmapper.rb -mapper...