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 – summarizing the shape data


Just as we provided a summarization for the overall UFO data set earlier, let's now do a more focused summarization on the data provided for UFO shapes:

  1. Save the following to shapemapper.rb:

    #!/usr/bin/env ruby
    
    while line = gets  
        parts = line.split("\t")    
        if parts.size == 6        
            shape = parts[3].strip     
            puts shape+"\t1" if !shape.empty?   
        end     
    end     
  2. Make the file executable:

    $ chmod +x shapemapper.rb
    
  3. Execute the job once again using the WordCount reducer:

    $ hadoop jar hadoop/contrib/streaming/hadoop-streaming-1.0.3.jarr --file shapemapper.rb -mapper shapemapper.rb -file wcreducer.rb -reducer wcreducer.rb -input ufo.tsv -output shapes
    
  4. Retrieve the shape info:

    $ hadoop fs -cat shapes/part-00000  
    

What just happened?

Our mapper here is pretty simple. It breaks each record into its constituent fields, discards any without exactly six fields, and gives a counter as the output for any non-empty shape value...