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 – performing the shape/time analysis from the command line


It may not be immediately obvious how to do this sort of local command-line analysis, so let's look at an example.

With the UFO datafile on the local filesystem, execute the following command:

$ cat ufo.tsv | shapetimemapper.rb | sort| shapetimereducer.rb

What just happened?

With a single Unixcommand line, we produced output identical to our previous full MapReduce job. If you look at what the command line does, this makes sense.

Firstly, the input file is sent—a line at a time—to the mapper. The output of this is passed through the Unix sort utility and this sorted output is passed a line at a time to the reducer. This is of course a very simplified representation of our general MapReduce job workflow.

Then the obvious question is why should we bother with Hadoop if we can do equivalent analysis at the command line. The answer of course is our old friend, scale. This simple approach works fine for a file such as the...