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

Chapter 3, Understanding MapReduce


Pop quiz – key/value pairs

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2

Q2

3

Pop quiz – walking through a run of WordCount

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1

Q2

3

Q3

2. Reducer C cannot be used because if such reduction were to occur, the final reducer could receive from the combiner a series of means with no knowledge of how many items were used to generate them, meaning the overall mean is impossible to calculate. Reducer D is subtle as the individual tasks of selecting a maximum or minimum are safe for use as combiner operations. But if the goal is to determine the overall variance between the maximum and minimum value for each key, this would not work. If the combiner that received the maximum key had values clustered around it, this would generate small results; similarly for the one receiving the minimum value. These subranges have little value in isolation and again the final reducer cannot construct the desired result.