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Hadoop Beginner's Guide

You're reading from   Hadoop Beginner's Guide Get your mountain of data under control with Hadoop. This guide requires no prior knowledge of the software or cloud services – just a willingness to learn the basics from this practical step-by-step tutorial.

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Product type Paperback
Published in Feb 2013
Publisher Packt
ISBN-13 9781849517300
Length 398 pages
Edition 1st Edition
Tools
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Table of Contents (19) Chapters Close

Hadoop Beginner's Guide
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. What It's All About 2. Getting Hadoop Up and Running FREE CHAPTER 3. Understanding MapReduce 4. Developing MapReduce Programs 5. Advanced MapReduce Techniques 6. When Things Break 7. Keeping Things Running 8. A Relational View on Data with Hive 9. Working with Relational Databases 10. Data Collection with Flume 11. Where to Go Next Pop Quiz Answers Index

Graph algorithms


Any good computer scientist will tell you that the graph data structure is one of the most powerful tools around. Many complex systems are best represented by graphs and a body of knowledge going back at least decades (centuries if you get more mathematical about it) provides very powerful algorithms to solve a vast variety of graph problems. But by their very nature, graphs and their algorithms are often very difficult to imagine in a MapReduce paradigm.

Graph 101

Let's take a step back and define some terminology. A graph is a structure comprising of nodes (also called vertices) that are connected by links called edges . Depending on the type of graph, the edges may be bidirectional or unidirectional and may have weights associated with them. For example, a city road network can be seen as a graph where the roads are the edges, and intersections and points of interest are nodes. Some streets are one-way and some are not, some have tolls, some are closed at certain times...

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