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Mastering Hadoop

You're reading from   Mastering Hadoop Go beyond the basics and master the next generation of Hadoop data processing platforms

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Product type Paperback
Published in Dec 2014
Publisher
ISBN-13 9781783983643
Length 374 pages
Edition 1st Edition
Tools
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Author (1):
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 Karanth Karanth
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Karanth
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Table of Contents (21) Chapters Close

Mastering Hadoop
Credits
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Preface
1. Hadoop 2.X FREE CHAPTER 2. Advanced MapReduce 3. Advanced Pig 4. Advanced Hive 5. Serialization and Hadoop I/O 6. YARN – Bringing Other Paradigms to Hadoop 7. Storm on YARN – Low Latency Processing in Hadoop 8. Hadoop on the Cloud 9. HDFS Replacements 10. HDFS Federation 11. Hadoop Security 12. Analytics Using Hadoop Hadoop for Microsoft Windows Index

Chapter 7. Storm on YARN – Low Latency Processing in Hadoop

Hadoop MapReduce builds on the concept of moving computation to data. Data is significantly larger than the instructions to manipulate it. The network is the slowest component in any distributed data processing system, so it is natural to move the smaller piece around, that is, the program itself. With assistance from the NameNode, Hadoop knows exactly how the data resides in a cluster of computers. It uses this data locality information to schedule tasks on appropriate nodes, putting in the best effort to locate the task very close to the data needed by the task.

In this chapter, we will discuss the opposite paradigm, that is, moving data to the compute, also known as the streaming paradigm. There are many frameworks that facilitate streaming, Apache Storm being a popular one. Apache Storm integrates with Hadoop YARN, bringing the streaming paradigm to Hadoop. In this chapter, we will cover the following topics:

  • Comparing and contrasting...

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