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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
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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 9. HDFS Replacements

The parallelism and scalability of the MapReduce computing paradigm are greatly influenced by the underlying filesystem. HDFS is the default filesystem that comes with most Hadoop distributions. The filesystem automatically chunks files into blocks and stores them in a replicated fashion across the cluster. The information of the distribution pattern is supplied to the MapReduce engine that can then smartly place tasks so that movement of data over the network is minimized.

However, there are many use cases where HDFS may not be ideal. In this chapter, we will look at the following topics:

  • The strengths and drawbacks of HDFS when compared to other POSIX filesystems.

  • Hadoop's support for other filesystems. One of them is Amazon's cloud storage service known as Simple Storage Service (S3). Reading and writing files from and to the S3 services is permitted within Hadoop.

  • Hadoop HDFS has extensibility features. Extending the framework can be of two kinds: by providing...

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