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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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Sandeep Karanth Sandeep Karanth
Author Profile Icon Sandeep Karanth
Sandeep Karanth
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Toc

Table of Contents (15) Chapters Close

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 A. Hadoop for Microsoft Windows Index

Summary

HDFS is a great filesystem for MapReduce workloads. But its sequential access pattern and non-compliance with POSIX interfaces make it tedious to work with in certain situations. Hadoop allows its users to extend HDFS or provide drop-in replacements. The key takeaways from this chapter are as follows:

  • There are a number of implementations that extend or provide drop-in replacements for HDFS. CephFS, MapRFS, GPFS from IBM, and Cassandra by DataStax are some examples of such extensions.
  • Interface to the Amazon S3 storage service is available out of the box in Hadoop. Both a native-storage S3 filesystem interface and a block-storage filesystem interface are available.
  • Extending Hadoop to incorporate other filesystems is done by extending the FileSystem abstract base class. The FSDataInputStream and FSDataOutputStream objects are used to wrap the input and output streams of the underlying filesystem respectively.
  • The security and access control mechanisms of the underlying filesystem can...
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