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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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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

Implementing a filesystem in Hadoop

Based on the situation, it might be a necessity to replace HDFS with a filesystem of your choice. Hadoop provides out-of-the-box support for a few filesystems such as S3. HDFS replacement can be done either as a drop-in replacement or, as in the case with S3, seamless integration with the S3 file store for input and output.

In this section, we will re-implement the S3 native filesystem and extend Hadoop. The code in this section illustrates the steps on how HDFS replacement can be done. Error handling and other features related to S3 have been omitted for brevity.

The major steps in implementing a filesystem for Hadoop are as follows:

  1. The org.apache.hadoop.fs.FileSystem abstract class needs to be extended and all the abstract methods need to be overridden. There are out-of-the-box implementations for FilterFileSystem, NativeS3FileSystem, S3FileSystem, RawLocalFileSystem, FTPFileSystem, and ViewFileSystem.
  2. The open method returns an FsDataInputStream object...
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