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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

File formats


Hive supports a number of file formats out of the box. In this section, we will inspect some of these file formats and their utilities.

Compressed files

For some use cases, storing files in a compressed format within HDFS is advantageous. This strategy not only uses less storage, it also can reduce query times. Hive provides importing files stored in GZIP and BZIP2 formats directly into tables. During query execution, these files are decompressed and given as inputs to Map tasks. However, files compressed with GZIP and BZIP2 compression schemes cannot be split and are processed within a single Map task.

In practice, files stored in these compressed file formats are loaded into a table whose underlying data format is a Sequence file. Sequence files can be split and distributed to different Map tasks.

Tip

The io.seqfile.compression.type property tells Hive how the compression of the Sequence file should happen. It can take two values, RECORD, where each record is compressed and, BLOCK...

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