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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

The Map task


The efficiency of the Map phase is decided by the specifications of the job inputs. We saw that having too many small files leads to proliferation of Map tasks because of a large number of splits. Another important statistic to note is the average runtime of a Map task. Too many or too few Map tasks are both detrimental for job performance. Striking a balance between the two is important, much of which depends on the nature of the application and data.

Tip

A rule of thumb is to have the runtime of a single Map task to be around a minute to three minutes, based on empirical evidence.

The dfs.blocksize attribute

The default block size of files in a cluster is overridden in the cluster configuration file, hdfs-site.xml, generally present in the etc/hadoop folder of the Hadoop installation. In some cases, a Map task might take only a few seconds to process a block. Giving a bigger block to the Map tasks in such cases is better. This can be done in the following ways:

  • Increasing the fileinputformat...

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