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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 Reduce task


The Reduce task is an aggregation step. If the number of Reduce tasks is not specified, the default number is one. The risk of running one Reduce task would mean overloading that particular node. Having too many Reduce tasks would mean shuffle complexity and proliferation of output files that puts pressure on the NameNode. It is important to understand the data distribution and the partitioning function to decide the optimal number of Reduce tasks.

Tip

The ideal setting for each Reduce task to process is a range of 1 GB to 5 GB.

The number of Reduce tasks can be set using the mapreduce.job.reduces parameter. It can be programmatically set by calling the setNumReduceTasks() method on the Job object. There is a cap on the number of Reduce tasks that can be executed by a single node. It is given by the mapreduce.tasktracker.reduce.maximum property.

Note

The heuristic to determine the right number of reducers is as follows:

0.95 * (nodes * mapreduce.tasktracker.reduce.maximum)

Alternatively...

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