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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

Job scheduling in YARN


Most cluster resources are multitenant in nature, that is, a number of teams or people share the cluster resources. Allocation of resources to satisfy the needs of all these tenants becomes important and is the responsibility of the scheduler. Individual clusters per team or person is not viable as they render poor utilization.

YARN provides a pluggable model to schedule policies. The initial versions of Hadoop had a simple First in First Out (FIFO) scheduler. However, FIFO was found to be inadequate in dealing with the complexities of multitenancy. We will discuss two other scheduling strategies that are used in Hadoop today, CapacityScheduler and FairScheduler.

CapacityScheduler

The concept behind CapacityScheduler is to guarantee a tenant-promised capacity on a shared cluster. If other tenants utilize less than the requested capacity, the scheduler allows the tenant to tap into these unused resources. The number one goal of CapacityScheduler is not to allow a single...

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