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Learning YARN

You're reading from   Learning YARN Moving beyond MapReduce - learn resource management and big data processing using YARN

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Product type Paperback
Published in Aug 2015
Publisher
ISBN-13 9781784393960
Length 278 pages
Edition 1st Edition
Tools
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Toc

Table of Contents (14) Chapters Close

Preface 1. Starting with YARN Basics FREE CHAPTER 2. Setting up a Hadoop-YARN Cluster 3. Administering a Hadoop-YARN Cluster 4. Executing Applications Using YARN 5. Understanding YARN Life Cycle Management 6. Migrating from MRv1 to MRv2 7. Writing Your Own YARN Applications 8. Dive Deep into YARN Components 9. Exploring YARN REST Services 10. Scheduling YARN Applications 11. Enabling Security in YARN 12. Real-time Data Analytics Using YARN Index

The integration of HAMA and Giraph with YARN

Apache HAMA is a distributed computing framework based on Bulk Synchronous Parallel algorithms. It provides high performance computing for performance-intensive, scientific, and iterative algorithms such as Matrix, Graph, and Machine Learning.

HAMA consists of three major components:

  • BSPMaster
  • GroomServers
  • Zookeeper

Deploying HAMA with YARN is a simple process and you can refer to the following references:

Apache Giraph is a framework for iterative processing of semi-structured graphs. It is inspired from Google's Pregel, which is also a graph processing framework. Giraph is also based on a Bulk Synchronous Parallel model of distributed computing.

For more details on Giraph, you can refer to the official website at http://giraph.apache.org/.

Initially, Giraph was used with the MapReduce framework for Hadoop 1.x. There were a few concerns, such as:

  • Defining...
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