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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

Understanding application execution flow

A YARN application can be a simple shell script, MapReduce job, or any group of jobs. This section will cover YARN application submission and execution flow. To manage application execution over YARN, a client needs to define an ApplicationMaster. The client submits an application context to the ResourceManager. As per the application needs, the ResourceManager then allocates memory for an ApplicationMaster and containers for application execution.

The complete process of application execution can be broadly divided into six phases, as shown in the following figure:

Understanding application execution flow

Phase 1 – Application initialization and submission

In the first phase of application execution, a client will connect to the applications manager service of the ResourceManager daemon and will request the ResourceManager for a new application ID. The ResourceManager will validate the client request and if the client is an authorized user, it will send a new and unique application...

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