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Learning YARN

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

Product type Paperback

Published in Aug 2015

Publisher

ISBN-13 9781784393960

Length 278 pages

Edition 1st Edition

Tools

Hadoop

Concepts

Data Processing

Table of Contents (14) Chapters

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

Starting with the basics

The Apache Hadoop 2.x version consists of three key components:

Hadoop Distributed File System (HDFS)
Yet Another Resource Negotiator (YARN)
The MapReduce API (Job execution, MRApplicationMaster, JobHistoryServer, and so on)

There are two master processes that manage the Hadoop 2.x cluster—the NameNode and the ResourceManager. All the slave nodes in the cluster have DataNode and NodeManager processes running as the worker daemons for the cluster. The NameNode and DataNode daemons are part of HDFS, whereas the ResourceManager and NodeManager belong to YARN.

When we configure Hadoop-YARN on a single node, we need to have all four processes running on the same system. Hadoop single node installation is generally used for learning purposes. If you are a beginner and need to understand the Hadoop-YARN concepts, you can use a single node Hadoop-YARN cluster.

In the production environment, a multi-node cluster is used. It is recommended to have separate nodes for NameNode...

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