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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

Chapter 12. Real-time Data Analytics Using YARN

Hadoop is known for batch processing of data available in HDFS through MapReduce programming. The data is placed in HDFS before it can be queried for analysis. The Hadoop services execute only MapReduce jobs. The cluster resources are not fully utilized for other operations when the resources are ideal.

This is considered as a limitation for use cases that required processing of data in real time. Apache Storm and Spark are the frameworks developed for processing data in real time. These frameworks need an efficient cluster's ResourceManager. Focusing on a common solution for the preceding limitations in Hadoop, YARN evolved as a generic framework to provide resource management and application execution over a cluster. It not only allows different frameworks other than MapReduce to use the same cluster but also provides efficient scheduling algorithms to the applications running on the cluster. Frameworks such as Storm, Spark...

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