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YARN Essentials

YARN Essentials

By : Fasale, Nirmal Kumar
4.2 (5)
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YARN Essentials

YARN Essentials

4.2 (5)
By: Fasale, Nirmal Kumar
Buy this Book

Overview of this book

If you have a working knowledge of Hadoop 1.x but want to start afresh with YARN, this book is ideal for you. You will be able to install and administer a YARN cluster and also discover the configuration settings to fine-tune your cluster both in terms of performance and scalability. This book will help you develop, deploy, and run multiple applications/frameworks on the same shared YARN cluster.
Table of Contents (12 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Need for YARN
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1. Need for YARN
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The redesign idea
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Summary
2
2. YARN Architecture
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2. YARN Architecture
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Core components of YARN architecture
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YARN scheduler policies
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Recent developments in YARN architecture
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Summary
3
3. YARN Installation
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3. YARN Installation
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Single-node installation
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The fully-distributed mode
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Operating Hadoop and YARN clusters
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Web interfaces of the Ecosystem
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Summary
4
4. YARN and Hadoop Ecosystems
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4. YARN and Hadoop Ecosystems
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The Hadoop 2 release
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A short introduction to Hadoop 1.x and MRv1
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MRv1 versus MRv2
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Understanding where YARN fits into Hadoop
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Old and new MapReduce APIs
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Backward compatibility of MRv2 APIs
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Practical examples of MRv1 and MRv2
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Summary
5
5. YARN Administration
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5. YARN Administration
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Container allocation
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Container configurations
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YARN scheduling policies
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YARN multitenancy application support
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Administration of YARN
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Summary
6
6. Developing and Running a Simple YARN Application
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6. Developing and Running a Simple YARN Application
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Running sample examples on YARN
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Monitoring YARN applications with web GUI
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YARN's MapReduce support
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The YARN application workflow
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Summary
7
7. YARN Frameworks
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7. YARN Frameworks
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Apache Samza
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Storm-YARN
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Apache Spark
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Apache Tez
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Apache Giraph
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HOYA (HBase on YARN)
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KOYA (Kafka on YARN)
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Summary
8
8. Failures in YARN
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8. Failures in YARN
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ResourceManager failures
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ApplicationMaster failures
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NodeManager failures
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Container failures
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Hardware Failures
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Summary
9
9. YARN – Alternative Solutions
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9. YARN – Alternative Solutions
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Mesos
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Omega
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Corona
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Summary
10
10. YARN – Future and Support
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10. YARN – Future and Support
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What YARN means to the big data industry
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Journey – present and future
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YARN-supported frameworks
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Summary
11
Index
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Index

A short introduction to Hadoop 1.x and MRv1

We will briefly look at the basic Apache Hadoop 1.x and its processing framework, MRv1 (Classic), so that we can get a clear picture of the differences in Apache Hadoop 2.x MRv2 (YARN) in terms of architecture, components, and processing framework.

Apache Hadoop is a scalable, fault-tolerant distributed system for data storage and processing. The core programming model in Hadoop is MapReduce.

Since 2004, Hadoop has emerged as the de facto standard to store, process, and analyze hundreds of terabytes and even petabytes of data.

The major components in Hadoop 1.x are as follows:

  • NameNode: This keeps the metadata in the main memory.
  • DataNode: This is where the data resides in the form of blocks.
  • JobTracker: This assigns/reassigns MapReduce tasks to TaskTrackers in the cluster and tracks the status of each TaskTracker.
  • TaskTracker: This executes the task assigned by the JobTracker and sends the status of the task to the JobTracker.

The major components of...

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