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  • Book Overview & Buying YARN Essentials
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YARN Essentials

YARN Essentials

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

Apache Spark

Apache Spark is a fast and general engine for large-scale data processing. It was originally developed in 2009 in UC Berkeley's AMPLab and open sourced in 2010.

The main features of Spark are as follows:

  • Speed: Spark enables applications in Hadoop clusters to run up to 100x faster in memory and 10x faster even when running on disk.

  • Ease of use: Spark lets you quickly write applications in Java, Scala, or Python. You can use it interactively to query big datasets from the Scala and Python shells.

  • Runs everywhere: Spark runs on Hadoop, Mesos, in standalone mode, or in the cloud. It can access diverse data sources, including HDFS, Cassandra, HBase, and S3. You can run Spark readily using its standalone cluster mode, on EC2, or run it on Hadoop YARN or Apache Mesos. It can read from HDFS, HBase, Cassandra, and any Hadoop data source.

  • Generality: Spark powers a stack of high-level tools, including Spark SQL, MLlib for machine learning, GraphX, and Spark Streaming. You can combine...

CONTINUE READING
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Tech Concepts
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Programming languages
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