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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

The integration of Spark with YARN

Spark is a distributed computing framework that uses in-memory primitives to process data available in a data store. It provides an in-memory representation of data to be processed and it is well suited for various machine learning algorithms. Spark allows easy connection to different data stores such as HDFS, Cassandra, and Amazon S3.

There are several companies that use Spark for big data processing. The complete list of companies and their use cases is available at https://cwiki.apache.org/confluence/display/SPARK/Powered+By+Spark.

Spark has two components: SparkContext (Driver) and Executor. SparkContext is a master service that connects with a cluster manager and acquires resources for Executor services on worker nodes. For cluster management, Spark supports YARN, Apache Mesos and an in-built standalone cluster manager.

In this section, we'll discuss how Spark is integrated with YARN and how you can submit Spark-YARN applications on a Hadoop-YARN...

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