Book Image

Learning YARN

By : Akhil Arora, Shrey Mehrotra, Shreyank Gupta
Book Image

Learning YARN

By: Akhil Arora, Shrey Mehrotra, Shreyank Gupta

Overview of this book

Today enterprises generate huge volumes of data. In order to provide effective services and to make smarter and more intelligent decisions from these huge volumes of data, enterprises use big-data analytics. In recent years, Hadoop has been used for massive data storage and efficient distributed processing of data. The Yet Another Resource Negotiator (YARN) framework solves the design problems related to resource management faced by the Hadoop 1.x framework by providing a more scalable, efficient, flexible, and highly available resource management framework for distributed data processing. This book starts with an overview of the YARN features and explains how YARN provides a business solution for growing big data needs. You will learn to provision and manage single, as well as multi-node, Hadoop-YARN clusters in the easiest way. You will walk through the YARN administration, life cycle management, application execution, REST APIs, schedulers, security framework and so on. You will gain insights about the YARN components and features such as ResourceManager, NodeManager, ApplicationMaster, Container, Timeline Server, High Availability, Resource Localisation and so on. The book explains Hadoop-YARN commands and the configurations of components and explores topics such as High Availability, Resource Localization and Log aggregation. You will then be ready to develop your own ApplicationMaster and execute it over a Hadoop-YARN cluster. Towards the end of the book, you will learn about the security architecture and integration of YARN with big data technologies like Spark and Storm. This book promises conceptual as well as practical knowledge of resource management using YARN.
Table of Contents (20 chapters)
Learning YARN
About the Authors
About the Reviewers

Writing your own application

YARN framework provides flexibility to run any application in a clustered environment. An application could be as simple as a Java process, a shell script, or a simple date command. The ResourceManager service manages the cluster resource allocation and the NodeManager services execute tasks as specified by the application framework; for example, the map and reduce tasks of Hadoop MapReduce jobs.

In this section, you will write your own applications to run in a distributed environment through YARN.

The complete process can be summarized in four simple steps, which are shown in the following diagram:

Step 1 – Create a new project and add Hadoop-YARN JAR files

We will create a new Java project in Eclipse and will use the YARN client APIs to write a simple YARN application. You can either create a simple Java project or a Maven project.

You need to add the following jar files to your project's build path:

  • hadoop-yarn-client-2.5.1.jar

  • hadoop-yarn-api-2.5.1.jar

  • hadoop...