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

Monitoring the YARN services

When we talk about handling big data and multi node clusters for distributed processing, we consider performance and efficiency as major factors. Monitoring of the YARN services includes collection of cluster, node, and service level metrics. Each of the YARN services exposes its monitoring information as JMX MBean object. As a cluster administrator, a person needs to monitor these metrics through detailed graphs and reporting tools, such as Jconsole, Ganglia, and so on. In this section, we'll discuss the different techniques used to monitor the YARN services.

JMX monitoring

JMX are the Java tools used for monitoring and managing applications, objects, and so on. The resources are represented as Managed Bean or simply MBean objects. An MBean represents a resource running in a Java Virtual Machine. The statistical information collected from these resources regarding performance, system resource usage, application events, and such, could be used to fine tune the...