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

Introducing MRv1 and MRv2

The MapReduce framework in Hadoop 1.x version is also known as MRv1. The MRv1 framework includes client communication, job execution and management, resource scheduling and resource management. The Hadoop daemons associated with MRv1 are JobTracker and TaskTracker as shown in the following figure:

JobTracker is a master service responsible for client communications, MapReduce job management, scheduling, resource management, and so on. The TaskTracker service is a worker daemon that runs on every slave of the Hadoop cluster. It is responsible for the execution of map reduce tasks. A client submits a job to the JobTracker service. The JobTracker validates the request and breaks the job into tasks. The JobTracker uses a data localization mechanism and assigns TaskTracker for the execution of tasks. The TaskTracker service runs a map reduce task as a separate JVM named as child as described in the following figure:

The following diagram shows the MRv1 services and their...