Learning YARN

More Information
  • Explore YARN features and offerings
  • Manage big data clusters efficiently using the YARN framework
  • Create single as well as multi-node Hadoop-YARN clusters on Linux machines
  • Understand YARN components and their administration
  • Gain insights into application execution flow over a YARN cluster
  • Write your own distributed application and execute it over YARN cluster
  • Work with schedulers and queues for efficient scheduling of applications
  • Integrate big data projects like Spark and Storm with YARN

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.

  • Deep dive into YARN components, schedulers, life cycle management and security architecture
  • Create your own Hadoop-YARN applications and integrate big data technologies with YARN
  • Step-by-step guide to provision, manage, and monitor Hadoop-YARN clusters with ease
Page Count 278
Course Length 8 hours 20 minutes
ISBN 9781784393960
Date Of Publication 27 Aug 2015


Akhil Arora

Akhil Arora works as a senior software engineer with Impetus Infotech and has around 5 years of extensive research and development experience. He joined Impetus Infotech in October 2012 and is working with the innovation labs team. He is a technology expert, good learner, and creative thinker. He is also passionate and enthusiastic about application development in Hadoop and other big data technologies. He loves to explore new technologies and is always ready to work on new challenges. Akhil attained a BE degree in computer science from the Apeejay College of Engineering in Sohna, Haryana, India.


A beginning for a new voyage, A first step towards my passion and to gain recognition, My first book Learning YARN..!!
-- Akhil Arora

Shrey Mehrotra

Shrey Mehrotra has over 8 years of IT experience and, for the past 6 years, has been designing the architecture of cloud and big-data solutions for the finance, media, and governance sectors. Having worked on research and development with big-data labs and been part of Risk Technologies, he has gained insights into Hadoop, with a focus on Spark, HBase, and Hive. His technical strengths also include Elasticsearch, Kafka, Java, YARN, Sqoop, and Flume. He likes spending time performing research and development on different big-data technologies. He is the coauthor of the books Learning YARN and Hive Cookbook, a certified Hadoop developer, and he has also written various technical papers.