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

Chapter 12. Real-time Data Analytics Using YARN

Hadoop is known for batch processing of data available in HDFS through MapReduce programming. The data is placed in HDFS before it can be queried for analysis. The Hadoop services execute only MapReduce jobs. The cluster resources are not fully utilized for other operations when the resources are ideal.

This is considered as a limitation for use cases that required processing of data in real time. Apache Storm and Spark are the frameworks developed for processing data in real time. These frameworks need an efficient cluster's ResourceManager. Focusing on a common solution for the preceding limitations in Hadoop, YARN evolved as a generic framework to provide resource management and application execution over a cluster. It not only allows different frameworks other than MapReduce to use the same cluster but also provides efficient scheduling algorithms to the applications running on the cluster. Frameworks such as Storm, Spark, and Giraph adopted...