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 1. Starting with YARN Basics

In early 2006, Apache Hadoop was introduced as a framework for the distributed processing of large datasets stored across clusters of computers, using a programming model. Hadoop was developed as a solution to handle big data in a cost effective and easiest way possible. Hadoop consisted of a storage layer, that is, Hadoop Distributed File System (HDFS) and the MapReduce framework for managing resource utilization and job execution on a cluster. With the ability to deliver high performance parallel data analysis and to work with commodity hardware, Hadoop is used for big data analysis and batch processing of historical data through MapReduce programming.

With the exponential increase in the usage of social networking sites such as Facebook, Twitter, and LinkedIn and e-commerce sites such as Amazon, there was the need of a framework to support not only MapReduce batch processing, but real-time and interactive data analysis as well. Enterprises should be able to execute other applications over the cluster to ensure that cluster capabilities are utilized to the fullest. The data storage framework of Hadoop was able to counter the growing data size, but resource management became a bottleneck. The resource management framework for Hadoop needed a new design to solve the growing needs of big data.

YARN, an acronym for Yet Another Resource Negotiator, has been introduced as a second-generation resource management framework for Hadoop. YARN is added as a subproject of Apache Hadoop. With MapReduce focusing only on batch processing, YARN is designed to provide a generic processing platform for data stored across a cluster and a robust cluster resource management framework.

In this chapter, we will cover the following topics:

  • Introduction to MapReduce v1

  • Shortcomings of MapReduce v1

  • An overview of the YARN components

  • The YARN architecture

  • How YARN satisfies big data needs

  • Projects powered by YARN