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

Projects powered by YARN

Efficient and reliable resource management is a basic need of a distributed application framework. YARN provides a generic resource management framework to support data analysis through multiple data processing algorithms. There are a lot of projects that have started using YARN for resource management. We've listed a few of these projects here and discussed how YARN integration solves their business requirements:

  • Apache Giraph: Giraph is a framework for offline batch processing of semistructured graph data stored using Hadoop. With the Hadoop 1.x version, Giraph had no control over the scheduling policies, heap memory of the mappers, and locality awareness for the running job. Also, defining a Giraph job on the basis of mappers / reducers slots was a bottleneck. YARN's flexible resource allocation model, locality awareness principle, and application master framework ease the Giraph's job management and resource allocation to tasks.

  • Apache Spark: Spark enables iterative data processing and machine learning algorithms to perform analysis over data available through HDFS, HBase, or other storage systems. Spark uses YARN's resource management capabilities and framework to submit the DAG of a job. The spark user can focus more on data analytics' use cases rather than how spark is integrated with Hadoop or how jobs are executed.

Some other projects powered by YARN are as follows:


A page on Hadoop wiki lists a number of projects/applications that are migrating to or using YARN as their resource management tool.

You can see this at