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

The integration of HAMA and Giraph with YARN

Apache HAMA is a distributed computing framework based on Bulk Synchronous Parallel algorithms. It provides high performance computing for performance-intensive, scientific, and iterative algorithms such as Matrix, Graph, and Machine Learning.

HAMA consists of three major components:

  • BSPMaster

  • GroomServers

  • Zookeeper

Deploying HAMA with YARN is a simple process and you can refer to the following references:

Apache Giraph is a framework for iterative processing of semi-structured graphs. It is inspired from Google's Pregel, which is also a graph processing framework. Giraph is also based on a Bulk Synchronous Parallel model of distributed computing.

For more details on Giraph, you can refer to the official website at

Initially, Giraph was used with the MapReduce framework for Hadoop 1.x. There were a few concerns, such as:

  • Defining...