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

MapReduce HistoryServer REST APIs

HistorySever maintains information of MapReduce applications executed over the cluster. The Rest API provides counters, attempts, and configuration information about the jobs and tasks. MapReduce HistoryServer starts at web address port 19888 by default. This could be configured by setting up the mapreduce.jobhistory.webapp.address property in the mapred-site.xml file.

MapReduce HistoryServer REST APIs provide information about the finished applications executed over the cluster. These APIs have similar URI structures and information types as the MapReduce ApplicationMaster API. The MapReduce ApplicationMaster API is used when the application is in RUNNING state. However, once the application is finished, the application data is accessed through the MapReduce HistoryServer API.

The URI format for MapReduce HistoryServer REST services is:

http://<history server http address:port>/ws/v1/history


MapReduce Job HistoryServer REST APIs are accessed using...