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

Analyzing transitions through logs

Both YARN services, ResourceManager and NodeManager generate logs and store them in a .log file locally inside the folder specified using the HADOOP_LOGS_DIR variable. By default, the logs are stored in HADOOP_PREFIX/logs. All the state transitions in YARN are recorded in the log files. In this section, we'll cover few state transitions and the logs generated during those transitions.


Setting the log level: Hadoop-YARN uses Apache Log4j library and it uses a file located in the configuration folder of the Hadoop-YARN bundle at HADOOP_PREFIX/etc/hadoop.

The Log4j library supports six log levels – TRACE, DEBUG, INFO, WARN, ERROR, and FATAL. A cluster administrator sets the log level for Hadoop-YARN services and the default log level is INFO. The hadoop.root.logger property is used to update the log level for Hadoop-YARN services. To read more about Apache Log4j library, you can refer to the official site at