Book Image

Hadoop 2.x Administration Cookbook

By : Aman Singh
Book Image

Hadoop 2.x Administration Cookbook

By: Aman Singh

Overview of this book

Hadoop enables the distributed storage and processing of large datasets across clusters of computers. Learning how to administer Hadoop is crucial to exploit its unique features. With this book, you will be able to overcome common problems encountered in Hadoop administration. The book begins with laying the foundation by showing you the steps needed to set up a Hadoop cluster and its various nodes. You will get a better understanding of how to maintain Hadoop cluster, especially on the HDFS layer and using YARN and MapReduce. Further on, you will explore durability and high availability of a Hadoop cluster. You’ll get a better understanding of the schedulers in Hadoop and how to configure and use them for your tasks. You will also get hands-on experience with the backup and recovery options and the performance tuning aspects of Hadoop. Finally, you will get a better understanding of troubleshooting, diagnostics, and best practices in Hadoop administration. By the end of this book, you will have a proper understanding of working with Hadoop clusters and will also be able to secure, encrypt it, and configure auditing for your Hadoop clusters.
Table of Contents (20 chapters)
Hadoop 2.x Administration Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Index

Fair Scheduler configuration


Getting ready

To go through the recipe in this section, we need Hadoop Cluster setup and running. By default, Apache Hadoop 1.x distribution uses FIFO scheduler and Hadoop 2.x uses Capacity Scheduler. In a cluster with multiple jobs, it is not good to use FIFO scheduler, as it will starve the jobs for resources and only the very first job in the queue is executed; all other jobs have to wait.

To address the preceding issue, there are two commonly used Schedulers: Fair Scheduler, and Capacity Scheduler, to allocate the cluster resources in a fair manner. In this recipe, we will see how to configure Fair Scheduler. Simply put, Fair Scheduler shares resources fairly among running jobs based on queues and weights assigned.

How to do it...

  1. Connect to the master1.cyrus.com master node in the cluster and switch as user hadoop.

  2. Edit the yarn-site.xml as follows:

    <property>
        <name>yarn.resourcemanager.scheduler.class</name>
        <value>org.apache...