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

Queuing mappings in Capacity Scheduler


In this recipe, we will be configuring users who can submit jobs to the queue and can also set rule for various job submissions.

Let's look at another use case where, if user hadoop submits a job, it should go to the prod queue and if any other users submits a job, it must go to dev queue. How can we set up something like this?

Getting ready

Make sure that the user has a running cluster with HDFS and YARN configured. It's best to have gone through at least the previous recipe.

How to do it...

  1. Connect to the master1.cyrus.com Namenode and switch as user hadoop.

  2. Edit the capacity-scheduler.xml allocation file as shown next:

    <property>
        <name>yarn.scheduler.capacity.queue-mappings</name>
        <value>u:d1:dev,g:group1:default,u:hadoop:prod</value>
    </property>
  3. Make the preceding changes and copy the file across all nodes and restart the YARN daemons.

  4. Whenever the d1 user submits a job, it should go to the dev queue and for user...