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

Hadoop streaming


In this recipe, we will look at how we can execute jobs on an Hadoop cluster using scripts written in Bash or Python. It is not mandatory to use only Java for programming MapReduce code; any language can be used by evoking the Hadoop streaming utility. Do not confuse this with real-time streaming, which is different from what we will be discussing here.

Getting ready

To step through the recipes in this chapter, make sure you have a running cluster with HDFS and YARN setup correctly as discussed in the previous chapters. This can be a single node cluster or a multinode cluster, as long as the cluster is configured correctly.

It is not necessary to know Java to run MapReduce programs on Hadoop. Users can carry forward their existing scripting knowledge and use Bash or Python to run the job on Hadoop.

How to do it...

  1. Connect to an edge node in the cluster and switch to user hadoop.

  2. The streaming JAR is also under the location as Hadoop /opt/cluster/hadoop/share/hadoop/tools/lib/hadoop...