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

Integration with Hive


In this recipe, we look at how we can integrate Hive with HBase and use Hive to perform all the data operations.

You will have realized from the previous recipe that it gets cumbersome to perform queries using just the native HBase commands.

Getting ready

Before going through the recipe, you must have completed the Hive metastore using MySQL recipe in Chapter 7, Data Ingestion and Workflow, and the Setting up multi-node HBase cluster recipe.

How to do it...

  1. Connect to the edge1.cyrus.com edge node in the cluster and switch to the user hadoop.

  2. We will create an external Hive table and point it to the HBase using the ZooKeeper ensemble.

  3. Create a table in HBase if it is not there already, as shown next:

    hbase> create 'hivetable', 'ratings'
    put 'hivetable', 'row1', 'ratings:userid', 'user1'
    put 'hivetable', 'row1', 'ratings:bookid', 'book1'
    put 'hivetable', 'row1', 'ratings:rating', '1'
    
  4. Connect either using a hive or beeline client and map by creating a table, as shown next...