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HBase Administration Cookbook

You're reading from HBase Administration Cookbook

Product type Book

Published in Aug 2012

Publisher Packt

ISBN-13 9781849517140

Pages 332 pages

Edition 1st Edition

Languages

Concepts

Database Administration

Author (1):

Yifeng Jiang

Table of Contents (16) Chapters

HBase Administration Cookbook

Credits

About the Author

Acknowledgement

About the Reviewers

www.PacktPub.com

Preface

Setting Up HBase Cluster

Data Migration

Using Administration Tools

Backing Up and Restoring HBase Data

Monitoring and Diagnosis

Maintenance and Security

Troubleshooting

Basic Performance Tuning

Advanced Configurations and Tuning

WAL tool—manually splitting and dumping WALs

An HBase edit will first be written to a region server's Write Ahead Log (WAL). After the log is written successfully, MemStore of the region server will be updated. As WAL is a sequence file on HDFS, it will be automatically replicated to the two other DataNode servers by default, so that a single region server crash will not cause a loss of the data stored on it.

As WAL is shared by all regions deployed on the region server, the WAL needs to first be split so that it can be replayed on each relative region, in order to recover from a region server crash. HBase handles region server failover automatically by using this algorithm.

HBase has a WAL tool providing manual WAL splitting and dumping facilities. We will describe how to use this tool in this recipe.

Getting ready

We will need to put some data into an HBase table to have HBase generate WAL files for our demonstration. Again, we will use the hly_temp table in this recipe. We will put the...

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Authors (1)

Yifeng Jiang

Yifeng Jiang is a Hadoop and HBase Administrator and Developer at Rakutenthe largest e-commerce company in Japan. After graduating from the University of Science and Technology of China with a B.S. in Information Management Systems, he started his career as a professional software engineer, focusing on Java development. In 2008, he started looking over the Hadoop project. In 2009, he led the development of his previous company's display advertisement data infrastructure using Hadoop and Hive. In 2010, he joined his current employer, where he designed and implemented the Hadoop- and HBase-based, large-scale item ranking system. He is also one of the members of the Hadoop team in the company, which operates several Hadoop/HBase clusters

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