Book Image

HBase Administration Cookbook

By : Yifeng Jiang
Book Image

HBase Administration Cookbook

By: Yifeng Jiang

Overview of this book

As an Open Source distributed big data store, HBase scales to billions of rows, with millions of columns and sits on top of the clusters of commodity machines. If you are looking for a way to store and access a huge amount of data in real-time, then look no further than HBase.HBase Administration Cookbook provides practical examples and simple step-by-step instructions for you to administrate HBase with ease. The recipes cover a wide range of processes for managing a fully distributed, highly available HBase cluster on the cloud. Working with such a huge amount of data means that an organized and manageable process is key and this book will help you to achieve that.The recipes in this practical cookbook start from setting up a fully distributed HBase cluster and moving data into it. You will learn how to use all of the tools for day-to-day administration tasks as well as for efficiently managing and monitoring the cluster to achieve the best performance possible. Understanding the relationship between Hadoop and HBase will allow you to get the best out of HBase so the book will show you how to set up Hadoop clusters, configure Hadoop to cooperate with HBase, and tune its performance.
Table of Contents (16 chapters)
HBase Administration Cookbook
Credits
About the Author
Acknowledgement
About the Reviewers
www.PacktPub.com
Preface

Managing a region split


Usually an HBase table starts with a single region. However, as data keeps growing and the region reaches its configured maximum size, it is automatically split into two halves, so that they can handle more data. The following diagram shows an HBase region splitting:

This is the default behavior of HBase region splitting. This mechanism works well for many cases, however there are situations wherein it encounters problems, such as the split/compaction storms issue.

With a roughly uniform data distribution and growth, eventually all the regions in the table will need to be split at the same time. Immediately following a split, compactions will run on the daughter regions to rewrite their data into separate files. This causes a large amount of disk I/O and network traffic.

In order to avoid this situation, you can turn off automatic splitting and manually invoke it. As you can control at what time to invoke the splitting, it helps spread the I/O load. Another advantage...