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 compactions


An HBase table has the following physical storage structure:

It consists of multiple regions. While a region may have several Stores, each holds a single column family. An edit first writes to the hosting region store's in-memory space, which is called MemStore. When the size of MemStore reaches a threshold, it is flushed to StoreFiles on HDFS.

As data increases, there may be many StoreFiles on HDFS, which is not good for its performance. Thus, HBase will automatically pick up a couple of the smaller StoreFiles and rewrite them into a bigger one. This process is called minor compaction. For certain situations, or when triggered by a configured interval (once a day by default), major compaction runs automatically. Major compaction will drop the deleted or expired cells and rewrite all the StoreFiles in the Store into a single StoreFile; this usually improves the performance.

However, as major compaction rewrites all of the Stores' data, lots of disk I/O and network...