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

Hive on HBase—querying HBase using a SQL-like language


HBase supports several interfaces to access data in its tables, such as the following:

  • HBase Shell

  • Java Client API

  • REST, Thrift, and Avro

HBase Shell is straightforward, but a little too simple to perform complex queries on. Other interfaces need programming, which is not suitable for ad hoc queries.

As data keeps growing, people might want an easy way to analyze the large amount of data stored in HBase. The analysis should be efficient, ad hoc, and it should not require programming. Hive is currently the best approach for this purpose.

Hive is a data warehouse infrastructure built for Hadoop. Hive is used for ad hoc querying, and analyzing a large data set without having to write a MapReduce program. Hive supports a SQL-like query language called HiveQL (HQL)  to access data in its table.

We can integrate HBase and Hive, so that we can use HQL statements to access HBase tables, both to read and write.

In this recipe, we will describe how...