Book Image

HBase High Performance Cookbook

By : Ruchir Choudhry
Book Image

HBase High Performance Cookbook

By: Ruchir Choudhry

Overview of this book

Apache HBase is a non-relational NoSQL database management system that runs on top of HDFS. It is an open source, disturbed, versioned, column-oriented store and is written in Java to provide random real-time access to big Data. We’ll start off by ensuring you have a solid understanding the basics of HBase, followed by giving you a thorough explanation of architecting a HBase cluster as per our project specifications. Next, we will explore the scalable structure of tables and we will be able to communicate with the HBase client. After this, we’ll show you the intricacies of MapReduce and the art of performance tuning with HBase. Following this, we’ll explain the concepts pertaining to scaling with HBase. Finally, you will get an understanding of how to integrate HBase with other tools such as ElasticSearch. By the end of this book, you will have learned enough to exploit HBase for boost system performance.
Table of Contents (19 chapters)
HBase High Performance Cookbook
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
7
Large-Scale MapReduce
Index

Monitoring HBase with CloudWatch


Cloud watch provides and extensive way of monitoring the Hbase/Hadoop nodes in various ways as listed here:

  • Cluster status

  • Node status

  • I/O

  • Hbase

Let's discuss Cluster status—it provides the following areas to look at:

Is Idle, Container allocated—container reserved, container pending, apps completed.

Apps failed, apps killed, apps pending, apps running, apps submitted. We will go over one area for explanation.

By double-clicking, a child window is popped up (overlay), which provides details as follows. You can explicitly get the detail by customizing the graph based on hours, days, or mins ago; the graph pulls the data accordingly.

Node status provides details of the following:

  • Core Nodes running /pending: As we have two nodes the graph is marked at 2

  • Live data nodes: The data node is fully live hence we see the 100 percentage

  • Map Reduce total nodes: The total MR node us 2 hence its showing 2

  • MR Active nodes: The total active MR nodes are 2 hence its showing 2...