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Learning Hadoop 2

You're reading from   Learning Hadoop 2 Design and implement data processing, lifecycle management, and analytic workflows with the cutting-edge toolbox of Hadoop 2

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Product type Paperback
Published in Feb 2015
Publisher Packt
ISBN-13 9781783285518
Length 382 pages
Edition 1st Edition
Tools
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Toc

Table of Contents (13) Chapters Close

Preface 1. Introduction FREE CHAPTER 2. Storage 3. Processing – MapReduce and Beyond 4. Real-time Computation with Samza 5. Iterative Computation with Spark 6. Data Analysis with Apache Pig 7. Hadoop and SQL 8. Data Lifecycle Management 9. Making Development Easier 10. Running a Hadoop Cluster 11. Where to Go Next Index

Monitoring


Earlier in this chapter, we discussed Cloudera Manager as a visual monitoring tool and hinted that it could also be programmatically integrated with other monitoring systems. But before plugging Hadoop into any monitoring framework, it's worth considering just what it means to operationally monitor a Hadoop cluster.

Hadoop – where failures don't matter

Traditional systems monitoring tends to be quite a binary tool; generally speaking, either something is working or it isn't. A host is alive or dead, and a web server is responding or it isn't. But in the Hadoop world, things are a little different; the important thing is service availability, and this can still be treated as live even if particular pieces of hardware or software have failed. No Hadoop cluster should be in trouble if a single worker node fails. As of Hadoop 2, even the failure of the server processes, such as the NameNode shouldn't really be a concern if HA is configured. So, any monitoring of Hadoop needs to take...

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