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Learning Hbase

You're reading from   Learning Hbase Learn the fundamentals of HBase administration and development with the help of real-time scenarios

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Product type Paperback
Published in Nov 2014
Publisher
ISBN-13 9781783985944
Length 326 pages
Edition 1st Edition
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Author (1):
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Shashwat Shriparv Shashwat Shriparv
Author Profile Icon Shashwat Shriparv
Shashwat Shriparv
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Table of Contents (12) Chapters Close

Preface 1. Understanding the HBase Ecosystem FREE CHAPTER 2. Let's Begin with HBase 3. Let's Start Building It 4. Optimizing the HBase/Hadoop Cluster 5. The Storage, Structure Layout, and Data Model of HBase 6. HBase Cluster Maintenance and Troubleshooting 7. Scripting in HBase 8. Coding HBase in Java 9. Advance Coding in Java for HBase 10. HBase Use Cases Index

Capacity planning


Suppose we have around 2 TB data with a replication factor of 3, which means 3 * 2 = 6 TB, which in turn means that 2 TB of extra space is still needed. So, for 2 TB of data, we can have a cluster with 4 to 8 DataNodes, totaling 8 TB of storage disk.

This extra space is needed for an intermediate temporary file that is generated during read/write operations and MapReduce jobs. If the data on which we run MapReduce is huge and the MapReduce code processes the whole data that requires a huge HDFS storage to store the temporary and intermediate result files, we will need to provide enough disk storage, the absence of which will result in a lot of failing tasks and blacklisted nodes. It is advisable to have 25 to 50 percent more storage than the original data size (without a replication factor) on the cluster; the minimum should be 25 percent more of the whole data size if we want to run a MapReduce task without much failing.

So, we can apply an approximate formula, as follows...

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