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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

HBase housekeeping

As data is being added to HBase, it writes an immutable file to store. Each store is made up of column families, and regions consist of these row-key ordered files as it's immutable. So, there will be more files rather than one on the fly. Due to many files, the I/O will be slower, and hence lag in reading and writing, resulting in slower operation. To overcome these types of problems, HBase uses the compaction methodology; let's look into it now. Refer to the following figure for a better understanding:

HBase housekeeping

Compaction

As the name suggests, compaction makes files more compact and, hence, efficient to read up files. When new data is written to HBase, HFile is generated and the number of HFiles might increase the I/O overhead. So, to minimize this, the HFiles are merged to one HFile periodically. As MemStore gets filled, a new HFile is created. If these files are not merged in time, there will be a huge overhead on the system. Compaction is nothing but the merging of...

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