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HBase High Performance Cookbook

You're reading from   HBase High Performance Cookbook Solutions for optimization, scaling and performance tuning

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Product type Paperback
Published in Jan 2017
Publisher Packt
ISBN-13 9781783983063
Length 350 pages
Edition 1st Edition
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Author (1):
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Ruchir Choudhry Ruchir Choudhry
Author Profile Icon Ruchir Choudhry
Ruchir Choudhry
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Table of Contents (13) Chapters Close

Preface 1. Configuring HBase 2. Loading Data from Various DBs FREE CHAPTER 3. Working with Large Distributed Systems Part I 4. Working with Large Distributed Systems Part II 5. Working with Scalable Structure of tables 6. HBase Clients 7. Large-Scale MapReduce 8. HBase Performance Tuning 9. Performing Advanced Tasks on HBase 10. Optimizing Hbase for Cloud 11. Case Study Index

Working with HDFS


To get the best performance from HBase, it is essential to get optimal performance from Hadoop/HDFS.

There are various parameters we can look at, but we will limit ourselves to make sure we get the benefits.

Multiple disk mount point:

Df.datanode.data.dir  -> use all attached disks to data node.
Block Side(DFS) =128 MB
Local file system buffer 
Io.file.buffer.size=131072 (128k)
Io.sort.factor=50 to 100
Data Node and NameNode  conconcurry
Dfs.namenode.handler.count(131072)
Dfs..datanode,max.transfer.tthread 4096

Give HDFS as many paths as possible to spread the disk I/O around and to increase the capacity of HDFS.

How to do it…

Please open the dfs.block.size section from dfs-site.xml and mapred.min.split.size from mapred.max.split.size in mapred-site.xml.

The split of the input size and the total input data size can be changed and can be mapped to the block size.

This also helps us reduce the number of map tasks. If the map task is reduced, the performance will increase. This...

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