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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

Auto Scaling HBase using AWS

Auto Scaling is a concept which came into existence because of the wide spread use of AWS Amazon Web Service platform. It allows the infrastructure to scale elastically, or words as needed. We can create auto-scaling groups from scratch and use the existing production instance to scale up as needed. Auto scaling gives an extra layer of fault tolerance, high availability and flexibility, at a very effective cost. If we are able to predict or plan the traffic pattern, then we can do predictive scaling on top of auto scaling.

In case of HBase we can create a Auto Scaling groups of region servers, namenode, secondary namenode, use Amazons Elastic Map Reduce (EMR), most of the know tools are already integrated with Amazon EMR, like Pig, Hive, Ganglia, DistCp and so on.

Getting Ready

This is based on the Amazon HBase EMR details and full credit is given to the Amazon doc.

  • To run HBase in AWS env there are guideline which a user needs to adhere to. Amazon Elastic MapReduce...
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