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Scaling Apache Solr

You're reading from   Scaling Apache Solr Optimize your searches using high-performance enterprise search repositories with Apache Solr

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Product type Paperback
Published in Jul 2014
Publisher
ISBN-13 9781783981748
Length 298 pages
Edition 1st Edition
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Author (1):
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Hrishikesh Vijay Karambelkar Hrishikesh Vijay Karambelkar
Author Profile Icon Hrishikesh Vijay Karambelkar
Hrishikesh Vijay Karambelkar
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Table of Contents (13) Chapters Close

Preface 1. Understanding Apache Solr FREE CHAPTER 2. Getting Started with Apache Solr 3. Analyzing Data with Apache Solr 4. Designing Enterprise Search 5. Integrating Apache Solr 6. Distributed Search Using Apache Solr 7. Scaling Solr through Sharding, Fault Tolerance, and Integration 8. Scaling Solr through High Performance 9. Solr and Cloud Computing 10. Scaling Solr Capabilities with Big Data A. Sample Configuration for Apache Solr Index

Scaling Solr through Storm


Apache Storm is a real-time distributed computation framework. It processes with humongous data in real time. Recently, Storm has been adapted by Apache as an incubating project and for the development of Apache Storm. You can read more information about Apache Storm features at http://storm.incubator.apache.org/.

Apache Storm can be used to process massive streams of data in a distributed manner. So, it provides excellent batch-oriented processing capabilities for time-sensitive analytics. With Apache Solr and Storm together, organizations can process Big Data in real time. For example, Apache Solr and Storm can be used if industrial plants would like to extract information from their plant systems that are emitting raw data continuously and process it to facilitate real-time analytics such as identifying the top problematic systems or look for recent errors/failures. Apache Solr and Storm can work together to get this batch processing for Big Data in real time...

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