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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

Understanding enterprise data


Many enterprise search applications consolidate data from various data sources. Each separate system may also use a different method of data organization and/or format. To use Apache Solr effectively in these systems, all the important data that is to be searched must be fed to the Solr engine, and it goes through a complete process chain (which is explained in brief in Chapter 1, Understanding Apache Solr). Interestingly, since this data is fed only to generate indexing, we do not really have to worry about the formatting, and other presentation aspects of this data. However, if the expectation from enterprise search engines is also to provide an excellent browsing experience, each data element should carry structure information. This information is extracted by Apache Solr and is used to provide further dimensional navigation for a better user experience, that is, facets.

Note

Each unit of data objects is called a document in Solr. Each document contains multiple...

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