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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

Need for distributed search


At the beginning of the chapter, we have already seen some of the reasons leading to the need for distributed searches. Any search engine would have two important functions: firstly to index the data, and secondly to provide a real-time search. As the data grows, single node enterprise search applications face the following issues:

  • There are times when an index on one machine is insufficient and it cannot accommodate enterprise information. This is mainly applicable for enterprises with growing data, which require the generation of large index sizes.

  • As more and more users start using enterprise search, there is huge traffic for search operations. Single node searches have a limitation on the number of requests they can serve within a stipulated time, even if the data is not huge.

  • For frequently changing data, the indexer has to index the data swiftly to avoid lagging and further delays. Often, index generation time is one of the primary expectations of enterprises...

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