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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

Solr with MongoDB


MongoDB is one of the popular NOSQL databases, just like Cassandra. It supports the storage of any random schemas in the document-oriented storage of its own. MongoDB supports JSON-based information pipe for any communication with the server. This database is designed to work with heavy data. Today, many organizations are focusing on utilizing MongoDB for various enterprise applications.

Understanding MongoDB

MongoDB provides high availability and load balancing. Each data unit is replicated, and a combination of data with its copes is called replica set. Replicas in MongoDB can be either primary or secondary. Primary is an active replica that is used for direct read-write operation; the secondary replica works like a backup for primary. MongoDB supports search by field, range queries, and regular expression searches. Queries can return specific fields of documents and also include user-defined JavaScript functions. Any field in a MongoDB document can be indexed. More information...

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