In this chapter, we have understood the distributed aspects of any enterprise search. We understood distributed search patterns, and how Apache Solr can be used as a distributed search. We started working with Apache SolrCloud, by understanding its architecture, and building a SolrCloud instance of development and production. We also looked at sharding strategies and fault tolerance. Finally, we went through Apache Solr and MongoDB together. In the coming chapter, we will see how Apache Hadoop and Solr can complement each other, alongside the various implementations of Solr with Hadoop.
Scaling Big Data with Hadoop and Solr, Second Edition
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Scaling Big Data with Hadoop and Solr, Second Edition
By:
Overview of this book
Table of Contents (13 chapters)
Scaling Big Data with Hadoop and Solr Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
Processing Big Data Using Hadoop and MapReduce
Understanding Apache Solr
Enabling Distributed Search using Apache Solr
Big Data Search Using Hadoop and Its Ecosystem
Scaling Search Performance
Use Cases for Big Data Search
Index
Customer Reviews