Book Image

Apache Solr Enterprise Search Server - Third Edition

By : David Smiley, Eric Pugh, Kranti Parisa, Matt Mitchell
Book Image

Apache Solr Enterprise Search Server - Third Edition

By: David Smiley, Eric Pugh, Kranti Parisa, Matt Mitchell

Overview of this book

<p>Solr Apache is a widely popular open source enterprise search server that delivers powerful search and faceted navigation features—features that are elusive with databases. Solr supports complex search criteria, faceting, result highlighting, query-completion, query spell-checking, relevancy tuning, geospatial searches, and much more.</p> <p>This book is a comprehensive resource for just about everything Solr has to offer, and it will take you from first exposure to development and deployment in no time. Even if you wish to use Solr 5, you should find the information to be just as applicable due to Solr's high regard for backward compatibility. The book includes some useful information specific to Solr 5.</p>

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Apache Solr Enterprise Search Server - Third Edition
David Smiley | Eric Pugh | Kranti Parisa | Matt Mitchell
May, 2015 | 432 pages
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Table of Contents (19 chapters)
Apache Solr Enterprise Search Server Third Edition
Apache Solr Enterprise Search Server Third Edition
Credits
Credits
About the Authors
About the Authors
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Quick Starting Solr
Quick Starting Solr
An introduction to Solr
A few differences between Solr 4 and Solr 5
Getting started
A quick tour of Solr
Configuration files
What's next?
Resources outside this book
Summary
2
Schema Design
Schema Design
Is Solr schemaless?
MusicBrainz.org
One combined index or separate indices
Schema design
The schema.xml file
Summary
3
Text Analysis
Text Analysis
Configuring field types
Character filters
Tokenization
Filtering
The multilingual search
Summary
4
Indexing Data
Indexing Data
Communicating with Solr
Solr's Update-XML format
Commit, optimize, and rollback the transaction log
Atomic updates and optimistic concurrency
Sending CSV-formatted data to Solr
The DataImportHandler framework
Indexing documents with Solr Cell
Update request processors
Summary
5
Searching
Searching
Your first search – a walk-through
Solr's generic XML structured data representation
Solr's XML response format
Understanding request handlers
Query parameters
Query parsers and local-params
Query syntax (the lucene query parser)
The DisMax query parser – part 1
Filtering
Sorting
Joining
Spatial search
Summary
6
Search Relevancy
Search Relevancy
Scoring
The DisMax query parser – part 2
Functions and function queries
Summary
7
Faceting
Faceting
A quick example – faceting release types
Field requirements
Types of faceting
Faceting field values
Faceting numeric and date ranges
Facet queries
Building a filter query from a facet
Pivot faceting
Excluding filters – multiselect faceting
Summary
8
Search Components
Search Components
About components
The highlight component
The SpellCheck component
Query complete/suggest
The QueryElevation component
The MoreLikeThis component
The Stats component
The Clustering component
Collapsing and expanding
The TermVector component
Summary
9
Integrating Solr
Integrating Solr
Working with the included examples
Solritas – the integrated search UI
SolrJ – Solr's Java client API
Using JavaScript/AJAX with Solr
Using XSLT to transform XML search results
Accessing Solr from PHP applications
Ruby on Rails integrations
Nutch for crawling web pages
Solr and Hadoop
ManifoldCF – a connector framework
Document-level security
Summary
10
Scaling Solr
Scaling Solr
Tuning complex systems is hard
Use SolrMeter to test Solr performance
Optimizing a single Solr server – scale up
Configuring Solr for near real-time search
Use SolrCloud to go big – scale wide
Summary
11
Deployment
Deployment
Deployment methodology for Solr
Installing Solr into a Servlet container
Configuring logging
A RequestHandler per search interface
Leveraging Solr cores
Setting up ZooKeeper for SolrCloud
Monitoring Solr performance
Securing Solr from prying eyes
Summary
Quick Reference
Quick Reference
Core search
Diagnostic
The Lucene query parser
The DisMax query parser
The Lucene query syntax
Faceting
Highlighting
Spell checking
Miscellaneous nonsearch
Index
Index

The Stats component

The stats component calculates some mathematical statistics of fields in the index. The main requirement is that the field be indexed. The following statistics are computed over the non-null values (except missing which counts the nulls):

  • min: The smallest value

  • max: The largest value

  • sum: The sum

  • count: The quantity of non-null values accumulated in these statistics

  • missing: The quantity of records skipped due to missing values

  • sumOfSquares: The sum of the square of each value; this is probably the least useful and is used internally to compute stddev efficiently

  • mean: The average value

  • stddev: The standard deviation of the values

  • distinctValues: A list of all distinct (non-duplicating) values

  • countDistinct: The size of distinctValues

If you calculate stats on a string or date field, then only min, max, count, missing, distinctValues, and countDistinct are calculated. The distinctValues and countDistinct are only present if stats.calcdistinct is enabled.

Configuring the...

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