May 2012 JaxDUG presentation by Zachary Gramana on using the Lucene.NET library to add search functionality to .NET applications. Contains an overview of search/information retrieval concepts and highlights some common use-cases.

1. SEARCH ME
Using Lucene.Net In Your Apps

2. About Me
 Zachary Johnson Gramana
 Engineer at Potts Consulting Group
 Proud new father of Rex

3. Search is...
 A vague term that encompasses multiple
problems.
 Better term is “information retrieval”, or IR
system.
 Interdisciplinary, drawing from:
 computer science (parsing, data structures)
 psychology (query grammar, human/computer
interact.)
 linguistics (textual analysis)
 information science (scoring/relevancy)
 maths (document retrieval strategy)

4. Problems Solved
 Information Overload
 Transparently handle all kinds of data:
 structured (hierarchical)
 semi-structured (markup)
 un-structured data (plain text)

5. Problems Solved
 Information Overload
 Find the information that users want,
not just the information they asked for.
 Transparently handle all kinds of data:
 structured (hierarchical)
 semi-structured (markup)
 un-structured data (plain text)
 Single portal to multiple data types and
sources.
 Do it fast!

6. Basic IR System Capabilities
 Collection (importing, crawling)
 Anonymous web page crawling (google)
 User-uploaded photographs (flickr)
 Publisher upload of .mp3 files (iTunes)
 Indexing
 Analysis
 Modify index data structure
 Querying
 Input parsing
 Query generation & execution
 Collecting the results
 Filtering the results (optional)

7. What is Lucene.Net?
 Port of the Apache Foundation‟s Lucene
libraries from Java to C#
 It‟s a search library.
 Lucene created by Doug Cutting
 Named after his wife.
 First released in 2000 on SourceForge
 Migrated to Apache Foundation in 9/2001.

8. Used By
 StackOverflow
 JIRA
 IBM
 Akamai
 Apple
 Autodesk
 Orchard
 RavenDB
 CouchDB

9. What Isn‟t Lucene.NET
 Not a complete information retrieval system
 Check out Google Search Appliance instead:
http://www.google.com/enterprise/search/
 Not a web-crawler.
 Check out Arachnode instead
http://arachnode.net
 Not a query service.
 Check out SOLR instead
http://lucene.apache.org/solr
 Not hard
 Check out Windows Search SDK instead
http://bit.ly/ImRtMk

10. Concept and Overview

11. What‟s In an Index?
 Stores a collection of Documents, each of
which represent a source record.
 Document contain:
 Metadata about the source record.
 (optionally) actual data from the source record.
 (optionally) derived analytical products.
 Documents store a collection of
token/frequency pairs (optionally position),
plus a document identifier.

12. Lucene‟s Index Structure
 Documents store a collection of fields.
 Fields are collection of terms, plus and identifier, and
optional term vectors.
 Terms are string key-value-pairs of a field name, and
a string value.
 Lucene provides special classes to deal with tricky
data, like the NumericField class.
 Term vectors are terms, along with their frequency
counts and positions.
 Fields can be indexed, stored, or both.
 Storing allows a term value to be retrieved after indexing.
 Indexing adds the term value to Lucene‟s inverted index.

13. The Inverted Index
(taken from Apple‟s excellent “Search Basics” article at http://bit.ly/JO59kH )

14. Lucene‟s Index Structure
 What an „inverted index‟?
 verted index: document points to collection of
terms
 inverted index: term points to a collection of
documents
 One or more segments
 Self-contained, independent partition of the
entire index.
 Stores: field names, field values, term dictionary,
term frequencies, term proximities, normalization
factor, term vectors, and (optional) deleted record
lookup table.

15. Analysis
(taken from Apple‟s excellent “Search Basics” article at http://bit.ly/JO59kH )

16. Tokenization
(taken from Thomas Koch‟s presentation “Search Basics and Lucene” at http://bit.ly/JTOrnH )

17. Tokenization
 Normalization: “Gramåna” > “gramana”
 Stemming: “preschooling” > “school”

18. Norms
(taken from Thomas Koch‟s presentation “Search Basics and Lucene” at http://bit.ly/JTOrnH )

19. Time to Look at Some Code

20. Getting a Query
 Two options:
 Parse a search string using a QueryParser class.
 Programatically build a query.
 QueryParser can build very complex queries
very quickly, but requires user to provide a
query string.
 Programatic building of a query requires less
overhead for simple queries.

21. General Query Types
(taken from the Wikipedia entry “Information Retrieval” at http://bitly.com/T1Qbw)

22. Some Lucene Query Types
 TermQuery (general purpose)
 BooleanQuery
 MultiPhraseQuery
 SpanQuery
 WildcardQuery
 FilteredQuery
 MoreLikeThisQuery
 BoostingQuery
 FuzzyQuery
 ConstantScoreRangeQuery

23. Time to Look at More Code

24. Lucene.Net Contribs
 Spatial (geo-spatial search)
 Similarity
 SimpleFactedSearch
 Highlighter
 SpellChecker
 WordNET (synonyms)
 Snowball (stemming library)
 RegEx

25. That‟s All!
Thanks for your time and attention.
twitter: @zgramana
blog: http://www.excitabyte.com/
Email: zgramanaATgee mail dot com