1. Lucene Boot Camp I Grant Ingersoll Lucid Imagination Nov. 3, 2008 New Orleans, LA

2. Intro My Background Goals for Tutorial –Understand Lucene core capabilities –Real examples, real code, real data Ask Questions!!!!!

3. Schedule Day I – Concepts – Indexing – Searching – Analysis – Lucene contrib: highlighter, spell checking, etc. Day II – In-depth Indexing/Searching Performance, Internals – Terms and Term Vectors – Class Project – Q & A

4. 4 Resources Slides at –http://www.lucenebootcamp.com/boot-camp-slides/http://www.lucenebootcamp.com/boot-camp-slides/ Lucene Java –http://lucene.apache.org/javahttp://lucene.apache.org/java –http://lucene.apache.org/java/2_4_0/ –http://lucene.apache.org/java/2_4_0/api/index.htmlhttp://lucene.apache.org/java/2_4_0/api/index.html Luke: –http://www.getopt.org/lukehttp://www.getopt.org/luke

5. 5 What is Search? Given a user’s information need (query), find documents relevant to the need –Very Subjective! Information Retrieval –Interdisciplinary –Comp. Sci, Math/Statistics, Library Sci., Linguistics, AI…

6. 6 Search Use Cases Web –Google, Y!, etc. Enterprise –Intranet, Content Repositories, email, etc. eCommerce/DB/CMS –Online Stores, websites, etc. Other –QA, Federated Yours? Why do you need Search?

7. 7 Your Content And You Only you know your content! –Key Features Title, body, price, margin, etc. –Important Terms –Synonyms/Jargon –Structures (tables, lists, etc.) –Importance –Priorities

8. Search Basics Many different Models: –Boolean, Probabilistic, Inference, Neural Net, and: Modified Vector Space Model (VSM) –Boolean + VSM –TF-IDF –The words in the document and the query each define a Vector in an n-dimensional space –Sim(q1, d1) = cos Θ q1q1 d1d1 Θ d j = q= w = weight assigned to term

9. 9 Inverted Index From “Taming Text”

10. 10 Lucene Background Created by Doug Cutting in 1999 Donated to ASF in 2001 Morphed into a Top Level Project (TLP) with many sub projects –Java (flagship) a.k.a. “Lucene” –Solr, Nutch, Mahout, Tika, several Lucene ports From here on out, Lucene refers to “Lucene Java”

11. Lucene is… NOT a crawler –See Nutch NOT an application –See PoweredBy on the Wiki NOT a library for doing Google PageRank or other link analysis algorithms –See Nutch A library for enabling text based search

12. 12 A Few Words about Solr HTTP-based Search Server XML Configuration XML, JSON, Ruby, PHP, Java support Many, many nice features that Lucene users need –Faceting, spell checking, highlighting –Caching, Replication, Distributed http://lucene.apache.org/solr

13. Indexing Process of preparing and adding text to Lucene, which stores it in an inverted index Key Point: Lucene only indexes Strings –What does this mean? Lucene doesn’t care about XML, Word, PDF, etc. –There are many good open source extractors available It’s our job to convert whatever file format we have into something Lucene can use

14. Indexing Classes Analyzer –Creates tokens using a Tokenizer and filters them through zero or more TokenFilter s IndexWriter –Responsible for converting text into internal Lucene format Directory –Where the Index is stored –RAMDirectory, FSDirectory, others

15. Indexing Classes Document –A collection of Field s –Can be boosted Field –Free text, keywords, dates, etc. –Defines attributes for storing, indexing –Can be boosted –Field Constructors and parameters Open up Fieldable and Field in IDE

16. How to Index Create IndexWriter For each input –Create a Document –Add Field s to the Document –Add the Document to the IndexWriter Close the IndexWriter Optimize (optional)

17. Indexing in a Nutshell For each Document –For each Field to be tokenized Create the tokens using the specified Tokenizer –Tokens consist of a String, position, type and offset information Pass the tokens through the chained TokenFilter s where they can be changed or removed Add the end result to the inverted index Position information can be altered –Useful when removing words or to prevent phrases from matching

18. Task 1.a From the Boot Camp Files, use the basic.ReutersIndexer skeleton to start Index the small Reuters Collection using the IndexWriter, a Directory and StandardAnalyzer –Boost every 10 documents by 3 Questions to Answer: –What Field s should I define? –What attributes should each Field have? –Pick a field to boost and give a reason why you think it should be boosted ~30 minutes

19. Use Luke

20. 5 minute Break

21. Searching Parse user query Lookup matching Documents Score Documents Return ranked list

22. 22 Key Classes: –Searcher –Provides methods for searching –Take a moment to look at the Searcher class declaration IndexSearcher, MultiSearcher, ParallelMultiSearcher –IndexReader –Loads a snapshot of the index into memory for searching –More tomorrow –TopDocs - The search results –QueryParser –http://lucene.apache.org/java/docs/queryparsersyntax.htmlhttp://lucene.apache.org/java/docs/queryparsersyntax.html –Query –Logical representation of program’s information need

23. Query Parsing Basic syntax: title:hockey +(body:stanley AND body:cup) OR/AND must be uppercase Default operator is OR (can be changed) Supports fairly advanced syntax, see the website –http://lucene.apache.org/java/docs/queryparsersyntax.html Doesn’t always play nice, so beware –Many applications construct queries programmatically or restrict syntax

24. 24 How to Search Create/Get an IndexSearcher Create a Query –Use a QueryParser –Construct it programmatically Display the results from the TopDocs –Retrieve Field values from Document More tomorrow on search lifecyle

25. Task 1.b Using the ReutersIndexerTest.java skeleton in the boot camp files –Search your newly created index using queries you develop Questions: –What is the default field for the QueryParser ? –What Analyzer to use? ~20 minutes

26. Task 1 Results Scores across queries are NOT comparable –They may not even be comparable for the same query over time (if the index changes) Performance –Caching –Warming –More Tomorrow

27. Lunch 1-2:30

28. 28 Discussion/Questions So far, we’ve seen the basics of search and indexing Next going to look into Analysis and Contrib modules

29. Analysis Analysis is the process of creating Token s to be indexed Analysis is usually done to improve results overall, but it comes with a price Lucene comes with many different Analyzer s, Tokenizer s and TokenFilter s, each with their own goals StandardAnalyzer is included with the core JAR and does a good job for most English and Latin-based tasks Often times you want the same content analyzed in different ways Consider a catch-all Field in addition to other Field s

30. 30 Solr’s Analysis tool If you use nothing else from Solr, the Admin analysis tool can really help you understand analysis Download Solr and unpack it cd apache-solr-1.3.0/example java -jar start.jar http://localhost:8983/solr/admin/analysis.jsp

31. 31 Analyzers StandardAnalyzer, WhitespaceAnalyzer, SimpleAnalyzer Contrib/analysis –Suite of Analyzers for many common situations Languages n-grams Payloads Contrib/snowball

32. Tokenization Split words into Token s to be processed Tokenization is fairly straightforward for most languages that use a space for word segmentation –More difficult for some East Asian languages –See the CJK Analyzer

33. Modifying Tokens TokenFilter s are used to alter the token stream to be indexed Common tasks: –Remove stopwords –Lower case –Stem/Normalize -> Wi-Fi -> Wi Fi –Add Synonyms StandardAnalyzer does things that you may not want

34. 34 Payloads Associate an arbitrary byte array with a term in the index Uses –Part of Speech –Font weight –URL Currently can search using the BoostingTermQuery

35. 35 n-grams Combine units of content together into a single token Character –2-grams for the word “Lucene”: Lu,uc, ce, en, ne –Can make search possible when data is noisy or hard to tokenize Word (“shingles” in Lucene parlance) –Pseudo Phrases

36. Custom Analyzers Problem: none of the Analyzers cover my problem Solution: write your own Analyzer Better solution: write a configurable Analyzer so you only need one Analyzer that you can easily change for your projects –See Solr

37. 37 Analysis APIs Have a look at the TokenStream and Token API s Token s and TokenStream s may be reused –Helps reduce allocations and speeds up indexing –Not all Analysis can take advantage: caching – Analyzer.reusableTokenStream() – TokenStream.next(Token)

38. Special Cases Dates and numbers need special treatment to be searchable –o.a.l.document.DateTools –org.apache.solr.util.NumberUtils Altering Position Information –Increase Position Gap between sentences to prevent phrases from crossing sentence boundaries –Index synonyms at the same position so query can match regardless of synonym used

39. Task 2 Take 15-20 minutes and write an Analyzer/Tokenizer/TokenFilter and Unit Test –Examine results in Luke –Run some searches Ideas: –Combine existing Tokenizer s and TokenFilter s –Normalize abbreviations –Add payloads –Filter out all words beginning with the letter A –Identify/Mark sentences

40. 40 Discussion What did you implement? What issues do you face with your content? To Stem or not to Stem? Stopwords: good or bad? Tradeoffs of different techniques

41. Lucene Contributions Many people have generously contributed code to help solve common problems These are in contrib directory of the source Popular: –Analyzers –Highlighter –Queries and MoreLikeThis –Snowball Stemmers –Spellchecker

42. 42 Highlighter Highlight query keywords in context –Often useful for display purposes Important Classes: – Highlighter - Main entry point, coordinates the work – Fragmenter - Splits up document for scoring – Formatter - Marks up the results – Scorer - Scores the fragments SpanScorer - Can score phrases Use term vectors for performance Look at example usage

43. 43 Spell Checking Suggest spelling corrections based on spellings of words in the index –Will/can suggest incorrectly spelled words Uses a distance measure to determine suggestions –Can also factor in document frequency –Distance Measure is pluggable

44. 44 Spell Checking Classes: Spellchecker, StringDistance See ContribExamplesTest Practical aspects: –It’s not as simple as just turning it on –Good results require testing and tuning Pay attention to accuracy settings Mind your Analysis (simple, no stemming) Consider alternate StringDistance ( JaroWinklerDistance )

45. 45 More Like This Given a Document, find other Document s that are similar –Variation on relevance feedback –“Find Similar” Extracts the most important terms from a Document and creates a new query –Many options available for determining important terms Classes: MoreLikeThis –See ContribExamplesTest

46. 46 Summary Indexing Searching Analysis Contrib Questions?

47. Resources http://lucene.apache.org/ http://en.wikipedia.org/wiki/Vector_space_model Modern Information Retrieval by Baeza-Yates and Ribeiro-Neto Lucene In Action by Hatcher and Gospodnetić Wiki Mailing Lists –java-user@lucene.apache.orgjava-user@lucene.apache.org Discussions on how to use Lucene –java-dev@lucene.apache.orgjava-dev@lucene.apache.org Discussions on how to develop Lucene Issue Tracking –https://issues.apache.org/jira/secure/Dashboard.jspa We always welcome patches –Ask on the mailing list before reporting a bug

48. Resources trainer@lucenebootcamp.com Lucid Imagination –Support –Training –Value Add –grant@lucidimagination.com