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Lucenefromtheory to real world Information retrieval Indexing Cluster Apache Performance tuning Parser Dictionary IndexReader Solr Real world Java Analysis Troubleshooting Vector Relevance Query Server Design Fields Document Probabilistic Production Model Search application Open Source Inverted index Doug Cutting Library Architecture
Agenda Introduction to Information Retrieval Luceneoverview Lucene in details Search applications design Performance tuning www.xebia.fr / blog.xebia.fr 2
www.xebia.fr / blog.xebia.fr 3 Information Retrieval
www.xebia.fr / blog.xebia.fr 4 Information Retrieval “ Information Retrieval (IR) is the science of searching for document ”
5
www.xebia.fr / blog.xebia.fr 6 Inverted Index
www.xebia.fr / blog.xebia.fr 7 Boolean Model ,[object Object],			Q = (T1 OR T2) AND (T3 OR T4) 		D1 = {T1, T3} 		D2 = {T2, T3, T4} ,[object Object],R = {D1, D2} withUnion for OR operator 		Intersection for AND operator
www.xebia.fr / blog.xebia.fr 8 VectorSpace Model ,[object Object]
Similaritycanbecomputedwith :dj = (w1,j,w2,j,...,wt,j) q = (w1,q,w2,q,...,wt,q)
www.xebia.fr / blog.xebia.fr 9 Lucene
www.xebia.fr / blog.xebia.fr 10 Lucene : where do we come from ?
www.xebia.fr / blog.xebia.fr 11 Lucene documentation
www.xebia.fr / blog.xebia.fr 12 Lucene : Simple indexingexample Directorydirectory= new RAMDirectory(); IndexWriter writer = new IndexWriter(directory, new WhitespaceAnalyzer(), 										   IndexWriter.MaxFieldLength.UNLIMITED); Documentdoc = new Document(); doc.add(new Field(“company”, “Xebia”, Field.Store.YES, Field.Index.NOT_ANALYZED)); doc.add(new Field(“country”, “France”, Field.Store.YES, Field.Index.NO)); writer.addDocument(doc); writer.close();
www.xebia.fr / blog.xebia.fr 13 Lucene : Simplesearchexample IndexSearcher searcher = new IndexSearcher(dir, true); Termt = new Term(“country”, “France”); Queryquery = new TermQuery(t); TopDocs docs = searcher.search(query, 10); assertEquals(1, docs.totalHits); searcher.close();
www.xebia.fr / blog.xebia.fr 14 Lucene - indexing
www.xebia.fr / blog.xebia.fr 15 Lucene - analyzers
www.xebia.fr / blog.xebia.fr 16 Lucene– Field types ,[object Object]
Index : NO / ANALYZED / NOT_ANALYZED / ANALYZED_NO_NORMS / NOT_ANALYZED_NO_NORMS
TermVector : NO / WITH_POSITIONS / WITH_OFFSETS / WITH_POSITIONS_OFFSETS / YES,[object Object]
www.xebia.fr / blog.xebia.fr 18 Lucenestorage - segments ,[object Object]
When documents are deleted, a marker isadded in the current segment,[object Object]
Or automaticallymergeddepending on : (int) log(max(minMergeMB, size))/log(mergeFactor)
www.xebia.fr / blog.xebia.fr 20 Lucene - search
www.xebia.fr / blog.xebia.fr 21 Lucene- search ,[object Object]
TermQuery
PhraseQuery
WildcardQuery
RangeQuery
FuzzyQuery
BooleanQuery,[object Object],[object Object]
3 layers :
QueryParser : Transforms a query string into an Abstract SyntaxTreerepresentation
QueryNodeProcessor : Processesnodes of the tree to move, remove or modifythem
QueryBuilder : builds a LuceneBooleanQuerytreefrom the abstract syntaxtree,[object Object]
www.xebia.fr / blog.xebia.fr 25 Lucene– PhraseQuery & SpanQuery ,[object Object]
PhraseQuery : SpanQuerywith a slop value of 0
Uses position information,[object Object]
www.xebia.fr / blog.xebia.fr 27 Inverted Index
www.xebia.fr / blog.xebia.fr 28 Lucene – Levenshtein distance ,[object Object]
the number of modifications required to switchfrom one word to another,[object Object]
LUCENE-2089 will use a Levenshteinautomaton,[object Object]
Can befullycustomized
By default limited to 50 KB characters
Uses FastVectorHighlighter for fasterresults (~2.5 times faster),[object Object]

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