![]() Some important points before you start indexing and searching using Apache Lucene:.Here are typical Lucene Maven dependencies (without hibernate search): Synonym Expansion – Adding in synonyms at the same token position as the current word can mean better matching when users search with words in the synonym set.īelow is an example Lucene analysis of a text/sentence:.Text Normalization – Stripping accents and other character markings can make for better searching.It may also reduce some “noise” and actually improve search quality. Removing them shrinks the index size and increases performance. Stop Words Filtering – Common words like “the”, “and” and “a” rarely add any value to a search.For instance, with English stemming, “bikes” is replaced with “bike” now the query “bike” can find both documents containing “bike” and those containing “bikes”. Stemming – Replacing words with their stems.Pre-Tokenization: Stripping HTML markup, transforming or removing text matching arbitrary patterns or sets of fixed strings. Here are the Lucene architectural layers and segment search:Īnd here is the typical data flow in a Lucene real-world application: Deleted Documents: An optional file indicating which documents are deleted.To add Term Vectors to your index see the Field constructors. ![]() A term vector consists of term text and term frequency. Term Vectors: For each field in each document, the term vector (sometimes called document vector) may be stored.Normalization Factors: For each field in each document, a value is stored that is multiplied into the score for hits on that field.Note that this will not exist if all fields in all documents omit position data. Term Proximity Data: For each term in the dictionary, the positions that the term occurs in each document.Term Frequency Data: For each term in the dictionary, the numbers of all the documents that contain that term, and the frequency of the term in that document, unless frequencies are omitted (IndexOptions.DOCS_ONLY).The dictionary also contains the number of documents which contain the term, and pointers to the term’s frequency and proximity data. Term Dictionary: A dictionary containing all of the terms used in all of the indexed fields of all of the documents.The set of stored fields are what is returned for each hit when searching. These are used to store auxiliary information about the document, such as its title, url, or an identifier to access a database. Stored Field Values: This contains, for each document, a list of attribute-value pairs, where the attributes are field names.Field Names: This contains the set of field names used in the index.Segment is a fragmented or chunked part of the entire Index, for better storage and faster retrieval.Įach segment index maintains the following:.String is simply a Token or an English language string.The set of distinct Terms is called the Vocabulary. This Term is the smallest piece of Information that will be Indexed to form the Inverted Index. ![]()
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