Apache lucene indexing example
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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).
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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.
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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.
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It’s an open source project available for free download, a cross-platform solution that offers scalable, high-performance indexing and powerful, accurate and efficient search algorithms. It is a technology suitable for nearly any application that requires full-text search, especially cross-platform. Apache Lucene introductionĪpache Lucene is a high-performance, full-featured text search engine library written entirely in Java. The most important aspects of Lucene are mentioned under each heading. We’ll start with Apache Lucene 5.3.x/5.4.y. This will also help you clarify a few terms before getting into search or information retrieval: Before we delve into Apache Lucene, the following are the most important terms that you need to be familiar with.