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Even if you break away from the level of bits and use content that can be read by humans instead, a document is still a series of characters: letters, punctuation marks, spaces. For a machine, a document is initially a collection of information. When documents are indexed, tokenization also takes place. For example, the field with the name title can have the value “Instructions for use for Apache Lucene.” So when creating the index, you can decide which metadata you want to include. These fields contain, for example, the name of the author, the title of the document, or the file name. However, from Lucene’s point of view, the documents themselves contain fields. The objects that Lucene works with are documents in every kind of form. To understand this, you have to go back one step. Developers decide which fields they want to include in the index during configuration. Lucene gives users the ability to configure this extraction individually. All terms must be taken from all the documents and stored in the index. In order to build an index, you first need to extract it. In principle, an inverted index is simply a table – the corresponding position is stored for each term.
#Apache lucene architecture pdf
It not only searches HTML documents, but also works with e-mail and PDF files.Īn index – the heart of Lucene – is decisive for the search, since all terms of all documents are stored here. Lucene can also be used for archives, libraries, or even on your home desktop PC. This shows that Lucene is not solely used in the context of the world wide web, even if the searches are mostly found here. This means, quite simply: a program searches a series of text documents for one or more terms that the user has specified. Apache Solr and Elasticsearch are powerful extensions that give the search function even more possibilities. Originally, Lucene was written completely in Java, but now there are also ports to other programming languages.
#Apache lucene architecture free
It is open source and free for everyone to use and modify.
#Apache lucene architecture update
All of the configuration is declarative, including the specification of update processor chains.Lucene is a program library published by the Apache Software Foundation. Each update handler can have it’s own Update Processor Chain that can do Document-level operations prior to indexing, or even redirect indexing to a different server or create multiple documents (or zero) from a single one. HTTP POST HTTP POST /update /update/csv /update/xml /update/extract XML Update Handler CSV Update Handler XML Update with custom processor chain Extracting RequestHandler (PDF, Word, …) Update Processor Chain (per handler) Text Index Analyzers Data Import Handler Database pull RSS pull Simple transforms Remove Duplicates processor Logging Index Custom Transform RSS feed Just like all request handlers, update handlers can be mapped to a specific URL and have their own set of default or invariant parameters. schema.xml Whitespace Tokenizer Analyzer for “title” CustomFilter SynonymFilter Porter Stemmer // declaratively defines types // and analyzers for fields /update /update/csv
Request Handlers Response Writers Update Handlers /admin /select /spell XML Binary JSON XML CSV binary Extracting Request Handler (PDF/WORD) Search Components Schema Update Processors Query Highlighting Signature Spelling Statistics Logging Faceting Debug Indexing Apache Tika More like this Clustering Query Parsing Config Distributed Search Data Import Handler (SQL/RSS) Analysis Faceting Filtering Search Caching High-lighting Index Replication Apache Lucene Core Search IndexReader/Searcher Indexing IndexWriter Text AnalysisĢ Lucene/Solr plugins RequestHandlers – handle a request at a URL like /select SearchComponents – part of a SearchHandler, a componentized request handler Includes, Query, Facet, Highlight, Debug, Stats Distributed Search capable UpdateHandlers – handle an indexing request Update Processor Chains – per-handler componentized chain that handle updates Query Parser plugins Mix and match query types in a single request Function plugins for Function Query Text Analysis plugins: Analyzers, Tokenizers, TokenFilters ResponseWriters serialize & stream response to clientĭeclarative Analysis per-field - Tokenizer to split text - TokenFilter to transform tokens - Analyzer for completely custom - Separate query / index analyzer QParser plugins - Support different query syntaxes - Support different query execution - Function Query supports pluggable custom functions - Excellent support for nesting/mixing different query types in the same request.
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