The following code will load the content from a MS Word, MS Excel, MS PowerPoint or Visio file, and the extracted content is form into a String representation so that it can be further processed by Lucene for indexing purposes.Įxtractors already exist for Excel, Word, PowerPoint and Visio if one of these objects is embedded into a worksheet, the ExtractorFactory class can be used to recover an extractor for it based on the file extension. Step 3: Defining the MS Document Indexer. Mvn archetype:generate -DartifactId=.demo -DgroupId=org.fazlan -Dversion=1.0-SNAPSHOT -DinteractiveMode=false In other words, it considers all documents, splits them into words or tokens, and then builds an index for each token so that it knows in advance exactly which document to look for if a term is searched. In action, Lucene uses an inverted full-text index. Creating new segments for newly added documents. You may also refer to Apache Lucene Tutorial: Indexing PDF Files Indexing is the first step for searching data fast. Seminars The Lucene Inverted Index Lucene directory (in memory, on disk, memory mapped) Collection of immutable segments (fully working) Each segment is composed by a set of binary les 1 1 Lucene File Format Documentation Indexes evolve by: 1.
You can read more about Apache POI.Īrticle applies to Lucene 3.6.0 and POI 3.8.0. One such library is Apache POI, which we'll use in the article. A unique kind of Lucene index has been used for all developed models, or in other words, all models share the same Lucene index. An Analyzer takes a series of terms or tokens and creates the terms to be indexed. Therefore, we need to use one of the APIs that enables us to perform text manipulation on MS documents files. To accomplish this, a Lucene index was created with a specific analyzer model-dependent. Apache Lucene doesn't have the build-in capability to process these files.
#Use apache lucene for indexing how to#
Here, we look at how to index content in a Microsoft documents such as Word, Excel and PowerPoint files.
This article is a sequel to Apache Lucene Tutorial: Lucene for Text Search.
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