In the next chapter, we will perform a simple Search application using Lucene Search library. Acquiring contents and displaying the results is left for the application part to handle. In a nutshell, Lucene is the heart of any search application and provides vital operations pertaining to indexing and searching.
Lucene plays role in steps 2 to step 7 mentioned above and provides classes to do the required operations. Analytics of search results is another important and advanced aspect of any search application. How much information is to be shown at first look and so on.Īpart from these basic operations, a search application can also provide administration user interface and help administrators of the application to control the level of search based on the user profiles.
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Once the result is received, the application should decide on how to show the results to the user using User Interface. Using a query object, the index database is then checked to get the relevant details and the content documents. Once a user makes a request to search a text, the application should prepare a Query object using that text which can be used to inquire index database to get the relevant details. To facilitate a user to make a search, the application must provide a user a mean or a user interface where a user can enter text and start the search process. Once a database of indexes is ready then the application can make any search.
Indexing process is similar to indexes at the end of a book where common words are shown with their page numbers so that these words can be tracked quickly instead of searching the complete book. Once documents are built and analyzed, the next step is to index them so that this document can be retrieved based on certain keys instead of the entire content of the document. This process is where the document is analyzed. The next step is to build the document(s) from the raw content, which the search application can understand and interpret easily.īefore the indexing process starts, the document is to be analyzed as to which part of the text is a candidate to be indexed. The first step of any search application is to collect the target contents on which search application is to be conducted. How Search Application works?Ī Search application performs all or a few of the following operations − Step Lucene library provides the core operations which are required by any search application. This high-performance library is used to index and search virtually any kind of text. It can be used in any application to add search capability to it.
#Apache lucene windows mac os#
The problem occurs running Docker on Ubuntu, Mac OS and Windows.Lucene is a simple yet powerful Java-based Search library.
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Our Docker image contains our application's code which uses Tika, as well as Apache DS. Also, if the standard Tika Embedded Document Extractor is used the same problem occurs. Interestingly, I found that if *anything at all* is added to the context via t the same problem occurs.
Under Tika 2.1.0, under Docker, the Microsoft documents are fully parsed, so this problem was introduced in 2.2.0 However when running under Docker the text withinMicrosoft documents (Word etc) is not parsed. This also works under Tika 2.2.0 when running in development environments (Eclipse, Apache Tomcat). This all works fine for us, and has been used in production for a few years. *this*.t(EmbeddedDocumentExtractor.*class*, nalyticsEmbeddedDocumentExtractor) NalyticsEmbeddedDocumentExtractor nalyticsEmbeddedDocumentExtractor = *new* NalyticsEmbeddedDocumentExtractor(*this*) We use EmbeddedDocumentExtractor, with this code: Microsoft documents are not text parsed when running under Docker
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