![]() Document ID: because in practice we may have thousands of documents, I have used base64 to generate unique IDs for documents.In the sample code below, I have define an Document instance with a number of attributes: This class allows to model a document instance with numerous attributes where for every attribute we can also define using the class Field . In the framework textual content is modelled using the class Document . * */ public Indexer ( String anIndexDir, String aDataDir ) 3.2 Modelling document * Create an Indexer using the given directory to store index files, and the There are other alternative solutions, such as SimpleFSDirectory , NIOFSDirectory . In the given code sample, we use FSDirectory . The purpose is I want to analyse documents in a particular way that suits my personal needs (e.g., I want to keep specific words that built-in analysers will certainly remove). In the code below, we can see that I have define an instance of IndexWriterConfig with a specialised Analyzer`. IndexWriterConfig conf holds all the configuration that is used to construct an IndexWriter.Directory d where the index will be stored.The IndexWriter constructors can be defined in various ways but most often we simply need to supply In Lucene, the index is created and maintained by IndexWriter. In this section, we will look at how we can create a new index and add documents to it. The framework already provides handy and powerful analysers that we can use without customising in a majority of problems.Īfter analysing, the textual contents are ready to be indexed. In Lucene, the analysis process is handled by Analyzer. We instead need to apply analysis process where we will discard unimportant terms from the textual content (e.g., punctuations, stopping words). More often than not, we do not index everything we retrieve from the input stream. It is the second procedure of our index process – creating an index. When parsing completes, we will have an input stream that needs to be indexed. One of widely known tools we can use to parse documents is Apache Tika. In extracting procedure, it is common to use a versatile parser that can extract textual contents from documents. Indexing with Lucene breaks down into three main operations: extracting text from source documents, analyzing it, and saving it to the index Typically we can divide indexing documents into two distinct procedures, extracting text and creating index (Figure 2).įigure 2. Instead, all segments will be stored in flat files. Furthermore, this strategy also allows Lucene to avoid complex B-trees to store segments. This strategy ensures there is no conflict between reading writing indexes. Instead, Lucene creates new segments when the document collection changes and later, it merges segments into new ones and deletes the old segments. More importantly, the segment will never be modified. The approach of merging and deleting segments is considerably useful as the document collection does not change frequently. After merging, all greyed segments will be removed and in total, Lucene merged 5 times after the indexing finishes. The process is similar when Lucene keeps merging a group of 3 segments until there is no more segment to merge. Lucene repeatedly creates a segment for a document and periodically merges a group of 3 documents. In the example, there are 14 documents in the collection. The index diagram with the merge factor b is 3įigure 1 illustrates an example of an index, where the merge factor equals three.
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