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This article focuses on "how the Lucene indexing process is". Interested friends may wish to take a look. The method introduced in this paper is simple, fast and practical. Let's let the editor take you to learn how the Lucene indexing process is.

In general, Lucene is:

An efficient, extensible, full-text search library.

All are implemented in Java without configuration.

Only index (Indexing) and search (Search) of plain text files are supported.

Is not responsible for the process of extracting plain text files from other formats or fetching files from the network.

In Lucene in action, the architecture and process of Lucene are shown in the following figure

It shows that Lucene has two processes of index and search, including index creation, index and search.

Let's take a closer look at the components of Lucene:

The indexed document is represented by a Document object.

IndexWriter adds the document to the index through the function addDocument to realize the process of creating the index.

Add the document to the index to implement the process of creating the index.

The index of Lucene is to apply the reverse index.

Query represents the query statement of the user when the user has a request.

IndexSearcher searches for Lucene Index through the function search.

IndexSearcher calculates term weight and score and returns the results to the user.

The collection of documents returned to the user is represented by TopDocsCollector.

So how do you apply these components?

Let's go into more detail about the call to Lucene API to implement the indexing and search process.

The analysis module of Lucene is mainly responsible for lexical analysis and language processing to form Term.

The index module of Lucene is mainly responsible for the creation of the index, which contains IndexWriter.

The store module of Lucene is mainly responsible for reading and writing the index.

Lucene's QueryParser is mainly responsible for syntax analysis.

The search module of Lucene is mainly responsible for searching the index.

The similarity module of Lucene is mainly responsible for the realization of correlation scoring.

At this point, I believe you have a deeper understanding of "what the Lucene indexing process is like". You might as well do it in practice. Here is the website, more related content can enter the relevant channels to inquire, follow us, continue to learn!

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