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1. #APACHE LUCENE INDEXING AND SEARCHING HOW TO#
2. #APACHE LUCENE INDEXING AND SEARCHING CODE#
3. #APACHE LUCENE INDEXING AND SEARCHING SERIES#
4. #APACHE LUCENE INDEXING AND SEARCHING DOWNLOAD#
5. #APACHE LUCENE INDEXING AND SEARCHING FREE#

KeyWordTokenizer, WhiteSpaceTokenizer, CharTokenizer are few of these. The answer is that Lucene provides lots of Tokeneziers to filter out information from the data being indexed.

One point remained undisclosed is how did we managed to remove “The” & “and” from beind indexed as terms from “The Girl and Boy”. So, when a search query “Girl” is fired on Lucene index, Lucene matches it with indexed terms and finds a Term “Girl” which points to a document “The Girl and Boy.”. Here, we indexed a document “The Girl and Boy” with terms Girl and Boy.

The index contains fields which is used to have the index or the original data itself (Its configurable to either only index the data or to store it too). So, when a index is created, the documents are specified with the terms pointing to it. The results are known as Documents and the search query terms are known as Term. When you create a index you can add Results which will be found for certain Search Query Terms. The indexes can be FileSystemBased, RAMBased, NIO Based Directory etc etc. The main part of Apache Lucene is its index. It can also be used as a base to create a highly efficient data analysis application. Its a very fast and efficient search framework which provides a search engine type of capability to your application. Then the index can be queried upon for data. It creates an index with the data to be searched. This blog mainly contains information about Lucene indexing and searching along with some less known facts about index and performance.Īpache Lucene is a text based search framework. I have been working on Lucene for last one year.

#APACHE LUCENE INDEXING AND SEARCHING SERIES#

Instead, we will extend CharTokenizer, which allows you to specify characters to “accept”, where those that are not “accepted” will be treated as delimiters between tokens and thrown away.It is the first part in series of blogs about Apache Lucene based on my practical experience.

#APACHE LUCENE INDEXING AND SEARCHING CODE#

The documentation for StandardTokenizer invites you to copy the source code and tailor it to your needs, but this solution would be unnecessarily complex. The Lucene StandardTokenizer throws away punctuation, and so our customization will begin here, as we need to preserve quotes. This pull proceeds back through the pipe until the first stage, the Tokenizer, reads from the InputStream.Īlso note that we don’t close the stream, as Lucene handles this for us. The IndexWriter pulls tokens from the end of the pipeline. The actual reading of the stream begins with addDocument. We don’t want to store the body of the ebook, however, as it is not needed when searching and would only waste disk space. Store.YES indicates that we store the title field, which is just the filename. We can see that each e-book will correspond to a single Lucene Document so, later on, our search results will be a list of matching books. )) ĭocument.add(new StringField("title", fileName, Store.YES)) ĭocument.add(new TextField("body", reader)) īufferedReader reader = new BufferedReader(new InputStreamReader(. The essential code for producing an index is: IndexWriter writer =. Creating a Lucene index and reading files are well travelled paths, so we won’t explore them much.

#APACHE LUCENE INDEXING AND SEARCHING DOWNLOAD#

To create an index for Project Gutenberg, we download the e-books, and create a small application to read these files and write them to the index. When documents are initially added to the index, the characters are read from a Java InputStream, and so they can come from files, databases, web service calls, etc.

#APACHE LUCENE INDEXING AND SEARCHING HOW TO#

We will see how to customize this pipeline to recognize regions of text marked by double-quotes, which I will call dialogue, and then bump up matches that occur when searching in those regions. The standard analysis pipeline can be visualized as such: The Lucene analysis JavaDoc provides a good overview of all the moving parts in the text analysis pipeline.Īt a high level, you can think of the analysis pipeline as consuming a raw stream of characters at the start and producing “terms”, roughly corresponding to words, at the end. Pieces of the Apache Lucene Analysis Pipeline So it is therefore in these early stages where our customization must begin.

In fact, they will throw away punctuation at the earliest stages of text analysis, which runs counter to being able to identify portions of the text that are dialogue. Neither Lucene, Elasticsearch, nor Solr provides out-of-the-box tools to identify content as dialogue.

Suppose we are especially interested in the dialogue within these novels. We know that many of these books are novels.

#APACHE LUCENE INDEXING AND SEARCHING FREE#

As an example of this sort of customization, in this Lucene tutorial we will index the corpus of Project Gutenberg, which offers thousands of free e-books.