Writing you own directory provider in Java

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Writing you own directory provider
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There may be a time when the Hibernate Search built-in directory providers are insufficient for your needs. It might be because you need to tweak things a bit, because you have written a custom Lucene Directory, or because you want to reuse a JBoss Cache, Terracotta, or GigaSpace Lucene directory. Hibernate Search lets you write your own custom DirectoryProvider. The DirectoryProvider implementation benefits from the same configuration infrastructure available for built-in directory providers. The list of properties matching the current index name is passed to the initialize
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Analyzers: doors to flexibility
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method. The property names are unqualified: Default properties or index-specific properties are merged and passed to the initialize method. Writing a DirectoryProvider might require some knowledge of Lucene. An example of a directory provider is in section 11.3. Once you know where your index structure will go, the questions in your agenda are, what happens during indexing Can you influence it Can you tweak it We ll cover this in the next section.
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Analyzers: doors to flexibility
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Analyzers are one of those things in Lucene that people tend to leave for later. Some people even tend to see them as some dark magic and haunted artifacts. While we cannot deny some dark magic things happen in some analyzers, they re not that complex. And they are indeed very useful and definitely worth the effort to learn about them. Some of them are fascinating in that they reflect the complexity of our languages. Before diving into the dark magic, let s see what an analyzer does.
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What s the job of an analyzer
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Analyzers are basically responsible for taking text as input, breaking it into individual words (called tokens in Lucene terminology), and optionally applying some operations on the tokens. We ll call these operations filters, but they do more than filter in the common sense of the word: A filter operation can alter the stream of tokens as it pleases. Said otherwise, it can remove, change, and add words. Once the filter centrifuge is finished, Lucene uses the list of words (a stream really). Each word is indexed, along with statistical information.
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TOKENIZING: SPLITTING TEXT INTO WORDS
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The first step of an analyzer is to take a stream of characters (text in human terminology) and return a stream of tokens (a list of words in human terminology). This looks like a piece of cake: We take the text and split it each time we find a space, a dot, or a comma (basically at every nonletter or number character), and we re good! This approach might work most of the time in classic Latin-based languages, but we ll reach some harder problems pretty fast:
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Hyphenation A dash is not always a word separator, especially in texts extracted from newspapers (because of thin columns). URLs, acronyms, and other particular groupings A dot is not a word separator. Elision (in languages like French, Dutch, Italian, Portuguese, and Spanish) The last vowel of a word might be suppressed when the following word starts with a vowel. An apostrophe separates the two words: l avion (the plane) should be considered as two words (literally le avion). Sometimes an apostrophe should be considered as a single word: aujourd hui (today). In case you didn t know, every rule of French grammar has an exception (except this one, maybe).
Indexing: where, how, what, and when
If we start to consider non-Latin languages, things get even worse. Some languages don t even have a clear notion of words. Chinese and Japanese, for example, do not separate words with a space. As a matter of fact, traditional Chinese does not have a word to designate the idea of word as an identifiable graphical unit.
Since when is a word a word
The idea of words is not as old as you might think. The grammar experts of ancient Greece and Rome didn t manage to clearly define the notion of words, and continuous script was the norm. Continuous script consisted of not having any space between words: thisisanexampleofcontinuousscriptwhilereadableitsquitehardtofindinformationquicklyinsuchaflow. Continuous scripting was good enough at that time because most reading was done aloud. The idea of adding word breaks was driven by the need for quick reference searching, by the need to read in a foreign language in the Middle Ages (reading in Latin, which was no longer the common language, was quite difficult in continuous script), and by the need to move away from reading aloud. For more information, you can read Space between Words: The Origins of Silent Reading, by Paul Saenger.
Depending on the targeted language, a tokenizer algorithm might be more accurate than another type of algorithm.
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