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WARNING The QueryParser class is not thread-safe! It s your responsibility to
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So how does a pile of unorganized information start to become an organized searchable index That s our next focus of attention. Let s start by reviewing some of the topics that were introduced in chapters 3 and 4, so that these idioms are fresh in your mind.
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Tokenization and fields
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A document unit (this is not the Document class of Lucene) is the initial piece of information that we wish to enter into an index. It could be the text of a book, a summary of a book, a paragraph, or even a sentence in short, any information that is index capable and searchable for our purposes. Our first step is to assemble these document units; after assembling them, we ll place each unit into a field or property.
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A field (Lucene) or property (Hibernate Search) is the basic container from which documents are composed. Fields hold the tokens/terms that are queried against. A field name followed by a colon followed by a term makes up a basic query, for example, description:adaptation. This is exactly what we were looking at with Luke in the first few figures of this chapter, so this should be a review of what we talked about then. Figure 7.6 shows Luke with this query.
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Luke querying the description field for the term adaptation
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In figure 7.6 B is the query we entered. After we clicked the Update button, our query was parsed and shown C. This is the most basic type of query, one term on one field, so there are no surprises here. Notice D the default field is set to title. This required us to enter the field name of the field we were querying. If the default field had been set to description, then the field name and colon would not have been necessary. Once we have our fields/properties assembled, we need to put them through a process called tokenization.
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Tokenizing is the task of chopping a document unit into pieces, called tokens, perhaps at the same time throwing away certain characters such as punctuation marks. We can even throw away whole words. These words are known as stop words and are common words that are unlikely to help determine the relevance of a document against a query. Words such as the, an, or, but, and so on are often defined as stop words but do not necessarily have to be. Although the usage is not strictly correct in the information-retrieval world, for our purposes term and word are synonymous, and we ll use these two idioms interchangeably. Analyzers perform the act of tokenizing a document unit. Let s look at them next.
Writing a Lucene query
Analyzers and their impact on queries
As you ve seen, analyzers generate tokens from document units. They perform more than tokenization. They can filter out the stop words we mentioned. They can convert all input to lowercase, filter all numeric input, or generate the stems of the query terms or a list of synonyms, and so on. In fact, if you write your own analyzer, it can process input text into anything you want. Figure 7.7 is an example of tokenization where the analyzer does not convert terms to lowercase. Some analyzers are designed to do this conversion automatically.
Tokenizing document unit input
Lucene comes with many analyzer classes and filter classes to process input text in a variety of ways as an index is built. These classes are contained in the org.apache.lucene.analysis package. An analyzer that is used quite often is the StandardAnalyzer in the org.apache.lucene.analysis.standard package. Recently, this analyzer has been significantly improved speedwise. Hibernate Search also supports the Apache Solr analyzers. Solr is an open source enterprise search server based on Lucene; the project home page is located at http:// lucene.apache.org/solr/. The Solr analyzer jar apache-solr-analyzer.jar contains an incredibly varied selection of filters that can be utilized in the manner explained in section 5.3.2. We want to take a minute here and discuss analyzers and the care you should take when working with them. That s the topic of our next section.
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