Lucene in Action: Meet Lucene Pt. 2 | WebReference

Lucene in Action: Meet Lucene Pt. 2

Lucene in Action: Meet Lucene Pt. 2

Written by Otis Gospodnetic and Erik Hatcher and reproduced from "Lucene in Action" by permission of Manning Publications Co. ISBN 1932394281, copyright 2004. All rights reserved. See http://www.manning.com for more information.

1.5 Understanding the core indexing classes

As you saw in our Indexer class, you need the following classes to perform the simplest indexing procedure:

What follows is a brief overview of these classes, to give you a rough idea about their role in Lucene. We'll use these classes throughout this book.


3 Neal Stephenson details this process nicely in "In the Beginning Was the Command Line": http:// www.cryptonomicon.com/beginning.html.

1.5.1 IndexWriter

IndexWriter is the central component of the indexing process. This class creates a new index and adds documents to an existing index. You can think of IndexWriter as an object that gives you write access to the index but doesn't let you read or search it. Despite its name, IndexWriter isn't the only class that's used to modify an index; section 2.2 describes how to use the Lucene API to modify an index.

1.5.2 Directory

The Directory class represents the location of a Lucene index. It's an abstract class that allows its subclasses (two of which are included in Lucene) to store the index as they see fit. In our Indexer example, we used a path to an actual file system directory to obtain an instance of Directory, which we passed to IndexWriter's constructor. IndexWriter then used one of the concrete Directory implementations, FSDirectory, and created our index in a directory in the file system.

In your applications, you will most likely be storing a Lucene index on a disk. To do so, use FSDirectory, a Directory subclass that maintains a list of real files in the file system, as we did in Indexer.

The other implementation of Directory is a class called RAMDirectory. Although it exposes an interface identical to that of FSDirectory, RAMDirectory holds all its data in memory. This implementation is therefore useful for smaller indices that can be fully loaded in memory and can be destroyed upon the termination of an application. Because all data is held in the fast-access memory and not on a slower hard disk, RAMDirectory is suitable for situations where you need very quick access to the index, whether during indexing or searching. For instance, Lucene's developers make extensive use of RAMDirectory in all their unit tests: When a test runs, a fast in-memory index is created or searched; and when a test completes, the index is automatically destroyed, leaving no residuals on the disk. Of course, the performance difference between RAMDirectory and FSDirectory is less visible when Lucene is used on operating systems that cache files in memory. You'll see both Directory implementations used in code snippets in this book.

1.5.3 Analyzer

Before text is indexed, it's passed through an Analyzer. The Analyzer, specified in the IndexWriter constructor, is in charge of extracting tokens out of text to be indexed and eliminating the rest. If the content to be indexed isn't plain text, it should first be converted to it, as depicted in figure 2.1. Chapter 7 shows how to extract text from the most common rich-media document formats. Analyzer is an abstract class, but Lucene comes with several implementations of it. Some of them deal with skipping stop words (frequently used words that don't help distinguish one document from the other, such as a, an, the, in, and on); some deal with conversion of tokens to lowercase letters, so that searches aren't case-sensitive; and so on. Analyzers are an important part of Lucene and can be used for much more than simple input filtering. For a developer integrating Lucene into an application, the choice of analyzer(s) is a critical element of application design. You'll learn much more about them in chapter 4.

1.5.4 Document

A Document represents a collection of fields. You can think of it as a virtual document— a chunk of data, such as a web page, an email message, or a text file— that you want to make retrievable at a later time. Fields of a document represent the document or meta-data associated with that document. The original source (such as a database record, a Word document, a chapter from a book, and so on) of document data is irrelevant to Lucene. The meta-data such as author, title, subject, date modified, and so on, are indexed and stored separately as fields of a document.

Lucene only deals with text. Lucene's core does not itself handle anything but java.lang.String and java.io.Reader. Although various types of documents can be indexed and made searchable, processing them isn't as straightforward as processing purely textual content that can easily be converted to a String or Reader Java type. You'll learn more about handling nontext documents in chapter 7. In our Indexer, we're concerned with indexing text files. So, for each text file we find, we create a new instance of the Document class, populate it with Fields (described next), and add that Document to the index, effectively indexing the file.

1.5.5 Field

Each Document in an index contains one or more named fields, embodied in a class called Field. Each field corresponds to a piece of data that is either queried against or retrieved from the index during search.

Lucene offers four different types of fields from which you can choose:

All fields consist of a name and value pair. Which field type you should use depends on how you want to use that field and its values. Strictly speaking, Lucene has a single Field type: Fields are distinguished from each other based on their characteristics. Some are analyzed, but others aren't; some are indexed, whereas others are stored verbatim; and so on.

Table 1.2 provides a summary of different field characteristics, showing you how fields are created, along with common usage examples.

Notice that all field types can be constructed with two Strings that represent the field's name and its value. In addition, a Keyword field can be passed both a String and a Date object, and the Text field accepts a Reader object in addition to the String. In all cases, the value is converted to a Reader before indexing; these additional methods exist to provide a friendlier API.

Finally, UnStored and Text fields can be used to create term vectors (an advanced topic, covered in section 5.7). To instruct Lucene to create term vectors for a given UnStored or Text field, you can use Field.UnStored(String, String, true), Field.Text(String, String, true), or Field.Text(String, Reader, true).

You'll apply this handful of classes most often when using Lucene for indexing. In order to implement basic search functionality, you need to be familiar with an equally small and simple set of Lucene search classes.


Created: March 27, 2003
Revised: January 31, 2005

URL: http://webreference.com/programming/lucene/2