Domestic ferrets are most commonly known for being quick, curious, and flexible, so it’s no surprise that the new search library for Ruby is known for these same qualities. A high-performance, full-featured text search engine library written specifically for Ruby, Ferret can quickly locate data on hard drives and servers.

But just like domestic ferrets, this program takes a bit of training before it’s “house-broken.” However, with the new release of Ferret, Ruby enthusiasts have no reason to worry. With this book, authored by David Balmain — the primary developer of this open source library — Ruby programmers can learn to build one of the fastest and most flexible search libraries available for locating data on hard drives and servers.

Ferret teaches you how to index different document types such as PDF, Microsoft Word, and HTML, as well as how to deal with foreign languages and different character encodings. This guide also describes the Ferret Query Language in detail along with the object-oriented approach to building queries.
Topics discussed in Ferret include:

• The Index class: an easy-to-use interface to the rest of the Ferret API
• Using Ferret out of the box: how to set up an index, set up fields for indexing different data types, and set up a field for sorting
• Going behind the scenes: learn about the indexing process, performance tuning, optimizing the index, Ferret’s index locking mechanism, and concurrency issues
• Ferret’s search API: find out how to search classes, build queries, and filter, sort, and highlight search results
• Ferret’s analysis API: use the standard Token, TokenStream, and Analyzer classes, and learn to build a custom analyzer
• Building a search application: index multiple document types, make indexing and search improvements, and put together a powerful PC search application

This book includes plenty of Ruby code samples, which are downloadable from the companion Web site. With Ferret you can now build a search library to help you find anything on your computer with lightning fast indexing and split-second query results.