Book Image

Solr 1.4 Enterprise Search Server

By : David Smiley, Eric Pugh
Book Image

Solr 1.4 Enterprise Search Server

By: David Smiley, Eric Pugh

Overview of this book

<p>If you are a developer building a high-traffic web site, you need to have a terrific search engine. Sites like Netflix.com and Zappos.com employ Solr, an open source enterprise search server, which uses and extends the Lucene search library. This is the first book in the market on Solr and it will show you how to optimize your web site for high volume web traffic with full-text search capabilities along with loads of customization options. So, let your users gain a terrific search experience.<br /><br />This book is a comprehensive reference guide for every feature Solr has to offer. It serves the reader right from initiation to development to deployment. It also comes with complete running examples to demonstrate its use and show how to integrate it with other languages and frameworks.<br /><br />This book first gives you a quick overview of Solr, and then gradually takes you from basic to advanced features that enhance your search. It starts off by discussing Solr and helping you understand how it fits into your architecture—where all databases and document/web crawlers fall short, and Solr shines. The main part of the book is a thorough exploration of nearly every feature that Solr offers. To keep this interesting and realistic, we use a large open source set of metadata about artists, releases, and tracks courtesy of the MusicBrainz.org project. Using this data as a testing ground for Solr, you will learn how to import this data in various ways from CSV to XML to database access. You will then learn how to search this data in a myriad of ways, including Solr's rich query syntax, "boosting" match scores based on record data and other means, about searching across multiple fields with different boosts, getting facets on the results, auto-complete user queries, spell-correcting searches, highlighting queried text in search results, and so on.<br /><br />After this thorough tour, we'll demonstrate working examples of integrating a variety of technologies with Solr such as Java, JavaScript, Drupal, Ruby, XSLT, PHP, and Python.<br /><br />Finally, we'll cover various deployment considerations to include indexing strategies and performance-oriented configuration that will enable you to scale Solr to meet the needs of a high-volume site.</p>
Table of Contents (15 chapters)
Solr 1.4 Enterprise Search Server
Credits
About the Authors
About the Reviewers
Preface
Index

Solr's XML response format


The <response/> element wraps the entire response.

The first child element is <lst name="responseHeader">, which is intuitively the response header that captures some basic metadata about the response.

  • status: Always zero unless something went very wrong.

  • QTime: The number of milliseconds Solr takes to process the entire request on the server. Due to internal caching, you should see this number drop to a couple of milliseconds or so for subsequent requests of the same query. If subsequent identical searches are much faster, yet you see the same QTime, then your web browser (or intermediate HTTP Proxy) cached the response. Solr's HTTP caching configuration is discussed in Chapter 9.

  • Other data may be present depending on query parameters.

The main body of the response is the search result listing enclosed by this: <result name="response" numFound="1002272" start="0" maxScore="1.0">, and it contains a <doc> child node for each returned document...