If you are a developer building an application today, then you know how important a good search experience is. Apache Solr, built on Apache Lucene, is a wildly popular open source enterprise search server that easily delivers the powerful search and faceted navigation features that are elusive with databases. Solr supports complex search criteria, faceting, result highlighting, query-completion, query spellcheck, relevancy tuning, and more.
Apache Solr Enterprise Search Server, Third Edition is a comprehensive resource to almost everything 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 Solr with other languages and frameworks—even Hadoop.
By using a large set of metadata, including artists, releases, and tracks, courtesy of the MusicBrainz.org project, you will have a testing ground for Solr and will learn how to import this data in various ways. You will then learn how to search this data in different ways, including Solr's rich query syntax and boosting match scores based on record data. 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.
Chapter 1, Quick Starting Solr, introduces Solr to you so that you understand its unique role in your application stack. You'll get started quickly by indexing example data and searching it with Solr's sample / browse UI. This chapter is oriented to Solr 5, but the majority of content applies to Solr 4 too.
Chapter 2, Schema Design, guides you through an approach to modeling your data within Solr into one or more Solr indices and schemas. It covers the schema thoroughly and explores some of Solr's field types.
Chapter 3, Text Analysis, covers how to customize text tokenization, stemming, synonyms, and related matters to have fine control over keyword search matching. It also covers multilingual strategies.
Chapter 4, Indexing Data, explores all of the options Solr offers for importing data, such as XML, CSV, databases (SQL), and text extraction from common documents. This includes important information on commits, atomic updates, and real-time search.
Chapter 5, Searching, covers the query syntax, from the basics to Boolean options to more advanced wildcard and fuzzy searches, join queries, and geospatial search.
Chapter 6, Search Relevancy, explains how Solr scores documents for relevancy ranking. We'll review different options to influence the score, called boosting, and apply it to common examples such as boosting recent documents and boosting by a user vote.
Chapter 7, Faceting, shows you how to use Solr's killer feature—faceting. You'll learn about the different types of facets and how to build filter queries for a faceted navigation interface.
Chapter 8, Search Components, explores how to use a variety of valuable search features implemented as Solr search components. This includes result highlighting, query spellcheck, query suggest / complete, result grouping / collapsing, and more.
Chapter 9, Integrating Solr, explores some external integration options to interface with Solr. This includes some language-specific frameworks for Java, JavaScript, Ruby, and PHP, as well as a web crawler, Hadoop, a quick prototyping option, and more.
Chapter 10, Scaling Solr, covers how to tune Solr to get the most out of it. Then we'll introduce how to scale beyond one instance with SolrCloud.
Chapter 11, Deployment, guides you through deployment considerations to include deploying Solr to Apache Tomcat, to logging, and to security, and setting up Apache ZooKeeper.
Appendix, Quick Reference, serves as a small parameter quick-reference guide you can print to have within reach when you need it.