Book Image

Apache Solr for Indexing Data

Book Image

Apache Solr for Indexing Data

Overview of this book

Apache Solr is a widely used, open source enterprise search server that delivers powerful indexing and searching features. These features help fetch relevant information from various sources and documentation. Solr also combines with other open source tools such as Apache Tika and Apache Nutch to provide more powerful features. This fast-paced guide starts by helping you set up Solr and get acquainted with its basic building blocks, to give you a better understanding of Solr indexing. You’ll quickly move on to indexing text and boosting the indexing time. Next, you’ll focus on basic indexing techniques, various index handlers designed to modify documents, and indexing a structured data source through Data Import Handler. Moving on, you will learn techniques to perform real-time indexing and atomic updates, as well as more advanced indexing techniques such as de-duplication. Later on, we’ll help you set up a cluster of Solr servers that combine fault tolerance and high availability. You will also gain insights into working scenarios of different aspects of Solr and how to use Solr with e-commerce data. By the end of the book, you will be competent and confident working with indexing and will have a good knowledge base to efficiently program elements.
Table of Contents (18 chapters)
Apache Solr for Indexing Data
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Index

Chapter 3. Indexing Data

In the previous chapter, we saw the various analyzers, tokenizers, and filters provided by Solr that help us select the most important data from a given document. In this chapter, we'll see how Solr provides us a way to index this data so that we can run queries on top of it. We'll cover the following topics in this chapter:

  • Defining field types in Solr

  • Creating a custom musicCatalogue example

  • Facet searching

The Solr indexing process can mainly be broken down into two major parts:

  • Converting the document from its native format to XML or JSON, both of which are supported by Solr

  • Adding documents into Solr datastore using API or HTTP POST

To better understand the preceding two parts, we'll create an example of a music catalogue that contains metadata related to songs. The music catalogue will contain metadata related to a song that can later be used to retrieve important information regarding the song.

We'll also see how Solr provides various ways of feeding this information...