Book Image

Mastering Apache Solr 7.x

By : Sandeep Nair, Chintan Mehta, Dharmesh Vasoya
Book Image

Mastering Apache Solr 7.x

By: Sandeep Nair, Chintan Mehta, Dharmesh Vasoya

Overview of this book

Apache Solr is the only standalone enterprise search server with a REST-like application interface. providing highly scalable, distributed search and index replication for many of the world's largest internet sites. To begin with, you would be introduced to how you perform full text search, multiple filter search, perform dynamic clustering and so on helping you to brush up the basics of Apache Solr. You will also explore the new features and advanced options released in Apache Solr 7.x which will get you numerous performance aspects and making data investigation simpler, easier and powerful. You will learn to build complex queries, extensive filters and how are they compiled in your system to bring relevance in your search tools. You will learn to carry out Solr scoring, elements affecting the document score and how you can optimize or tune the score for the application at hand. You will learn to extract features of documents, writing complex queries in re-ranking the documents. You will also learn advanced options helping you to know what content is indexed and how the extracted content is indexed. Throughout the book, you would go through complex problems with solutions along with varied approaches to tackle your business needs. By the end of this book, you will gain advanced proficiency to build out-of-box smart search solutions for your enterprise demands.
Table of Contents (14 chapters)
Title Page
Packt Upsell
Contributors
Preface
Index

Summary


In this chapter, we saw an overview of text analysis, analyzers, tokenizers, filters, and how to configure an analyzer along with tokenizers and filters. We also saw the implementation approach for putting tokenizers and filters together. Then we moved on to multiple search. Here we explored how Solr determines a language, two approaches to creating separate fields and separate indexes per language for multiple-language search, and the pros and cons of each approach. Finally, we understood Solr phonetic matching mechanics using the BMPM algorithm.

In the next chapter, we will see how to do indexing using client API, upload data using index handlers, upload data using Apache Tika with Solr Cell, and detect languages while indexing.