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

Understanding phonetic matching


Phonetic matching algorithms are used to match different spellings that are pronounced similarly by encoding them. Some examples are Sandeep and Sandip; TaylorTailer, and Tailor; and so on. Solr provides several filters for phonetic matching.

Understanding Beider-Morse phonetic matching

Beider-Morse Phonetic Matching (BMPM) helps you search for personal names or surnames. It is a very intelligent algorithm compared to soundex, metaphone, caverphone, and so on. Its purpose is to match names that are phonetically equivalent to the expected name. BMPM does not split spellings and does not generate false hits. It extracts names that are phonetically equivalent.

It executes these steps to extract names that are phonetically equivalent:

  • Determines the language from the spelling of the name
  • Applies phonetic rules to identify the language and translates the name into phonetic alphabets
  • In the case of a language not identified from the name, it applies generic phonetics...