Book Image

Mastering PostgreSQL 13 - Fourth Edition

By : Hans-Jürgen Schönig
Book Image

Mastering PostgreSQL 13 - Fourth Edition

By: Hans-Jürgen Schönig

Overview of this book

Thanks to its reliability, robustness, and high performance, PostgreSQL has become one of the most advanced open source databases on the market. This updated fourth edition will help you understand PostgreSQL administration and how to build dynamic database solutions for enterprise apps with the latest release of PostgreSQL, including designing both physical and technical aspects of the system architecture with ease. Starting with an introduction to the new features in PostgreSQL 13, this book will guide you in building efficient and fault-tolerant PostgreSQL apps. You’ll explore advanced PostgreSQL features, such as logical replication, database clusters, performance tuning, advanced indexing, monitoring, and user management, to manage and maintain your database. You’ll then work with the PostgreSQL optimizer, configure PostgreSQL for high speed, and move from Oracle to PostgreSQL. The book also covers transactions, locking, and indexes, and shows you how to improve performance with query optimization. You’ll also focus on how to manage network security and work with backups and replication while exploring useful PostgreSQL extensions that optimize the performance of large databases. By the end of this PostgreSQL book, you’ll be able to get the most out of your database by executing advanced administrative tasks.
Table of Contents (15 chapters)

Understanding full-text search

If you are looking up names or looking for simple strings, you are usually querying the entire content of a field. In full-text search, this is different. The purpose of the full-text search is to look for words or groups of words that can be found in a text. Therefore, full-text search is more of a contains operation, as you are basically never looking for an exact string.

In PostgreSQL, full-text search can be done using GIN indexes. The idea is to dissect a text, extract valuable lexemes (= "preprocessed tokens of words") string, and index those elements rather than the underlying text. To make your search even more successful, those words are preprocessed.

Here is an example:

test=# SELECT to_tsvector('english', 
'A car, I want a car. I would not even mind
having many cars');
to_tsvector
---------------------------------------------------------...