Book Image

Mastering PostgreSQL 11 - Second Edition

By : Hans-Jürgen Schönig
Book Image

Mastering PostgreSQL 11 - Second Edition

By: Hans-Jürgen Schönig

Overview of this book

This second edition of Mastering PostgreSQL 11 helps you build dynamic database solutions for enterprise applications using the latest release of PostgreSQL, which enables database analysts to design both the physical and technical aspects of the system architecture with ease. This book begins with an introduction to the newly released features in PostgreSQL 11 to help you build efficient and fault-tolerant PostgreSQL applications. You’ll examine all of the advanced aspects of PostgreSQL in detail, including logical replication, database clusters, performance tuning, monitoring, and user management. You will also work with the PostgreSQL optimizer, configuring PostgreSQL for high speed, and see how to move from Oracle to PostgreSQL. As you progress through the chapters, you will cover transactions, locking, indexes, and optimizing queries to improve performance. Additionally, you’ll learn to manage network security and explore backups and replications, while understanding the useful extensions of PostgreSQL so that you can optimize the speed and performance of large databases. By the end of this book, you will be able to use your database to its utmost capacity by implementing advanced administrative tasks with ease.
Table of Contents (15 chapters)
Free Chapter
1
PostgreSQL Overview

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 (FTS), 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, FTS is more of a contains operation, as you are basically never looking for an exact string.

In PostgreSQL, FTS can be done using GIN indexes. The idea is to dissect a text, extract valuable lexemes (= "preprocessed tokens of words"), 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
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