Book Image

Learning PostgreSQL 11 - Third Edition

By : Salahaldin Juba, Andrey Volkov
Book Image

Learning PostgreSQL 11 - Third Edition

By: Salahaldin Juba, Andrey Volkov

Overview of this book

PostgreSQL is one of the most popular open source database management systems in the world, and it supports advanced features included in SQL standards. This book will familiarize you with the latest features in PostgreSQL 11, and get you up and running with building efficient PostgreSQL database solutions from scratch. Learning PostgreSQL, 11 begins by covering the concepts of relational databases and their core principles. You’ll explore the Data Definition Language (DDL) and commonly used DDL commands supported by ANSI SQL. You’ll also learn how to create tables, define integrity constraints, build indexes, and set up views and other schema objects. As you advance, you’ll come to understand Data Manipulation Language (DML) and server-side programming capabilities using PL/pgSQL, giving you a robust background to develop, tune, test, and troubleshoot your database application. The book will guide you in exploring NoSQL capabilities and connecting to your database to manipulate data objects. You’ll get to grips with using data warehousing in analytical solutions and reports, and scaling the database for high availability and performance. By the end of this book, you’ll have gained a thorough understanding of PostgreSQL 11 and developed the necessary skills to build efficient database solutions.
Table of Contents (18 chapters)

PostgreSQL native data types

When designing a database table, you should take care to pick the appropriate data type. When the database goes to production, changing the data type of a column can become a very costly operation, especially for heavily-loaded tables. The cost often comes from locking the table, and in some cases, rewriting it. When picking a data type, consider a balance between the following factors:

  • Extensibility: Can the maximum length of a type be increased or decreased without a full table rewrite and a full table scan?
  • Data type size: Going for a safe option, such as choosing big integers instead of integers, will cause more storage consumption.
  • Support: This factor is important for rich data types, such as XML, JSON, and hstore. If the drivers, such as JDBC drivers, don't support rich types, you need to write your own code to serialize and deserialize...