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

Making use of parallel queries

Starting with version 9.6, PostgreSQL supports parallel queries. This support for parallelism has been improved gradually over time, and version 11 has added even more functionality to this important feature. In this section, we will take a look at how parallelism works and what can be done to speed up things.

Before digging into the details, it is necessary to create some sample data, as shown in the following section:

test=# CREATE TABLE t_parallel AS 
SELECT * FROM generate_series(1, 25000000) AS id;
SELECT 25000000

After loading the initial data, we can run our first parallel query. A simple count will show what a parallel query looks like in general:

test=# explain SELECT count(*) FROM t_parallel;
QUERY PLAN
------------------------------------------------------------------------------------
Finalize Aggregate...