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)

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 things up.

Before digging into the details, it is necessary to create some sample data, as follows:

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 (cost=241829.17..241829.18 rows=1 width=8)
-> Gather (cost=241828.96..241829.17 rows=2 width=8)
Workers...