Book Image

PostgreSQL 11 Administration Cookbook

By : Simon Riggs, Gianni Ciolli, Sudheer Kumar Meesala
Book Image

PostgreSQL 11 Administration Cookbook

By: Simon Riggs, Gianni Ciolli, Sudheer Kumar Meesala

Overview of this book

PostgreSQL is a powerful, open source database management system with an enviable reputation for high performance and stability. With many new features in its arsenal, PostgreSQL 11 allows you to scale up your PostgreSQL infrastructure. This book takes a step-by-step, recipe-based approach to effective PostgreSQL administration. The book will introduce you to new features such as logical replication, native table partitioning, additional query parallelism, and much more to help you to understand and control, crash recovery and plan backups. You will learn how to tackle a variety of problems and pain points for any database administrator such as creating tables, managing views, improving performance, and securing your database. As you make steady progress, the book will draw attention to important topics such as monitoring roles, backup, and recovery of your PostgreSQL 11 database to help you understand roles and produce a summary of log files, ensuring high availability, concurrency, and replication. By the end of this book, you will have the necessary knowledge to manage your PostgreSQL 11 database efficiently.
Table of Contents (19 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Index

Using parallel query


PostgreSQL now has an increasingly effective parallel query feature.

Response times from long-running queries can be improved by the use of parallel processing. The concept is that we divide a large task up into multiple smaller pieces. We get the answer faster, but we use more resources to do that.

Very short queries won't get faster by using parallel query, so if you have lots of those you'll gain more by thinking about better indexing strategies. Parallel query is aimed at making very large tasks faster, so it is useful for reporting and business intelligence queries.

How to do it…

Take a query that needs to do a big chunk of work, such as the following:

\timing
SELECT count(*) FROM accounts;
count
---------
1000000
(1 row)
Time: 261.652 ms
SET max_parallel_workers_per_gather = 8;
SELECT count(*) FROM accounts;
count
---------
1000000
(1 row)
Time: 180.513 ms

By setting the max_parallel_workers_per_gather parameter, we've improved performance using parallel query. Note...