Book Image

PostgreSQL 16 Administration Cookbook

By : Gianni Ciolli, Boriss Mejías, Jimmy Angelakos, Vibhor Kumar, Simon Riggs
5 (1)
Book Image

PostgreSQL 16 Administration Cookbook

5 (1)
By: Gianni Ciolli, Boriss Mejías, Jimmy Angelakos, Vibhor Kumar, Simon Riggs

Overview of this book

PostgreSQL has seen a huge increase in its customer base in the past few years and is becoming one of the go-to solutions for anyone who has a database-specific challenge. This PostgreSQL book touches on all the fundamentals of Database Administration in a problem-solution format. It is intended to be the perfect desk reference guide. This new edition focuses on recipes based on the new PostgreSQL 16 release. The additions include handling complex batch loading scenarios with the SQL MERGE statement, security improvements, running Postgres on Kubernetes or with TPA and Ansible, and more. This edition also focuses on certain performance gains, such as query optimization, and the acceleration of specific operations, such as sort. It will help you understand roles, ensuring high availability, concurrency, and replication. It also draws your attention to aspects like validating backups, recovery, monitoring, and scaling aspects. This book will act as a one-stop solution to all your real-world database administration challenges. By the end of this book, you will be able to manage, monitor, and replicate your PostgreSQL 16 database for efficient administration and maintenance with the best practices from experts.
Table of Contents (15 chapters)
13
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14
Index

Creating time-series tables using partitioning

In many applications, we need to store data in time series. There are various mechanisms in PostgreSQL that are designed to support this.

How to do it…

If you have a huge table and a query to select only a subset of that table, then you may wish to use a block range index (BRIN index). These indexes give performance improvements when the data is naturally ordered as it is added to the table, such as logtime columns or a naturally ascending OrderId column. Adding a BRIN index is fast and very easy, and works well for the use case of time-series data logging, though it works less well under intensive updates, even with the new BRIN features in PostgreSQL 16. INSERT commands into BRIN indexes are specifically designed to not slow down as the table gets bigger, so they perform much better than B-tree indexes for write-heavy applications. B-trees do have faster retrieval performance but require more resources. To try BRIN, just...