Book Image

PostgreSQL High Availability Cookbook - Second Edition

By : Shaun Thomas
Book Image

PostgreSQL High Availability Cookbook - Second Edition

By: Shaun Thomas

Overview of this book

Databases are nothing without the data they store. In the event of a failure - catastrophic or otherwise - immediate recovery is essential. By carefully combining multiple servers, it’s even possible to hide the fact a failure occurred at all. From hardware selection to software stacks and horizontal scalability, this book will help you build a versatile PostgreSQL cluster that will survive crashes, resist data corruption, and grow smoothly with customer demand. It all begins with hardware selection for the skeleton of an efficient PostgreSQL database cluster. Then it’s on to preventing downtime as well as troubleshooting some real life problems that administrators commonly face. Next, we add database monitoring to the stack, using collectd, Nagios, and Graphite. And no stack is complete without replication using multiple internal and external tools, including the newly released pglogical extension. Pacemaker or Raft consensus tools are the final piece to grant the cluster the ability to heal itself. We even round off by tackling the complex problem of data scalability. This book exploits many new features introduced in PostgreSQL 9.6 to make the database more efficient and adaptive, and most importantly, keep it running.
Table of Contents (18 chapters)
Title Page
About the Author
About the Reviewer
Customer Feedback

Identifying important tables

Another aspect of maintaining a highly-available database is to know all of the important information about the contents of the database itself. In this case, we aim to focus on tables and indexes that receive the most activity. If any problems that might require maintenance or a restart arise, the most active portions are the likely origin.

What is activity? Inserts, updates, deletes, and selects are a good start. PostgreSQL collects statistics on all of this information, making it easy to collect and track. It also tracks how often indexes or tables are scanned and how many rows were affected by each. In addition, we can find out how much disk space any object consumes, and given the help of a couple of contributed tools, we can also find out how much of this space is currently reusable.

Data like this tells us which tables and indexes are the most active, which objects have the highest row turnover, and which objects require a high disk I/O. Armed with these...