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

Figuring out what to monitor

Modern servers have a lot of active hardware and software that can stop working at any time. A failure can start with the operating system, storage, database, network connectivity, heat, or a number of other sources.

So, which elements do we rank highest to ensure system availability? Which hardware needs the closest monitoring? What kind of tests should we use to ensure that the software is operating as expected?

When dedicating monitoring resources to check hardware and software, we must answer several questions to distribute effort efficiently. Every test takes time, uses network resources, and must save its results to a status file. If our system checks are too frequent or numerous, we could end up overwhelming our monitor server. Failing to prioritize the alerting criteria can actually be more dangerous; if we become too accustomed to ignoring irrelevant alerts, legitimate system issues can propagate unchecked.

Thus, the first step in building a monitoring...