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


Every business has the goal of being successful. The consequence of having a successful business when there's a database involved is increasingly high volume. This volume can be composed of query activity, data accumulation, or both. A PostgreSQL database that is not prepared for vast amounts of data or a huge transaction load will slowly falter until the platform suffers.

Customers notice bad performance just as readily as outages. If our database is struggling to service queries, we have three options:

  • Spend time optimizing the platform to reduce database interaction
  • Buy a more capable database server
  • Store data on several PostgreSQL servers

Indeed, we should probably always implement step one in any case. Yet, there is a limit to candidates for optimization. If the platform is using an ORM (Object-relational Mapping), making query changes can be difficult because they are generated from the framework. Frontend caching can prevent a vast amount of database accesses, but we need...