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

Creating a scalable nextval replacement

Now that we have all of the tools to communicate between disparate servers, we can start building a very rudimentary API to generate ID values that are distinct across a pool of database servers. By doing so, database-level function calls are available to the application and encourage data distribution, otherwise known as application-level sharding. This, in turn, increases our scalability and availability, as it will take far more than a single database outage to truly derail the application.

A company that did this early in the development cycle of their platform is Instagram. In fact, they're very open about the process they used, as described in this blog post:

The idea they implemented may seem complicated but is actually deceptively simple. Here's a basic breakdown of what they were trying to create:

  • The system should accommodate several thousand logical shards
  • Generated...