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

Upgrading to asynchronous replication

Since the release of PostgreSQL 9.0, DBAs have had access to asynchronous streaming replication. Unlike the older hot standby methods used in earlier versions, replica servers can connect to an upstream PostgreSQL server and consume data modifications directly. With low network latency and fast transactions, this means that it is fairly common for streaming replicas to lag behind the master by only a few milliseconds.

In the context of high availability, this means we can scale horizontally by copying the database to multiple servers. Of course, this means that we need to copy the entire database to each server. For small-to medium-sized database instances, this is a relatively minor requirement. This also means that we can produce up-to-date backups, perform ad hoc queries on practically live data, and aggregate information into reports without disrupting our primary database.

This recipe will explain how to set up a streaming asynchronous database replica...