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

Logging checkpoints properly

Checkpoints are an integral part of a PostgreSQL server. Table data is not modified during query execution until modified rows, index pages, and other structures are committed to the Write Ahead Log (WAL). WAL files are also known as checkpoint segments. When the cumulative size of these files exceeds max_wal_size-or the time since the last checkpoint exceeds checkpoint_timeout-the data files are modified to reflect the changes.


In versions older than PostgreSQL 9.5, checkpoints were specified as a count of 16MB files with the checkpoint_segments parameter, rather than a cumulative total size. The setting for max_wal_size in MB is roughly equivalent to checkpoint_segments * 16.

This decoupled writing ensures database integrity at the cost of doubling the necessary disk writes. This is the main reason why experienced PostgreSQL DBAs interested in performance move the WAL location to a separate storage device. However, even moving the WAL files to another device...