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

Checking the pg_stat_statements view

We mentioned in another recipe that logging every query on a highly-available database that handles high volumes of query traffic is undesirable. DBAs often solve this problem by only logging slow queries by setting log_min_duration_statement to a reasonable number of milliseconds in postgresql.conf. Later, only queries that cross this threshold are logged, along with binding parameters if the query was a prepared statement.

We strongly encourage this practice, as it is invaluable for catching outlying queries that could benefit from optimization. Unfortunately, faster queries are still invisible to us. Worse, queries that execute often probably have their data sources cached in memory, so it's unlikely that they contribute to I/O. The database could be executing an inefficient or redundant query thousands of times per second, and besides an elevated server load, we would never know.

This situation is not conducive to long-term viability of a highly-available...