postgres_exporter VS runbooks

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An easy way forward would be to export it as a metric through something like https://github.com/prometheus-community/postgres_exporter and then set up an alert on the metric reporting >= 1.

We use postgres as RDBMS for our database needs and if you guys are already using postgres_exporter(https://github.com/prometheus-community/postgres_exporter) for exporting PostgreSQL server metrics to prometheus, then you can surely give coroot-pg-agent a try.

It’s not that simple…https://github.com/prometheus-community/postgres_exporter :)

New server and want a basic config for a production server? (I'm assuming brand new app here, no prior monitoring and knowledge on what to tune.)

Use this to get the values you need https://pgtune.leopard.in.ua/#/ .

This saved me a lot of headaches, and it just gets the server into a good enough state from which you can observe and optimise later.

I'd also add in monitoring early, add a Prometheus exporter https://github.com/prometheus-community/postgres_exporter and alerts https://awesome-prometheus-alerts.grep.to/rules#postgresql . There are a few Grafana dashboards available for the prometheus exporter, start with those.

I’m setting up some monitoring for a PostgreSQL instance and came across two seemingly mature projects for Prometheus monitoring, those being postgres_exporter and pg_exporter . From the outside it looks like postgres_exporter is linked from the Prometheus pages, but has fewer recent updates, and pg_exporter has a smaller user base but a more active development community.

PostgreSQL monitoring with Prometheus is an easy thing to do thanks to the PostgreSQL Exporter.

Thanks, that is probably a better fit. I was looking to use this part of the PostgreSQL exporter: https://github.com/prometheus-community/postgres_exporter/blob/master/queries.yaml and just not care about the metrics about Postgres itself.

The postgres_exporter provides the data about the state of replication. Then you have rules like these to validate the data.

There are also good patterns for monitoring the status of jobs.

Check out this job monitoring pattern.

For a freely available resource, I found GitLab’s Runvbooks a good example to strive towards. But this is the output of a large team and several years. My advice is to just start with something simple.

It's a weighted combination of all services. The SLA weightings for all service areas are defined in the gitlab.com service catalog.

Take a look at this pushgateway-based job monitoring pattern.

I want to use the GitLab dashboards here in my local Grafana environment - https://gitlab.com/gitlab-com/runbooks/-/tree/master/dashboards

Also, take a look at the code https://gitlab.com/gitlab-com/runbooks

GitLab puts everything in a git repo in markdown. A single repo that contains a few different things. All of the production alerts are sourced from there. This makes it possible to review alerts and runbook changes in the same code review.