Shipping Metrics to Centralized Instance
We want to ship metrics from our OpenShift clusters to a centralized instance. This will allow us to keep an overview over our managed clusters and keep us up-to-date on meta topics like managed upgrades.
OpenShift clusters may have millions of metrics, which - while technically easily possible - may come with a lot of resource usage, and thus cost. To reduce spending we need to be able to select which metrics to ship to the centralized instance.
All recording rules are shipped to the centralized instance.
This automatically ships alerts and many series from the OpenShift console dashboards.
It makes aggregation of metrics very easy and forces the user to think how and if the metrics should be aggregated.
Metrics that should be shipped are labeled with a static label like
This moves the configuration very close to the metric implementation.
The labeling would need to be done in the ServiceMonitor or PodMonitor resources. Most of those resources can’t be edited or patched on OpenShift clusters. This would make many metrics non-shippable.
We currently have a huge Regex in the Commodore hierarchy that selects metrics to be shipped. This is hard to maintain and not very flexible.
Through some Jsonnet functions, building the Regex, we can engineer a better way to manage which metrics to ship.
This allows us to store the configuration in a single place and have it easily editable.
We could use a separate tool to ship metrics to the centralized instance with more ergonomic shipping rules.
Grafana Agent has the exact same configuration as Prometheus and would not improve the configuration. It would also require us to run another tool on the cluster.
Telegraf doesn’t seem to be configurable and can’t filter metrics.
There’s some SaaS offerings, such as NewRelic, that have fancier filtering options. They’re not free and would require us to ship metrics to a third party.
We should ship all recording rules AND have an explicit list of time series to ship in the config hierarchy.
Shipping all recording rules is a manageable amount of metrics and gives us a head start on identifying the most important metrics. It allows users to define recording rules for their own metrics and have them automatically shipped to the centralized instance.
Having an explicit list of time series to ship in the config hierarchy allows us to have a single source of truth for the metrics we want to ship. Allows experimentation and makes shipping metrics more accessible to users.