This document describes a configuration for rule and alert evaluation in a Managed Service for Prometheus deployment that uses managed collection.
The following diagram illustrates a deployment that uses multiple clusters in two Google Cloud projects and uses both rule and alert evaluation, as well as the optional GlobalRules resource:
To set up and use a deployment like the one in the diagram, note the following:
The managed rule evaluator is automatically deployed in any cluster where managed collection is running. These evaluators are configured as follows:
Use Rules resources to run rules on data within a namespace. Rules resources must be applied in every namespace in which you want to execute the rule.
Use ClusterRules resources to run rules on data across a cluster. ClusterRules resources should be applied once per cluster.
All rule evaluation executes against the global data store, Monarch.
- Rules resources automatically filter rules to the project, location, cluster, and namespace in which they are installed.
- ClusterRules resources automatically filter rules to the project, location, and cluster in which they are installed.
- All rule results are written to Monarch after evaluation.
A Prometheus AlertManager instance is manually deployed in every single cluster. Managed rule evaluators are configured by editing the OperatorConfig resource to send fired alerting rules to their local AlertManager instance. Silences, acknowledgements, and incident management workflows are typically handled in a third-party tool such as PagerDuty.
You can centralize alert management across multiple clusters into a single AlertManager by using a Kubernetes Endpoints resource.
The preceding diagram also shows the optional GlobalRules resource. Use GlobalRules very sparingly, for tasks like calculating global SLOs across projects or for evaluating rules across clusters within a single Google Cloud project. We strongly recommend using Rules and ClusterRules whenever possible; these resources provide superior reliability and are better fits for common Kubernetes deployment mechanisms and tenancy models.
If you use the GlobalRules resource, note the following from the preceding diagram:
One single cluster running inside Google Cloud is designated as the global rule-evaluation cluster for a metrics scope. This managed rule evaluator is configured to use scoping_project_A, which contains Projects 1 and 2. Rules executed against scoping_project_A automatically fan out to Projects 1 and 2.
The underlying service account must be given the Monitoring Viewer permissions for scoping_project_A. For additional information on how to set these fields, see Multi-project and global rule evaluation.
As in all other clusters, this rule evaluator is set up with Rules and ClusterRules resources that evaluate rules scoped to a namespace or cluster. These rules are automatically filtered to the local project—Project 1, in this case. Because scoping_project_A contains Project 1, Rules and ClusterRules-configured rules execute only against data from the local project as expected.
This cluster also has GlobalRules resources that execute rules against scoping_project_A. GlobalRules are not automatically filtered, and therefore GlobalRules execute exactly as written across all projects, locations, clusters, and namespaces in scoping_project_A.
Fired alerting rules will be sent to the self-hosted AlertManager as expected.
Using GlobalRules may have unexpected effects, depending on whether you
preserve or aggregate the
namespace labels in your rules:
If your GlobalRules rule preserves the
project_idlabel (by using a
by(project_id)clause), then rule results are written back to Monarch using the original
project_idvalue of the underlying time series.
In this scenario, you need to ensure the underlying service account has the Monitoring Metric Writer permissions for each monitored project in scoping_project_A. If you add a new monitored project to scoping_project_A, then you must also manually add a new permission to the service account.
If your GlobalRules rule does not preserve the
project_idlabel (by not using a
by(project_id)clause), then rule results are written back to Monarch using the
project_idvalue of the cluster where the global rule evaluator is running.
In this scenario, you do not need to further modify the underlying service account.
If your GlobalRules preserves the
locationlabel (by using a
by(location)clause), then rule results are written back to Monarch using each original Google Cloud region from which the underlying time series originated.
If your GlobalRules does not preserve the
locationlabel, then data is written back to the location of the cluster where the global rule evaluator is running.
We strongly recommend preserving the
namespace labels in
rule evaluation results unless the purpose of the rule is to aggregate away
those labels. Otherwise, query performance might decline and you might
encounter cardinality limits. Removing both labels is strongly discouraged.