feat: Add pod affinity/anti-affinity metrics for deployments

[SoumyaRaikwar]

What this PR does / why we need it

Adds explicit rule-based pod affinity and anti-affinity metrics for deployments to provide granular visibility into Kubernetes scheduling constraints, addressing issue #2701.

Refactored from count-based to explicit rule-based approach following maintainer feedback for enhanced operational value.

Which issue(s) this PR fixes

Fixes #2701

Metrics Added

• kube_deployment_spec_affinity - Pod affinity and anti-affinity rules with granular labels

Labels provided:

• affinity - podaffinity | podantiaffinity
• type - requiredDuringSchedulingIgnoredDuringExecution | preferredDuringSchedulingIgnoredDuringExecution
• topology_key - The topology key for the rule
• label_selector - The formatted label selector string

[mrueg]

How would you use this metric for alerting and/or showing information about the deployment?

[SoumyaRaikwar]

How would you use this metric for alerting and/or showing information about the deployment?

These metrics enable critical alerting on scheduling constraint violations. For example: (kube_deployment_spec_pod_anti_affinity_preferred_rules > 0) and (kube_deployment_spec_pod_anti_affinity_required_rules == 0) alerts when deployments rely only on soft anti-affinity rules that can be ignored during node pressure, creating single points of failure.

They also help monitor missing protection: (kube_deployment_spec_pod_anti_affinity_required_rules == 0) and (kube_deployment_spec_pod_anti_affinity_preferred_rules == 0) identifies deployments without any anti-affinity rules.

For dashboards, you can visualize cluster-wide scheduling health with count(kube_deployment_spec_pod_anti_affinity_required_rules > 0) to show how many deployments have proper distribution protection.

During incidents, these metrics help correlate why workloads ended up co-located or why pods failed to schedule due to overly complex constraints.

This addresses #2701's core need: visibility into "preferred vs required" scheduling logic to maintain reliable workload distribution during cluster events. Thanks @mrueg for the question - these use cases demonstrate the operational value of these scheduling constraint metrics!

[CatherineF-dev]

/triage accepted
/assign @mrueg

[mrueg]

i think the metric should be explicit, something like:

kube_deployment_affinity{affinity="podaffinity", type="requiredDuringSchedulingIgnoredDuringExecution",topologyKey="foo",labelSelector="matchExpression foo in bar,baz"}  1

then you can count over these and get the desired result, as well as gather exactly that information about the specific affinity setting.

I'm not sure about the labelSelector at this point, if this should be split into subtypes as well or just calling https://github.com/kubernetes/apimachinery/blob/master/pkg/apis/meta/v1/helpers.go#L171 is enough.

[k8s-ci-robot]

[APPROVALNOTIFIER] This PR is NOT APPROVED

This pull-request has been approved by: SoumyaRaikwar
Once this PR has been reviewed and has the lgtm label, please ask for approval from mrueg. For more information see the Code Review Process.

The full list of commands accepted by this bot can be found here.

Needs approval from an approver in each of these files:

• OWNERS

Approvers can indicate their approval by writing /approve in a comment
Approvers can cancel approval by writing /approve cancel in a comment

[SoumyaRaikwar]

Thanks @mrueg for the feedback! I understand you're looking for more explicit metrics that expose individual affinity rule details rather than just counts.

You're absolutely right that explicit metrics would provide much more granular visibility. Instead of simple count metrics

[SoumyaRaikwar]

Hi @mrueg,

I've successfully refactored the implementation to use explicit rule-based metrics as you requested.

Key Changes:

• Replaced 4 count-based metrics with single kube_deployment_spec_affinity metric
• Added granular labels for individual rule visibility and flexible querying
• Used metav1.FormatLabelSelector() for consistent labelSelector formatting
• Updated comprehensive tests and documentation

The new approach provides exponentially more operational value while maintaining low cardinality and following the individual object-level data principle from the best practices document.