feat: add node_readiness_rules with enforcement_mode and dry_run labels

[rawadhossain]

Description

• Adds node_readiness_rules{enforcement_mode, dry_run} while keeping node_readiness_rules_total unchanged for compatibility.
• Serves node_readiness_rules from the scrape-time ReadinessCollector. the original ruleCache push approach showed staleness scaling with rule count under bulk changes
• Updates monitoring.md and docs/TEST_README.md.
• Adds related tests, also contributes to testing: Add Test Coverage for Prometheus Metrics #269.

from the design doc:

Related to #446

Type of Change

/kind feature

Testing

make test, make lint, go vet, gofmt -l all pass.

Checklist

• make test passes
• make lint passes

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[kubernetes-prow]

Hi @rawadhossain. Thanks for your PR.

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[rawadhossain]

/cc @ajaysundark

[rawadhossain]

/cc @AvineshTripathi

[AvineshTripathi]

/ok-to-test

[rawadhossain]

/retest

[AvineshTripathi]

Since we're already calling ListRules in the collector, we can compute the enforcement_mode/dry_run breakdown there instead of maintaining a separate in-memory sync. The local ruleCache can lag behind actual cluster state and can be unreliable.

cc @ajaysundark wdyt?

[rawadhossain]

@AvineshTripathi Good point. I tested both ways and found ruleCache can be stale on startup (~200-300ms) and for ~1s after creating a rule, and ListRules reflects the current state right away. The stale window is relatively small, so I think keeping the current approach is reasonable for now.

@ajaysundark since this metric wasn't part of the collector scope, should we switch or keep it separate for now?

[AvineshTripathi]

The stale window is relatively small, so I think keeping the current approach is reasonable for now.

I would prefer testing this delay on the scale before doing the scale. Can we run the scan?

[rawadhossain]

Yes we can and thanks it helped.

Ran the scale test. Used the test/scale setup. So here’s the findings:

ruleCache staleness (current one):

Scenario	N=100	N=1000	N=5000
One rule changes	~130ms	~135ms	~145ms
One rule gets created	~1.15s	~1.15s	~1.2s
All N rules created at once	~2s	~14s	~79s
All N rules dryRun flipped at once	~1.5s	~11s	~62s
All N rules deleted at once	~1s	~11s	~57s

Single changes are fine, but bigger difference shows up when a large number of rules change together, delay grows quite long.

Scrape-time approach:

N	100	1000	5000
Standalone lookup	~0.37ms	~4.1ms	~23ms
Added to existing collector	~3µs	~26µs	~128µs

Since the collector already does that fetch, adding this one on barely adds anything.

Based on these, would it make more sense to go with collector instead, specially for scale? Staleness gets pretty noticeable with bulk changes, also extra cost in the collector is very small.

@AvineshTripathi @ajaysundark wdyt? I can make the changes if we decide to go with collector approach.

[AvineshTripathi]

@rawadhossain I'll go with the collector approach! that can be a standard we can go for

[rawadhossain]

@AvineshTripathi I also think so collector approach is the one to go for. Made the changes. Tested and everything's working as expected.
PTAL. Thanks!

cc @ajaysundark

[AvineshTripathi]

/lgtm

havn't tested in local but looks good!

[ajaysundark]

/lgtm

[ajaysundark]

/lgtm
/approve

[kubernetes-prow]

[APPROVALNOTIFIER] This PR is APPROVED

This pull-request has been approved by: ajaysundark, rawadhossain

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