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- [ Alpha] ( #alpha )
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- [ Upgrade / Downgrade Strategy] ( #upgrade--downgrade-strategy )
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- [ Version Skew Strategy] ( #version-skew-strategy )
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+ - [ Production Readiness Review Questionnaire] ( #production-readiness-review-questionnaire )
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+ - [ Feature Enablement and Rollback] ( #feature-enablement-and-rollback )
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+ - [ Rollout, Upgrade and Rollback Planning] ( #rollout-upgrade-and-rollback-planning )
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+ - [ Monitoring Requirements] ( #monitoring-requirements )
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+ - [ Dependencies] ( #dependencies )
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+ - [ Scalability] ( #scalability )
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+ - [ Troubleshooting] ( #troubleshooting )
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- [ Implementation History] ( #implementation-history )
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- [ Drawbacks] ( #drawbacks )
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- [ Alternatives] ( #alternatives )
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## Release Signoff Checklist
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- [X] Enhancement issue in release milestone, which links to KEP dir in [ kubernetes/enhancements] (not the initial KEP PR)
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- - [ ] KEP approvers have approved the KEP status as ` implementable `
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- - [ ] Design details are appropriately documented
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- - [ ] Test plan is in place, giving consideration to SIG Architecture and SIG Testing input
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- - [ ] Graduation criteria is in place
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+ - [X ] KEP approvers have approved the KEP status as ` implementable `
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+ - [X ] Design details are appropriately documented
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+ - [X ] Test plan is in place, giving consideration to SIG Architecture and SIG Testing input
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+ - [X ] Graduation criteria is in place
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- [ ] "Implementation History" section is up-to-date for milestone
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- [ ] User-facing documentation has been created in [ kubernetes/website] , for publication to [ kubernetes.io]
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- [ ] Supporting documentation e.g., additional design documents, links to mailing list discussions/SIG meetings, relevant PRs/issues, release notes
@@ -117,16 +124,16 @@ kube-proxy unit tests:
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#### E2E Tests
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E2E tests will be added to validate that no traffic is dropped during a rolling update for a Service with ExternalTrafficPolicy=Local.
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- This test may be marked "Flaky" as the behavior is largely also dependant on the cloud provider's loadbalancer.
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All existing E2E tests for Services should continue to pass.
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### Graduation Criteria
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#### Alpha
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- * kube-proxy internally tracks the terminating condition of an endpoint.
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- * feature is only enabled if the feature gate ` EndpointSliceTerminatingCondition ` is on.
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+ * kube-proxy internally tracks the ` terminating ` and ` serving ` condition from EndpointSlice
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+ * kube-proxy falls back to terminating endpoints if and only if they are the only available endpoints.
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+ * feature is only enabled if the feature gate ` ProxyTerminatingEndpoints ` is on.
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* unit tests in kube-proxy.
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### Upgrade / Downgrade Strategy
@@ -141,6 +148,266 @@ This would either happen if a version of the control plane was not aware of the
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There's not much risk involved as the worse case scenario is falling back to existing behavior.
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+ ## Production Readiness Review Questionnaire
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+
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+ ### Feature Enablement and Rollback
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+
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+ ###### How can this feature be enabled / disabled in a live cluster?
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+
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+ - [X] Feature gate (also fill in values in ` kep.yaml ` )
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+ - Feature gate name: ProxyTerminatingEndpoints
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+ - Components depending on the feature gate: kube-proxy
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+
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+ ###### Does enabling the feature change any default behavior?
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+
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+ Yes, when externalTrafficPolicy=Local and there are only terminating endpoints,
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+ kube-proxy will route traffic to those endpoints. Before this change, kube-proxy
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+ dropped this traffic instead.
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+
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+ ###### Can the feature be disabled once it has been enabled (i.e. can we roll back the enablement)?
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+
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+ Yes.
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+
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+ ###### What happens if we reenable the feature if it was previously rolled back?
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+
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+ kube-proxy will no longer drop traffic if only terminating endpoints are available.
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+
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+ ###### Are there any tests for feature enablement/disablement?
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+
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+ Yes, there will be unit tests in kube-proxy with the feature gate enabled and disabled.
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+
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+ ### Rollout, Upgrade and Rollback Planning
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+
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+ <!--
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+ This section must be completed when targeting beta to a release.
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+ -->
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+
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+ ###### How can a rollout fail? Can it impact already running workloads?
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+
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+ <!--
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+ Try to be as paranoid as possible - e.g., what if some components will restart
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+ mid-rollout?
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+ -->
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+
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+ TBD for beta.
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+
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+ ###### What specific metrics should inform a rollback?
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+
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+ <!--
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+ What signals should users be paying attention to when the feature is young
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+ that might indicate a serious problem?
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+ -->
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+
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+ TBD for beta.
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+
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+ ###### Were upgrade and rollback tested? Was the upgrade->downgrade->upgrade path tested?
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+
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+ <!--
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+ Describe manual testing that was done and the outcomes.
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+ Longer term, we may want to require automated upgrade/rollback tests, but we
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+ are missing a bunch of machinery and tooling and can't do that now.
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+ -->
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+
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+ TBD for beta.
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+
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+ ###### Is the rollout accompanied by any deprecations and/or removals of features, APIs, fields of API types, flags, etc.?
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+
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+ <!--
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+ Even if applying deprecation policies, they may still surprise some users.
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+ -->
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+
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+ TBD for beta.
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+
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+ ### Monitoring Requirements
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+
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+ <!--
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+ This section must be completed when targeting beta to a release.
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+ -->
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+
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+ ###### How can an operator determine if the feature is in use by workloads?
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+
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+ <!--
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+ Ideally, this should be a metric. Operations against the Kubernetes API (e.g.,
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+ checking if there are objects with field X set) may be a last resort. Avoid
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+ logs or events for this purpose.
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+ -->
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+
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+ TBD for beta.
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+
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+ ###### What are the SLIs (Service Level Indicators) an operator can use to determine the health of the service?
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+
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+ <!--
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+ Pick one more of these and delete the rest.
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+ -->
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+
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+ TBD for beta.
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+
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+ - [ ] Metrics
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+ - Metric name:
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+ - [ Optional] Aggregation method:
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+ - Components exposing the metric:
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+ - [ ] Other (treat as last resort)
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+ - Details:
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+
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+ ###### What are the reasonable SLOs (Service Level Objectives) for the above SLIs?
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+
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+ <!--
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+ At a high level, this usually will be in the form of "high percentile of SLI
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+ per day <= X". It's impossible to provide comprehensive guidance, but at the very
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+ high level (needs more precise definitions) those may be things like:
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+ - per-day percentage of API calls finishing with 5XX errors <= 1%
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+ - 99% percentile over day of absolute value from (job creation time minus expected
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+ job creation time) for cron job <= 10%
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+ - 99,9% of /health requests per day finish with 200 code
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+ -->
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+
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+ TBD for beta.
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+
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+ ###### Are there any missing metrics that would be useful to have to improve observability of this feature?
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+
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+ <!--
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+ Describe the metrics themselves and the reasons why they weren't added (e.g., cost,
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+ implementation difficulties, etc.).
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+ -->
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+
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+ TBD for beta.
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+
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+ ### Dependencies
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+
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+ <!--
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+ This section must be completed when targeting beta to a release.
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+ -->
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+
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+ ###### Does this feature depend on any specific services running in the cluster?
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+
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+ <!--
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+ Think about both cluster-level services (e.g. metrics-server) as well
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+ as node-level agents (e.g. specific version of CRI). Focus on external or
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+ optional services that are needed. For example, if this feature depends on
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+ a cloud provider API, or upon an external software-defined storage or network
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+ control plane.
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+
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+ For each of these, fill in the following—thinking about running existing user workloads
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+ and creating new ones, as well as about cluster-level services (e.g. DNS):
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+ - [Dependency name]
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+ - Usage description:
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+ - Impact of its outage on the feature:
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+ - Impact of its degraded performance or high-error rates on the feature:
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+ -->
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+
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+ TBD for beta.
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+
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+ ### Scalability
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+
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+ <!--
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+ For alpha, this section is encouraged: reviewers should consider these questions
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+ and attempt to answer them.
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+
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+ For beta, this section is required: reviewers must answer these questions.
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+
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+ For GA, this section is required: approvers should be able to confirm the
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+ previous answers based on experience in the field.
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+ -->
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+
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+ TBD for beta.
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+
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+ ###### Will enabling / using this feature result in any new API calls?
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+
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+ <!--
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+ Describe them, providing:
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+ - API call type (e.g. PATCH pods)
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+ - estimated throughput
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+ - originating component(s) (e.g. Kubelet, Feature-X-controller)
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+ Focusing mostly on:
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+ - components listing and/or watching resources they didn't before
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+ - API calls that may be triggered by changes of some Kubernetes resources
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+ (e.g. update of object X triggers new updates of object Y)
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+ - periodic API calls to reconcile state (e.g. periodic fetching state,
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+ heartbeats, leader election, etc.)
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+ -->
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+
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+ TBD for beta.
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+
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+ ###### Will enabling / using this feature result in introducing new API types?
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+
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+ <!--
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+ Describe them, providing:
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+ - API type
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+ - Supported number of objects per cluster
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+ - Supported number of objects per namespace (for namespace-scoped objects)
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+ -->
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+
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+ TBD for beta.
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+
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+ ###### Will enabling / using this feature result in any new calls to the cloud provider?
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+
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+ <!--
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+ Describe them, providing:
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+ - Which API(s):
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+ - Estimated increase:
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+ -->
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+
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+ ###### Will enabling / using this feature result in increasing size or count of the existing API objects?
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+
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+ <!--
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+ Describe them, providing:
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+ - API type(s):
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+ - Estimated increase in size: (e.g., new annotation of size 32B)
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+ - Estimated amount of new objects: (e.g., new Object X for every existing Pod)
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+ -->
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+
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+ ###### Will enabling / using this feature result in increasing time taken by any operations covered by existing SLIs/SLOs?
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+
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+ <!--
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+ Look at the [existing SLIs/SLOs].
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+
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+ Think about adding additional work or introducing new steps in between
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+ (e.g. need to do X to start a container), etc. Please describe the details.
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+
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+ [existing SLIs/SLOs]: https://git.k8s.io/community/sig-scalability/slos/slos.md#kubernetes-slisslos
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+ -->
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+
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+ ###### Will enabling / using this feature result in non-negligible increase of resource usage (CPU, RAM, disk, IO, ...) in any components?
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+
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+ <!--
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+ Things to keep in mind include: additional in-memory state, additional
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+ non-trivial computations, excessive access to disks (including increased log
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+ volume), significant amount of data sent and/or received over network, etc.
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+ This through this both in small and large cases, again with respect to the
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+ [supported limits].
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+
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+ [supported limits]: https://git.k8s.io/community//sig-scalability/configs-and-limits/thresholds.md
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+ -->
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+
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+ ### Troubleshooting
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+
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+ <!--
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+ This section must be completed when targeting beta to a release.
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+
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+ The Troubleshooting section currently serves the `Playbook` role. We may consider
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+ splitting it into a dedicated `Playbook` document (potentially with some monitoring
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+ details). For now, we leave it here.
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+ -->
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+
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+ ###### How does this feature react if the API server and/or etcd is unavailable?
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+
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+ ###### What are other known failure modes?
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+
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+ <!--
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+ For each of them, fill in the following information by copying the below template:
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+ - [Failure mode brief description]
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+ - Detection: How can it be detected via metrics? Stated another way:
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+ how can an operator troubleshoot without logging into a master or worker node?
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+ - Mitigations: What can be done to stop the bleeding, especially for already
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+ running user workloads?
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+ - Diagnostics: What are the useful log messages and their required logging
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+ levels that could help debug the issue?
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+ Not required until feature graduated to beta.
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+ - Testing: Are there any tests for failure mode? If not, describe why.
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+ -->
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+
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+ ###### What steps should be taken if SLOs are not being met to determine the problem?
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+
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## Implementation History
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- [x] 2020-04-23: KEP accepted as implementable for v1.19
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