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Manifest based registration of Admission webhooks

Table of Contents

Release Signoff Checklist

ACTION REQUIRED: In order to merge code into a release, there must be an issue in kubernetes/enhancements referencing this KEP and targeting a release milestone before Enhancement Freeze of the targeted release.

For enhancements that make changes to code or processes/procedures in core Kubernetes i.e., kubernetes/kubernetes, we require the following Release Signoff checklist to be completed.

Check these off as they are completed for the Release Team to track. These checklist items must be updated for the enhancement to be released.

  • kubernetes/enhancements issue in release milestone, which links to KEP (this should be a link to the KEP location in kubernetes/enhancements, not the initial KEP PR)
  • KEP approvers have set the KEP status to implementable
  • Design details are appropriately documented
  • Test plan is in place, giving consideration to SIG Architecture and SIG Testing input
  • Graduation criteria is in place
  • "Implementation History" section is up-to-date for milestone
  • User-facing documentation has been created in kubernetes/website, for publication to kubernetes.io
  • Supporting documentation e.g., additional design documents, links to mailing list discussions/SIG meetings, relevant PRs/issues, release notes

Note: Any PRs to move a KEP to implementable or significant changes once it is marked implementable should be approved by each of the KEP approvers. If any of those approvers is no longer appropriate than changes to that list should be approved by the remaining approvers and/or the owning SIG (or SIG-arch for cross cutting KEPs).

Note: This checklist is iterative and should be reviewed and updated every time this enhancement is being considered for a milestone.

Summary

Manifest based webhook configuration allows registering admission webhooks during kube-apiserver start up allowing for no delays in policy enforcement between policy addition and kube-apiserver startup.

Motivation

Today most policy enforcement is implemented through MutatingAdmissionWebhooks and/or ValidatingAdmissionWebhooks. These admission webhooks are registered through creating MutatingWebhookConfiguration or ValidatingWebhookConfiguration objects. Any policy enforcement is not in place until these webhook configurations are created, thereby registering the webhook. This creates a gap in enforcement spanning from when the kube-apiserver is started to until the webhook configuration objects are created and picked up by the dynamic admission controller. Another gap is the inability of the cluster administrator to protect against deletion of these webhook configuration objects as MutatingWebhookConfiguration and ValidatingWebhookConfiguration objects are not subject to webhook admission policy.

This KEP aims to address these issues.

Goals

  • A robust admission webhook registration process where there is no period of time between the kube-apiserver coming up and the registration of an admission webhook.

  • The registration of webhooks registered through this process should be protected from alteration by API requests made by cluster users.

  • It should be possible to alter webhooks registered through this new process without restarting the kube-apiserver.

Proposal

In short, this proposal is about augmenting AdmissionConfig's plugin configuration specification to include a path to a configuration file containing one of the following:

  • admissionregistration.k8s.io/v1.ValidatingWebhookConfigurationList
  • admissionregistration.k8s.io/v1.MutatingWebhookConfigurationList
  • v1.List with items of admissionregistration.k8s.io/v1.ValidatingWebhookConfiguration
  • v1.List with items of admissionregistration.k8s.io/v1.MutatingWebhookConfiguration. This configuration is loaded as manifest based webhooks in api servers. These webhooks are called by validating and mutating admission plugins along with dynamically loaded webhooks for relevant admission requests. This also means that these webhooks are not API visible objects and hence cannot be modified by an API user.

Naming

All webhooks in the manifest file need to have unique names. If a new webhook configuration API object with a same name as a webhook in the manifest is added, both webhook would be invoked. Essentially, webhooks in the manifest file will be treated as belonging to a different domain from the webhooks registered through the API.

Metrics and audit annotations

Metrics and audit annotations for webhooks registered through the manifest file will be surfaced differently. This allows the administrator to unique monitor activity of these webhooks as they would be the only party able to take action against issues with these webhooks.

All admission metrics for webhooks will have an additional label manifest_based with a value true for webhooks registered via manifest.

Audit annotations for mutation webhooks will include another field manifestBased with a value true for webhooks registered via manifest.

<<[UNRESOLVED sig-instrumentation ]>>
get feedback on how to indicate this in metrics
<<[/UNRESOLVED]>>

New AdmissionConfig schema

A webhooksFile field is added to configuration field of a plugin object in AdmissionConfig.

apiVersion: apiserver.config.k8s.io/v1
kind: AdmissionConfiguration
plugins:
- name: ValidatingAdmissionWebhook
  configuration:
    ....
    webhooksFile: "<path-to-manifest-file>"
- name: MutatingAdmissionWebhook
  configuration:
    ....
    webhooksFile: "<path-to-manifest-file>"

Reconfiguring manifest file

The manifest file is watched so that the webhook configuration can be dynamically changed by editing the contents of the file.

<<[UNRESOLVED]>>
need to define behavior in the following cases:
1. file goes missing
2. file cannot be read
3. parse error reading file contents
4. some webhook configurations listed in the file do not pass validation
<<[/UNRESOLVED]>>

Design Details

Test Plan

Graduation Criteria

Alpha -> Beta Graduation

Beta -> GA Graduation

Removing a deprecated flag

Upgrade / Downgrade Strategy

Version Skew Strategy

Production Readiness Review Questionnaire

Feature enablement and rollback

  • How can this feature be enabled / disabled in a live cluster?

    • Feature gate (also fill in values in kep.yaml)
      • Feature gate name:
      • Components depending on the feature gate:
    • Other
      • Describe the mechanism:
      • Will enabling / disabling the feature require downtime of the control plane?
      • Will enabling / disabling the feature require downtime or reprovisioning of a node?

  • Does enabling the feature change any default behavior? Any change of default behavior may be surprising to users or break existing automations, so be extremely careful here.

  • Can the feature be disabled once it has been enabled (i.e. can we roll back the enablement)? Also set disable-supported to true or false in kep.yaml. Describe the consequences on existing workloads (e.g., if this is a runtime feature, can it break the existing applications?).

  • What happens if we reenable the feature if it was previously rolled back?

  • Are there any tests for feature enablement/disablement? The e2e framework does not currently support enabling or disabling feature gates. However, unit tests in each component dealing with managing data, created with and without the feature, are necessary. At the very least, think about conversion tests if API types are being modified.

Rollout, Upgrade and Rollback Planning

This section must be completed when targeting beta graduation to a release.

  • How can a rollout fail? Can it impact already running workloads? Try to be as paranoid as possible - e.g., what if some components will restart mid-rollout?

  • What specific metrics should inform a rollback?

  • Were upgrade and rollback tested? Was the upgrade->downgrade->upgrade path tested? Describe manual testing that was done and the outcomes. Longer term, we may want to require automated upgrade/rollback tests, but we are missing a bunch of machinery and tooling and can't do that now.

  • Is the rollout accompanied by any deprecations and/or removals of features, APIs, fields of API types, flags, etc.? Even if applying deprecation policies, they may still surprise some users.

Monitoring Requirements

This section must be completed when targeting beta graduation to a release.

  • How can an operator determine if the feature is in use by workloads? Ideally, this should be a metric. Operations against the Kubernetes API (e.g., checking if there are objects with field X set) may be a last resort. Avoid logs or events for this purpose.

  • What are the SLIs (Service Level Indicators) an operator can use to determine the health of the service?

    • Metrics
      • Metric name:
      • [Optional] Aggregation method:
      • Components exposing the metric:
    • Other (treat as last resort)
      • Details:

  • What are the reasonable SLOs (Service Level Objectives) for the above SLIs? At a high level, this usually will be in the form of "high percentile of SLI per day <= X". It's impossible to provide comprehensive guidance, but at the very high level (needs more precise definitions) those may be things like:

    • per-day percentage of API calls finishing with 5XX errors <= 1%
    • 99% percentile over day of absolute value from (job creation time minus expected job creation time) for cron job <= 10%
    • 99,9% of /health requests per day finish with 200 code

  • Are there any missing metrics that would be useful to have to improve observability of this feature? Describe the metrics themselves and the reasons why they weren't added (e.g., cost, implementation difficulties, etc.).

Dependencies

This section must be completed when targeting beta graduation to a release.

  • Does this feature depend on any specific services running in the cluster? Think about both cluster-level services (e.g. metrics-server) as well as node-level agents (e.g. specific version of CRI). Focus on external or optional services that are needed. For example, if this feature depends on a cloud provider API, or upon an external software-defined storage or network control plane.

    For each of these, fill in the following—thinking about running existing user workloads and creating new ones, as well as about cluster-level services (e.g. DNS):

    • [Dependency name]
      • Usage description:
        • Impact of its outage on the feature:
        • Impact of its degraded performance or high-error rates on the feature:

Scalability

For alpha, this section is encouraged: reviewers should consider these questions and attempt to answer them.

For beta, this section is required: reviewers must answer these questions.

For GA, this section is required: approvers should be able to confirm the previous answers based on experience in the field.

  • Will enabling / using this feature result in any new API calls? Describe them, providing:

    • API call type (e.g. PATCH pods)
    • estimated throughput
    • originating component(s) (e.g. Kubelet, Feature-X-controller) focusing mostly on:
    • components listing and/or watching resources they didn't before
    • API calls that may be triggered by changes of some Kubernetes resources (e.g. update of object X triggers new updates of object Y)
    • periodic API calls to reconcile state (e.g. periodic fetching state, heartbeats, leader election, etc.)

  • Will enabling / using this feature result in introducing new API types? Describe them, providing:

    • API type
    • Supported number of objects per cluster
    • Supported number of objects per namespace (for namespace-scoped objects)

  • Will enabling / using this feature result in any new calls to the cloud provider?

  • Will enabling / using this feature result in increasing size or count of the existing API objects? Describe them, providing:

    • API type(s):
    • Estimated increase in size: (e.g., new annotation of size 32B)
    • Estimated amount of new objects: (e.g., new Object X for every existing Pod)

  • Will enabling / using this feature result in increasing time taken by any operations covered by existing SLIs/SLOs? Think about adding additional work or introducing new steps in between (e.g. need to do X to start a container), etc. Please describe the details.

  • Will enabling / using this feature result in non-negligible increase of resource usage (CPU, RAM, disk, IO, ...) in any components? Things to keep in mind include: additional in-memory state, additional non-trivial computations, excessive access to disks (including increased log volume), significant amount of data sent and/or received over network, etc. This through this both in small and large cases, again with respect to the supported limits.

Troubleshooting

The Troubleshooting section currently serves the Playbook role. We may consider splitting it into a dedicated Playbook document (potentially with some monitoring details). For now, we leave it here.

This section must be completed when targeting beta graduation to a release.

  • How does this feature react if the API server and/or etcd is unavailable?

  • What are other known failure modes? For each of them, fill in the following information by copying the below template:

    • [Failure mode brief description]
      • Detection: How can it be detected via metrics? Stated another way: how can an operator troubleshoot without logging into a master or worker node?
      • Mitigations: What can be done to stop the bleeding, especially for already running user workloads?
      • Diagnostics: What are the useful log messages and their required logging levels that could help debug the issue? Not required until feature graduated to beta.
      • Testing: Are there any tests for failure mode? If not, describe why.

  • What steps should be taken if SLOs are not being met to determine the problem?

Implementation History

  • 2020-04-21: KEP introduced

Drawbacks

  • Reduced visibility for users wanting to list all active admission webhooks. This KEP has similar visibility characteristics as compiled in admission controllers.

Alternatives

Adding Deny policies to RBAC allowing a cluster administrator to create roles that deny access to certain webhook configuration objects was considered. But, adding Deny policies to RBAC has far reaching consequences like redesigning/changing the implementation of object watchers.