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# KEP-5366: Graceful Leader Transition

<!--
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<!-- toc -->
- [Release Signoff Checklist](#release-signoff-checklist)
- [Summary](#summary)
- [Motivation](#motivation)
  - [Goals](#goals)
  - [Non-Goals](#non-goals)
- [Proposal](#proposal)
  - [Phase 1: Controller Sanitation (Pre-Alpha)](#phase-1-controller-sanitation-pre-alpha)
  - [Phase 2: Fast Lease Release](#phase-2-fast-lease-release)
  - [Risks and Mitigations](#risks-and-mitigations)
- [Design Details](#design-details)
  - [Phase 1 Implementation](#phase-1-implementation)
  - [Phase 2 Implementation](#phase-2-implementation)
  - [Test Plan](#test-plan)
      - [Prerequisite testing updates](#prerequisite-testing-updates)
      - [Unit tests](#unit-tests)
      - [Integration tests](#integration-tests)
      - [e2e tests](#e2e-tests)
  - [Graduation Criteria](#graduation-criteria)
    - [Alpha](#alpha)
    - [Beta](#beta)
    - [GA](#ga)
  - [Upgrade / Downgrade Strategy](#upgrade--downgrade-strategy)
  - [Version Skew Strategy](#version-skew-strategy)
- [Production Readiness Review Questionnaire](#production-readiness-review-questionnaire)
  - [Feature Enablement and Rollback](#feature-enablement-and-rollback)
  - [Rollout, Upgrade and Rollback Planning](#rollout-upgrade-and-rollback-planning)
  - [Monitoring Requirements](#monitoring-requirements)
  - [Dependencies](#dependencies)
  - [Scalability](#scalability)
  - [Troubleshooting](#troubleshooting)
- [Implementation History](#implementation-history)
- [Drawbacks](#drawbacks)
- [Alternatives](#alternatives)
- [Future Work (Stories)](#future-work-stories)
    - [Graceful Leader Transition](#graceful-leader-transition)
    - [Story 1](#story-1)
    - [Story 2](#story-2)
- [Infrastructure Needed (Optional)](#infrastructure-needed-optional)
<!-- /toc -->

## Release Signoff Checklist

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- [ ] (R) Test plan is in place, giving consideration to SIG Architecture and SIG Testing input (including test refactors)
  - [ ] e2e Tests for all Beta API Operations (endpoints)
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- [ ] 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

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[kubernetes.io]: https://kubernetes.io/
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[kubernetes/website]: https://git.k8s.io/website

## Summary

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This proposal outlines a plan to modify the leader election mechanism for key
Kubernetes components (kube-scheduler, kube-controller-manager,
cloud-controller-manager). The goal is to enable these components to gracefully
release the leader lock and transition back to a follower state without a full
process restart. This change will be introduced behind a new feature gate.

## Motivation

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Many high-availability (HA) Kubernetes components, including kube-scheduler and
kube-controller-manager, rely on the leader election library in client-go. The
current implementation mandates that when a component loses its leader lock, it
must shut down immediately. This is typically handled by calling
`klog.FlushAndExit()` in the `OnStoppedLeading` callback.

When the leadership is lost, the component shuts down and waits for the kubelet
to detect that the component is unhealthy and restart it. This has several
significant drawbacks:

- High Overhead: Restarting a component process incurs unnecessary computational
  overhead and increases latency during a leadership transition
- No Graceful Shutdown: The immediate call to `os.Exit()` prevents any graaceful
  shutdown or cleanup operations.

### Goals

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- Allow leader-elected components to transition to a follower state without restarting the process
- Enable graceful handling of leader lock loss

### Non-Goals

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and make progress.
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- This KEP allows work towards graceful shutdown of controllers, but the actual
  mechanisms of how we do graceful shutdown is outside the scope of this KEP.

## Proposal

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We propose a phased approach. Phase 1 ensures controllers can safely shut down, and Phase 2 builds on that to enable faster failovers by actively releasing the lease on shutdown. Phases 1 and 2 are the committed scope of this KEP. A future Phase 3 would remove the need for process restarts entirely (graceful leader transition). It is out of scope here and captured as [future work](#future-work-stories).

### Phase 1: Controller Sanitation (Pre-Alpha)

This phase ensures that `kube-controller-manager` controllers gracefully terminate without leaking
goroutines by strictly enforcing context cancellation within their control loops.

*Note: This phase is largely addressed by [PR #134910](https://github.com/kubernetes/kubernetes/pull/134910) and [PR #134945](https://github.com/kubernetes/kubernetes/pull/134945), which standardizes `Run` termination.*

- **Objective**: Ensure that kcm controller goroutines properly terminate when context is cancelled.
- **Mechanism**: Refactor controller management to track all spawned goroutines via `wg.Go()` and `wg.Wait()`.
- **Feature Gate**: None (Technical debt cleanup).

### Phase 2: Fast Lease Release

Once we are confident that controllers shut down gracefully (Phase 1), we can optimize the leadership transition. Instead of waiting for the lease TTL to expire, the leader will actively release the lock upon shutdown. Note: `kube-scheduler` already implements this behavior, so this phase only targets `kube-controller-manager`.

- **Objective**: Reduce failover latency.
- **Mechanism**: Modify `client-go/tools/leaderelection` to perform an active release of the `Lease` object (removing the holder identity) when the context is cancelled.
- **Feature Gate**: `ControllerManagerReleaseLeaderElectionLockOnExit`

### Risks and Mitigations

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Kubernetes leader elected controllers and the scheduler have been running
without graceful shutdown for years. 

Risk 1: Resource exhaustion: Memory leaks may exist in the processes that were
previously masked by doing a full shutdown and restart loop. 

- Severity: Medium high
- Controllers will continue to function (potentially in degraded state due to
  lack of resources), and may be restarted frequently. However, cluster should
  continue to function.

Risk 2: Wedged KCM: There is a risk that controllers and the scheduler are not
properly respecting context shutdowns. This can either result in multiple
instances of controllers running or no instances running despite the lock being
held.

- Severity: High
- Breaking mutual exclusion guarantees can put the cluster into a non-desirable
  state. A manual user intervention is possible but if the problem is triggered
  due to a problematic component, the issue will resurface and the best path for
  mitigation is to turn off the feature.

Risk 3: Futureproofing: An additional risk is that even if all the current code
is safe and respects shutting down gracefully, new controllers/modifications to
kcm or scheduler could create subtle problems in shutdown and transition.

- Severity: High
- Leads to either risk 1 or 2.


Mitigations:

- Audit and add tests for the existing controllers and the scheduler to ensure
  proper handling of context shutdowns. See test plan section for more details.
- Graceful shutdown modifications will not be guarded by a feature gate, but the
  code change to remove the `os.Exit()` line will be guarded by a feature gate.
- Document the new development best practices for graceful shutdown requirements
  for modified components that are leader elected.

## Design Details

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### Phase 1 Implementation

All controllers must standardize their startup sequences. When the controller returns and the leader
lock is released, all associated goroutines generally must be cancelled.

### Phase 2 Implementation

The leader lock will be proactively released when the context is cancelled and the leader prepares to
step down. This release must occur only after all controller goroutines have returned. This behavior will
be guarded by the `ControllerManagerReleaseLeaderElectionLockOnExit` feature gate.

### Test Plan

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[x] I/we understand the owners of the involved components may require updates to
existing tests to make this code solid enough prior to committing the changes necessary
to implement this enhancement.

To properly test that components can respect context cancellation shutdowns and
not leak memory, we will run both manual tests with profiling, and automated
testing that transitions the leader lock multiple times.

We will also test that things like caches, reflectors, and go funcs are properly
cleared/stopped when the leader lock is lost.

Scenarios:

- Component acquires leader lock should start its control **loop**
- Component releasing leader lock should shut down its control loop
- Component acquiring and releasing the leader lock multiple times should not leak memory
- Component acquiring and releasing the leader lock multiple times should have ONLY ONE control loop running
- If kube-apiserver takes a long time to release client calls, the graceful release will properly wait until the controller has returned
- Releasing a lock will stop the control loop
- Only one control loop should be running at all times
- Components in follower mode should not start control loop or otherwise allocate unnecessary memory

##### Prerequisite testing updates

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implementing this enhancement to ensure the enhancements have also solid foundations.
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##### Unit tests

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This can inform certain test coverage improvements that we want to do before
extending the production code to implement this enhancement.
-->

This is primarily testing for leader election's interaction with components, and will be tested via integration and e2e tests.


##### Integration tests

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See the above scenarios for test plan. kube-scheduler and kcm in particular will be integration tested that they shut down properly.

##### e2e tests

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We expect no non-infra related flakes in the last month as a GA graduation criteria.
-->

See the above scenarios for test plan.

### Graduation Criteria

#### Alpha

- `ControllerManagerReleaseLeaderElectionLockOnExit` feature gate implemented.
- Phase 1 implemented and controller startup and shutdown logic is handled gracefully.
- Runtime detection of leaked goroutines.
- Test that controller-manager and scheduler do not leak memory on leadership transitions.

#### Beta

- `ControllerManagerReleaseLeaderElectionLockOnExit` graduates to beta, enabled by default in v1.37.
- e2e tests
- Address how to minimize risks of putting KCM or scheduler in a "wedged" state

#### GA

- TBD

### Upgrade / Downgrade Strategy

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- What changes (in invocations, configurations, API use, etc.) is an existing
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No changes.

### Version Skew Strategy

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- How does an n-3 kubelet or kube-proxy without this feature available behave when this feature is used?
- How does an n-1 kube-controller-manager or kube-scheduler without this feature available behave when this feature is used?
- Will any other components on the node change? For example, changes to CSI,
  CRI or CNI may require updating that component before the kubelet.
-->

This is a control plane change. Skew should not affect the feature.

## Production Readiness Review Questionnaire

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### Feature Enablement and Rollback

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

<!--
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-->

- [x] Feature gate (also fill in values in `kep.yaml`)
  - Feature gate name: ControllerManagerReleaseLeaderElectionLockOnExit
  - Components depending on the feature gate: kube-controller-manager
- Will enabling / disabling the feature require downtime of the control plane? Yes, components need to be restarted.
- Will enabling / disabling the feature require downtime or reprovisioning of a node? No.

###### 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.
-->
Yes. With the `ControllerManagerReleaseLeaderElectionLockOnExit` feature gate
enabled, kube-controller-manager actively releases its leader lease on shutdown
(clearing the holder identity) instead of leaving it to expire by TTL, so a
standby instance can acquire leadership without waiting out the lease duration.
The component still exits when it loses leadership.

###### Can the feature be disabled once it has been enabled (i.e. can we roll back the enablement)?

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Yes, the feature can be disabled by setting the
`ControllerManagerReleaseLeaderElectionLockOnExit` feature gate to `false` and
restarting kube-controller-manager. This reverts to the previous behavior where
the leader lease is left to expire by TTL on shutdown. This should not break
existing workloads as it restores the prior, well-understood behavior.

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

If the feature is re-enabled after being rolled back, kube-controller-manager
will once again actively release its lease on shutdown. There are no special
considerations for re-enabling.

###### Are there any tests for feature enablement/disablement?

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are exercising the `switch` of feature gate itself (what happens if I disable a
feature gate after having objects written with the new field) are also critical.
You can take a look at one potential example of such test in:
https://github.com/kubernetes/kubernetes/pull/97058/files#diff-7826f7adbc1996a05ab52e3f5f02429e94b68ce6bce0dc534d1be636154fded3R246-R282
-->

No.

### Rollout, Upgrade and Rollback Planning

<!--
This section must be completed when targeting beta to a release.
-->

###### How can a rollout or rollback fail? Can it impact already running workloads?

<!--
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mid-rollout?

Be sure to consider highly-available clusters, where, for example,
feature flags will be enabled on some API servers and not others during the
rollout. Similarly, consider large clusters and how enablement/disablement
will rollout across nodes.
-->
A rollout could fail if:
- kube-controller-manager does not correctly handle context cancellation when
  losing leadership, leading to incomplete shutdown of internal controllers.
- The lease is released before controllers have fully stopped, briefly allowing
  a standby instance to start while the old leader is still finishing work.

Impact on workloads:
- If a leader component becomes unstable (e.g., due to memory leaks or improper
  shutdown), its ability to perform its duties (scheduling, controller
  management) could be impaired. In an HA setup, another instance should take
  over leadership, but frequent transitions or instability could degrade overall
  cluster performance or reliability.
- If a component fails to release resources correctly upon losing leadership, it
might lead to resource contention or incorrect behavior. Rollback (disabling the
feature gate and restarting components) should revert to the previous stable
behavior.

###### What specific metrics should inform a rollback?

<!--
What signals should users be paying attention to when the feature is young
that might indicate a serious problem?
-->
Some things to look at:
- Component restart counts: An increase in restarts for kube-controller-manager or kube-scheduler.
- Log messages indicating errors during leader transition or resource cleanup.
- General cluster health indicators like API server latency, pod scheduling latency, or controller reconciliation errors.

###### 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.
-->

n/a

###### 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.
-->
No. This feature introduces new behavior behind a feature gate and does not
deprecate or remove any existing features, APIs, fields, or flags.

### Monitoring Requirements

<!--
This section must be completed when targeting beta to a release.

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

An operator can determine if the feature is active by inspecting the
command-line flags of kube-controller-manager to verify that the
`ControllerManagerReleaseLeaderElectionLockOnExit` feature gate is enabled.

Observing component logs for messages indicating an active lease release on
shutdown (rather than waiting for the lease to expire) would also confirm its
use.

###### How can someone using this feature know that it is working for their instance?

<!--
For instance, if this is a pod-related feature, it should be possible to determine if the feature is functioning properly
for each individual pod.
Pick one more of these and delete the rest.
Please describe all items visible to end users below with sufficient detail so that they can verify correct enablement
and operation of this feature.
Recall that end users cannot usually observe component logs or access metrics.
-->

This feature is primarily for cluster operators. Operators can verify its
operation by:

- Observing failover latency: after a graceful kube-controller-manager shutdown
  (e.g. during a rolling upgrade), a standby instance should acquire leadership
  promptly rather than waiting out the lease duration.
- Inspecting the Lease object: the holder identity is cleared when the leader
  shuts down, instead of remaining set until the lease expires.

###### What are the reasonable SLOs (Service Level Objectives) for the enhancement?

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  - 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

These goals will help you determine what you need to measure (SLIs) in the next
question.
-->

- No increase to leader transition time with the feature enabled.
- No increase to memory usage with feature is enabled

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

 Component logs can be used to verify graceful shutdown steps are being executed.

###### Are there any missing metrics that would be useful to have to improve observability of this feature?

<!--
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implementation difficulties, etc.).
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n/a

### Dependencies

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This section must be completed when targeting beta to a release.
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###### Does this feature depend on any specific services running in the cluster?

This is a control plane feature and requires the components that the feature
runs on (kube-scheduler, kube-controller-manager) to be active, as well as the
kube-apiserver and etcd for leader election.

### Scalability

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###### Will enabling / using this feature result in any new API calls?

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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.)
-->

No

###### Will enabling / using this feature result in introducing new API types?

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  - Supported number of objects per cluster
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No

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

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  - Which API(s):
  - Estimated increase:
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No

###### Will enabling / using this feature result in increasing size or count of the existing API objects?

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  - API type(s):
  - Estimated increase in size: (e.g., new annotation of size 32B)
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No

###### Will enabling / using this feature result in increasing time taken by any operations covered by existing SLIs/SLOs?

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Look at the [existing SLIs/SLOs].

Think about adding additional work or introducing new steps in between
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[existing SLIs/SLOs]: https://git.k8s.io/community/sig-scalability/slos/slos.md#kubernetes-slisslos
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No

###### Will enabling / using this feature result in non-negligible increase of resource usage (CPU, RAM, disk, IO, ...) in any components?

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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].

[supported limits]: https://git.k8s.io/community//sig-scalability/configs-and-limits/thresholds.md
-->

No.

###### Can enabling / using this feature result in resource exhaustion of some node resources (PIDs, sockets, inodes, etc.)?

<!--
Focus not just on happy cases, but primarily on more pathological cases
(e.g. probes taking a minute instead of milliseconds, failed pods consuming resources, etc.).
If any of the resources can be exhausted, how this is mitigated with the existing limits
(e.g. pods per node) or new limits added by this KEP?

Are there any tests that were run/should be run to understand performance characteristics better
and validate the declared limits?
-->

No.

### Troubleshooting

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

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.
-->

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

Leader election cannot function without apiserver or etcd.

###### What are other known failure modes?

- Premature lease release
  - Detection: briefly more than one active kube-controller-manager after a
    leadership change (duplicate controller activity or conflicting writes).
  - Mitigations: the lease is released only after controller goroutines return.
    Turn off the feature to fall back to TTL expiry.
  - Diagnostics: inspect the Lease object and controller logs around handoff.
  - Testing: integration tests for shutdown ordering.

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

Disable the feature.

## Implementation History

<!--
Major milestones in the lifecycle of a KEP should be tracked in this section.
Major milestones might include:
- the `Summary` and `Motivation` sections being merged, signaling SIG acceptance
- the `Proposal` section being merged, signaling agreement on a proposed design
- the date implementation started
- the first Kubernetes release where an initial version of the KEP was available
- the version of Kubernetes where the KEP graduated to general availability
- when the KEP was retired or superseded
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- 2025-06-03 - KEP was marked as implementable

## Drawbacks

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Why should this KEP _not_ be implemented?
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Adds a small risk of briefly running two leaders if the lease is released before controllers have fully stopped.

## Alternatives

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What other approaches did you consider, and why did you rule them out? These do
not need to be as detailed as the proposal, but should include enough
information to express the idea and why it was not acceptable.
-->

n/a

## Future Work (Stories)

This feature enables the user stories below, but require additional modification to the kcm and scheduler code that they are outside the scope of this KEP.

#### Graceful Leader Transition

A possible future direction is for leader-elected components to keep running and
return to a follower state on lost leadership instead of exiting the process,
decoupling "stop leading" from "process exit". This would be gated separately
(e.g. a `GracefulLeaderTransition` gate) and requires resolving metric
re-registration conflicts, health-check deregistration, and resource cleanup on
transition.

#### Story 1

In an HA configuration, cloud provider A wants to balance controllers over
multiple control plane instances. With graceful transitions, multiple locks can
be used by a single KCM instance such that a subset of components run under each
lock.

#### Story 2

A extension developer has controller manager that should dynamically start and
shutdown controllers based on cluster state (such as the registration of
resources that declare how a CRD should be reconciled). The extension developer
requires that controllers shutdown gracefully so that only the controller loops
that SHOULD be running continue to run, and that no resources are leaked over
time as controllers are started and stopped.



## Infrastructure Needed (Optional)

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Use this section if you need things from the project/SIG. Examples include a
new subproject, repos requested, or GitHub details. Listing these here allows a
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-->
