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# KEP-2926: Allow updating scheduling directives of jobs


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<!-- toc -->
- [Release Signoff Checklist](#release-signoff-checklist)
- [Summary](#summary)
- [Motivation](#motivation)
  - [Goals](#goals)
  - [Non-Goals](#non-goals)
- [Proposal](#proposal)
  - [User Stories (Optional)](#user-stories-optional)
    - [Story 1](#story-1)
  - [Risks and Mitigations](#risks-and-mitigations)
- [Design Details](#design-details)
  - [Update related to KEP-5440](#update-related-to-kep-5440)
  - [Test Plan](#test-plan)
    - [Unit tests](#unit-tests)
    - [Integration tests](#integration-tests)
    - [e2e tests](#e2e-tests)
  - [Graduation Criteria](#graduation-criteria)
    - [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)
- [Alternatives](#alternatives)
  - [Alternative 2: allow updating jobs with no restrictions.](#alternative-2-allow-updating-jobs-with-no-restrictions)
  - [Allow updating all suspended jobs](#allow-updating-all-suspended-jobs)
<!-- /toc -->

## Release Signoff Checklist

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  - [ ] e2e Tests for all Beta API Operations (endpoints)
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[kubernetes/website]: https://git.k8s.io/website

## Summary

In [#2232](https://github.com/kubernetes/enhancements/issues/2232) we added a new flag
to allow suspending jobs to control when the Pods of a Job get created by controller-manager. 
This was proposed as a primitive to allow a higher-level queue controller to implement
job queuing: the queue controller unsuspends the job when resources become available.

To complement the above capability, a queue controller may also want to control on 
which group of nodes a job should run. For example, it may want to send the job to a 
specific partition (e.g., preemptibles) or a specific location (e.g., zone x).

This is a proposal to relax update validation on jobs that have never been unsuspended to 
allow mutating node scheduling directives, namely the pod template's node affinity, 
node selector, tolerations, schedulingGates, annotations and labels of the job's pod template. This 
enables a higher-level queue controller to inject such directives before un-suspending a job to
influence its placement.

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## Motivation

Most kubernetes components are pod centric, including the scheduler and cluster 
autoscaler. This works well for service type workloads where the pods of a service
are mostly independent and all services are expected to be running at all times.

However, for batch jobs, it doesn't make sense to focus only on pods. In most cases
a parallel job will want the pods to run within specific constraints, like all in 
the same zone, or all either on GPU model x or y, but not a mix of both.

We made the first step towards achieving those semantics by introducing the `suspend`
flag to the Job API, which allowed a queue controller to decide when a job should start.
The idea was that such a controller would unsuspend the job when specific constraints
are met (e.g., capacity is provisioned). However, once a job is unsuspended, the queue 
controller has no influence on where the pods of a job will actually land.

Adding the ability to mutate a job's scheduling bits before it starts gives a queue controller
the ability to influence placement while at the same time offloading actual pod-to-node
assignment to the existing kube-scheduler.

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### Goals

- Allow mutating node affinity, node selector, tolerations, annotations and labels of 
  the pod template of jobs that have never been unsuspended.


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### Non-Goals

- Implement a queue controller.
- Allow mutating scheduling bits of jobs that started at least once. This will not 
  allow queue controllers to support preempting jobs and schedule them on a different 
  partition of the cluster. As discussed in alternatives, this can be addressed in the future.
- Allow mutating scheduling bits of pods.
- Allow mutating scheduling bits of apps other than jobs.

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## Proposal

The proposal is to relax update validation of scheduling bits of jobs that have never
been unsuspended, specifically node affinity, node selector, tolerations, annotations and 
labels of the pod template.

This has no impact on the job-controller. The job controller has no dependency
on those scheduling directives expressed in a job's pod template.

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### User Stories (Optional)

#### Story 1

I want to build a controller that implements job queueing and influences when and where a 
job should run. Users create v1.Job objects, and to control when the job can run, I have 
a webhook that forces the jobs to be created in a suspended state, and the controller unsuspends 
them when capacity becomes available.

At the time of creating the job, we may not know on which part of the cluster (.e.g., a zone or 
a VM type) a job should run. To control where a job should run, the controller can update 
node affinity or tolerations of the Job's pod template; by doing so, the queue controller is 
able to influence the scheduler and autoscaler decisions.

<!--
#### Story 2


### Notes/Constraints/Caveats (Optional)
-->

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### Risks and Mitigations

- New calls from a queue controller to the API server to update affinity or tolerations. 
  The mitigation is for such controllers to make a single API call for both updating
  affinity/tolerations and unsuspending the job.
  
- A race condition could happen that may result in having a mix of pods with different 
  affinity/tolerations, consider the following scenario: 
  1. unsuspend the job
  2. suspend the job
  3. update affinity
  
  After unsuspending the job, the job-controller could have created some pods and not yet
  updated `Status.StartTime` by the time kube-apiserver received the suspend then affinity updates.
  This is not a typical sequence of events, but it is something that users should watch for.

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## Design Details

The pod template validation logic in the API server, what we need to do is relax the validation
of the Job's Template field which is currently immutable to be mutable for node affinity, 
node selector, tolerations, annotations and labels.

The condition we will check to verify that the job has never been unsuspended before is 
`Job.Spec.Suspend=true && Job.Status.StartTime=nil`.

### Update related to KEP-5440

As part of the [KEP-5440](https://github.com/kubernetes/enhancements/issues/5440) we adjust
the condition to be `Job.Spec.Suspend=true && hasCondition(Job.Status, "JobSuspended") && Job.Status.Active=0`
which is more flexible as it allows to mutate the Job after it was started, but got suspended.
This possibility is already used in the Kueue project, and the check had to be workarounded by clearing
the `Job.Status.StartTime`, see [here](https://github.com/kubernetes-sigs/kueue/blob/eb8a0e8c5c60d5771c593cca2fe9f7be0ea5b122/pkg/controller/jobs/job/job_controller.go#L184-L192).

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### Test Plan

- Unit and integration tests veryfing that:
  - pod template's node affinity, node selector, tolerations, annotations and labels not mutable for jobs that have been unsuspended before.
  - pod template's node affinity, node selector tolerations, annotations or labels not mutable for apps other than jobs.
  - job controller observes the update and creates pods with the new scheduling directives.

#### Unit tests

- `k8s.io/kubernetes/pkg/registry/batch/job/`: `1/30/2023` - `76.8%`

#### Integration tests

Available under [Job integrations tests](https://github.com/kubernetes/kubernetes/blob/457341c3d408097025af5a9b6f5917439c0debdd/test/integration/job/job_test.go#L1397)

#### e2e tests

Integration tests offer enough coverage.


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### Graduation Criteria

We will release the feature directly in Beta state. Because the feature is opt-in and doesn't add
a new field, there is no benefit in having an alpha release.

#### Beta
- Feature implemented behind a feature flag
- Unit and integration tests passing 

#### GA
- Fix any potentially reported bugs

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### Upgrade / Downgrade Strategy

No changes required to existing cluster to use this feature.

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### Version Skew Strategy

N/A. This feature doesn't impact nodes.

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  CRI or CNI may require updating that component before the kubelet.
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## 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?

<!--
Pick one of these and delete the rest.
-->

- [X] Feature gate (also fill in values in `kep.yaml`)
  - Feature gate name: MutableSchedulingDirectivesForJobs
  - Components depending on the feature gate: kube-apiserver
- [ ] 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?

Yes, it relaxes validation of updates to jobs. Specifically, it will allow
mutating the node affinity, node selector and tolerations.

<!--
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-->

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

Yes. If disabled, kube-apiserver will start rejecting mutating node affinity/tolerations of jobs.

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

kube-apiserver will accept node affinity/tolerations updates of jobs.

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

Tests are in place. See [Job integrations tests](https://github.com/kubernetes/kubernetes/blob/457341c3d408097025af5a9b6f5917439c0debdd/test/integration/job/job_test.go#L1397).
The tests do not directly test switching between enable/disable, that was tested manually (see below).

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### Rollout, Upgrade and Rollback Planning

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

The change is opt-in, it doesn't impact already running workloads. But problems with the 
updated validation logic may cause crashes in the apiserver.

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

Crashes in the apiserver because of potential problems with the updated validation logic.

<!--
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that might indicate a serious problem?
-->

###### Were upgrade and rollback tested? Was the upgrade->downgrade->upgrade path tested?

Tested manually. 
1. Cluster on 1.22 was upgraded to 1.23
2. Created a suspended job
3. Mutated scheduling directives and checked that mutation was successful
4. Started the job, verifying that the mutation took effect in practice as well
5. Created a suspended job
6. Downgraded the cluster back to 1.22
7. Attempt to mutate the scheduling directives was rejected.

Findings: the feature and the cluster are behaving as expected.

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###### Is the rollout accompanied by any deprecations and/or removals of features, APIs, fields of API types, flags, etc.?

No.

<!--
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-->

### Monitoring Requirements

<!--
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###### How can an operator determine if the feature is in use by workloads?

N/A. This is not a feature that workloads use directly.

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

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- [ ] Events
  - Event Reason: 
- [ ] API .status
  - Condition name: 
  - Other field: 
- [X] Other (treat as last resort)
  - Details: Create a job then update the node affinity/tolerations of the pod template of the job.

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

N/A

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- [x] Metrics
  - Metric name: apiserver_request_total[resource=job, group=batch, verb=UPDATE, code=400]
  - [Optional] Aggregation method:
  - Components exposing the metric: kube-apiserver
- [ ] Other (treat as last resort)
  - Details:

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

N/A

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### Dependencies

<!--
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###### Does this feature depend on any specific services running in the cluster?

No.

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### Scalability

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

The feature itself doesn't generate API calls. But it will allow the
apiserver to accept update requests to mutate part of the job spec, 
which will encourage implementing controllers that does this.

<!--
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    (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?

No.

<!--
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?

No.

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Describe them, providing:
  - Which API(s):
  - Estimated increase:
-->

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

No.

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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?

No.

<!--
Look at the [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.

[existing SLIs/SLOs]: https://git.k8s.io/community/sig-scalability/slos/slos.md#kubernetes-slisslos
-->

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

No.

<!--
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
-->

### Troubleshooting

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

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?

Update requests will be rejected.

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

In a multi-master setup, when the cluster has skewed apiservers, some update requests
may get accepted and some may get rejected.

<!--
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?

N/A.

## Implementation History

- 2021-09-01: Proposed KEP starting in beta status.
- 2021-10-28: Updated the KEP to include annotations and labels of the pod template.
- 2023-01-03: Updated the KEP to include schedulingGates of the pod template and graduated the feature to stable.


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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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<!--
## Drawbacks


Why should this KEP _not_ be implemented?
-->


## Alternatives

### Alternative 2: allow updating jobs with no restrictions.
This risk with this alternative is the potential for ending up with a mix of
pods, existing ones use the old constraints while new ones using the updated
constraints. This may surprise users since the old pods may not be schedulable, 
and the user's intention for changing the constraints is to make the job schedulable.

The solution could be to offer guaranteed behavior by updating the job-controller
to re-create existing pods, but we decided to consider this as a followup and start
with the proposed limited scope for now.

### Allow updating all suspended jobs
This is similar to this KEP's proposal but for all suspended jobs, not just ones that have
never been unsuspended before. Without ensuring that the job is indeed scaled down before accepting 
the update, this approach faces race conditions that may result in pods with mixed scheduling directives
and so create unpleasnt surprises.



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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.
-->

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## Infrastructure Needed (Optional)


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
SIG to get the process for these resources started right away.
-->
