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# KEP-4443: More granular Job failure reasons for PodFailurePolicyRule

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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)
    - [Story 2](#story-2)
  - [Notes/Constraints/Caveats (Optional)](#notesconstraintscaveats-optional)
  - [Risks and Mitigations](#risks-and-mitigations)
- [Design Details](#design-details)
    - [Defaulting](#defaulting)
    - [Validation](#validation)
    - [Business logic](#business-logic)
  - [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)
- [Infrastructure Needed (Optional)](#infrastructure-needed-optional)
<!-- /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

This KEP proposes to extend the Job API by adding an optional `Name` field to `PodFailurePolicyRule`. If unset, it would default to the index of
the rule in the `podFailurePolicy.rules` slice. 

The purpose of giving the rule a name is to expose more detailed failure information inside the
`JobFailed` condition reason. When a pod failure policy rule triggers a Job failure, the rule name would be appended as a suffix to the `JobFailed` condition reason, in the
format: `PodFailurePolicy_{ruleName}`. This will allow users to set multiple pod failure policy rules
and distinguish which one (if any) triggered a Job failure.

## Motivation

Higher level K8s APIs are built via a composition of features, using primitive K8S APIs as building blocks to implement more advanced features.
These higher level APIs using the Job API as a building block need to be able to distinguish between different types of Job failures in order to
make informed decisions about how to react to these failures.

Currently, no mechanism exists in the Job API to propagate granular failure reason information (e.g., container exit codes) up to be 
programmatically consumed by higher level software managing Jobs. A `PodFailurePolicy` can be configured to add a condition reason of `PodFailurePolicy`
to the `JobFailed` condition added to the Job when it fails, but different pod failure policies targeting different container exit codes all use the
same condition reason of `PodFailurePolicy`. This prevents higher level APIs like JobSet from distinguishing them and being able to take different
actions depending on the type of Job failure that occurred.

For a concrete use case, see the JobSet [Configurable Failury Policy KEP](https://github.com/kubernetes-sigs/jobset/blob/main/keps/262-ConfigurableFailurePolicy/README.md) which illuminated the need for more granular pod failure policy reasons.

### Goals

For pod failure policies to be able communicate different failure types to higher level APIs.

### Non-Goals

- Modifying PodFailurePolicy behavior
- The Job controller using this new field for any purpose not explicitly defined in the proposal.

## Proposal

The proposal is to add an optional `Name` field to the `PodFailurePolicyRule`. If unset, it will default to the index of the  `PodFailurePolicyRule` in the `PodFailurePolicy.Rules` slice.

When a `PodFailurePolicyRule` matches a pod failure and the `Action` is `FailJob`, the Job
controller will append the name of the pod failure policy rule which triggered the failure
to the JobFailed [condition](https://github.com/kubernetes/kubernetes/blob/6a4e93e776a35d14a61244185c848c3b5832621c/pkg/controller/job/job_controller.go#L816)
reason. The exact format of the JobFailed condition reason will be `PodFailurePolicy_{ruleName}`.

### User Stories (Optional)

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#### Story 1
As a user, I am using a JobSet to manage a group of jobs, and I want to be able to decide whether to fail the
JobSet or not, based on the exact container exit code that caused a child job failure.

**Example JobSet for this use case**:

```yaml
apiVersion: jobset.x-k8s.io/v1alpha2
kind: JobSet
metadata:
  name: fail-jobset-example
spec:
  failurePolicy:
    rules:
    # If Job fails due to a pod failing with exit code 2, fail the JobSet immediately, without attempting any restarts.
    - action: FailJobSet
      targetReplicatedJobs:
      - workers
      onJobFailureReasons:
      - PodFailurePolicy_ExitCode2 # Job failure reason format: PodFailurePolicy_{ruleName}
    maxRestarts: 10
  replicatedJobs:
  - name: workers
    replicas: 10
    template:
      spec:
        parallelism: 1
        completions: 1
        backoffLimit: 0
        # If a pod fails with exit code 2, fail the job with the user-defined reason.
        podFailurePolicy:
          rules:
          - name: "ExitCode2"  # Will be added as a suffix to the reason "PodFailurePolicy" condition reason.
            action: FailJob
            onExitCodes:
              containerName: main
              operator: In
              values: [2]
        template:
          spec:
            restartPolicy: Never
            containers:
            - name: main
              image: python:3.10
              command: ["..."]
```

#### Story 2

As a user, I am using a JobSet to manage a group of jobs, each running a HPC simulation.
Each job runs a simulation with different random initial parameters. When a simulation ends, the
application will exit with one of two exit codes:

- Exit code 2, which indicates the simulation produced an invalid result due to bad starting parameters, and should
not be retried.
- Exit code 3, which indicates the simulation produced an invalid result but the initial parameters were reasonable,
so the simulation should be restarted.

When a Job fails due to a pod failing with exit code 2, I want my job management software to leave the Job in
a failed state.

When a Job fails due to a pod failing with exit code 3, I want my job management software to to restart the Job.

**Example JobSet for this use case**:

```yaml
apiVersion: jobset.x-k8s.io/v1alpha2
kind: JobSet
metadata:
  name: restart-job-example
  annotations:
    alpha.jobset.sigs.k8s.io/exclusive-topology: {{topologyDomain}} # 1:1 job replica to topology domain assignment
spec:
  failurePolicy:
    rules:
    # If Job fails due to a pod failing with exit code 2, leave it in a failed state.
    - action: FailJob
      targetReplicatedJobs:
      - simulations
      onJobFailureReasons:
      - PodFailurePolicy_ExitCode2  # Job failure reason format: PodFailurePolicy_{ruleName}
    # If Job fails due to a pod failing with exit code 3, restart that Job.
    - action: RestartJob
      targetReplicatedJobs:
      - simulations
      onJobFailureReasons:
      - PodFailurePolicy_ExitCode3  # Job failure reason format: PodFailurePolicy_{ruleName}
    maxRestarts: 10
  replicatedJobs:
  - name: simulations
    replicas: 10
    template:
      spec:
        parallelism: 1
        completions: 1
        backoffLimit: 0
        # Pod failure policy rules, the names of which are referenced in the JobSet failure policy.
        podFailurePolicy:
          rules:
          - name: ExitCode2
            action: FailJob
            onExitCodes:
              containerName: main
              operator: In
              values: [2]
          - name: ExitCode3
            action: FailJob
            onExitCodes:
              containerName: main
              operator: In
              values: [3]
        template:
          spec:
            restartPolicy: Never
            containers:
            - name: main
              image: python:3.10
              command: ["..."]
```

### Notes/Constraints/Caveats (Optional)

It should be noted that upon pod failure, the Job's pod failure policy rules
are evaluated in order, and only the first matching rule is executed, even
if multiple rules match a pod failure.

### Risks and Mitigations

<!--
What are the risks of this proposal, and how do we mitigate? Think broadly.
For example, consider both security and how this will impact the larger
Kubernetes ecosystem.
Validate all pod failure policy rule names are unique
How will security be reviewed, and by whom?

How will UX be reviewed, and by whom?

Consider including folks who also work outside the SIG or subproject.
-->
There is a risk to making a field that was previously exclusively managed by the controller,
to now being configurable by the user. However, as described in the validation section below,
we are validating against malformed/invalid inputs.


## Design Details

#### Defaulting

There will be no defaulting for the new pod failure policy `Name` field.
When `Name` is unset, the Job controller will set the reason suffix to the index of the rule in the `podFailurePolicy.rules` slice (e.g. `PodFailurePolicy_{index}`).

#### Validation
- Validate all pod failure policy rule names are unique.
- Validate pod failure policy rule names do not have integer values of any of the existing indexes
(unless the value is the rule's own index).
- We will validate the Job failure condition reason that would be generated from a given
PodFailurePolicy rule name (i.e., `PodFailurePolicy_{ruleName}`) will be a valid reason (CamelCase, max length of 128 characters, and matches the regex defined [here](https://github.com/kubernetes/kubernetes/blob/dd301d0f23a63acc2501a13049c74b38d7ebc04d/staging/src/k8s.io/apimachinery/pkg/apis/meta/v1/types.go#L1559)).
- We will also validate the pod failure policy rule does not conflict with any [K8s internal reasons used by the Job controller](https://github.com/kubernetes/kubernetes/blob/862ff187baad9373d59d19e5d736dcda1e25e90d/staging/src/k8s.io/api/batch/v1/types.go#L542-L553). 

#### Business logic
When a `PodFailurePolicyRule` matches a pod failure and the `Action` is `FailJob`, the Job controller will
set the JobFailed condition reason deterministically in the format `PodFailurePolicy_{ruleName}`. This 
suffix will be added to the condition [condition](https://github.com/kubernetes/kubernetes/blob/6a4e93e776a35d14a61244185c848c3b5832621c/pkg/controller/job/job_controller.go#L816) here.

Note: If the PodFailurePolicy feature gate is disabled, but the `PodFailurePolicyName` feature gate is enabled, there will be 
no adverse effect and neither feature will actually be used, since the only place the proposed new field `Name` will be
used is inside of a [code block](https://github.com/kubernetes/kubernetes/blob/6a4e93e776a35d14a61244185c848c3b5832621c/pkg/controller/job/job_controller.go#L816) protected by the PodFailurePolicy feature gate.

### Test Plan

[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.

##### Prerequisite testing updates

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##### Unit tests

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- `k8s.io/kubernetes/pkg/controller/job`: `02/05/2024` - `91.5%`
- `k8s.io/kubernetes/pkg/apis/batch/v1`: `06/05/2024` - `87.3%`
- `k8s.io/kubernetes/pkg/apis/batch/v1beta1`: `06/05/2024` - `78.3%`
- `k8s.io/kubernetes/pkg/apis/batch/validation`: `06/05/2024` - `87.7%`

##### Integration tests

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<!-- - <test>: <link to test coverage> -->
- Test that when the feature flag is enabled and a Job's PodFailurePolicy triggers a Job failure, due to a matching PodFailurePolicyRule, check that the `JobFailed` condition has a reason of `PodFailurePolicy_{Name}`.

- Test that when the feature flag is off, but when it was previously enabled, there is an existing Job
which already had the `JobFailed` condition reason set with the new suffix (i.e., `PodFailurePolicy_{Name}`), that the Job controller does not overwrite the reason to `PodFailurePolicy`, and that it remains set to the existing value.

- Add test cases for both onPodConditions and onExitCodes to ensure the `Name` or the rule's index (when `Name` is empty) is properly added.

##### e2e tests

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

<!-- - <test>: <link to test coverage> -->
We will a test case similar to the integration test case:

- When the feature flag is enabled and a Job's PodFailurePolicy triggers a Job failure, due to a
matching PodFailurePolicyRule, check that the `JobFailed` condition has the `PodFailurePolicy_{Reason}` reason set correctly.

### Graduation Criteria

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#### GA

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in back-to-back releases.

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[conformance tests].**

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#### Deprecation

- Announce deprecation and support policy of the existing flag
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- Deprecate the flag
-->
#### Alpha

- Feature implemented behind a feature flag
- Initial unit and integration tests are implemented
- Documentation is updated

#### Beta
- Address reviews and bug reports from Alpha users
- Feature is stable in Alpha for 1 release cycle
- Feature flag enabled by default

#### GA
- Address reviews and bug reports from Beta users
- Feature is stable in Beta for 2 full release cycles

### Upgrade / Downgrade Strategy

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Consider the following in developing an upgrade/downgrade strategy for this
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  cluster required to make on upgrade, in order to maintain previous behavior?
- What changes (in invocations, configurations, API use, etc.) is an existing
  cluster required to make on upgrade, in order to make use of the enhancement?
-->
After a user upgrades their cluster to a k8s version which supports this feature, 
the user can use this feature by simply specifying the new field in their podFailurePolicy
config.

When a user downgrades from a k8s version that supports this field to one that does
not support this field:
- for existing Jobs, this new field will be ignored by the Job controller,
resulting in the condition reason being set to the previous default of `PodFailurePolicy`
for any Job failures triggered by a pod failure policy.
- for new Jobs, the kube-apiserver would remove this field when the Job is submitted.

### 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 feature is limited to control plane, so the version skew with kubelet does
not matter.

In case kube-apiserver is running in HA mode, and the versions are skewed, then
the old version of kube-apiserver (from before this change) may not handle
the the new `Name` field if it is set in a Job PodFailurePolicy spec.

In case the version of the kube-controller-manager leader is skewed (old), the
built-in Job controller would reconcile the Jobs with the new `Name` field and
simply drop the field, thereby not using it when setting the `JobFailed` condition
reason.

## Production Readiness Review Questionnaire

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-->

### Feature Enablement and Rollback

<!--
This section must be completed when targeting alpha to a release.
-->
- Upgrade to k8s version 1.31+
- Enable feature flag `PodFailurePolicyName`

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

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[existing list]: https://kubernetes.io/docs/reference/command-line-tools-reference/feature-gates/
-->

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

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Any change of default behavior may be surprising to users or break existing
automations, so be extremely careful here.
-->
No

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

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-->
Yes, by disabling the feature flag `PodFailurePolicyName`.

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

For new Jobs, the apiserver will stop wiping out the new field (`Name`).
For existing Jobs, the Job controller will stop ignoring the new field, and begin
using it as described in previous sections.

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

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conversion tests if API types are being modified.

Additionally, for features that are introducing a new API field, unit tests that
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
-->
We can add unit tests for:
- feature enabled and field set
- feature disabled and field set
- feature disabled after Jobs have `JobFailed` condition with reason set using the
new format

### Rollout, Upgrade and Rollback Planning


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

<!--
Try to be as paranoid as possible - e.g., what if some components will restart
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.
-->
If any component has not yet rolled out, or fails to rollout, the existing
default behavior will continue to apply, but there is no downtime during partial
rollout or rollback.


###### 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?
-->
A substantial increase in the `job_sync_duration_seconds` metric may suggest the
processing of the configured job pod failure policy rules consumes too much time.

An operator can also observe `job_pods_finished_total` to check if the reason count
of taken actions (`FailJob`, `Count` or `Ignore`) correlates with the expected
changes based on the Job workload specificity.

Additionally, an operator should check if the failed Jobs have the correct condition
reason set on the `JobFailed` reason, as described in the [design details](#design-details).

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

Feature is not implemented yet so we cannot test these paths.

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

`PodFailurePolicy` reason format will be deprecated in GA and replaced by `PodFailurePolicy_{RuleName}`.
Until then, we will maintain both based on conditional logic behind a feature flag.

### 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.
-->

###### 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.
-->

###### 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.
-->

- [ ] Events
  - Event Reason: 
- [ ] API .status
  - Condition name: 
  - Other field: 
- [ ] Other (treat as last resort)
  - Details:

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

<!--
This is your opportunity to define what "normal" quality of service looks like
for a feature.

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

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

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

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

- [x] Metrics
  - Metric name:
    - `job_sync_duration_seconds` (existing): can be used to see how much the
feature enablement increases the time spent in the sync job
  - Components exposing the metric: kube-controller-manager

###### 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.).
-->

No.

### Dependencies

<!--
This section must be completed when targeting beta 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.)
-->
No

###### 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)
-->
No.

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

<!--
Describe them, providing:
  - Which API(s):
  - Estimated increase:
-->
No

###### 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)
-->
If the optional `name` field is specified, the podFailurePolicy object size will increase by 1 byte per
character in the `name` string. The name field will be no longer than 128 characters, thus the max size
increase of the PodFailurePolicy object will be 128 bytes.
Otherwise, if unset, it will default to the index of the rule, and thus increase by 1 byte per digit in
the index number.

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

<!--
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
-->
No

###### 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].

[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

<!--
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.

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?

###### 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

<!--
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
-->
- 2024-05-24: KEP Published
- 2026-06-09: Update the milestone to v1.38

## Drawbacks

It is a less intuitive user experience to have the PodFailurePolicy rule name be appended
as a suffix to the Job failure reason, rather than 

## Alternatives

We discussed the idea of having a new optional `PodFailurePolicyRule` field `SetConditionReason`, which will enable
the user to explicitly set the condition reason they want on the JobFailed condition set on the Job when that pod failure
policy rule triggers a Job failure. However, ultimately it was decided we didn't want to open up the reason field to be
explicitly set by the user to any arbitrary value, as this would be tricky to validate, and would diverge from the current
paradigm of having only machine set reasons which are determined programatically.


## 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.
-->
