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Blog: Kubernetes 1.28: Improved failure handling for Jobs

Authors: Kevin Hannon (G-Research), Michał Woźniak (Google)

This blog discusses two new features in Kubernetes 1.28 to improve Jobs for batch users: Pod replacement policyand Backoff limit per index.

These features continue the effort started by thePod failure policyto improve the handling of Pod failures in a Job.

Pod replacement policy

By default, when a pod enters a terminating state (e.g. due to preemption or eviction), Kubernetes immediately creates a replacement Pod. Therefore, both Pods are running at the same time. In API terms, a pod is considered terminating when it has adeletionTimestamp and it has a phase Pending or Running.

The scenario when two Pods are running at a given time is problematic for some popular machine learning frameworks, such as TensorFlow and JAX, which require at most one Pod running at the same time, for a given index. Tensorflow gives the following error if two pods are running for a given index.

 /job:worker/task:4: Duplicate task registration with task_name=/job:worker/replica:0/task:4

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See more details in the (issue).

Creating the replacement Pod before the previous one fully terminates can also cause problems in clusters with scarce resources or with tight budgets, such as:

  • cluster resources can be difficult to obtain for Pods pending to be scheduled, as Kubernetes might take a long time to find available nodes until the existing Pods are fully terminated.
  • if cluster autoscaler is enabled, the replacement Pods might produce undesired scale ups.

How can you use it?

This is an alpha feature, which you can enable by turning on JobPodReplacementPolicyfeature gate in your cluster.

Once the feature is enabled in your cluster, you can use it by creating a new Job that specifies apodReplacementPolicy field as shown here:

kind: Job
 name: new
 podReplacementPolicy: Failed

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In that Job, the Pods would only be replaced once they reached the Failed phase, and not when they are terminating.

Additionally, you can inspect the .status.terminating field of a Job. The value of the field is the number of Pods owned by the Job that are currently terminating.

kubectl get jobs/myjob -o=jsonpath='{.items[*].status.terminating}'

3 # three Pods are terminating and have not yet reached the Failed phase

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This can be particularly useful for external queueing controllers, such asKueue, that tracks quota from running Pods of a Job until the resources are reclaimed from the currently terminating Job.

Note that the podReplacementPolicy: Failed is the default when using a customPod failure policy.

Backoff limit per index

By default, Pod failures for Indexed Jobsare counted towards the global limit of retries, represented by .spec.backoffLimit. This means, that if there is a consistently failing index, it is restarted repeatedly until it exhausts the limit. Once the limit is reached the entire Job is marked failed and some indexes may never be even started.

This is problematic for use cases where you want to handle Pod failures for every index independently. For example, if you use Indexed Jobs for running integration tests where each index corresponds to a testing suite. In that case, you may want to account for possible flake tests allowing for 1 or 2 retries per suite. There might be some buggy suites, making the corresponding indexes fail consistently. In that case you may prefer to limit retries for the buggy suites, yet allowing other suites to complete.

The feature allows you to:

  • complete execution of all indexes, despite some indexes failing.
  • better utilize the computational resources by avoiding unnecessary retries of consistently failing indexes.

How can you use it?

This is an alpha feature, which you can enable by turning on theJobBackoffLimitPerIndexfeature gatein your cluster.

Once the feature is enabled in your cluster, you can create an Indexed Job with the.spec.backoffLimitPerIndex field specified.


The following example demonstrates how to use this feature to make sure the Job executes all indexes (provided there is no other reason for the early Job termination, such as reaching the activeDeadlineSeconds timeout, or being manually deleted by the user), and the number of failures is controlled per index.

apiVersion: batch/v1
kind: Job
 name: job-backoff-limit-per-index-execute-all
 completions: 8
 parallelism: 2
 completionMode: Indexed
 backoffLimitPerIndex: 1
 restartPolicy: Never
 - name: example # this example container returns an error, and fails,
 # when it is run as the second or third index in any Job
 # (even after a retry) 
 image: python
 - python3
 - -c
 - |
 import os, sys, time
 id = int(os.environ.get("JOB_COMPLETION_INDEX"))
 if id == 1 or id == 2:

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Now, inspect the Pods after the job is finished:

kubectl get pods -l job-name=job-backoff-limit-per-index-execute-all

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Returns output similar to this:

job-backoff-limit-per-index-execute-all-0-b26vc 0/1 Completed 0 49s
job-backoff-limit-per-index-execute-all-1-6j5gd 0/1 Error 0 49s
job-backoff-limit-per-index-execute-all-1-6wd82 0/1 Error 0 37s
job-backoff-limit-per-index-execute-all-2-c66hg 0/1 Error 0 32s
job-backoff-limit-per-index-execute-all-2-nf982 0/1 Error 0 43s
job-backoff-limit-per-index-execute-all-3-cxmhf 0/1 Completed 0 33s
job-backoff-limit-per-index-execute-all-4-9q6kq 0/1 Completed 0 28s
job-backoff-limit-per-index-execute-all-5-z9hqf 0/1 Completed 0 28s
job-backoff-limit-per-index-execute-all-6-tbkr8 0/1 Completed 0 23s
job-backoff-limit-per-index-execute-all-7-hxjsq 0/1 Completed 0 22s

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Additionally, you can take a look at the status for that Job:

kubectl get jobs job-backoff-limit-per-index-fail-index -o yaml

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The output ends with a status similar to:

 completedIndexes: 0,3-7
 failedIndexes: 1,2
 succeeded: 6
 failed: 4
 - message: Job has failed indexes
 reason: FailedIndexes
 status: "True"
 type: Failed

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Here, indexes 1 and 2 were both retried once. After the second failure, in each of them, the specified .spec.backoffLimitPerIndex was exceeded, so the retries were stopped. For comparison, if the per-index backoff was disabled, then the buggy indexes would retry until the global backoffLimit was exceeded, and then the entire Job would be marked failed, before some of the higher indexes are started.

How can you learn more?

Getting Involved

These features were sponsored by SIG Apps. Batch use cases are actively being improved for Kubernetes users in thebatch working group. Working groups are relatively short-lived initiatives focused on specific goals. The goal of the WG Batch is to improve experience for batch workload users, offer support for batch processing use cases, and enhance the Job API for common use cases. If that interests you, please join the working group either by subscriping to ourmailing list or onSlack.


As with any Kubernetes feature, multiple people contributed to getting this done, from testing and filing bugs to reviewing code.

We would not have been able to achieve either of these features without Aldo Culquicondor (Google) providing excellent domain knowledge and expertise throughout the Kubernetes ecosystem.

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