Jobs

    A simple case is to create one Job object in order to reliably run one Pod to completion. The Job object will start a new Pod if the first Pod fails or is deleted (for example due to a node hardware failure or a node reboot).

    You can also use a Job to run multiple Pods in parallel.

    If you want to run a Job (either a single task, or several in parallel) on a schedule, see CronJob.

    Here is an example Job config. It computes π to 2000 places and prints it out. It takes around 10s to complete.

    You can run the example with this command:

    The output is similar to this:

    1. job.batch/pi created

    Check on the status of the Job with kubectl:

    1. kubectl describe jobs/pi

    The output is similar to this:

    1. Name:           pi
    2. Namespace:      default
    3. Selector:       controller-uid=c9948307-e56d-4b5d-8302-ae2d7b7da67c
    4. Labels:         controller-uid=c9948307-e56d-4b5d-8302-ae2d7b7da67c
    5.                 job-name=pi
    6. Annotations:    kubectl.kubernetes.io/last-applied-configuration:
    7.                   {"apiVersion":"batch/v1","kind":"Job","metadata":{"annotations":{},"name":"pi","namespace":"default"},"spec":{"backoffLimit":4,"template":...
    8. Parallelism:    1
    9. Completions:    1
    10. Start Time:     Mon, 02 Dec 2019 15:20:11 +0200
    11. Completed At:   Mon, 02 Dec 2019 15:21:16 +0200
    12. Duration:       65s
    13. Pods Statuses:  0 Running / 1 Succeeded / 0 Failed
    14. Pod Template:
    15.   Labels:  controller-uid=c9948307-e56d-4b5d-8302-ae2d7b7da67c
    16.            job-name=pi
    17.   Containers:
    18.    pi:
    19.     Image:      perl
    20.     Port:       <none>
    21.     Host Port:  <none>
    22.     Command:
    23.       perl
    24.       -Mbignum=bpi
    25.       -wle
    26.       print bpi(2000)
    27.     Environment:  <none>
    28.     Mounts:       <none>
    29. Events:
    30.   Type    Reason            Age   From            Message
    31.   ----    ------            ----  ----            -------
    32.   Normal  SuccessfulCreate  14m   job-controller  Created pod: pi-5rwd7

    To view completed Pods of a Job, use kubectl get pods.

    To list all the Pods that belong to a Job in a machine readable form, you can use a command like this:

    1. pods=$(kubectl get pods --selector=job-name=pi --output=jsonpath='{.items[*].metadata.name}')
    2. echo $pods

    The output is similar to this:

    Here, the selector is the same as the selector for the Job. The --output=jsonpath option specifies an expression with the name from each Pod in the returned list.

    View the standard output of one of the pods:

    1. kubectl logs $pods

    The output is similar to this:

    1. 3.1415926535897932384626433832795028841971693993751058209749445923078164062862089986280348253421170679821480865132823066470938446095505822317253594081284811174502841027019385211055596446229489549303819644288109756659334461284756482337867831652712019091456485669234603486104543266482133936072602491412737245870066063155881748815209209628292540917153643678925903600113305305488204665213841469519415116094330572703657595919530921861173819326117931051185480744623799627495673518857527248912279381830119491298336733624406566430860213949463952247371907021798609437027705392171762931767523846748184676694051320005681271452635608277857713427577896091736371787214684409012249534301465495853710507922796892589235420199561121290219608640344181598136297747713099605187072113499999983729780499510597317328160963185950244594553469083026425223082533446850352619311881710100031378387528865875332083814206171776691473035982534904287554687311595628638823537875937519577818577805321712268066130019278766111959092164201989380952572010654858632788659361533818279682303019520353018529689957736225994138912497217752834791315155748572424541506959508295331168617278558890750983817546374649393192550604009277016711390098488240128583616035637076601047101819429555961989467678374494482553797747268471040475346462080466842590694912933136770289891521047521620569660240580381501935112533824300355876402474964732639141992726042699227967823547816360093417216412199245863150302861829745557067498385054945885869269956909272107975093029553211653449872027559602364806654991198818347977535663698074265425278625518184175746728909777727938000816470600161452491921732172147723501414419735685481613611573525521334757418494684385233239073941433345477624168625189835694855620992192221842725502542568876717904946016534668049886272327917860857843838279679766814541009538837863609506800642251252051173929848960841284886269456042419652850222106611863067442786220391949450471237137869609563643719172874677646575739624138908658326459958133904780275901

    Writing a Job spec

    As with all other Kubernetes config, a Job needs apiVersion, kind, and metadata fields. Its name must be a valid .

    A Job also needs a .spec section.

    The .spec.template is the only required field of the .spec.

    The .spec.template is a . It has exactly the same schema as a Pod, except it is nested and does not have an apiVersion or kind.

    In addition to required fields for a Pod, a pod template in a Job must specify appropriate labels (see ) and an appropriate restart policy.

    Only a RestartPolicy equal to Never or OnFailure is allowed.

    Pod selector

    The .spec.selector field is optional. In almost all cases you should not specify it. See section specifying your own pod selector.

    Parallel execution for Jobs

    There are three main types of task suitable to run as a Job:

    1. Non-parallel Jobs
      • normally, only one Pod is started, unless the Pod fails.
      • the Job is complete as soon as its Pod terminates successfully.
    2. Parallel Jobs with a fixed completion count:
      • specify a non-zero positive value for .spec.completions.
      • the Job represents the overall task, and is complete when there are .spec.completions successful Pods.
      • when using .spec.completionMode="Indexed", each Pod gets a different index in the range 0 to .spec.completions-1.
    3. Parallel Jobs with a work queue:
      • do not specify .spec.completions, default to .spec.parallelism.
      • the Pods must coordinate amongst themselves or an external service to determine what each should work on. For example, a Pod might fetch a batch of up to N items from the work queue.
      • each Pod is independently capable of determining whether or not all its peers are done, and thus that the entire Job is done.
      • when any Pod from the Job terminates with success, no new Pods are created.
      • once at least one Pod has terminated with success and all Pods are terminated, then the Job is completed with success.
      • once any Pod has exited with success, no other Pod should still be doing any work for this task or writing any output. They should all be in the process of exiting.

    For a non-parallel Job, you can leave both .spec.completions and .spec.parallelism unset. When both are unset, both are defaulted to 1.

    For a fixed completion count Job, you should set .spec.completions to the number of completions needed. You can set .spec.parallelism, or leave it unset and it will default to 1.

    For a work queue Job, you must leave .spec.completions unset, and set .spec.parallelism to a non-negative integer.

    For more information about how to make use of the different types of job, see the job patterns section.

    Controlling parallelism

    The requested parallelism (.spec.parallelism) can be set to any non-negative value. If it is unspecified, it defaults to 1. If it is specified as 0, then the Job is effectively paused until it is increased.

    Actual parallelism (number of pods running at any instant) may be more or less than requested parallelism, for a variety of reasons:

    • For fixed completion count Jobs, the actual number of pods running in parallel will not exceed the number of remaining completions. Higher values of .spec.parallelism are effectively ignored.
    • For work queue Jobs, no new Pods are started after any Pod has succeeded — remaining Pods are allowed to complete, however.
    • If the Job Controller has not had time to react.
    • If the Job controller failed to create Pods for any reason (lack of ResourceQuota, lack of permission, etc.), then there may be fewer pods than requested.
    • The Job controller may throttle new Pod creation due to excessive previous pod failures in the same Job.
    • When a Pod is gracefully shut down, it takes time to stop.

    Completion mode

    FEATURE STATE: Kubernetes v1.22 [beta]

    Jobs with fixed completion count - that is, jobs that have non null .spec.completions - can have a completion mode that is specified in .spec.completionMode:

    • NonIndexed (default): the Job is considered complete when there have been .spec.completions successfully completed Pods. In other words, each Pod completion is homologous to each other. Note that Jobs that have null .spec.completions are implicitly NonIndexed.

    • : the Pods of a Job get an associated completion index from 0 to .spec.completions-1. The index is available through three mechanisms:

      • The Pod annotation batch.kubernetes.io/job-completion-index.
      • As part of the Pod hostname, following the pattern $(job-name)-$(index). When you use an Indexed Job in combination with a Service, Pods within the Job can use the deterministic hostnames to address each other via DNS.
      • From the containarized task, in the environment variable JOB_COMPLETION_INDEX.

    Handling Pod and container failures

    A container in a Pod may fail for a number of reasons, such as because the process in it exited with a non-zero exit code, or the container was killed for exceeding a memory limit, etc. If this happens, and the .spec.template.spec.restartPolicy = "OnFailure", then the Pod stays on the node, but the container is re-run. Therefore, your program needs to handle the case when it is restarted locally, or else specify .spec.template.spec.restartPolicy = "Never". See pod lifecycle for more information on restartPolicy.

    An entire Pod can also fail, for a number of reasons, such as when the pod is kicked off the node (node is upgraded, rebooted, deleted, etc.), or if a container of the Pod fails and the .spec.template.spec.restartPolicy = "Never". When a Pod fails, then the Job controller starts a new Pod. This means that your application needs to handle the case when it is restarted in a new pod. In particular, it needs to handle temporary files, locks, incomplete output and the like caused by previous runs.

    Note that even if you specify .spec.parallelism = 1 and .spec.completions = 1 and .spec.template.spec.restartPolicy = "Never", the same program may sometimes be started twice.

    If you do specify .spec.parallelism and .spec.completions both greater than 1, then there may be multiple pods running at once. Therefore, your pods must also be tolerant of concurrency.

    There are situations where you want to fail a Job after some amount of retries due to a logical error in configuration etc. To do so, set .spec.backoffLimit to specify the number of retries before considering a Job as failed. The back-off limit is set by default to 6. Failed Pods associated with the Job are recreated by the Job controller with an exponential back-off delay (10s, 20s, 40s …) capped at six minutes. The back-off count is reset when a Job’s Pod is deleted or successful without any other Pods for the Job failing around that time.

    Note: If your job has restartPolicy = "OnFailure", keep in mind that your Pod running the Job will be terminated once the job backoff limit has been reached. This can make debugging the Job’s executable more difficult. We suggest setting restartPolicy = "Never" when debugging the Job or using a logging system to ensure output from failed Jobs is not lost inadvertently.

    When a Job completes, no more Pods are created, but the Pods are not deleted either. Keeping them around allows you to still view the logs of completed pods to check for errors, warnings, or other diagnostic output. The job object also remains after it is completed so that you can view its status. It is up to the user to delete old jobs after noting their status. Delete the job with kubectl (e.g. kubectl delete jobs/pi or kubectl delete -f ./job.yaml). When you delete the job using kubectl, all the pods it created are deleted too.

    By default, a Job will run uninterrupted unless a Pod fails (restartPolicy=Never) or a Container exits in error (restartPolicy=OnFailure), at which point the Job defers to the .spec.backoffLimit described above. Once .spec.backoffLimit has been reached the Job will be marked as failed and any running Pods will be terminated.

    Another way to terminate a Job is by setting an active deadline. Do this by setting the .spec.activeDeadlineSeconds field of the Job to a number of seconds. The activeDeadlineSeconds applies to the duration of the job, no matter how many Pods are created. Once a Job reaches activeDeadlineSeconds, all of its running Pods are terminated and the Job status will become type: Failed with reason: DeadlineExceeded.

    Note that a Job’s .spec.activeDeadlineSeconds takes precedence over its .spec.backoffLimit. Therefore, a Job that is retrying one or more failed Pods will not deploy additional Pods once it reaches the time limit specified by activeDeadlineSeconds, even if the backoffLimit is not yet reached.

    Example:

    1. apiVersion: batch/v1
    2. kind: Job
    3. metadata:
    4.   name: pi-with-timeout
    5. spec:
    6.   backoffLimit: 5
    7.   activeDeadlineSeconds: 100
    8.   template:
    9.     spec:
    10.       containers:
    11.       - name: pi
    12.         image: perl
    13.         command: ["perl",  "-Mbignum=bpi", "-wle", "print bpi(2000)"]
    14.       restartPolicy: Never

    Note that both the Job spec and the Pod template spec within the Job have an activeDeadlineSeconds field. Ensure that you set this field at the proper level.

    Keep in mind that the restartPolicy applies to the Pod, and not to the Job itself: there is no automatic Job restart once the Job status is type: Failed. That is, the Job termination mechanisms activated with .spec.activeDeadlineSeconds and .spec.backoffLimit result in a permanent Job failure that requires manual intervention to resolve.

    Clean up finished jobs automatically

    Finished Jobs are usually no longer needed in the system. Keeping them around in the system will put pressure on the API server. If the Jobs are managed directly by a higher level controller, such as CronJobs, the Jobs can be cleaned up by CronJobs based on the specified capacity-based cleanup policy.

    TTL mechanism for finished Jobs

    FEATURE STATE: Kubernetes v1.21 [beta]

    Another way to clean up finished Jobs (either Complete or Failed) automatically is to use a TTL mechanism provided by a TTL controller for finished resources, by specifying the .spec.ttlSecondsAfterFinished field of the Job.

    When the TTL controller cleans up the Job, it will delete the Job cascadingly, i.e. delete its dependent objects, such as Pods, together with the Job. Note that when the Job is deleted, its lifecycle guarantees, such as finalizers, will be honored.

    For example:

    1. apiVersion: batch/v1
    2. kind: Job
    3. metadata:
    4.   name: pi-with-ttl
    5. spec:
    6.   ttlSecondsAfterFinished: 100
    7.   template:
    8.       containers:
    9.       - name: pi
    10.         image: perl
    11.         command: ["perl",  "-Mbignum=bpi", "-wle", "print bpi(2000)"]
    12.       restartPolicy: Never

    The Job pi-with-ttl will be eligible to be automatically deleted, 100 seconds after it finishes.

    If the field is set to 0, the Job will be eligible to be automatically deleted immediately after it finishes. If the field is unset, this Job won’t be cleaned up by the TTL controller after it finishes.

    Note:

    It is recommended to set ttlSecondsAfterFinished field because unmanaged jobs (Jobs that you created directly, and not indirectly through other workload APIs such as CronJob) have a default deletion policy of orphanDependents causing Pods created by an unmanaged Job to be left around after that Job is fully deleted. Even though the eventually garbage collects the Pods from a deleted Job after they either fail or complete, sometimes those lingering pods may cause cluster performance degradation or in worst case cause the cluster to go offline due to this degradation.

    You can use and ResourceQuotas to place a cap on the amount of resources that a particular namespace can consume.

    Job patterns

    The Job object can be used to support reliable parallel execution of Pods. The Job object is not designed to support closely-communicating parallel processes, as commonly found in scientific computing. It does support parallel processing of a set of independent but related work items. These might be emails to be sent, frames to be rendered, files to be transcoded, ranges of keys in a NoSQL database to scan, and so on.

    In a complex system, there may be multiple different sets of work items. Here we are just considering one set of work items that the user wants to manage together — a batch job.

    There are several different patterns for parallel computation, each with strengths and weaknesses. The tradeoffs are:

    • One Job object for each work item, vs. a single Job object for all work items. The latter is better for large numbers of work items. The former creates some overhead for the user and for the system to manage large numbers of Job objects.
    • Number of pods created equals number of work items, vs. each Pod can process multiple work items. The former typically requires less modification to existing code and containers. The latter is better for large numbers of work items, for similar reasons to the previous bullet.
    • Several approaches use a work queue. This requires running a queue service, and modifications to the existing program or container to make it use the work queue. Other approaches are easier to adapt to an existing containerised application.

    The tradeoffs are summarized here, with columns 2 to 4 corresponding to the above tradeoffs. The pattern names are also links to examples and more detailed description.

    When you specify completions with .spec.completions, each Pod created by the Job controller has an identical spec. This means that all pods for a task will have the same command line and the same image, the same volumes, and (almost) the same environment variables. These patterns are different ways to arrange for pods to work on different things.

    This table shows the required settings for and .spec.completions for each of the patterns. Here, W is the number of work items.

    Pattern.spec.completions.spec.parallelism
    Wany
    Queue with Variable Pod Countnullany
    Wany
    Job Template Expansion1should be 1

    Suspending a Job

    FEATURE STATE: Kubernetes v1.22 [beta]

    Note: In Kubernetes version 1.21, this feature was in alpha, which required additional steps to enable this feature; make sure to read the right documentation for the version of Kubernetes you’re using.

    When a Job is created, the Job controller will immediately begin creating Pods to satisfy the Job’s requirements and will continue to do so until the Job is complete. However, you may want to temporarily suspend a Job’s execution and resume it later, or start Jobs in suspended state and have a custom controller decide later when to start them.

    To suspend a Job, you can update the .spec.suspend field of the Job to true; later, when you want to resume it again, update it to false. Creating a Job with .spec.suspend set to true will create it in the suspended state.

    Remember that suspending a Job will delete all active Pods. When the Job is suspended, your with a SIGTERM signal. The Pod’s graceful termination period will be honored and your Pod must handle this signal in this period. This may involve saving progress for later or undoing changes. Pods terminated this way will not count towards the Job’s completions count.

    An example Job definition in the suspended state can be like so:

    1. kubectl get job myjob -o yaml

    The Job’s status can be used to determine if a Job is suspended or has been suspended in the past:

    1. kubectl get jobs/myjob -o yaml
    1. apiVersion: batch/v1
    2. kind: Job
    3. # .metadata and .spec omitted
    4. status:
    5.   conditions:
    6.   - lastProbeTime: "2021-02-05T13:14:33Z"
    7.     lastTransitionTime: "2021-02-05T13:14:33Z"
    8.     status: "True"
    9.     type: Suspended
    10.   startTime: "2021-02-05T13:13:48Z"

    The Job condition of type “Suspended” with status “True” means the Job is suspended; the lastTransitionTime field can be used to determine how long the Job has been suspended for. If the status of that condition is “False”, then the Job was previously suspended and is now running. If such a condition does not exist in the Job’s status, the Job has never been stopped.

    Events are also created when the Job is suspended and resumed:

    1. kubectl describe jobs/myjob
    1. Name:           myjob
    2. ...
    3. Events:
    4.   Type    Reason            Age   From            Message
    5.   ----    ------            ----  ----            -------
    6.   Normal  SuccessfulCreate  12m   job-controller  Created pod: myjob-hlrpl
    7.   Normal  SuccessfulDelete  11m   job-controller  Deleted pod: myjob-hlrpl
    8.   Normal  Suspended         11m   job-controller  Job suspended
    9.   Normal  SuccessfulCreate  3s    job-controller  Created pod: myjob-jvb44
    10.   Normal  Resumed           3s    job-controller  Job resumed

    The last four events, particularly the “Suspended” and “Resumed” events, are directly a result of toggling the .spec.suspend field. In the time between these two events, we see that no Pods were created, but Pod creation restarted as soon as the Job was resumed.

    Mutable Scheduling Directives

    FEATURE STATE: Kubernetes v1.23 [beta]

    Note: In order to use this behavior, you must enable the JobMutableNodeSchedulingDirectives on the API server. It is enabled by default.

    In most cases a parallel job will want the pods to run with constraints, like all in the same zone, or all either on GPU model x or y but not a mix of both.

    The field is the first step towards achieving those semantics. Suspend allows a custom queue controller to decide when a job should start; However, once a job is unsuspended, a custom queue controller has no influence on where the pods of a job will actually land.

    This feature allows updating a Job’s scheduling directives before it starts, which gives custom queue controllers the ability to influence pod placement while at the same time offloading actual pod-to-node assignment to kube-scheduler. This is allowed only for suspended Jobs that have never been unsuspended before.

    The fields in a Job’s pod template that can be updated are node affinity, node selector, tolerations, labels and annotations.

    Normally, when you create a Job object, you do not specify .spec.selector. The system defaulting logic adds this field when the Job is created. It picks a selector value that will not overlap with any other jobs.

    However, in some cases, you might need to override this automatically set selector. To do this, you can specify the .spec.selector of the Job.

    Be very careful when doing this. If you specify a label selector which is not unique to the pods of that Job, and which matches unrelated Pods, then pods of the unrelated job may be deleted, or this Job may count other Pods as completing it, or one or both Jobs may refuse to create Pods or run to completion. If a non-unique selector is chosen, then other controllers (e.g. ReplicationController) and their Pods may behave in unpredictable ways too. Kubernetes will not stop you from making a mistake when specifying .spec.selector.

    Here is an example of a case when you might want to use this feature.

    Say Job old is already running. You want existing Pods to keep running, but you want the rest of the Pods it creates to use a different pod template and for the Job to have a new name. You cannot update the Job because these fields are not updatable. Therefore, you delete Job old but leave its pods running, using kubectl delete jobs/old --cascade=orphan. Before deleting it, you make a note of what selector it uses:

    1. kubectl get job old -o yaml

    The output is similar to this:

    Then you create a new Job with name new and you explicitly specify the same selector. Since the existing Pods have label controller-uid=a8f3d00d-c6d2-11e5-9f87-42010af00002, they are controlled by Job new as well.

    You need to specify manualSelector: true in the new Job since you are not using the selector that the system normally generates for you automatically.

    1. kind: Job
    2. metadata:
    3.   name: new
    4.   ...
    5. spec:
    6.   manualSelector: true
    7.   selector:
    8.     matchLabels:
    9.       controller-uid: a8f3d00d-c6d2-11e5-9f87-42010af00002
    10.   ...

    The new Job itself will have a different uid from a8f3d00d-c6d2-11e5-9f87-42010af00002. Setting manualSelector: true tells the system to that you know what you are doing and to allow this mismatch.

    Job tracking with finalizers

    FEATURE STATE: Kubernetes v1.23 [beta]

    Note:

    In order to use this behavior, you must enable the JobTrackingWithFinalizers on the API server and the . It is enabled by default.

    When enabled, the control plane tracks new Jobs using the behavior described below. Jobs created before the feature was enabled are unaffected. As a user, the only difference you would see is that the control plane tracking of Job completion is more accurate.

    When this feature isn’t enabled, the Job Controller relies on counting the Pods that exist in the cluster to track the Job status, that is, to keep the counters for succeeded and failed Pods. However, Pods can be removed for a number of reasons, including:

    • The garbage collector that removes orphan Pods when a Node goes down.
    • The garbage collector that removes finished Pods (in Succeeded or Failed phase) after a threshold.
    • Human intervention to delete Pods belonging to a Job.
    • An external controller (not provided as part of Kubernetes) that removes or replaces Pods.

    If you enable the JobTrackingWithFinalizers feature for your cluster, the control plane keeps track of the Pods that belong to any Job and notices if any such Pod is removed from the API server. To do that, the Job controller creates Pods with the finalizer batch.kubernetes.io/job-tracking. The controller removes the finalizer only after the Pod has been accounted for in the Job status, allowing the Pod to be removed by other controllers or users.

    The Job controller uses the new algorithm for new Jobs only. Jobs created before the feature is enabled are unaffected. You can determine if the Job controller is tracking a Job using Pod finalizers by checking if the Job has the annotation batch.kubernetes.io/job-tracking. You should not manually add or remove this annotation from Jobs.

    Alternatives

    Bare Pods

    When the node that a Pod is running on reboots or fails, the pod is terminated and will not be restarted. However, a Job will create new Pods to replace terminated ones. For this reason, we recommend that you use a Job rather than a bare Pod, even if your application requires only a single Pod.

    Replication Controller

    Jobs are complementary to Replication Controllers. A Replication Controller manages Pods which are not expected to terminate (e.g. web servers), and a Job manages Pods that are expected to terminate (e.g. batch tasks).

    As discussed in , Job is only appropriate for pods with RestartPolicy equal to OnFailure or Never. (Note: If RestartPolicy is not set, the default value is Always.)

    Another pattern is for a single Job to create a Pod which then creates other Pods, acting as a sort of custom controller for those Pods. This allows the most flexibility, but may be somewhat complicated to get started with and offers less integration with Kubernetes.

    One example of this pattern would be a Job which starts a Pod which runs a script that in turn starts a Spark master controller (see spark example), runs a spark driver, and then cleans up.

    What’s next