website/content/en/docs/concepts/workloads/controllers/jobs-run-to-completion.md

---
reviewers:
- erictune
- soltysh
title: Jobs - Run to Completion
content_template: templates/concept
feature:
  title: Batch execution
  description: >
    In addition to services, Kubernetes can manage your batch and CI workloads, replacing containers that fail, if desired.
weight: 70
---

{{% capture overview %}}

A Job creates one or more Pods and ensures that a specified number of them successfully terminate.
As pods successfully complete, the Job tracks the successful completions.  When a specified number
of successful completions is reached, the task (ie, Job) is complete.  Deleting a Job will clean up
the Pods it created.

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.

{{% /capture %}}


{{% capture body %}}

## Running an example Job

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

{{< codenew file="controllers/job.yaml" >}}

You can run the example with this command:

```shell
$ kubectl create -f https://k8s.io/examples/controllers/job.yaml
job "pi" created
```

Check on the status of the Job with `kubectl`:

```shell
$ kubectl describe jobs/pi
Name:             pi
Namespace:        default
Selector:         controller-uid=b1db589a-2c8d-11e6-b324-0209dc45a495
Labels:           controller-uid=b1db589a-2c8d-11e6-b324-0209dc45a495
                  job-name=pi
Annotations:      <none>
Parallelism:      1
Completions:      1
Start Time:       Tue, 07 Jun 2016 10:56:16 +0200
Pods Statuses:    0 Running / 1 Succeeded / 0 Failed
Pod Template:
  Labels:       controller-uid=b1db589a-2c8d-11e6-b324-0209dc45a495
                job-name=pi
  Containers:
   pi:
    Image:      perl
    Port:
    Command:
      perl
      -Mbignum=bpi
      -wle
      print bpi(2000)
    Environment:        <none>
    Mounts:             <none>
  Volumes:              <none>
Events:
  FirstSeen    LastSeen    Count    From            SubobjectPath    Type        Reason            Message
  ---------    --------    -----    ----            -------------    --------    ------            -------
  1m           1m          1        {job-controller }                Normal      SuccessfulCreate  Created pod: pi-dtn4q
```

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:

```shell
$ pods=$(kubectl get pods --selector=job-name=pi --output=jsonpath='{.items[*].metadata.name}')
$ echo $pods
pi-aiw0a
```

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

View the standard output of one of the pods:

```shell
$ kubectl logs $pods
3.1415926535897932384626433832795028841971693993751058209749445923078164062862089986280348253421170679821480865132823066470938446095505822317253594081284811174502841027019385211055596446229489549303819644288109756659334461284756482337867831652712019091456485669234603486104543266482133936072602491412737245870066063155881748815209209628292540917153643678925903600113305305488204665213841469519415116094330572703657595919530921861173819326117931051185480744623799627495673518857527248912279381830119491298336733624406566430860213949463952247371907021798609437027705392171762931767523846748184676694051320005681271452635608277857713427577896091736371787214684409012249534301465495853710507922796892589235420199561121290219608640344181598136297747713099605187072113499999983729780499510597317328160963185950244594553469083026425223082533446850352619311881710100031378387528865875332083814206171776691473035982534904287554687311595628638823537875937519577818577805321712268066130019278766111959092164201989380952572010654858632788659361533818279682303019520353018529689957736225994138912497217752834791315155748572424541506959508295331168617278558890750983817546374649393192550604009277016711390098488240128583616035637076601047101819429555961989467678374494482553797747268471040475346462080466842590694912933136770289891521047521620569660240580381501935112533824300355876402474964732639141992726042699227967823547816360093417216412199245863150302861829745557067498385054945885869269956909272107975093029553211653449872027559602364806654991198818347977535663698074265425278625518184175746728909777727938000816470600161452491921732172147723501414419735685481613611573525521334757418494684385233239073941433345477624168625189835694855620992192221842725502542568876717904946016534668049886272327917860857843838279679766814541009538837863609506800642251252051173929848960841284886269456042419652850222106611863067442786220391949450471237137869609563643719172874677646575739624138908658326459958133904780275901
```

## Writing a Job Spec

As with all other Kubernetes config, a Job needs `apiVersion`, `kind`, and `metadata` fields.

A Job also needs a [`.spec` section](https://git.k8s.io/community/contributors/devel/api-conventions.md#spec-and-status).

### Pod Template

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

The `.spec.template` is a [pod template](/docs/concepts/workloads/pods/pod-overview/#pod-templates). It has exactly the same schema as a [pod](/docs/user-guide/pods), 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 [pod selector](#pod-selector)) and an appropriate restart policy.

Only a [`RestartPolicy`](/docs/concepts/workloads/pods/pod-lifecycle/#restart-policy) 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](#specifying-your-own-pod-selector).


### Parallel 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.
1. 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 is one successful Pod for each value in the range 1 to `.spec.completions`.
  - **not implemented yet:** Each Pod is passed a different index in the range 1 to `.spec.completions`.
1. 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](#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 controller has not had time to react.
- If the controller failed to create Pods for any reason (lack of `ResourceQuota`, lack of permission, etc.),
  then there may be fewer pods than requested.
- The 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.

## 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](/docs/concepts/workloads/pods/pod-lifecycle/#example-states) 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.

### Pod backoff failure policy

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 if no new failed Pods appear before the Job's next
status check.

{{< note >}}
Issue [#54870](https://github.com/kubernetes/kubernetes/issues/54870) still exists for versions of Kubernetes prior to version 1.12
{{< /note >}}

## Job Termination and Cleanup

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, at which point the Job defers to the
`.spec.backoffLimit` described above. 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 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:

```yaml
apiVersion: batch/v1
kind: Job
metadata:
  name: pi-with-timeout
spec:
  backoffLimit: 5
  activeDeadlineSeconds: 100
  template:
    spec:
      containers:
      - name: pi
        image: perl
        command: ["perl",  "-Mbignum=bpi", "-wle", "print bpi(2000)"]
      restartPolicy: Never
```

Note that both the Job spec and the [Pod template spec](https://kubernetes.io/docs/concepts/workloads/pods/init-containers/#detailed-behavior) within the Job have an `activeDeadlineSeconds` field. Ensure that you set this field at the proper level.

## 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](/docs/concepts/workloads/controllers/cron-jobs/), the Jobs can be
cleaned up by CronJobs based on the specified capacity-based cleanup policy.

### TTL Mechanism for Finished Jobs

{{< feature-state for_k8s_version="v1.12" state="alpha" >}}

Another way to clean up finished Jobs (either `Complete` or `Failed`)
automatically is to use a TTL mechanism provided by a
[TTL controller](/docs/concepts/workloads/controllers/ttlafterfinished/) 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:

```yaml
apiVersion: batch/v1
kind: Job
metadata:
  name: pi-with-ttl
spec:
  ttlSecondsAfterFinished: 100
  template:
    spec:
      containers:
      - name: pi
        image: perl
        command: ["perl",  "-Mbignum=bpi", "-wle", "print bpi(2000)"]
      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 that this TTL mechanism is alpha, with feature gate `TTLAfterFinished`. For
more information, see the documentation for
[TTL controller](/docs/concepts/workloads/controllers/ttlafterfinished/) for
finished resources.

## 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 &mdash; 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.

|                            Pattern                                   | Single Job object | Fewer pods than work items? | Use app unmodified? |  Works in Kube 1.1? |
| -------------------------------------------------------------------- |:-----------------:|:---------------------------:|:-------------------:|:-------------------:|
| [Job Template Expansion](/docs/tasks/job/parallel-processing-expansion/)            |                   |                             |          ✓          |          ✓          |
| [Queue with Pod Per Work Item](/docs/tasks/job/coarse-parallel-processing-work-queue/)   |         ✓         |                             |      sometimes      |          ✓          |
| [Queue with Variable Pod Count](/docs/tasks/job/fine-parallel-processing-work-queue/)  |         ✓         |             ✓               |                     |          ✓          |
| Single Job with Static Work Assignment                               |         ✓         |                             |          ✓          |                     |

When you specify completions with `.spec.completions`, each Pod created by the Job controller
has an identical [`spec`](https://git.k8s.io/community/contributors/devel/api-conventions.md#spec-and-status).  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 `.spec.parallelism` and `.spec.completions` for each of the patterns.
Here, `W` is the number of work items.

|                             Pattern                                  | `.spec.completions` |  `.spec.parallelism` |
| -------------------------------------------------------------------- |:-------------------:|:--------------------:|
| [Job Template Expansion](/docs/tasks/job/parallel-processing-expansion/)           |          1          |     should be 1      |
| [Queue with Pod Per Work Item](/docs/tasks/job/coarse-parallel-processing-work-queue/)   |          W          |        any           |
| [Queue with Variable Pod Count](/docs/tasks/job/fine-parallel-processing-work-queue/)  |          1          |        any           |
| Single Job with Static Work Assignment                               |          W          |        any           |


## Advanced Usage

### Specifying your own pod selector

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=false`.
Before deleting it, you make a note of what selector it uses:

```
kind: Job
metadata:
  name: old
  ...
spec:
  selector:
    matchLabels:
      job-uid: a8f3d00d-c6d2-11e5-9f87-42010af00002
  ...
```

Then you create a new Job with name `new` and you explicitly specify the same selector.
Since the existing Pods have label `job-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.

```
kind: Job
metadata:
  name: new
  ...
spec:
  manualSelector: true
  selector:
    matchLabels:
      job-uid: a8f3d00d-c6d2-11e5-9f87-42010af00002
  ...
```

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.

## 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](/docs/user-guide/replication-controller).
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 [Pod Lifecycle](/docs/concepts/workloads/pods/pod-lifecycle/), `Job` is *only* appropriate
for pods with `RestartPolicy` equal to `OnFailure` or `Never`.
(Note: If `RestartPolicy` is not set, the default value is `Always`.)

### Single Job starts Controller Pod

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](https://github.com/kubernetes/examples/tree/{{< param "githubbranch" >}}/staging/spark/README.md)), runs a spark
driver, and then cleans up.

An advantage of this approach is that the overall process gets the completion guarantee of a Job
object, but complete control over what Pods are created and how work is assigned to them.

## Cron Jobs {#cron-jobs}

You can use a [`CronJob`](/docs/concepts/workloads/controllers/cron-jobs/) to create a Job that will run at specified times/dates, similar to the Unix tool `cron`.

{{% /capture %}}