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Jobs

• What is a job?
• Running an example Job
• Writing a Job Spec
  • Pod Template
  • Pod Selector
  • Parallel Jobs
    • Controlling Parallelism
• Handling Pod and Container Failures
• Job Termination and Cleanup
• Job Patterns
• Advanced Usage
  • Specifying your own pod selector
• Alternatives
  • Bare Pods
  • Replication Controller
  • Single Job starts Controller Pod
• Future work

What is a job?

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 job itself is complete. Deleting a Job will cleanup 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).

A Job can also be used to run multiple pods in parallel.

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.

job.yaml
apiVersion: batch/v1 kind: Job metadata: name: pi spec: template: metadata: name: pi spec: containers: - name: pi image: perl command: ["perl", "-Mbignum=bpi", "-wle", "print bpi(2000)"] restartPolicy: Never

Run the example job by downloading the example file and then running this command:

$ kubectl create -f ./job.yaml
job "pi" created

Check on the status of the job using this command:

$ kubectl describe jobs/pi
Name:		pi
Namespace:	default
Image(s):	perl
Selector:	app in (pi)
Parallelism:	1
Completions:	1
Start Time:	Mon, 11 Jan 2016 15:35:52 -0800
Labels:		app=pi
Pods Statuses:	0 Running / 1 Succeeded / 0 Failed
No volumes.
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 --show-all. The --show-all will show completed pods too.

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

$ pods=$(kubectl get pods --selector=app=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:

$ kubectl logs $pods
3.1415926535897932384626433832795028841971693993751058209749445923078164062862089986280348253421170679821480865132823066470938446095505822317253594081284811174502841027019385211055596446229489549303819644288109756659334461284756482337867831652712019091456485669234603486104543266482133936072602491412737245870066063155881748815209209628292540917153643678925903600113305305488204665213841469519415116094330572703657595919530921861173819326117931051185480744623799627495673518857527248912279381830119491298336733624406566430860213949463952247371907021798609437027705392171762931767523846748184676694051320005681271452635608277857713427577896091736371787214684409012249534301465495853710507922796892589235420199561121290219608640344181598136297747713099605187072113499999983729780499510597317328160963185950244594553469083026425223082533446850352619311881710100031378387528865875332083814206171776691473035982534904287554687311595628638823537875937519577818577805321712268066130019278766111959092164201989380952572010654858632788659361533818279682303019520353018529689957736225994138912497217752834791315155748572424541506959508295331168617278558890750983817546374649393192550604009277016711390098488240128583616035637076601047101819429555961989467678374494482553797747268471040475346462080466842590694912933136770289891521047521620569660240580381501935112533824300355876402474964732639141992726042699227967823547816360093417216412199245863150302861829745557067498385054945885869269956909272107975093029553211653449872027559602364806654991198818347977535663698074265425278625518184175746728909777727938000816470600161452491921732172147723501414419735685481613611573525521334757418494684385233239073941433345477624168625189835694855620992192221842725502542568876717904946016534668049886272327917860857843838279679766814541009538837863609506800642251252051173929848960841284886269456042419652850222106611863067442786220391949450471237137869609563643719172874677646575739624138908658326459958133904780275901

Writing a Job Spec

As with all other Kubernetes config, a Job needs apiVersion, kind, and metadata fields. For general information about working with config files, see here, here, and here.

A Job also needs a .spec section.

Pod Template

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

The .spec.template is a pod template. 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 pod selector) and an appropriate restart policy.

Only a RestartPolicy equal to Never or OnFailure are 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 Jobs

There are three main types of jobs:

1. Non-parallel Jobs
   • normally only one pod is started, unless the pod fails.
   • job is complete as soon as Pod terminates successfully.
2. Parallel Jobs with a fixed completion count:
   • specify a non-zero positive value for .spec.completions
   • the job is complete when there is one successful pod for each value in the range 1 to .spec.completions.
   • not implemented yet: each pod passed a different index in the range 1 to .spec.completions.
3. Parallel Jobs with a work queue:
   • do not specify .spec.completions
   • the pods must coordinate with themselves or an external service to determine what each should work on
   • each pod is independently capable of determining whether or not all its peers are done, thus the entire Job is done.
   • when any pod 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 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.

A job can be scaled up using the kubectl scale command. For example, the following command sets .spec.parallelism of a job called myjob to 10:

$ kubectl scale  --replicas=$N jobs/myjob
job "myjob" scaled

You can also use the scale subresource of the Job resource.

Actual parallelism (number of pods running at any instant) may be more or less than requested parallelism, for a variety or 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 shutdown, it make take 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.containers[].restartPolicy = "OnFailure", then the Pod stays on the node, but the Container is re-run. Therefore, your program needs to handle the the case when it is restarted locally, or else specify .spec.template.containers[].restartPolicy = "Never". See pods-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.containers[].restartPolicy = "Never". When a Pod fails, then the Job controller starts a new Pod. Therefore, your program 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.containers[].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.

Job Termination and Cleanup

When a Job completes, no more Pods are created, but the Pods are not deleted either. Since they are terminated, they don’t show up with kubectl get pods, but they will show up with kubectl get pods -a. 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.

If a Job’s pods are failing repeatedly, the Job will keep creating new pods forever, by default. Retrying forever can be a useful pattern. If an external dependency of the Job’s pods is missing (for example an input file on a networked storage volume is not present), then the Job will keep trying Pods, and when you later resolve the external dependency (for example, creating the missing file) the Job will then complete without any further action.

However, if you prefer not to retry forever, you can set a deadline on the job. Do this by setting the spec.activeDeadlineSeconds field of the job to a number of seconds. The job will have status with reason: DeadlineExceeded. No more pods will be created, and existing pods will be deleted.

apiVersion: batch/v1
kind: Job
metadata:
  name: pi-with-timeout
spec:
  activeDeadlineSeconds: 100
  template:
    metadata:
      name: pi
    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 within the Job have a field with the same name. Set the one on the Job.

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. Also, with the latter, the resource usage of the job (number of concurrently running pods) can be easily adjusted using the kubectl scale command.
• 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 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.

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 of the 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 unpredicatable 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-one --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. 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 jobs).

As discussed in life of a pod, 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), 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.

Future work

Support for creating Jobs at specified times/dates (i.e. cron) is expected in 1.3.