I'm learning about semaphores in the Argo project workflows to avoid concurrent workflows using the same resource.
My use case is that I have several external resources which only one workflow can use one at a time. So far so good, but sometimes the resource needs some maintenance and during that period I don't want to Argo to start any workflow.
I guess I have two options:
I tested manually setting the semaphore value in the configMap to the value 0, but Argo started one workflow anyway.
I can start a workflow that runs forever, until it is deleted, claiming the synchronization lock, but that requires some extra overhead to have workflows running that don't do anything.
So I wonder how it is supposed to work if I set the semaphore value to 0, I think it should not start the workflow then since it says 0. Anyone have any info about this?
This is the steps I carried out:
First I apply my configmap with kubectl -f.
I then submit some workflows and since they all use the same semaphore Argo will start one and the rest will be executed in order one at a time.
I then change value of the semapore with kubectl edit configMap
Submit new job which then Argo will execute.
Perhaps Argo will not reload the configMap when I update the configMap through kubectl edit? I would like to update the configmap programatically in the future but used kubectl edit now for testing.
Quick fix: after applying the ConfigMap change, cycle the workflow-controller pod. That will force it to reload semaphore state.
I couldn't reproduce your exact issue. After using kubectl edit to set the semaphore to 0, any newly submitted workflows remained Pending.
I did encounter an issue where using kubectl edit to bump up the semaphore limit did not automatically kick off any of the Pending workflows. Cycling the workflow controller pod allowed the workflows to start running again.
Besides using the quick fix, I'd recommend submitting an issue. Synchronization is a newer feature, and it's possible it's not 100% robust yet.
Related
Is there a way for a Deployment or Job to completely delete itself upon completion?
I want it such that when I do kubectl get jobs/deployments/pods they don't show up after they finished (container exited).
One possible way I thought of was to call kubectl delete jobs/deployments/pods from within the container but I'm not sure if that's safe.
I found a related question and answer, but found out it is for CronJobs.
Following that answer, there is a TTL Controller introduced Alpha in 1.12 and Beta in 1.21.
You have to manually enable it in Feature Gates before v1.21.
According to the docs:
provides a TTL (time to live) mechanism to limit the lifetime of resource objects that have finished execution. TTL controller only handles Jobs for now, and may be expanded to handle other resources that will finish execution, such as Pods and custom resources.
It could clean up Jobs currently and others in plan.
I have a Kube cronjob that has a concurrencyPolicy of Replace. As I'd have expected, documentation suggests this means if there is a job running when the next cycle in the schedule is met while the previous job is running that the previous job would be killed off / cancelled.
What I want to know is, if I manually kick off a job with kubectl create job --from, does the concurrencyPolicy still play a part? It seems as though the answer is no from the testing I've been doing (and then I'll have multiple concurrent jobs), but would like to confirm.
If I'm correct and they don't work together, is there a way to have this functionality? Basically wanting to be able to deploy a job and then test it without having to wait around for it to kick off, but also don't want to have two jobs running at the same time.
Thanks!
I have a simple containerised python script which I am trying to parallelise with Kubernetes. This script guesses hashes until it finds a hashed value below a certain threshold.
I am only interested in the first such value, so I wish to create a Kubernetes job that spawns n worker pods and completes as soon as one worker pod finds a suitable value.
By default, Kubernetes jobs wait until all worker pods complete before marking the job as complete. I have so far been unable to find a way around this (no mention of this job pattern in the documentation), and have been relying on checking the logs of bare pods via a bash script to determine whether one has completed.
Is there a native means to achieve this? And, if not, what would be the best approach?
Hi look this link https://kubernetes.io/docs/concepts/workloads/controllers/jobs-run-to-completion/#parallel-jobs.
I've never tried it but it seems possible to launch several pods and configure the end of the job when x pods have finished. In your case x is 1.
We can define two specifications for parallel Jobs:
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.
2. 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 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.
You can also take a look on single job which starts controller pod:
This 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.
At the same time take under consideration that completition status of Job set by dafault - when specified number of successful completions is reached it ensure that all tasks are processed properly. Applying this status before all tasks are finished is not secure solution.
You should also know that 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.
Here is official documentations: jobs-parallel-processing , parallel-jobs.
Useful blog: article-parallel job.
EDIT:
Another option is that you can create special script which will continuously check values you look for. Using job then will not be necessary, you can simply use deployment.
Because Kubernetes handles situations where there's a typo in the job spec, and therefore a container image can't be found, by leaving the job in a running state forever, I've got a process that monitors job events to detect cases like this and deletes the job when one occurs.
I'd prefer to just stop the job so there's a record of it. Is there a way to stop a job?
1) According to the K8S documentation here.
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.
Here are the details for the failedJobsHistoryLimit property in the CronJobSpec.
This is another way of retaining the details of the failed job for a specific duration. The failedJobsHistoryLimit property can be set based on the approximate number of jobs run per day and the number of days the logs have to be retained. Agree that the Jobs will be still there and put pressure on the API server.
This is interesting. Once the job completes with failure as in the case of a wrong typo for image, the pod is getting deleted and the resources are not blocked or consumed anymore. Not sure exactly what kubectl job stop will achieve in this case. But, when the Job with a proper image is run with success, I can still see the pod in kubectl get pods.
2) Another approach without using the CronJob is to specify the ttlSecondsAfterFinished as mentioned here.
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.
Not really, no such mechanism exists in Kubernetes yet afaik.
You can workaround is to ssh into the machine and run a: (if you're are using Docker)
# Save the logs
$ docker log <container-id-that-is-running-your-job> 2>&1 > save.log
$ docker stop <main-container-id-for-your-job>
It's better to stream log with something like Fluentd, or logspout, or Filebeat and forward the logs to an ELK or EFK stack.
In any case, I've opened this
You can suspend cronjobs by using the suspend attribute. From the Kubernetes documentation:
https://kubernetes.io/docs/tasks/job/automated-tasks-with-cron-jobs/#suspend
Documentation says:
The .spec.suspend field is also optional. If it is set to true, all
subsequent executions are suspended. This setting does not apply to
already started executions. Defaults to false.
So, to pause a cron you could:
run and edit "suspend" from False to True.
kubectl edit cronjob CRON_NAME (if not in default namespace, then add "-n NAMESPACE_NAME" at the end)
you could potentially create a loop using "for" or whatever you like, and have them all changed at once.
you could just save the yaml file locally and then just run:
kubectl create -f cron_YAML
and this would recreate the cron.
The other answers hint around the .spec.suspend solution for the CronJob API, which works, but since the OP asked specifically about Jobs it is worth noting the solution that does not require a CronJob.
As of Kubernetes 1.21, there alpha support for the .spec.suspend field in the Job API as well, (see docs here). The feature is behind the SuspendJob feature gate.
I am eagerly awaiting the release of Kubernetes v1.3 in mid to late June, so that I can access cron scheduling for jobs. In the meantime, what I plan to do is the following:
Deploy a job on my Kubernetes cluster
Use jenkins as a cron tool to trigger the job in defined intervals (e.g. 1 hour).
I have two questions:
How do I update a job? For replication controllers, I would simply do a rolling update, but in the jobs API spec (http://kubernetes.io/docs/user-guide/jobs/) there are no details about how to do this. For example, lets say that I want to use my jenkins deploy system to update the job whenever I do a git commit.
Is it possible to use the kubernetes API to trigger jobs? For example, I have a job that runs and then the pod is terminated on completion. Then, 1 hour later, I want to use jenkins to trigger the job again.
Thanks so much!
I am not sure if there is any fancy way to trigger a completed job, but one way to do it can be to delete and recreate the job.
Re: rolling-update: that is required for long running pods, which is what RCs control.
For jobs: You can update the podTemplateSpec in jobSpec and that will ensure that any new pod created by the job after the update will have the updated podTemplateSpec (note: already running pods will not be affected).
Hope this helps!