After running for 17 hours, my Dataflow job failed with the following message:
The job failed because a work item has failed 4 times. Look in previous log entries for the cause of each one of the 4 failures.
The 4 failures consist of 3 workers losing contact with the service, and one worker reported dead:
****-q15f Root cause: The worker lost contact with the service.
****-pq33 Root cause: The worker lost contact with the service.
****-fzdp Root cause: The worker ****-fzdp has been reported dead. Aborting lease 4624388267005979538.
****-nd4r Root cause: The worker lost contact with the service.
I don't see any errors in the worker logs for the job in Stackdriver. Is this just bad luck? I don't know how frequently work items need to be retried, so I don't know what the probability is that a single work item will fail 4 times over the course of a 24 hour job. But this same type of job failure happens frequently for this long-running job, so it seems like we need some way to either decrease the failure rate of work items, or increase the allowed number of retries. Is either possible? This doesn't seem related to my pipeline code, but in case it's relevant, I'm using the Python SDK with apache-beam==2.15.0. I'd appreciate any advice on how to debug this.
Update: The "STACK TRACES" section in the console is totally empty.
I was having the same problem and it was solved by scaling up my workers resources. Specifically, I set --machine_type=n1-highcpu-96 in my pipeline configs. See this for a more extensive list on machine type options.
Edit: Set it to highcpu or highmem depending on the requirements of your pipeline process
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We have a service running that orchestrates starting Fargate ECS tasks on messages from a RabbitMQ-queue. Sometimes tasks weirdly fail to start.
Info:
It starts a task somewhere between every other minute and every ten minutes.
It uses a set amount of task definitions. It re-uses the task definitions.
It consistently uses the same subnet in the same VPC.
The problem:
The vast majority of tasks starts fine. Say 98%. Sometimes tasks fail to start, and I get error messages. The error messages are not always the same, but they seem to be network-related.
Error messages I have gotten the last 36 hours:
'Timeout waiting for network interface provisioning to complete.'
'ResourceInitializationError: failed to configure ENI: failed to setup regular eni: netplugin failed with no error message'
'CannotPullContainerError: ref pull has been retried 5 time(s): failed to resolve reference <image that exists in repository>: failed to do request: Head https:<account-id>.dkr.ecr.eu-west-1.amazonaws.com/v2/k1-d...'
'ResourceInitializationError: failed to configure ENI: failed to setup regular eni: context deadline exceeded'
Thoughts:
It looks to me like there is a network-connectivity error of some sort.
The result of my Googling tells me that at least some of the errors can arise from having wrongly configured VPC or route-tables.
This is not the case here, I assume, since starting the exact same task with the exact same task definition in the same subnet works fine most of the time.
The ENI problem could maybe arise from me running out of ENI:s (?) on an EC2-instance, but since these tasks are started through Fargate I feel like that should not be the problem.
It seems like at least the network provisioning error can sometimes be an AWS issue.
Questions:
Why is this happening? Is it me or AWS?
Depending on the answer to the first question, is there something I can do to avoid this?
If there is nothing I can do, is there something I can do to mitigate it while it's happening? Should I simply just retry starting the task and hope that solves it?
Thanks very much in advance, I have been chasing this problem for months and feel like I am at least closing in on it, but this is as far as I can get on my own, I fear.
It is possible that tasks may fail to start due to a certain amount of reasons. Some of them are transient and are more "AWS" some others are more structural of your configuration and are more "you". For example the network time out is often due to a network misconfiguration where the task ENI does not have a proper route to the registry (e.g. Docker Hub). In all other cases it is possible that it's a transient one-off issue of the Fargate internals.
These problems may be transparent to you OR you may need to take action depending on how you use Fargate. For example, if you use Fargate tasks as part of an ECS service or an EKS deployment, the ECS/EKS routines will make sure they retry to instantiate the task to meet the service/deployment target configuration.
If you are launching the Fargate task using a one-off RunTask API call (i.e. not part of an orchestrator control loop that can monitor its failure) then it depends how you are calling that API. If you are calling it from tools such as AWS Step Functions, AWS Batch and possibly others, they all have retry mechanisms so if a task fails to launch they are smart enough to re-launch it.
However, if you are launching the task from an imperative line of code (or CLI command etc) then it's on your code to make sure the task has been launched properly and that you don't need to re-launch it upon an error message.
Does this means that my service tasks are stopping or it's ok to get these log messages?
actually opposite this. The service scheduler reports status periodically. A normal state indicates that there is nothing for it to do -- all tasks are healthy, there are no scaling requests or deployments.
No it doesn't mean that any of your tasks had stopped. If a task stops you will see an event that clearly states so and will include a link to the specific task that was stopped. For example you will get something like this "service xxx has stopped 1 running tasks: task xxx."
If no tasks have been created or stopped in the last six hours the ECS console will duplicate the last event message to let you know that everything works as expected.
From the ECS docs:
"To ensure that this event view is helpful, we only show the 100 most recent events and duplicate event messages are omitted until either the cause is resolved or six hours passes. If the cause is not resolved within six hours, you will receive another service event message for that cause."
https://docs.aws.amazon.com/AmazonECS/latest/developerguide/service-event-messages.html
Check this thread here on the aws forums. https://forums.aws.amazon.com/thread.jspa?threadID=182793
This sounds like normal behavior. The service scheduler reports status periodically. A normal state indicates that there is nothing for it to do -- all tasks are healthy, there are no scaling requests or deployments. Are you seeing any issues?
I run Airflow in a managed Cloud-composer environment (version 1.9.0), whic runs on a Kubernetes 1.10.9-gke.5 cluster.
All my DAGs run daily at 3:00 AM or 4:00 AM. But sometime in the morning, I see a few Tasks failed without a reason during the night.
When checking the log using the UI - I see no log and I see no log either when I check the log folder in the GCS bucket
In the instance details, it reads "Dependencies Blocking Task From Getting Scheduled" but the dependency is the dagrun itself.
Although the DAG is set with 5 retries and an email message it does not look as if any retry took place and I haven't received an email about the failure.
I usually just clear the task instance and it run successfully on the first try.
Has anyone encountered a similar problem?
Empty logs often means the Airflow worker pod was evicted (i.e., it died before it could flush logs to GCS), which is usually due to an out of memory condition. If you go to your GKE cluster (the one under Composer's hood) you will probably see that there is indeed a evicted pod (GKE > Workloads > "airflow-worker").
You will probably see in "Tasks Instances" that said tasks have no Start Date nor Job Id or worker (Hostname) assigned, which, added to no logs, is a proof of the death of the pod.
Since this normally happens in highly parallelised DAGs, a way to avoid this is to reduce the worker concurrency or use a better machine.
EDIT: I filed this Feature Request on your behalf to get emails in case of failure, even if the pod was evicted.
We've got Celery/SQS set up for asynchronous task management. We're running Django for our framework. We have a celery task that has a self.retry() in it. Max_retries is set to 15. The retry is happening with an exponential backoff and takes 182 hours to complete all 15 retries.
Last week, this task went haywire, I think due to a bug in our code not properly handling a service outage. It resulted in exponential creation (retrying?) of the same celery task. It eventually used up all available memory and the worker crashed. Restarting the worker results in another crash a couple hours later, since all those tasks (and their retries) keep retrying and spawning new retries until we run out of memory again. Ultimately we ended up with nearly 600k tasks created!
We need our workers to ignore all the tasks with a specific celery GUID. Ideally we could just get rid of them for good. I was going to use revoke() but, per documentation (http://docs.celeryproject.org/en/3.1/userguide/workers.html#commands), this is only implemented for Redis and RabbitMQ, not SQS. Furthermore, when I go to the SQS service in the AWS console, it's showing zero messages in flight so it's not like I can just flush it.
Is there a way to delete or revoke a specific message from SQS using the Celery task ID? Or is there another way to fix this problem? Obviously we need to fix our code so we don't get into this situation again, but first we need to get our worker up and running because without it our website has reduced functionality. Thanks!
I am running Dataflow-Jobs on Google Cloud Platform and one new Error I get is "Workflow failed" without any explanations.
The logs I get are the following:
2017-08-25 (00:06:01) Executing operation ReadNewXXXFromStorage/Read+JsonStringsToXXX+RemoveLanguagesFromXXX...
2017-08-25 (00:06:01) Executing operation ReadOldXYZ_ABC_1234_123_ns_123123123123123/GroupByKey/Create
2017-08-25 (00:06:01) Starting 1 workers in europe-west1-b...
2017-08-25 (00:06:01) Executing operation ReadOldXYZ_ABC_1234_123_ns_123123123123123/ParDo(SplitQuery)+ReadOldXYZ...
2017-08-25 (00:06:48) Workflow failed.
2017-08-25 (00:06:48) Stopping worker pool...
2017-08-25 (00:06:58) Worker pool stopped.
How am I supposed to find out whats going wrong? It should not be a problem with rights on the object, as similar jobs run successfully.
When I try to rerun the template from Google Cloud Console, I get the message:
No metadata file found for this template
But I am able to start the template and now it runs successfully. May this have to do with exceeded quotas? We just increased our CPU and IP-Quota for Dataflow and I increased our parallel running jobs from 5 to 15 to be able to use the quota. When I rerun the template without any other Jobs running, everything seems to work fine.
Any Input is highly appreciated. Thanks
EDIT: Seems like the Jobs failed because of exceeded CPU-Quota, but usually we would get an error-description where it says "could not spawn enough workers". Nevertheless, Everything works fine after I reduced the maximum number of workers per job, so that our quota cannot be exceeded.
I believe the "No metadata file found for this template" should be considered a warning, not an error. A template is able to have a "metadata" file associated with it which allows validation of parameters. If no such file is present, the parameters aren't validated, but everything else works as normal -- the message is just the indicator of this situation.
It sounds like the problem was the job being unable for other reasons. Based on your description and the edit, it sounds like this was because of lack of quota to run the job.