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<@ULVA73B9P> Why did I receive this error in prefect 2.x error occurred while monitoring flow run 'f...
# ask-marvin
m
@Marvin Why did I receive this error in prefect 2.x error occurred while monitoring flow run 'f943ba9c-7964-42c6-b46f-26fbcfc17499'. The flow run will not be marked as failed, but an issue may have occurred.
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Traceback (most recent call last):
 .......
  File "/usr/local/lib/python3.11/site-packages/prefect_kubernetes/worker.py", line 1031, in _watch_job
    for event in self._job_events(
  File "/usr/local/lib/python3.11/site-packages/kubernetes/watch/watch.py", line 195, in stream
    raise client.rest.ApiException(
kubernetes.client.exceptions.ApiException: (410)
Reason: Expired: too old resource version: 580605559 (580938156)
m
This error you're encountering is related to the Kubernetes API when Prefect is trying to monitor a flow run. The specific error, 
ApiException: (410)
, happens when the Kubernetes client tries to stream events and encounters an expired resource version. This behavior occurs because the Kubernetes watch API uses resource versions to manage event consistency. When a client tries to use a resource version that's too old, Kubernetes responds that the version has expired, leading to this 410 error. Here's a relevant issue from Prefect's GitHub repository discussing this problem: Prefect Kubernetes Agent doesn't retry 410 Expired k8s API errors from Watch(). The issue indicates that when Prefect runs a KubernetesJob FlowRun, it watches the job using 
kubernetes.watch.Watch()
. The 
watch.stream()
function may only retry once for an expired resource, which sometimes fails, leading to the 410 error. Although the flow often continues running, the error might imply that the agent might lose contact with the flow, potentially affecting operations like flow cancellation. The suggested approach to handle this is to implement additional retries or catching mechanisms for these 410 errors within the Prefect infrastructure code for Kubernetes, as indicated by similar practices in other projects. You can keep track of this issue on GitHub for any updates or fixes that the Prefect team might provide. If you need an immediate workaround, you might consider modifying the Kubernetes agent parameters or retry mechanisms, though this might involve diving into Prefect's or Kubernetes client's code. For more details, you can check the documentation related to Prefect's Kubernetes integration here.
The error you're experiencing is related to the Kubernetes API and the way Prefect handles the connection to monitor your Kubernetes jobs. The 
ApiException 410
error occurs when the resource version you are attempting to watch is too old, leading to an "expired resource version" error. This is a common issue when using Kubernetes' 
watch
mechanism to follow resource changes like job status. Here are some insights based on the information: 1. Issue with Kubernetes Watch: The Kubernetes API may return a 
410 Gone
error if the resource version is outdated. This typically happens if there are a large number of resource versions generated since the client last updated the watched resource. 2. Retries and Recovery: Prefect attempts to handle these cases by retrying the watch. However, retries may fail if the number of updates outpaces the client's ability to catch up, or if the job is particularly long-running. 3. Effect on Flow Run: Typically, such an error in monitoring doesn't stop the flow run itself but can mean Prefect loses visibility of the run, potentially missing log updates or completion notifications. The flow might still complete correctly without Prefect marking it as unsuccessful. 4. GitHub Issues Highlighting the Problem: - There is a GitHub issue discussing this behavior, specifically about Prefect not retrying 
410 Expired
errors adequately. The conversation highlights the need for Prefect to more effectively handle these expired errors with retries. - Another PR aimed to improve the way Prefect's Kubernetes agents handle long-running jobs, reducing the occurrence of these issues by allowing the watch to restart after timeouts. Recommendations: - Configure Timeout and Resource Versioning: Ensure your Kubernetes is configured to handle long-running watch connections if possible, and consider setting the 
watch
timeout in Prefect to a higher value. - Update Prefect: Ensure you are running an updated version of Prefect, as improvements and bug fixes are regularly released. - Kubernetes Configuration: Consult with your Kubernetes administrator to see if adjustments to the API server's configuration may help mitigate these kinds of errors. These issues are part of broader discussions in Prefect's community, and ongoing improvements are likely. Stay connected with Prefect's GitHub repository for the latest updates and community discussions.
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