Based on the research and issue reports, here's how to debug this situation: 1. Check Kubernetes Pod Logs

bash
   # Get the pod name
   kubectl get pods | grep kappa-manifold
   
   # Check the pod logs
   kubectl logs <pod-name>
   
   # For previous attempts, add -p flag
   kubectl logs <pod-name> -p

2. Check Pod Events and Status

bash
   kubectl describe pod <pod-name>
   kubectl describe job <job-name>

3. Common Causes to Check: - Resource Limits: Verify if the pods are being OOMKilled (Out of Memory) or hitting CPU limits - Image Pull Issues: Ensure the container image is accessible - Permissions: Verify RBAC permissions are correctly set - Environment Variables: Check if all required environment variables are properly set 4. Specific Solutions: a. If it's a resource issue:

python
   from prefect.infrastructure import KubernetesJob
   
   kubernetes_job = KubernetesJob(
       job_template={
           "spec": {
               "template": {
                   "spec": {
                       "containers": [{
                           "resources": {
                               "requests": {
                                   "memory": "1Gi",
                                   "cpu": "500m"
                               },
                               "limits": {
                                   "memory": "2Gi",
                                   "cpu": "1"
                               }
                           }
                       }]
                   }
               }
           }
       }
   )

b. If you need to adjust the backoff limit:

python
   kubernetes_job = KubernetesJob(
       job_template={
           "spec": {
               "backoffLimit": 6  # default is 6
           }
       }
   )

5. To Unstick the Flow Run: - Navigate to the flow run in the Prefect UI - Mark the flow run as failed to clear the stuck state - Investigate the root cause before retrying Remember that in Prefect 3.x, it's recommended to use work pools and workers instead of the older agent-based approach. If you're experiencing frequent issues, consider migrating to a Kubernetes work pool:

bash
prefect work-pool create my-k8s-pool --type kubernetes

This provides better visibility into infrastructure issues and more granular control over job execution. Links for further reading: - Prefect 3.x Kubernetes Infrastructure - Prefect 3.x States Documentation

Let me explain how backoffLimit works in the context of Kubernetes jobs and Prefect: What is backoffLimit? - It's a Kubernetes Job specification that determines how many times a Job should retry its Pods before marking the Job as failed - The default value is 6 attempts - After each failed attempt, Kubernetes implements an exponential backoff delay before retrying How Backoff Works: 1. First failure: 10 seconds 2. Second failure: 20 seconds 3. Third failure: 40 seconds 4. And so on, with an exponential increase 5. Maximum backoff is capped at 6 minutes Configuring backoffLimit in Prefect:

python
from prefect.infrastructure import Infrastructure

infrastructure = Infrastructure.from_job_template(
    job_template={
        "apiVersion": "batch/v1",
        "kind": "Job",
        "spec": {
            "backoffLimit": 3,  # Set to 3 retries instead of default 6
            "template": {
                "spec": {
                    "containers": [{
                        "name": "flow",
                        # ... other container specs ...
                    }],
                    "restartPolicy": "Never"  # Important: Use "Never" with backoffLimit
                }
            }
        }
    }
)

Important Considerations: 1. RestartPolicy Interaction: - Must be set to "Never" (not "OnFailure") when using backoffLimit - "OnFailure" causes the same pod to restart, while "Never" creates new pods 2. When to Adjust backoffLimit: - Increase it when your job: - Has known intermittent failures - Depends on external services that might be temporarily unavailable - Needs more retry attempts for self-recovery - Decrease it when: - Quick failure detection is needed - Resources are constrained - Retries won't help (e.g., syntax errors) 3. Debugging Tips:

bash
   # View pod history for a job
   kubectl get pods --selector=job-name=<job-name>
   
   # Check events for the job
   kubectl describe job <job-name>
   
   # View logs from failed attempts
   kubectl logs <pod-name> --previous

4. Common Failure Scenarios: - Image pull errors - Resource constraints (CPU/Memory) - Configuration errors - Application crashes Best Practices: 1. Set Appropriate Values:

python
   # For jobs that need more resilience
   infrastructure = Infrastructure.from_job_template(
       job_template={
           "spec": {
               "backoffLimit": 10,
               "activeDeadlineSeconds": 3600  # Optional: Set maximum time for all retries
           }
       }
   )

2. Combine with Timeouts:

python
   # Add both backoff and timeout controls
   infrastructure = Infrastructure.from_job_template(
       job_template={
           "spec": {
               "backoffLimit": 4,
               "activeDeadlineSeconds": 1800,  # 30 minutes total
               "template": {
                   "spec": {
                       "containers": [{
                           "name": "flow",
                           "command": ["python", "-c", "..."],
                           "resources": {
                               "requests": {
                                   "memory": "1Gi"
                               }
                           }
                       }]
                   }
               }
           }
       }
   )

3. Monitor and Log: - Always include proper logging in your flows - Use Prefect's logging capabilities to track retry attempts - Consider implementing custom handling for specific failure types Remember that backoffLimit is just one part of a robust retry strategy. Consider combining it with Prefect's built-in retry mechanisms for task-level retries when appropriate. Links: - Kubernetes Jobs Documentation - [Prefect Kubernetes Infrastructure Guide](https://docs.prefect.io/latest/guides