Based on the research and GitHub issues, here are the most common reasons why your tasks might be stuck in pending state when using Kubernetes workers in Prefect 2.x: 1. Resource Constraints: - Insufficient CPU or memory resources in your Kubernetes cluster - Incorrect resource requests and limits in your job manifests - Pods might be unable to schedule due to resource quotas 2. Kubernetes Configuration Issues: - Incorrect namespace permissions - Missing or incorrectly configured service accounts - Issues with image pull secrets or policies - Network connectivity problems preventing image pulls 3. Worker-Related Issues: - Worker crashes due to API server overload (as seen in GitHub issue #16977) - Worker restarts while tasks are being scheduled can leave tasks in a pending state - Multiple workers trying to schedule the same tasks simultaneously To troubleshoot this, I recommend: 1. Check Kubernetes Events:

bash
   kubectl get events --sort-by='.lastTimestamp' -n your-namespace

This will show if there are scheduling issues, resource constraints, or permission problems. 2. Verify Pod Status:

bash
   kubectl get pods -n your-namespace
   kubectl describe pod <pod-name> -n your-namespace

Look for events or warnings that might indicate why pods aren't starting. 3. Check Worker Logs:

bash
   kubectl logs -n your-namespace <worker-pod-name>

Look for any error messages or warnings about task scheduling. 4. Verify Resources: - Check your cluster's available resources - Verify the resource requests and limits in your work pool configuration - Ensure you haven't hit any namespace quotas 5. Check Prefect Worker Health:

bash
   prefect worker ls

Verify that your workers are healthy and properly connected. Would you be able to share: 1. Are you seeing any specific error messages in your Kubernetes events or worker logs? 2. How many workers do you have running? 3. Are you using Prefect Cloud or a self-hosted server? This information would help me provide more specific guidance for your situation. Also, since you're using Prefect 2.x, I should mention that Prefect 3.x has significant improvements in worker reliability and task scheduling. If possible, consider upgrading to take advantage of these improvements.

Based on this new context, I can provide more targeted troubleshooting advice. If tasks are meant to run in the same pod as the flow, there are a few specific things to check: 1. Task Runner Configuration: - Make sure you're using the correct task runner for in-pod execution - The default

ConcurrentTaskRunner

or

SequentialTaskRunner

should be used for same-pod execution - Check if you're accidentally using a

KubernetesTaskRunner

which would try to spawn new pods 2. Flow Configuration: Could you share your flow code? It would be helpful to see how the tasks are defined and submitted. Here's what to check in your code:

python
   from prefect import flow, task
   
   @task
   def my_task():
       # task code
       pass
   
   @flow
   def my_flow():
       # This will run in the same pod
       result = my_task()
       
       # This might cause issues if not configured correctly
       result = my_task.submit()

3. Common Issues to Check: - Are you using

.submit()

on your tasks? This can sometimes cause issues if not configured correctly - Are you using any specific task runners or flow runners? - Are there any concurrent execution settings that might be affecting task execution? To help diagnose this further, could you please share: 1. A simplified version of your flow code showing how tasks are defined and called 2. Are you seeing any error messages in your flow logs? 3. How many tasks are you trying to run simultaneously? 4. Are you using any specific task runners or flow configuration? This information would help me provide more specific guidance for your situation. Also, if you're using task submission with

.submit()

, you might want to check if there are any concurrency limits set that could be preventing task execution. In Prefect 2.x, these settings can sometimes cause tasks to remain in a pending state if not configured correctly.