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<@ULVA73B9P> why are flows stuck in a pending stat...
# ask-marvin
j
@Marvin why are flows stuck in a pending state. This is prefect 3 and they are deployed as docker containers. Worker logs: EventsWorker - Still processing items: 1 items remaining..
m
✅ thought for 41.8 seconds
Based on the research and the symptoms you're describing, there are a few potential issues that could be causing your flows to get stuck in a pending state. Let me break down the most likely causes and solutions: 1. Worker Event Processing Issue The message "EventsWorker - Still processing items: 1 items remaining..." suggests there might be a problem with event processing. This could be caused by: - Network connectivity issues between the worker and Prefect Cloud - Authentication problems - Resource constraints 2. Docker Container Configuration Since you're using Docker containers, ensure: - The worker has proper permissions to pull and run Docker images - Docker daemon is running and accessible - Network connectivity between the worker and Docker daemon is working Here are the steps I recommend to troubleshoot: 1. Check Worker Logs - Look for any error messages beyond the "Still processing items" message - Check for any Docker-related errors - Verify network connectivity logs 2. Verify Worker Configuration
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   prefect worker status
This will show the current state of your worker. 3. Check Flow Run Logs In the Prefect UI, look at the flow run details for any additional error messages or warnings. 4. Verify Docker Configuration - Ensure the worker has access to Docker - Check if Docker images can be pulled manually - Verify Docker daemon status 5. Network Connectivity - Verify the worker can reach Prefect Cloud - Check if there are any firewall rules blocking connections - Ensure Docker network settings are correct If you could provide: 1. Complete worker logs (beyond just the EventsWorker message) 2. Any error messages from the Prefect UI 3. Docker configuration details This would help pinpoint the exact cause of the issue. Also, as a best practice, you might want to try: 1. Restarting the worker 2. Running a simple test flow (non-Docker) to isolate if the issue is Docker-specific 3. Checking if the issue occurs with all flows or just specific ones Would you be able to share any of these additional details to help narrow down the cause?
j
@Marvin this is self hosted prefect. restarted worker but issue persists. issue happens with all flows
m
✅ thought for 36.0 seconds
Based on the research and similar issues reported, I can suggest several specific troubleshooting steps for your self-hosted Prefect 3.x environment: 1. Check Database Connectivity - The EventsWorker issue often occurs when there are problems with the database connection - Verify that your Prefect server has stable connectivity to its database - Check database logs for any connection timeouts or errors 2. Verify Server Configuration
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   prefect config view
- Ensure all server-related configurations are correct - Check that the PREFECT_API_URL is properly set - Verify any custom configurations in your 
prefect.yaml
3. Check Worker Pool Configuration
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   prefect work-pool ls
   prefect worker ls
- Verify that your work pool is properly configured for Docker - Ensure the worker has the correct permissions to access Docker 4. Monitor Server Resources - Check server CPU, memory, and disk usage - Ensure there's enough capacity to handle the event processing - Look for any resource constraints that might be affecting the EventsWorker 5. Review Docker Setup
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   docker ps
   docker logs <worker-container-id>
- Verify Docker daemon is running - Check if containers can be created successfully - Look for any Docker-related errors in the logs 6. Clear Pending States You might need to clear any stuck states:
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prefect flow-run delete --state Pending
7. Temporary Workaround If the issue persists, you can try restarting both:
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# Restart Prefect server
prefect server stop
prefect server start

# Restart worker
prefect worker stop
prefect worker start -p <your-pool-name>
Could you please provide: 1. The output of 
prefect config view
(with sensitive information redacted)? 2. The complete worker logs beyond just the EventsWorker message? 3. Are you seeing any errors in your Prefect server logs? This would help us identify if this is a configuration issue, a resource constraint, or potentially a bug in the event processing system. Also, what version of Prefect 3.x are you running specifically? Some earlier versions of 3.x had known issues with event processing that were fixed in later releases.
✅ thought for 24.0 seconds
Since this is affecting all flows and persists after worker restart, let's try some more systematic troubleshooting. Here are specific steps to diagnose and potentially fix the issue: 1. Check Server Services Status
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prefect server services ls
This will show the status of all server services, including the EventsWorker. 2. Verify Database Health
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prefect server database reset-failed-jobs
This can help clear any stuck database jobs. 3. Check Server Configuration
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prefect config view
Look specifically for: - Database connection settings - API URL configuration - Worker settings 4. Diagnostic Steps Try these commands and note any errors:
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# Check work pool status
prefect work-pool ls

# Check worker status
prefect worker ls

# Check flow runs status
prefect flow-run ls --state Pending
5. Potential Solutions A. Reset Server Services
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prefect server stop
prefect server start
B. Clear Database Connections
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prefect server database clear-db-connections
C. Check Server Logs Look in your server logs for errors, particularly around the time flows get stuck. Could you please provide: 1. What specific version of Prefect 3.x are you running? (
prefect version
) 2. What database are you using for your self-hosted setup (SQLite, PostgreSQL, etc.)? 3. Are you seeing any specific error messages in: - Server logs - Database logs - Worker logs (beyond the EventsWorker message) This information would help narrow down whether this is: - A database connection issue - A server configuration problem - A service orchestration issue - A potential bug in your specific Prefect version Also, as a temporary diagnostic measure, you could try: 1. Running a very simple test flow (no Docker, just a basic Python function) 2. Monitoring the server logs while this flow runs 3. Checking if the EventsWorker message appears for this simple flow as well This would help isolate whether the issue is related to Docker specifically or if it's a more fundamental server/worker communication problem.
j
@Marvin is it advisable to delete all events logs
m
✅ thought for 40.1 seconds
Based on the research, I would NOT recommend deleting all event logs as a first troubleshooting step. Here's why: 1. Diagnostic Value - Event logs are crucial for diagnosing the root cause of issues - They contain important information about flow execution patterns - They help track dependencies and infrastructure problems 2. Audit Trail - Logs provide an audit trail of what happened and when - This is valuable for understanding system behavior over time - Important for compliance and debugging purposes Instead of deleting logs, I recommend these steps in order: 1. First, Try Database Maintenance
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# Clear failed database jobs
prefect server database reset-failed-jobs

# Clear stale DB connections
prefect server database clear-db-connections
2. Increase Logging Level
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# Set to DEBUG level for more detailed information
prefect config set PREFECT_LOGGING_LEVEL="DEBUG"
3. If Necessary, Selective Cleanup Instead of deleting all logs, you could: - Clear only specific flow runs that are stuck - Remove only the oldest logs while keeping recent ones - Archive logs before deletion for future reference 4. If You Must Clear Logs If you decide you need to clear logs, do it systematically:
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# First, stop the server
prefect server stop

# Clear specific flow runs
prefect flow-run delete --state Pending

# Restart the server
prefect server start
Before proceeding with any log deletion, could you share: 1. How much disk space is being used by logs? 2. Are you experiencing any specific performance issues? 3. What's the age of the oldest logs in your system? This would help determine if log deletion is really necessary or if there might be a better solution to your specific problem. Remember: If you do decide to delete logs, make sure to: 1. Back up your database first 2. Document the current state of your system 3. Have a plan to restore if needed 4. Monitor the system closely after deletion Would you like to explore any of these alternative approaches before proceeding with log deletion?
j
@Marvin turns out the issue was flow run terminating after running a entry command configured in the docker work pool. What are some ways to ensure the flow run continues after the initial command runs successfully
m
✅ thought for 43.8 seconds
Based on the research, here are several ways to ensure your flow runs continue after the initial entry command in a Docker work pool: 1. Use Command Chaining When configuring your Docker work pool, chain commands using 
&&
to ensure both the initial command and the flow run execute:
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your_setup_command && python -m prefect.engine
2. Create a Startup Script Create a shell script (e.g., 
entrypoint.sh
) that handles both your setup and the flow:
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#!/bin/bash
# Run your setup command
your_setup_command

# Start the Prefect flow
python -m prefect.engine
Then use this script as your entry point in the work pool configuration. 3. Use Docker ENTRYPOINT with CMD In your Dockerfile:
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ENTRYPOINT ["./entrypoint.sh"]
CMD ["python", "-m", "prefect.engine"]
4. Configure Work Pool Base Job Template You can modify the work pool's base job template to properly handle command execution:
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prefect work-pool create \
  --type docker \
  --base-job-template '{
    "job_configuration": {
      "command": ["bash", "-c", "your_setup_command && python -m prefect.engine"]
    }
  }' \
  my-docker-pool
5. Use Environment Variables Set up environment variables in your work pool configuration and reference them in your flow:
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from prefect import flow
import os

@flow
def my_flow():
    # Access setup results via environment variables
    setup_result = os.getenv("SETUP_RESULT")
    # Rest of your flow logic
Here's a complete example putting it all together: 1. Create a startup script (
entrypoint.sh
):
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#!/bin/bash
# Run your setup
setup_command

# Export any necessary environment variables
export SETUP_RESULT=$?

# Start the Prefect flow
python -m prefect.engine
2. Update your Dockerfile:
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FROM prefecthq/prefect:3-python3.9

COPY entrypoint.sh /opt/prefect/entrypoint.sh
RUN chmod +x /opt/prefect/entrypoint.sh

ENTRYPOINT ["/opt/prefect/entrypoint.sh"]
3. Configure your work pool:
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prefect work-pool create \
  --type docker \
  --base-job-template '{
    "job_configuration": {
      "entrypoint": ["/opt/prefect/entrypoint.sh"]
    }
  }' \
  my-docker-pool
Best Practices: 1. Always make your entry commands idempotent 2. Use environment variables to pass information between the setup command and the flow 3. Include proper error handling in your startup scripts 4. Add logging to help debug issues 5. Consider using health checks to ensure your container is ready Would you like me to provide more specific examples based on your particular setup or entry command? What type of initial setup command are you running?
j
@Marvin why python -m prefect.engine
m
✅ thought for 35.3 seconds
I apologize for my previous response - I made a mistake in suggesting 
python -m prefect.engine
. This is not the correct approach in Prefect 3.x. Let me provide the correct way to handle this: In Prefect 3.x, you should instead: 1. Use the Standard Flow Runner
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from prefect import flow

@flow
def your_flow():
    # your flow code here
    pass

if __name__ == "__main__":
    your_flow()
2. For Deployments, Use the Proper CLI Commands
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# Start a worker for your work pool
prefect worker start -p "your-work-pool-name"
For your specific case with Docker work pools, here are the correct approaches to ensure flow runs continue after initial commands: 1. Using a Shell Script (entrypoint.sh)
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#!/bin/bash
# Run your setup command
your_setup_command

# Start the actual flow
python your_flow_file.py
2. Direct Command in Work Pool Configuration
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prefect work-pool create \
  --type docker \
  --base-job-template '{
    "job_configuration": {
      "command": ["bash", "-c", "your_setup_command && python your_flow_file.py"]
    }
  }' \
  my-docker-pool
3. Using Docker's ENTRYPOINT/CMD
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dockerfile
FROM prefecthq/prefect:3-python3.9

COPY entrypoint.sh /opt/prefect/entrypoint.sh
RUN chmod +x /opt/prefect/entrypoint.sh

ENTRYPOINT ["/opt/prefect/entrypoint.sh"]
CMD ["python", "your_flow_file.py"]
The key differences from my previous response are: - We're not using 
python -m prefect.engine
(which was incorrect) - We're directly running the Python file containing your flow - We're using the standard Prefect 3.x deployment patterns Would you like me to provide more specific examples based on your actual setup? What kind of setup command are you running before your flow?
k
when you don't supply a startup command in your work pool, the one we use by default is 
prefect flow-run execute
for custom commands, make sure they always end with that. so, 
my-other-command && prefect flow-run execute
✅ 1
j
Thanks @Kevin Grismore Can you help me to understand what is causing the docker container to exit? The custom command was a pip install. Also the flow run status stays at pending without 
prefect flow-run execute
so there is a disconnect but I'm not sure between which entities.
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