Based on reported issues, here are some additional gotchas to watch out for: 6. Version Consistency Issues: - Ensure consistent Prefect versions across your environment, especially when using pull steps in deployments - Be careful when mixing different 3.x versions, as some internal classes may have changed names - The

prefect:managed

and push pools use

prefecthq/prefect:3-latest

by default, which might not match your pinned version 7. Debugging Changes: - There are some known issues with

ipdb

breakpoints in async functions when using Prefect 3.x - Use

pdb

instead if you need to debug async functions 8. Long-Running Flows: - Some users have reported log streaming issues with flows running longer than 30 minutes - These don't typically affect flow execution but might impact log visibility Recommendations for a Smooth Upgrade: 1. Start by updating your deployment creation code to use the new

flow.from_source(...).deploy(...)

pattern 2. Replace any agent configurations with appropriate workers 3. Review and update your task execution patterns to explicitly use

.result()

or

.wait()

4. Pin your Prefect version consistently across all environments 5. Test your flows thoroughly, especially if they involve async operations or long-running processes For detailed guidance on upgrading, refer to the official migration guide: https://docs-3.prefect.io/v3/how-to-guides/migrate/upgrade-to-prefect-3 Would you like me to elaborate on any of these points or provide specific examples for any particular aspect of the upgrade process?

Let me break down both topics in detail: ### 6. Version Consistency Issues #### The Problem Version inconsistencies can manifest in several ways: 1. Pull Step Conflicts:

python
# Example of problematic setup
# In your Dockerfile
FROM prefecthq/prefect:3-latest  # This might have Prefect 3.4.7

# In your requirements.txt
prefect==3.4.6  # This gets installed during pull step

This mismatch can cause import errors like:

python
ImportError: cannot import name 'TaskRunInput' from 'prefect.client.schemas.objects'

2. Worker/Flow Version Mismatches:

yaml
# Work pool configuration using latest
prefecthq/prefect:3-latest

# While your flow might be using a specific version
requirements:
  - prefect==3.4.6

#### Best Practices for Version Management 1. Pin Versions Explicitly:

python
# In your deployment code
deployment = flow.from_source(
    source="<https://github.com/your/repo>",
    entrypoint="flows/main.py"
).deploy(
    name="my-deployment",
    work_pool_name="my-pool",
    image="prefecthq/prefect:3.4.6-python3.9"  # Pin specific version
)

2. Maintain Version Consistency:

yaml
# docker-compose.yml example
services:
  worker:
    image: prefecthq/prefect:3.4.6-python3.9
    environment:
      PREFECT_API_URL: ${PREFECT_API_URL}
      
  flow:
    build:
      context: .
      dockerfile: Dockerfile
    image: your-flow-image:3.4.6  # Match worker version

3. Version Control in Requirements:

txt
# requirements.txt
prefect==3.4.6  # Pin exact version
# Instead of prefect>=3.4.0  # Avoid this

### 8. Long-Running Flows #### Known Issues 1. Log Streaming Timeout: - After 30 minutes, you might see errors like:

Response payload is not completed: <TransferEncodingError: 400, message='Not enough data for satisfy transfer length header.'>

- This is primarily a logging issue and doesn't affect flow execution 2. Worker Communication:

The worker's watch for job 'job-name' has exited early. Check the logs for more information. 
The job is still running, but the worker will not wait for it to complete.

#### Best Practices for Long-Running Flows 1. Implement Proper Heartbeat Monitoring:

python
from prefect import flow, get_run_logger
from datetime import timedelta

@flow(timeout_seconds=7200)  # Set appropriate timeout
def long_running_flow():
    logger = get_run_logger()
    
    # Implement periodic logging
    <http://logger.info|logger.info>("Flow progress update")
    
    # Consider breaking into smaller subflows
    result = subprocess_flow()
    return result

2. Use Appropriate Infrastructure Settings:

python
# When deploying long-running flows
deployment = flow.from_source(
    source="<https://github.com/your/repo>",
    entrypoint="flows/main.py"
).deploy(
    name="long-running-deployment",
    work_pool_name="my-pool",
    job_variables={
        "timeout": 7200,  # Adjust timeout settings
        "retry_policy": {"max_retries": 3}
    }
)

3. Monitor and Handle Logs: - Implement custom logging handlers for critical information - Don't rely solely on real-time log streaming for critical data - Consider persisting important progress metrics to external storage 4. Break Down Long Flows:

python
@flow
def main_flow():
    # Break into smaller, manageable subflows
    data = extract_flow()
    processed = transform_flow(data)
    load_flow(processed)

#### Monitoring Long-Running Flows You can monitor long-running flows using the Prefect UI or API:

python
from prefect.client import get_client

async with get_client() as client:
    flow_run = await client.read_flow_run(flow_run_id)
    # Check status, logs, etc.

Remember that while log streaming might have issues after 30 minutes, the flow execution itself continues, and you can still: - Monitor flow status through the UI - Access logs after completion - Set up notifications for flow completion or failure

Would you like me to elaborate on any specific aspect of these topics or provide more concrete examples for your use case?