Based on the research and available information, here are the recommended approaches for handling long-running flows (72+ hours) with push work pools: 1. Break Down the Flow The most reliable approach is to break your 72-hour flow into smaller, more manageable segments that can be chained together. Here are two ways to do this:

python
from prefect import flow

@flow(name="sub_flow")
def sub_flow(data):
    # Process a portion of your work
    return processed_data

@flow(name="main_flow")
def main_flow():
    # Break your 72-hour work into smaller chunks
    for chunk in data_chunks:
        result = sub_flow(chunk)
        # Each sub-flow runs independently and can be monitored separately

2. Use Flow Retries and State Persistence Configure your flow with robust retry mechanisms:

python
from prefect import flow
from datetime import timedelta

@flow(
    name="long_running_flow",
    retries=3,
    retry_delay_seconds=300,  # 5 minutes between retries
    persist_result=True
)
def your_flow():
    # Your long-running logic here
    pass

3. Consider Infrastructure Configuration When deploying to push work pools, you'll need to ensure your infrastructure configuration accounts for long-running processes: - For AWS ECS: Adjust the

stopTimeout

in your task definition - For Google Cloud Run: Use Cloud Run jobs instead of standard Cloud Run services - For Kubernetes: Set appropriate

activeDeadlineSeconds

and resource limits 4. Implement Progress Tracking Use Prefect's state management to track progress and enable recovery:

python
from prefect import flow, get_run_logger

@flow
def long_running_flow():
    logger = get_run_logger()
    
    # Save progress periodically
    for i, item in enumerate(large_dataset):
        <http://logger.info|logger.info>(f"Processing item {i}")
        # Process item
        # Optionally save checkpoint

Important Considerations: 1. Infrastructure Limits: Most serverless platforms have built-in timeouts that can't be extended indefinitely. You'll need to verify the maximum execution time allowed by your specific infrastructure. 2. Cost Efficiency: Running a single flow for 72+ hours on serverless infrastructure might not be cost-effective. Breaking it into smaller units can help manage costs and resources better. 3. Monitoring and Recovery: Implement proper logging and monitoring to track the progress of your long-running flows and enable recovery if needed. 4. State Management: Use Prefect's built-in state management to track progress and handle failures gracefully. If these approaches don't meet your needs, you might want to consider: 1. Using a hybrid work pool instead (though you mentioned you don't want this) 2. Running your long process outside of Prefect and using Prefect only for orchestration and monitoring 3. Implementing a custom solution using subflows with state persistence Would you like me to elaborate on any of these approaches or provide more specific examples for your use case?