Ritesh
06/28/2025, 3:30 PMMarvin
06/28/2025, 3:30 PMMarvin
06/28/2025, 3:31 PMrun_once
Mode
python
from prefect.worker import Worker
worker = Worker(work_pool_name="your-pool")
worker.start(run_once=True) # Worker will check for work once and then exit
This makes the worker poll exactly once and then terminate. Not exactly "automatic" but at least it's deterministic.
2. Infrastructure-Level Management
- If you're running workers in containers or cloud instances, you can implement shutdown logic at the infrastructure level
- For example, use container orchestration platforms' job definitions or cloud provider auto-scaling policies to terminate instances after a period of inactivity
3. Custom Implementation
- You could wrap your worker process in a custom script that monitors flow run completion and terminates the worker process
- This would require additional logic to track your specific flows and determine when it's safe to shut down
The most straightforward approach is using run_once=True
if you just want the worker to process currently available work and exit. Otherwise, you'll need to implement custom logic or infrastructure-level controls to manage worker lifecycle.
Remember that workers are meant to be long-running processes by design. If you find yourself frequently starting and stopping workers, you might want to reconsider your deployment architecture or use case.
Would you like me to elaborate on any of these approaches?Ritesh
06/28/2025, 3:35 PMRitesh
06/28/2025, 3:53 PMMarvin
06/28/2025, 3:53 PMMarvin
06/28/2025, 3:54 PMpython
from prefect import flow
from prefect.deployments import run_deployment
from prefect.states import Completed
@flow
def flow_1():
# Your flow 1 logic here
pass
@flow
def flow_2():
# Your flow 2 logic here
pass
@flow
def orchestrator_flow():
# Run first deployment and wait for completion
flow_1_state = run_deployment(
name="flow-1/deployment-1",
timeout=0, # wait indefinitely
)
# Only if flow 1 completed successfully, run flow 2
if isinstance(flow_1_state, Completed):
flow_2_state = run_deployment(
name="flow-2/deployment-2",
timeout=0,
)
2. Using Automation Rules with SequenceTrigger
python
from prefect.automations import SequenceTrigger
from prefect.automations.actions import RunDeployment
# Create a sequence trigger
trigger = SequenceTrigger(
name="Sequential Deployment Trigger",
match=[
{"deployment_name": "flow-1/deployment-1", "state_type": "COMPLETED"}
]
)
# Define the action to run the next deployment
action = RunDeployment(
name="Run Flow 2",
deployment_name="flow-2/deployment-2"
)
# Link them together
automation = trigger.automate(action)
For your YAML configuration (prefect.yaml
), you would define your deployments like this:
yaml
deployments:
- name: deployment-1
entrypoint: flows.py:flow_1
work_pool:
name: your-work-pool
- name: deployment-2
entrypoint: flows.py:flow_2
work_pool:
name: your-work-pool
A few important notes:
1. The first approach gives you more programmatic control and is easier to debug
2. The second approach using automations is more declarative and can be managed through the Prefect UI
3. Both approaches ensure that the second flow only deploys after the first flow's successful completion
4. Make sure your worker is running to execute these deployments
Would you like me to elaborate on either of these approaches or explain how to handle more complex deployment sequences?Ritesh
06/28/2025, 4:44 PMMarvin
06/28/2025, 4:44 PMMarvin
06/28/2025, 4:45 PMrun_once
parameter works as designed, but perhaps not as expected. Here's what's happening and how to handle it:
When a worker starts with run_once=True
, it:
1. Checks the work pool ONCE for any existing work
2. If it finds nothing at that exact moment, it exits
3. It doesn't wait for future deployments
Here are three better approaches to handle this timing-dependent scenario:
1. Use a Short Polling Duration (Recommended)
python
from prefect.worker import Worker
worker = Worker(work_pool_name="your-pool")
# Poll for 5 minutes then exit
worker.start(poll_interval=10, limit_seconds=300)
2. Create a Simple Wrapper Script
python
import time
from prefect.worker import Worker
from prefect.client import get_client
async def wait_for_deployment_and_run():
client = get_client()
worker = Worker(work_pool_name="your-pool")
# Wait for deployment to exist
while True:
deployments = await client.read_deployments()
if deployments: # or check for specific deployment
break
time.sleep(10)
# Now start the worker
worker.start(run_once=True)
# Run this instead of starting worker directly
if __name__ == "__main__":
import asyncio
asyncio.run(wait_for_deployment_and_run())
3. Use Infrastructure-Level Scheduling
- Deploy your worker after your deployment script completes
- This could be done through shell scripts, CI/CD pipelines, or orchestration tools:
bash
# Example shell script
python deploy_flows.py
# Wait for deployment to complete
sleep 10
# Start worker
python start_worker.py
The first approach (using limit_seconds
) is probably the most robust as it:
- Gives your deployment time to be created
- Will pick up the work when it appears
- Will eventually stop (unlike a regular worker)
- Doesn't require complex scripting
Would you like me to elaborate on any of these approaches or suggest alternative solutions?