@Marvin How to properly cleanup inside tasks when the flow receives a Cancellation signal?

To help you with cleaning up inside tasks when a flow receives a Cancellation signal, I'll need to check the Prefect documentation and any relevant GitHub issues for both Prefect 2.x and 3.x versions. This will help me provide you with an accurate and version-specific solution. Please hold on for a moment while I gather the information. To clean up inside tasks when the flow receives a Cancellation signal in Prefect, you should consider the following steps, depending on whether you're using Prefect 2.x or 3.x: 1. Cancellation Process: Both versions handle cancellation with some robustness, but there can be issues if the infrastructure does not support cancellation. Refer to the Prefect 3.0 documentation on cancellation and the Prefect 2.x documentation. 2. Using State Handlers: Prefect allows the use of state handlers to execute cleanup code when certain events occur, including task cancellation. You can use these handlers to define custom cleanup logic that runs when a task is cancelled. 3. on_cancellation Hooks: Prefect provides hooks like

on_cancellation

which you can utilize to perform cleanup actions. For example, setting up the

on_cancellation

state change hook allows you to execute a clean-up function when a task is cancelled. However, be aware of race conditions and limitations, such as those discussed in this GitHub issue. 4. Experimental Features: You could also enable experimental features like enhanced cancellation to ensure more reliable handling of cancellations. You can set this feature using:

bash
   prefect config set PREFECT_EXPERIMENTAL_ENABLE_ENHANCED_CANCELLATION=True

5. Generic Hooks for Cleanup: For concise code, you might want to use a generic hook parameter that handles cleanup for multiple states, such as completion, failure, or cancellation. This approach minimizes code repetition as shown in Generic flow and task hooks GitHub issue. By setting appropriate state handlers or hooks, you can ensure that cleanup operations are executed when a cancellation occurs, helping ensure that resources are released correctly. You may also need to handle specific infrastructure-related constraints, such as with Dask or Kubernetes integrations.

@Marvin How to define code in a task that should run for cleaup? on cancellation hook works only for flows

To perform cleanup operations specifically within a task when a cancellation occurs, you'll need to include the cleanup logic directly within the task function itself, as Prefect currently does not provide a direct

on_cancellation

hook for tasks. Instead, you can handle cancellations using Python's exception handling and Prefect's task state. Here's a straightforward way to implement cleanup within a task function: 1. Use Try-Finally or Try-Except-Finally Blocks: You can use a

try-finally

block within your task to ensure that cleanup code runs irrespective of whether the task completes successfully or is cancelled. 2. Check for Cancellations: Prefect raises an

Abort

exception when a task is cancelled. You can catch and handle this exception to perform any necessary cleanup. Here's an example:

python
from prefect import task, flow
import time

@task
def my_task():
    try:
        # Simulating task activity
        print("Task is starting. It will sleep for 10 seconds...")
        time.sleep(10)
        
    except Exception as e:
        print(f"Task was cancelled or encountered an error: {e}")
        
    finally:
        # This cleanup code will always run, even if the task is cancelled
        print("Running cleanup operations...")
        # Insert your cleanup logic here, such as closing files or releasing resources

@flow
def my_flow():
    my_task()

# To test it, you'd trigger this flow and use cancellation features provided by Prefect

In this example, the

finally

block ensures that cleanup operations are executed whether the task is cancelled or completes normally. This simple pattern allows you to clean up resources within a task context without relying on flow-level hooks.

@Marvin Unfortunately this doesn't seem to work. The task finishes in crashed state

I understand the frustration. Let me verify the setup for handling task cancellations in Prefect for the specific version you're using. It's crucial to ensure that cleanup is possible even when Prefect interrupts or cancels a task. Let me gather more details on how tasks handle cancellations internally in both Prefect 2.x and 3.x to provide a viable solution. I will take a closer look at the documentation and community discussions around this challenge. Please hold on for a moment. Handling cleanup for cancellation within a task is indeed tricky, as Prefect's task handling can not explicitly provide an

on_cancellation

hook similar to flows. Here are some refined recommendations: 1. Use Exception Handling: Prefect raises an internal exception to handle task cancellations, but directly relying on catching it can be challenging due to how backend task executors handle cancellations. Regular try-except blocks are often not catching these signals as they operate below application-level exceptions. 2. Use Subprocess Handling: One approach involves running the main task workload in a subprocess or in parallel, which allows for catching termination signals at the Python level:

python
   from prefect import task, flow
   import subprocess
   import signal
   import time

   @task
   def my_task():
       def handler(signum, frame):
           print("Cleanup code executed due to cancellation")
           # Insert your cleanup logic here
           
       # Attach the handler to the signal
       signal.signal(signal.SIGTERM, handler)

       try:
           # Simulate task workload
           print("Task is running")
           time.sleep(10)
       except Exception as e:
           print(f"Exception during task execution: {e}")
       finally:
           print("Task cleanup completed.")

   @flow
   def my_flow():
       my_task()

   if __name__ == '__main__':
       my_flow()

3. Community Suggestions: From the Prefect forums and issues, handling cancellations gracefully often involves infrastructure level automation or having cleanup delegated to other tasks or infrastructure-specific features like AWS Lambda finalizers or Kubernetes preStop Hooks. 4. Infra-Level Graceful Stops: When running tasks on an infrastructure like Kubernetes, using Pre-Stop hooks or termination grace periods defined in the deployment manifest can allow you to execute cleanup scripts even when a Prefect task does not expose direct hooks. 5. Concurrent & Asynchronous Execution: Depending on your runtime environment, using a robust asyncio pattern or threading for task internals can help implement timeouts or manual signals. However, this usually requires more control over the runtime environment. These strategies utilize Python’s signal handling and subprocess protocols to force a cleanup sequence when cancellation occurs. Keep in mind that the precise implementation and its effectiveness will depend on the task execution environment and scheduler behavior you are using.