@Marvin While updating a parameter inside a task like to cancel the task after it run for 3600 seconds under the parameters name ‘timeout_seconds’. It works for few task, but if my task is accessing an api which is not giving any response until 3600 seconds but even after my task didn’t get cancelled. What might be the reason and how can I solve this ?

Prefect uses the

timeout_seconds

parameter to prevent long-running tasks, but there are some nuances and issues that might affect its behavior: 1. Documentation Insights: - Prefect allows task and flow timeouts using the

timeout_seconds

parameter to mark tasks as failed if they exceed the specified duration (source: Prefect Documentation). - The cancellation process might encounter problems if the infrastructure supporting the task does not support it, or if identifiers and permissions mismatch during cancellation attempts. 2. Common Issues and Workarounds: - RayTaskRunner Timeout Issue: There's a known issue with

RayTaskRunner

where the

timeout_seconds

is not enforced, leading to tasks that run longer than the expected timeout (source: GitHub Issue #13004). - Kubernetes Concurrency Slots: Concurrency slots may not be released correctly when tasks are cancelled in Kubernetes, which might also interfere with timing and task cancellation (source: GitHub Issue #8566). - Subflow Cancellations: If you are using subflows, managing cancellations can be tricky, especially if the parent flow encounters an error and the subflows continue running (source: GitHub Issue #12864). ### Recommended Actions 1. Ensure Compatibility with Infrastructure: - Verify that task's infrastructure supports timeout and cancellation. For example, certain Kubernetes configurations may influence task management. 2. Review TaskRunner Configuration: - If using

RayTaskRunner

, explore alternatives or keep track of the linked issue for a resolution. 3. Manual Cancellation: - In scenarios where the automated process fails, consider manually triggering cancellations using

prefect flow-run cancel

command via CLI or use the UI to cancel problematic tasks. 4. Subflow Management: - For complex workflows involving subflows, handle cancellations explicitly by monitoring parent and child task states and implementing custom logic to enforce cancellations if automated processes lag. For more details or continued updates, consider monitoring the linked GitHub issues and Prefect documentation.

@Marvin In my case I am using ConcurrentTaskrunner for activating each task. How can I resolve this ?

When using

ConcurrentTaskRunner

in Prefect, some problems related to task timeouts and cancellation can still arise due to the specific nature of concurrent execution. Here's what I found and steps you can take to potentially resolve these issues: ### Documentation Insights 1. Concurrency Limits (Prefect Docs): Prefect's

ConcurrentTaskRunner

allows handling multiple tasks concurrently. However, the task's infrastructure must support proper timeout and cancellation behaviors. ### Common Issues and Related GitHub Discussions 1. Aiohttp Timeout Issue (GitHub Issue #12857): - When using

aiohttp

with

ConcurrentTaskRunner

, there might be issues with the context manager used for timeouts. - Ensure that the timeout context manager, if used within a task, handles the timeout correctly inside the asynchronous context. 2. Parallel Execution Exceptions (GitHub Issue #9296): - Running two different flows using multi-threading might raise exceptions, indicating potential conflicts in concurrent execution. 3. Task Queue Pool Issue (GitHub Issue #6492): - If you have a very large number of tasks (e.g., 1500+), it may lead to pooling issues, and handling timeouts properly becomes challenging. ### Recommended Actions 1. Ensure Proper Asynchronous Timeout Handling: - Make sure that tasks using async operations like

aiohttp

handle timeouts properly. This means ensuring that the timeout context manager is properly used within the task's async context. 2. Limit Concurrency: - When dealing with many concurrent tasks, set appropriate concurrency limits to avoid overload and ensure proper handling of task state transitions. 3. Review Tasks and TaskRunner Configurations: - Specifically, review the configuration of the

ConcurrentTaskRunner

and validate that all tasks have the correct setup to be interrupted by timeouts. 4. Custom Timeout Management: - Consider implementing custom timeout management inside the task if the built-in

timeout_seconds

is not sufficient due to specific concurrency issues. Here’s a revised code snippet to help you implement proper timeout handling when using `ConcurrentTaskRunner`:

python
import asyncio
from contextlib import asynccontextmanager
from prefect import flow, task
from prefect.task_runners import ConcurrentTaskRunner

@asynccontextmanager
async def timeout_manager(timeout):
    try:
        yield await asyncio.wait_for(asyncio.sleep(timeout), timeout=timeout)
    except asyncio.TimeoutError:
        print("Operation timed out!")

@task(timeout_seconds=3600)
async def call_api_task():
    async with timeout_manager(3600):
        # Replace with your async API call
        print("Starting API call")
        await asyncio.sleep(10)  # Simulating a long API call
        print("API call completed")

@flow(task_runner=ConcurrentTaskRunner())
async def my_flow():
    await call_api_task()

if __name__ == "__main__":
    asyncio.run(my_flow())

For detailed analysis and continued updates, monitor the related GitHub issues and Prefect documentation. Adjust your task and flow configurations to ensure they adhere to Prefect’s concurrency and timeout management principles.