@Marvin In Prefect 2.20 I am running concurrent subflows by using asyncio.TaskGroup in the context manager. I notice that if one of those subflows fails before the other complete, the entire TaskGroup execution will crash. The other subflows will error with

Crash detected! Execution was cancelled by the runtime environment.

The main flow will finish in state failed with

Finished in state Failed('Flow run encountered an exception. ExceptionGroup: unhandled errors in a TaskGroup (1 sub-exception)')

If the failing subflow retries and then fails after the other subflows are complete, the main subflow will finish gracefully in state Failed,

Finished in state Failed('Flow run encountered an exception.

with a descriptive error message. What is the best way to handle subflow failures for concurrent subflows running inside of an asyncio.TaskGroup context?

Handling failures in concurrent subflows using

asyncio.TaskGroup

can be tricky, especially when one subflow's failure affects the entire group. Here are some strategies to consider: 1. Use `allow_failure`: Prefect provides an

allow_failure

utility that can be used to handle specific tasks or subflows that are expected to fail without affecting the entire flow. This might be useful if you can anticipate certain failures and want to handle them gracefully. 2. Custom Exception Handling: You can wrap your subflows with try-except blocks to catch exceptions and handle them as needed. This can prevent the entire

TaskGroup

from crashing due to a single failure. 3. Retries: Implement retries for subflows that might fail due to transient issues. Prefect supports retries to make your flows more resilient. You can specify the number of retries and delay between retries in your subflow definitions. 4. Flow State Management: Check the flow state and handle different outcomes explicitly. Prefect allows you to manage and inspect the state of your flows, which can be useful in deciding how to proceed after a subflow failure. 5. Logging and Monitoring: Utilize Prefect's logging and state management to monitor subflow executions and gather insights into failures. This can help in diagnosing issues and implementing more robust error-handling strategies. For more detailed guidance, you may want to explore Prefect's documentation on flows and subflows and consider using Prefect's built-in tools like

allow_failure

and retry mechanisms. If you need specific syntax or examples, let me know, and I can help gather that information!

@Marvin great job marvin! How do I use allow_failure? I can't tell from the docs.

The

allow_failure

utility in Prefect is used to handle failures gracefully without affecting the entire flow. Here's a basic understanding of how you can use it: ### Usage - Parameters: - `value`: The task or subflow that you want to allow to fail without propagating the failure to the entire flow. - Returns: - A modified version of the task or subflow that does not raise an exception on failure. ### Example While the exact usage example isn't explicitly detailed in the documents, the general idea is to wrap tasks or subflows that you anticipate might fail with

allow_failure

. This way, the failure of these tasks won't cause the entire flow to fail. For example, you might use it like this:

python
from prefect import flow, task, allow_failure

@task
def might_fail():
    # Some operation that might fail
    ...

@flow
def my_flow():
    # Wrap the task with allow_failure
    result = allow_failure(might_fail.submit())
    # Continue with other tasks or logic
    ...

This would ensure that if

might_fail

encounters an error, it won't crash the entire flow, allowing other tasks to proceed. For more details, you might want to explore Prefect's documentation on flows. If you have more specific questions or need further examples, feel free to ask!

@Marvin give an example where might_fail is a flow instead of task.

Here's how you can use

allow_failure

with a subflow in Prefect:

python
from prefect import flow, task, allow_failure

@flow
def might_fail_flow():
    # Some operation that might fail
    raise ValueError("This flow failed!")

@task
def another_task():
    print("This task runs regardless of the subflow's outcome.")

@flow
def my_main_flow():
    # Wrap the subflow with allow_failure
    result = allow_failure(might_fail_flow())
    # Continue with other tasks or logic
    another_task()

if __name__ == "__main__":
    my_main_flow()

### Explanation - `might_fail_flow`: This is a subflow that intentionally raises an exception to simulate a failure. - `another_task`: This task will run regardless of the outcome of

might_fail_flow

. - `my_main_flow`: The main flow where

might_fail_flow

is wrapped with

allow_failure

. This ensures that even if

might_fail_flow

fails, it doesn't affect the execution of

another_task

. This pattern allows you to handle failures in subflows without interrupting the overall execution of your main flow.

@Marvin OK, how to I apply the allow_failure wrapper to an async call of my flow dbx_extract, when it currently has this syntax:

asyncio.to_thread(dbx_extract, extract_filepath=extractsql)

To use

allow_failure

with an async call for your flow

dbx_extract

, you can wrap the call within the

asyncio.to_thread

function. Here's how you might apply it:

python
from prefect import allow_failure
import asyncio

# Assuming dbx_extract is defined as a flow
async def run_dbx_extract(extract_filepath):
    # Use allow_failure to permit failure without crashing the entire flow
    result = await asyncio.to_thread(allow_failure, dbx_extract, extract_filepath=extract_filepath)
    return result

# Example usage within an async context
async def main():
    extractsql = "path/to/extract.sql"
    result = await run_dbx_extract(extractsql)
    print("Result:", result)

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

### Explanation - `allow_failure`: This utility is used to wrap the

dbx_extract

call, allowing it to fail without causing the entire flow to crash. - `asyncio.to_thread`: This function runs the

dbx_extract

flow in a separate thread, which is useful for blocking operations within an async context. - `run_dbx_extract`: An async function that wraps the

dbx_extract

call with

allow_failure

. This approach allows the

dbx_extract

flow to be executed asynchronously while handling any potential failures gracefully.

@Marvin The dbx_extract function takes an argument extract_filepath, its not fitting the syntax quite right:

TypeError: BaseAnnotation.__new__() got an unexpected keyword argument 'extract_filepath'

The error you're encountering suggests that

allow_failure

is being called incorrectly. It seems like

allow_failure

might not directly accept keyword arguments for the function it wraps. Instead, you should wrap the function call itself. Here's how you can modify your code:

python
from prefect import allow_failure
import asyncio

# Assuming dbx_extract is defined as a flow
async def run_dbx_extract(extract_filepath):
    # Define a wrapper function to pass the argument correctly
    def extract_wrapper():
        return dbx_extract(extract_filepath)

    # Use allow_failure to permit failure without crashing the entire flow
    result = await asyncio.to_thread(allow_failure, extract_wrapper)
    return result

# Example usage within an async context
async def main():
    extractsql = "path/to/extract.sql"
    result = await run_dbx_extract(extractsql)
    print("Result:", result)

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

### Explanation - `extract_wrapper`: A simple wrapper function is created to pass the

extract_filepath

argument to

dbx_extract

. - `allow_failure`: Now wraps the

extract_wrapper

function, which correctly calls

dbx_extract

with the necessary argument. This should resolve the

TypeError

by ensuring that

allow_failure

is used to wrap a callable that doesn't directly take keyword arguments.