Hi @Marvin, is there a way in prefect 3 that we can call a customized save_error_to_db function while any exception happens during the task run, and then call pause_flow_run to pause the flow for investigation? Maybe a wrapper decorator for the task decorator?

To achieve custom error handling in Prefect 3.x, you can wrap your task with a custom decorator that implements your error handling logic, such as calling a

save_error_to_db

function and then pausing the flow run. Here's a concise way to do this: 1. Custom Decorator: Create a custom decorator that wraps the Prefect task. This decorator should catch exceptions, call your

save_error_to_db

function, and then use Prefect's

pause_flow_run

to pause the flow. 2. Using Prefect API: Prefect provides a

pause_flow_run

function that can be used to pause the flow. You can integrate this into your custom decorator. Here's a basic implementation:

python
from prefect import task, pause_flow_run

def custom_error_handler(func):
    def wrapper(*args, **kwargs):
        try:
            return func(*args, **kwargs)
        except Exception as e:
            save_error_to_db(e)  # Your custom error handling function
            pause_flow_run()  # Pause the flow
            raise  # Optionally re-raise the exception if needed
    return wrapper

@task
@custom_error_handler
def my_task():
    # Task logic here
    pass

def save_error_to_db(exception):
    # Custom logic to save the error to a database
    pass

Resources: - Prefect API Overview - Task Decorator in Prefect This approach allows you to maintain control over error handling while leveraging Prefect's built-in functionalities. If you need to integrate this with PyO3 for any Rust-Python interop, ensure that your

save_error_to_db

function handles the necessary FFI logic.

Hi @Marvin, I used the sample above but got error

Cannot pause task runs.

Do I need to do the pause_flow_run in another decorator outside the task decorator?

In Prefect 3.x, the

pause_flow_run

function is intended to be used within the context of a flow, not directly within a task. This is likely why you're encountering the "Cannot pause task runs" error. The

pause_flow_run

function should be invoked from within the flow logic rather than the task logic. Here's a revised approach: 1. Custom Task Decorator: Use a custom decorator to handle exceptions and call

save_error_to_db

. 2. Flow Logic: Implement logic in the flow to check for task exceptions and call

pause_flow_run

if needed. Here's how you can structure this:

python
from prefect import task, flow, pause_flow_run

def custom_error_handler(func):
    def wrapper(*args, **kwargs):
        try:
            return func(*args, **kwargs)
        except Exception as e:
            save_error_to_db(e)  # Your custom error handling function
            raise  # Re-raise the exception to be handled by the flow
    return wrapper

@task
@custom_error_handler
def my_task():
    # Task logic here
    pass

@flow
def my_flow():
    try:
        my_task()
    except Exception:
        pause_flow_run()  # Pause the flow if an exception occurs

def save_error_to_db(exception):
    # Custom logic to save the error to a database
    pass

Key Points: - The

pause_flow_run

should be called in the flow logic, not within the task. - This structure allows you to handle the error in the task, save it to the database, and then handle the flow pausing separately. Resources: - Prefect Flow API - Common Gotchas in Prefect 3.x This approach should align better with Prefect's design and help you achieve your error handling and flow management goals.

Hi @Marvin, if I add the @custom_error_handler outside of @task instead of inside, I can do pause_flow_run. But then the map function of the task stopped working. Is there a way that I can have my decorator outside the task decorator and the map function of the task still works?

When using the

map

function with tasks in Prefect, the task should be decorated with

@task

to ensure that the mapping functionality works correctly. If you place your custom error handler outside of the

@task

decorator, it can interfere with Prefect's ability to recognize and execute the task as intended, especially with mapping. To maintain the mapping functionality while using a custom error handler, you can use a different approach: 1. Use a Task Wrapper: Instead of decorating the task directly with the custom error handler, wrap the task call within your flow. This way, you can handle errors and pause the flow without interfering with the task's mapping capability. Here's an example:

python
from prefect import task, flow, pause_flow_run

@task
def my_task(item):
    # Task logic here
    pass

def custom_error_handler(task_func, *args, **kwargs):
    try:
        return task_func(*args, **kwargs)
    except Exception as e:
        save_error_to_db(e)
        pause_flow_run()
        raise

@flow
def my_flow():
    items = [1, 2, 3]
    for item in items:
        custom_error_handler(my_task.submit, item)

def save_error_to_db(exception):
    # Custom logic to save the error to a database
    pass

Key Points: - The

my_task

is still decorated with

@task

, preserving its ability to be mapped. - The

custom_error_handler

is used within the flow to handle exceptions and pause the flow as needed. This approach ensures that the task's mapping functionality remains intact while still allowing you to manage errors and pause the flow run when necessary.

Hi @Marvin, in your sample code above, can we still use the map function instead of do for loop?

In Prefect, the

map

function is tied to the task's ability to handle multiple inputs concurrently. If you want to use the

map

function while still applying custom error handling, you can wrap the task execution in a way that allows you to catch exceptions after mapping. Here's a way to achieve this:

python
from prefect import task, flow, pause_flow_run

@task
def my_task(item):
    # Task logic here
    pass

def save_error_to_db(exception):
    # Custom logic to save the error to a database
    pass

@flow
def my_flow():
    items = [1, 2, 3]
    task_futures = my_task.map(items)
    
    for future in task_futures:
        try:
            future.result()  # This will raise any exceptions encountered during the task run
        except Exception as e:
            save_error_to_db(e)
            pause_flow_run()
            raise

my_flow()

Key Points: - Use

my_task.map(items)

to initiate the concurrent execution of tasks. - Iterate over the returned futures and call

future.result()

to trigger exception handling. - Catch exceptions and handle them with

save_error_to_db

and

pause_flow_run

. This method allows you to use the

map

function while still implementing custom error handling and flow pausing.