Bruno Nirello
12/10/2024, 8:02 PMMarvin
12/10/2024, 8:02 PMresult(raise_on_failure=False)
method, which will allow the flow to continue running even if a task encounters an error.
Here's a snippet illustrating this concept:
python
from prefect import flow, task
@task
def always_fails_task():
raise ValueError("I fail successfully")
@task
def always_succeeds_task():
print("I'm fail safe!")
return "success"
@flow
def always_succeeds_flow():
x = always_fails_task.submit().result(raise_on_failure=False)
y = always_succeeds_task.submit(wait_for=[x])
return y
if __name__ == "__main__":
always_succeeds_flow()
In this example, always_fails_task
will fail, but always_succeeds_task
will still run successfully because the result
method was called with raise_on_failure=False
.
You can find more details and context in the Prefect 2.x documentation on flows.
Isn't it nice when things just work, even with a hiccup or two? Just like life, flowing along despite minor bumps!Bruno Nirello
12/10/2024, 8:19 PMMarvin
12/10/2024, 8:19 PM.map()
in Prefect 2.x can indeed handle scenarios where tasks might fail, just like when using .result(raise_on_failure=False)
. However, it requires some additional considerations.
When you use the .map()
function, it’s like calling a task multiple times in parallel, and handling failures in this parallelized context can be trickier. Here’s an approach you might take:
1. Handle Individual Task Failures: Within the mapped task, you can handle exceptions so that each individual task can continue or return a default value if it fails. This way, the .map()
function can complete without stopping prematurely due to failed tasks.
2. Use result(raise_on_failure=False)
: Just as with individual tasks, you can ensure that a mapped task doesn't raise an exception to the calling flow by using .result(raise_on_failure=False)
.
For more theoretical explanations and code examples, you might want to explore the Prefect 2.x documentation and see how mapping can be used effectively: Prefect's Mapping Documentation.
There's nothing like watching tasks fail spectacularly in parallel... and your flow confidently shrugging it off!