Comments (2)
What I tend to do is to define one base task for the whole project codebase, something like:
class Base(SelinonTask):
def run(self, node_args) -> Any:
# ... perform pre-processing
return self.execute(*args, **kwargs)
def execute(self, *args, **kwargs) -> Any:
raise NotImplementedError("execute() not implemented")
Then, each task in the codebase can derive from Base
and make sure the shared functionality is reused (e.g. preparing args
/kwargs
or any other desired behavior):
def Task1(Base):
def execute(self, my_int: int) -> int:
return my_int * 2
To include args
/kwargs
expansion to Selinon (as a generic library) - we would need to come up with a flexible enough approach for a user and, at the same time, be safe with types/unpacking.
from selinon.
Agreed, I have come up with the same approach as yours for this kind of usage, but I will try to think of a new (and safe way) to implement this properly
from selinon.
Related Issues (20)
- Guidance for integrating Selinon as a library HOT 3
- Comment in code thoughts ? HOT 3
- Switch from raven to sentry-sdk
- Dynamic flow creation for workers HOT 23
- how can I get the progress and state of flow? HOT 3
- Any method can get all flow jobs? HOT 5
- Using Environment variables in storage? HOT 3
- Failure Fallback task loses parent information HOT 5
- Flow error observed very often HOT 14
- Trace logging failing due to AsyncResult not serializable HOT 1
- cannot find task module HOT 1
- Configure CI for this repo
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- Race condition with filesystem storage
- Cannot have condition on edge with python >= 3.8 HOT 3
- using celery beat with selinon HOT 1
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- bug: fieldEqual does not work with boolean values HOT 3
- feature request: Ability to use the return of a task as arguments for the next task / sub-flow HOT 3
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from selinon.