Comments (1)
Planning to switch from the load_task
model to the run_task
model. Difference in usage:
# load_task usage
task = task_store.load_task(taskid)
result = task()
# run_task usage
task_details = task_store.load_task_details(taskid)
result = task_store.run_task(task_details)
The concern is that the environment where the task runs may be changing over time (e.g., files needed to define the task may change). The load_task
model risks that everything is baked into the task when it is loaded, and the task
could theoretically hang around a bit before being used, meaning that the world can change around it.
The task_details
are always the same, regardless of serialization state. So using run_task
only interfaces with the environment at the moment the task is run.
(Granted, good programming practices will avoid problems in either model, but it seems like a potential footgun to me.)
It is also probably more natural to normal people to write a function that returns a result, rather than a function that returns a function that returns a result. (I'm clearly not normal.)
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Related Issues (13)
- TaskStatusDB: Set up an empty database
- TaskStatusDB: Methods to add a task/network of tasks to a database
- TaskStatusDB: Method to update task status
- ResultStore HOT 1
- Example client code
- TaskStatusDB: Update DAG after task completion
- TaskStatusDB: Rebuild task network object
- Migrate setup to pyproject
- Worker: Method for selecting task to work on
- Worker: Method to run a single task
- Worker: Method to run tasks sequentially until out of queue time
- DISCUSS: TaskStatusDB: Switch to storing status name instead of value?
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