def test_reduce_task_properly_applies_trigger_across_all_mapped_states_for_deep_pipelines(
executor, ):
@prefect.task
def ll():
return [0, 1]
@prefect.task(max_retries=5, retry_delay=datetime.timedelta(hours=1))
def div(x):
return 1 / x
@prefect.task
def take_sum(x):
return sum(x)
with Flow(name="test") as f:
d = div.map(ll)
s = take_sum(d)
state = FlowRunner(flow=f).run(executor=executor, return_tasks=[s])
assert state.is_running()
assert state.result[s].is_pending()
def test_manual_only_trigger_caches_inputs(self, executor):
with Flow(name="test") as f:
x = Parameter("x")
inp = SuccessTask()
t = AddTask(trigger=manual_only)
res = t(x, inp)
first_state = FlowRunner(flow=f).run(executor=executor,
parameters=dict(x=11),
return_tasks=f.tasks)
assert first_state.is_running()
first_state.result.update(
{res: Resume(cached_inputs=first_state.result[res].cached_inputs)})
second_state = FlowRunner(flow=f).run(
executor=executor,
parameters=dict(x=1),
return_tasks=[res],
task_states=first_state.result,
)
assert isinstance(second_state, Success)
assert second_state.result[res].result == 12
def test_retries_ignore_cached_inputs_if_upstream_results_are_available(
self, executor
):
with Flow(name="test") as f:
a = CountTask()
b = ReturnTask(max_retries=1, retry_delay=datetime.timedelta(0))
a_res = a()
b_res = b(a_res)
first_state = FlowRunner(flow=f).run(executor=executor, return_tasks=f.tasks)
assert first_state.is_running()
a_state = first_state.result[a_res]
a_state.result = 100 # modify the result
b_state = first_state.result[b_res]
b_state.cached_inputs = dict(x=Result(2)) # artificially alter state
with raise_on_exception(): # without caching we'd expect a KeyError
second_state = FlowRunner(flow=f).run(
executor=executor, return_tasks=[b_res], task_states=first_state.result
)
assert isinstance(second_state, Success)
assert second_state.result[b_res].result == 1 / 99
def test_retries_use_cached_inputs(self, executor):
with Flow(name="test") as f:
a = CountTask()
b = ReturnTask(max_retries=1, retry_delay=datetime.timedelta(0))
a_res = a()
b_res = b(a_res)
first_state = FlowRunner(flow=f).run(executor=executor, return_tasks=f.tasks)
assert first_state.is_running()
a_state = first_state.result[a_res]
a_state.result = (
NoResult
) # remove the result to see if the cached results are picked up
b_state = first_state.result[b_res]
b_state.cached_inputs = dict(x=Result(2)) # artificially alter state
with raise_on_exception(): # without caching we'd expect a KeyError
second_state = FlowRunner(flow=f).run(
executor=executor, return_tasks=[b_res], task_states=first_state.result
)
assert isinstance(second_state, Success)
assert second_state.result[b_res].result == 1
def test_running_stays_running(self):
state = Running()
flow = Flow(name="test", tasks=[Task()])
new_state = FlowRunner(flow=flow).set_flow_to_running(state=state)
assert new_state.is_running()
def test_pending_becomes_running(self, state):
flow = Flow(name="test", tasks=[Task()])
new_state = FlowRunner(flow=flow).set_flow_to_running(state=state)
assert new_state.is_running()
