TaskFlow/Inputs and Outputs
Revised on: 10/26/2013 by Harlowja
Contents
Overview
In taskflow there are multiple ways to design how your tasks/flows and engines get inputs and produce outputs. This document will help you understand what those ways are and how to use those ways to accomplish your desired taskflow usage pattern as well as include examples that show common ways of providing input and getting output.
Task & Flow Inputs and Outputs
A task accept inputs via task arguments and provides outputs via task results (see task arguments and results for more details). This the standard and recommended way to pass data from one task to another. Of course not every task argument needs to be provided to some other task of a flow, and not every task result should be consumed by every task.
If some value is required by one or more tasks of a flow, but is not provided
by any task, it is considered to be flow input, and MUST be put into the
storage before the flow is run. A set of names
required by a flow can be retrieved via that flows requires property.
These names can be used to determine what names may be applicable for placing
in storage ahead of time and which names are not applicable.
All values provided by tasks of the flow are considered to be flow outputs;
the set of names of such values is available via the provides
property of the flow.
For example:
>>> class MyTask(task.Task):
... def execute(self, **kwargs):
... return 1, 2
...
>>> flow = linear_flow.Flow('test').add(
... MyTask(requires='a', provides=('b', 'c')),
... MyTask(requires='b', provides='d')
... )
>>> flow.requires
set(['a'])
>>> flow.provides
set(['c', 'b', 'd'])
As you can see, this flow does not require b, as it is provided by the
fist task.
Engine Inputs and Outputs
Storage
The storage layer is how an engine persists flow and task details.
For more in-depth design details: persistence.
Inputs
The problem: you should prepopulate storage with all required flow inputs before running it:
>>> from taskflow import task
>>> from taskflow import engines
>>> from taskflow.patterns import linear_flow as lf
>>>
>>> class CatTalk(task.Task):
... def execute(self, meow):
... print meow
... return "cat"
...
>>> class DogTalk(task.Task):
... def execute(self, woof):
... print woof
... return "dog"
...
>>> flo = lf.Flow("cat-dog")
>>> flo.add(CatTalk(), DogTalk(provides="dog"))
>>> engines.run(flo)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/lib/python2.6/site-packages/taskflow/engines/helpers.py", line 110, in run
engine.run()
File "/usr/lib/python2.6/site-packages/taskflow/utils/lock_utils.py", line 51, in wrapper
return f(*args, **kwargs)
File "/usr/lib/python2.6/site-packages/taskflow/engines/action_engine/engine.py", line 104, in run
raise exc.MissingDependencies(self._flow, sorted(missing))
taskflow.exceptions.MissingDependencies: taskflow.patterns.linear_flow.Flow: cat-dog;
2 requires ['meow', 'woof'] but no other entity produces said requirements
The solution: provide necessary data via store parameter of engines.run:
>>> from taskflow import task
>>> from taskflow import engines
>>> from taskflow.patterns import linear_flow as lf
>>>
>>> class CatTalk(task.Task):
... def execute(self, meow):
... print meow
... return "cat"
...
>>> class DogTalk(task.Task):
... def execute(self, woof):
... print woof
... return "dog"
...
>>> flo = lf.Flow("cat-dog")
>>> flo.add(CatTalk(), DogTalk(provides="dog"))
>>> engines.run(flo, store={'meow': 'meow', 'woof': 'woof'})
meow
woof
{'meow': 'meow', 'woof': 'woof', 'dog': 'dog'}
Note: you can also directly interact with the engine storage layer to add additional values although you must use the load method instead.
>>> flo = lf.Flow("cat-dog")
>>> flo.add(CatTalk(), DogTalk(provides="dog"))
>>> eng = engines.load(flo, store={'meow': 'meow'})
>>> eng.storage.inject({"woof": "bark"})
>>> eng.run()
meow
bark
Outputs
As you can see from examples above, run method returns all flow outputs
in a dict. This same data can be fetched via fetch_all method
of the storage, or in a more precise manner by using fetch method.
For example:
>>> eng = engines.load(flo, store={'meow': 'meow', 'woof': 'woof'})
>>> eng.run()
meow
woof
>>> print(eng.storage.fetch_all())
{'meow': 'meow', 'woof': 'woof', 'dog': 'dog'}
>>> print(eng.storage.fetch("dog"))
dog
Notifications
What: engines provide a way to receive notification on task and flow state transitions.
Why: state transition notifications are useful for monitoring, logging, metrics, debugging, affecting further engine state (and other unknown future usage).
Flow notifications
A basic example is the following:
>>> from taskflow import task
>>> from taskflow import engines
>>> from taskflow.patterns import linear_flow as lf
>>>
>>> class CatTalk(task.Task):
... def execute(self, meow):
... print(meow)
... return "cat"
...
>>> class DogTalk(task.Task):
... def execute(self, woof):
... print(woof)
... return 'dog'
...
>>> def flow_transition(state, details):
... print("Flow '%s' transition to state %s" % (details['flow_name'], state))
...
>>>
>>> flo = lf.Flow("cat-dog")
>>> flo.add(CatTalk(), DogTalk(provides="dog"))
<taskflow.patterns.linear_flow.Flow object at 0x2263050>
>>> eng = engines.load(flo, store={'meow': 'meow', 'woof': 'woof'})
>>> eng.notifier.register("*", flow_transition)
>>> eng.run()
Flow 'cat-dog' transition to state RUNNING
meow
woof
Flow 'cat-dog' transition to state SUCCESS
Task notifications
A basic example is the following:
>>> from taskflow import task
>>> from taskflow import engines
>>> from taskflow.patterns import linear_flow as lf
>>>
>>> class CatTalk(task.Task):
... def execute(self, meow):
... print(meow)
... return "cat"
...
>>> class DogTalk(task.Task):
... def execute(self, woof):
... print(woof)
... return 'dog'
...
>>> def task_transition(state, details):
... print("Task '%s' transition to state %s" % (details['task_name'], state))
...
>>>
>>> flo = lf.Flow("cat-dog")
>>> flo.add(CatTalk(), DogTalk(provides="dog"))
<taskflow.patterns.linear_flow.Flow object at 0x22634d0>
>>> eng = engines.load(flo, store={'meow': 'meow', 'woof': 'woof'})
>>> eng.task_notifier.register("*", task_transition)
>>> eng.run()
Task '__main__.CatTalk' transition to state RUNNING
meow
Task '__main__.CatTalk' transition to state SUCCESS
Task '__main__.DogTalk' transition to state RUNNING
woof
Task '__main__.DogTalk' transition to state SUCCESS
Common notification classes
There exists common helper classes that can be used to accomplish common ways of notifying.
A basic example is the following:
>>> from taskflow import task
>>> from taskflow import engines
>>> from taskflow.listeners import printing
>>> from taskflow.patterns import linear_flow as lf
>>>
>>> class CatTalk(task.Task):
... def execute(self, meow):
... print(meow)
... return "cat"
...
>>> class DogTalk(task.Task):
... def execute(self, woof):
... print(woof)
... return 'dog'
...
>>>
>>> flo = lf.Flow("cat-dog")
>>> flo.add(CatTalk(), DogTalk(provides="dog"))
>>> eng = engines.load(flo, store={'meow': 'meow', 'woof': 'woof'})
>>> with printing.PrintingListener(eng):
... eng.run()
...
SingleThreadedActionEngine: 49258576 has moved flow 'cat-dog' (...c) into state 'RUNNING'
SingleThreadedActionEngine: 49258576 has moved task '__main__.CatTalk' (...) into state 'RUNNING'
meow
SingleThreadedActionEngine: 49258576 has moved task '__main__.CatTalk' (...) into state 'SUCCESS' with result 'cat' (failure=False)
SingleThreadedActionEngine: 49258576 has moved task '__main__.DogTalk' (...) into state 'RUNNING'
woof
SingleThreadedActionEngine: 49258576 has moved task '__main__.DogTalk' (...) into state 'SUCCESS' with result 'dog' (failure=False)
SingleThreadedActionEngine: 49258576 has moved flow 'cat-dog' (...) into state 'SUCCESS'
