TaskFlow/Inputs and Outputs
Revised on: 10/24/2013 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
See: Task & Flow Arguments and Results
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: how to prepopulate your engine with arguments (so that dependent tasks can immediately start running).
>>> 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
To solve this you would want to do the following to make your flow run smoothly:
>>> 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
Note: as you can see the result of the previous run
method is the results of all tasks that have ran.
This same data can be fetched in a more precise manner by doing the following:
>>> 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.