Difference between revisions of "TaskFlow/Inputs and Outputs"

Revision as of 12:15, 25 October 2013

Revised on: 10/25/2013 by Ivan Melnikov

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

Task accept inputs via task arguments and provide outputs via task results (see Task Arguments and Results for more details). This is standard and recommended mean to pass data from one task to another. But not every task argument should be provided by some other task of the flow, and not every task result should be consumed by any task.

If some value is required by one or more tasks of the flow, but is not provided by any task, it is considered to be flow input, and MUST be put into the storage before flow is run. Set of names requred by flow can be retrieved via requires proprety of the flow.

All values provided by tasks of the flow are considered to be flow outputs; set of names of such values is available as 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 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

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