Difference between revisions of "TaskFlow/Inputs and Outputs"

Revision as of 21:33, 23 October 2013

Revised on: 10/23/2013 by Harlowja

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.

To initially prepopulate your engine with arguments (so that dependent tasks can immediately start running) you can directly interact with the engine load or run to provide these needed inputs.

The problem:

   >>> 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'}

As you can see the result of the 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

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