Files
deb-python-taskflow/doc/source/notifications.rst
Joshua Harlow 6bbf85b5a5 Add a timing listener that also prints the results
Instead of just recording them it can also be quite
useful (especially for debugging) to print the start
and stop timings as they occur.

Also adds an example that shows how this can be used
and an explanation of why it is useful to have this type
of capability.

Part of blueprint more-examples

Change-Id: Id2dc3f8dc9ac94e511470e39f499f325b33537ee
2014-09-18 11:22:15 -07:00

170 lines
5.3 KiB
ReStructuredText

===========================
Notifications and Listeners
===========================
.. testsetup::
from taskflow import task
from taskflow.patterns import linear_flow
from taskflow import engines
--------
Overview
--------
Engines provide a way to receive notification on task and flow state
transitions, which is useful for monitoring, logging, metrics, debugging
and plenty of other tasks.
To receive these notifications you should register a callback with
an instance of the the :py:class:`notifier <taskflow.utils.misc.Notifier>`
class that is attached
to :py:class:`engine <taskflow.engines.base.EngineBase>`
attributes ``task_notifier`` and ``notifier``.
TaskFlow also comes with a set of predefined :ref:`listeners <listeners>`, and
provides means to write your own listeners, which can be more convenient than
using raw callbacks.
--------------------------------------
Receiving notifications with callbacks
--------------------------------------
To manage notifications instances of
:py:class:`~taskflow.utils.misc.Notifier` are used.
.. autoclass:: taskflow.utils.misc.Notifier
Flow notifications
------------------
To receive notification on flow state changes use
:py:class:`~taskflow.utils.misc.Notifier` available as
``notifier`` property of the engine. A basic example is:
.. doctest::
>>> 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 = linear_flow.Flow("cat-dog").add(
... CatTalk(), DogTalk(provides="dog"))
>>> 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
------------------
To receive notification on task state changes use
:py:class:`~taskflow.utils.misc.Notifier` available as
``task_notifier`` property of the engine. A basic example is:
.. doctest::
>>> 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 = linear_flow.Flow("cat-dog")
>>> flo.add(CatTalk(), DogTalk(provides="dog"))
<taskflow.patterns.linear_flow.Flow object at 0x...>
>>> eng = engines.load(flo, store={'meow': 'meow', 'woof': 'woof'})
>>> eng.task_notifier.register("*", task_transition)
>>> eng.run()
Task 'CatTalk' transition to state RUNNING
meow
Task 'CatTalk' transition to state SUCCESS
Task 'DogTalk' transition to state RUNNING
woof
Task 'DogTalk' transition to state SUCCESS
.. _listeners:
---------
Listeners
---------
TaskFlow comes with a set of predefined listeners -- helper classes that can be
used to do various actions on flow and/or tasks transitions. You can also
create your own listeners easily, which may be more convenient than using raw
callbacks for some use cases.
For example, this is how you can use
:py:class:`~taskflow.listeners.printing.PrintingListener`:
.. doctest::
>>> from taskflow.listeners import printing
>>> 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 = linear_flow.Flow("cat-dog").add(
... CatTalk(), DogTalk(provides="dog"))
>>> eng = engines.load(flo, store={'meow': 'meow', 'woof': 'woof'})
>>> with printing.PrintingListener(eng):
... eng.run()
...
taskflow.engines.action_engine.engine.SingleThreadedActionEngine: ... has moved flow 'cat-dog' (...) into state 'RUNNING'
taskflow.engines.action_engine.engine.SingleThreadedActionEngine: ... has moved task 'CatTalk' (...) into state 'RUNNING'
meow
taskflow.engines.action_engine.engine.SingleThreadedActionEngine: ... has moved task 'CatTalk' (...) into state 'SUCCESS' with result 'cat' (failure=False)
taskflow.engines.action_engine.engine.SingleThreadedActionEngine: ... has moved task 'DogTalk' (...) into state 'RUNNING'
woof
taskflow.engines.action_engine.engine.SingleThreadedActionEngine: ... has moved task 'DogTalk' (...) into state 'SUCCESS' with result 'dog' (failure=False)
taskflow.engines.action_engine.engine.SingleThreadedActionEngine: ... has moved flow 'cat-dog' (...) into state 'SUCCESS'
Basic listener
--------------
.. autoclass:: taskflow.listeners.base.ListenerBase
Printing and logging listeners
------------------------------
.. autoclass:: taskflow.listeners.base.LoggingBase
.. autoclass:: taskflow.listeners.logging.LoggingListener
.. autoclass:: taskflow.listeners.printing.PrintingListener
Timing listener
---------------
.. autoclass:: taskflow.listeners.timing.TimingListener
.. autoclass:: taskflow.listeners.timing.PrintingTimingListener