deb-python-taskflow/doc/source/arguments_and_results.rst

Arguments and results

In TaskFlow, all flow and task state goes to (potentially persistent) storage (see persistence <persistence> for more details). That includes all the information that atoms <atoms> (e.g. tasks, retry objects...) in the workflow need when they are executed, and all the information task/retry produces (via serializable results). A developer who implements tasks/retries or flows can specify what arguments a task/retry accepts and what result it returns in several ways. This document will help you understand what those ways are and how to use those ways to accomplish your desired usage pattern.

Task/retry arguments

Set of names of task/retry arguments available as the requires and/or optional property of the task/retry instance. When a task or retry object is about to be executed values with these names are retrieved from storage and passed to the execute method of the task/retry. If any names in the requires property cannot be found in storage, an exception will be thrown. Any names in the optional property that cannot be found are ignored.

Task/retry results

Set of names of task/retry results (what task/retry provides) available as provides property of task or retry instance. After a task/retry finishes successfully, its result(s) (what the execute method returns) are available by these names from storage (see examples below).

from taskflow import task

Arguments specification

There are different ways to specify the task argument requires set.

Arguments inference

Task/retry arguments can be inferred from arguments of the :py~taskflow.atom.Atom.execute method of a task (or the :py~taskflow.retry.Retry.execute of a retry object).

>>> class MyTask(task.Task): ... def execute(self, spam, eggs, bacon=None): ... return spam + eggs ... >>> sorted(MyTask().requires) ['eggs', 'spam'] >>> sorted(MyTask().optional) ['bacon']

Inference from the method signature is the ''simplest'' way to specify arguments. Special arguments like self, *args and **kwargs are ignored during inference (as these names have special meaning/usage in python).

>>> class UniTask(task.Task): ... def execute(self, args,*kwargs): ... pass ... >>> sorted(UniTask().requires) []

Rebinding

Why: There are cases when the value you want to pass to a task/retry is stored with a name other than the corresponding arguments name. That's when the rebind constructor parameter comes in handy. Using it the flow author can instruct the engine to fetch a value from storage by one name, but pass it to a tasks/retries execute method with another name. There are two possible ways of accomplishing this.

The first is to pass a dictionary that maps the argument name to the name of a saved value.

For example, if you have task:

class SpawnVMTask(task.Task):

    def execute(self, vm_name, vm_image_id, **kwargs):
        pass  # TODO(imelnikov): use parameters to spawn vm

and you saved 'vm_name' with 'name' key in storage, you can spawn a vm with such 'name' like this:

SpawnVMTask(rebind={'vm_name': 'name'})

The second way is to pass a tuple/list/dict of argument names. The length of the tuple/list/dict should not be less then number of required parameters.

For example, you can achieve the same effect as the previous example with:

SpawnVMTask(rebind_args=('name', 'vm_image_id'))

This is equivalent to a more elaborate:

SpawnVMTask(rebind=dict(vm_name='name',
                        vm_image_id='vm_image_id'))

In both cases, if your task (or retry) accepts arbitrary arguments with the **kwargs construct, you can specify extra arguments.

SpawnVMTask(rebind=('name', 'vm_image_id', 'admin_key_name'))

When such task is about to be executed, name, vm_image_id and admin_key_name values are fetched from storage and value from name is passed to :py~taskflow.atom.Atom.execute method as vm_name, value from vm_image_id is passed as vm_image_id, and value from admin_key_name is passed as admin_key_name parameter in kwargs.

Manually specifying requirements

Why: It is often useful to manually specify the requirements of a task, either by a task author or by the flow author (allowing the flow author to override the task requirements).

To accomplish this when creating your task use the constructor to specify manual requirements. Those manual requirements (if they are not functional arguments) will appear in the kwargs of the :py~taskflow.atom.Atom.execute method.

>>> class Cat(task.Task): ... def __init__(self, kwargs): ... if 'requires' not in kwargs: ... kwargs['requires'] = ("food", "milk") ... super(Cat, self).__init__(kwargs) ... def execute(self, food, **kwargs): ... pass ... >>> cat = Cat() >>> sorted(cat.requires) ['food', 'milk']

When constructing a task instance the flow author can also add more requirements if desired. Those manual requirements (if they are not functional arguments) will appear in the kwargs parameter of the :py~taskflow.atom.Atom.execute method.

>>> class Dog(task.Task): ... def execute(self, food, **kwargs): ... pass >>> dog = Dog(requires=("water", "grass")) >>> sorted(dog.requires) ['food', 'grass', 'water']

If the flow author desires she can turn the argument inference off and override requirements manually. Use this at your own risk as you must be careful to avoid invalid argument mappings.

>>> class Bird(task.Task): ... def execute(self, food, **kwargs): ... pass >>> bird = Bird(requires=("food", "water", "grass"), auto_extract=False) >>> sorted(bird.requires) ['food', 'grass', 'water']

Results specification

In python, function results are not named, so we can not infer what a task/retry returns. This is important since the complete result (what the task :py~taskflow.atom.Atom.execute or retry :py~taskflow.retry.Retry.execute method returns) is saved in (potentially persistent) storage, and it is typically (but not always) desirable to make those results accessible to others. To accomplish this the task/retry specifies names of those values via its provides constructor parameter or by its default provides attribute.

Examples

Returning one value

If task returns just one value, provides should be string -- the name of the value.

>>> class TheAnswerReturningTask(task.Task): ... def execute(self): ... return 42 ... >>> sorted(TheAnswerReturningTask(provides='the_answer').provides) ['the_answer']

Returning a tuple

For a task that returns several values, one option (as usual in python) is to return those values via a tuple.

class BitsAndPiecesTask(task.Task):
    def execute(self):
        return 'BITs', 'PIECEs'

Then, you can give the value individual names, by passing a tuple or list as provides parameter:

BitsAndPiecesTask(provides=('bits', 'pieces'))

After such task is executed, you (and the engine, which is useful for other tasks) will be able to get those elements from storage by name:

>>> storage.fetch('bits')
'BITs'
>>> storage.fetch('pieces')
'PIECEs'

Provides argument can be shorter then the actual tuple returned by a task --then extra values are ignored (but, as expected, all those values are saved and passed to the task :py~taskflow.atom.Atom.revert or retry :py~taskflow.retry.Retry.revert method).

Note

Provides arguments tuple can also be longer then the actual tuple returned by task -- when this happens the extra parameters are left undefined: a warning is printed to logs and if use of such parameter is attempted a :py~taskflow.exceptions.NotFound exception is raised.

Returning a dictionary

Another option is to return several values as a dictionary (aka a dict).

class BitsAndPiecesTask(task.Task):

    def execute(self):
        return {
            'bits': 'BITs',
            'pieces': 'PIECEs'
        }

TaskFlow expects that a dict will be returned if provides argument is a set:

BitsAndPiecesTask(provides=set(['bits', 'pieces']))

After such task executes, you (and the engine, which is useful for other tasks) will be able to get elements from storage by name:

>>> storage.fetch('bits')
'BITs'
>>> storage.fetch('pieces')
'PIECEs'

Note

If some items from the dict returned by the task are not present in the provides arguments -- then extra values are ignored (but, of course, saved and passed to the :py~taskflow.atom.Atom.revert method). If the provides argument has some items not present in the actual dict returned by the task -- then extra parameters are left undefined: a warning is printed to logs and if use of such parameter is attempted a :py~taskflow.exceptions.NotFound exception is raised.

Default provides

As mentioned above, the default base class provides nothing, which means results are not accessible to other tasks/retries in the flow.

The author can override this and specify default value for provides using the default_provides class/instance variable:

class BitsAndPiecesTask(task.Task):
    default_provides = ('bits', 'pieces')
    def execute(self):
        return 'BITs', 'PIECEs'

Of course, the flow author can override this to change names if needed:

BitsAndPiecesTask(provides=('b', 'p'))

or to change structure -- e.g. this instance will make tuple accessible to other tasks by name 'bnp':

BitsAndPiecesTask(provides='bnp')

or the flow author may want to return default behavior and hide the results of the task from other tasks in the flow (e.g. to avoid naming conflicts):

BitsAndPiecesTask(provides=())

Revert arguments

To revert a task the engine <engines> calls the tasks :py~taskflow.atom.Atom.revert method. This method should accept the same arguments as the :py~taskflow.atom.Atom.execute method of the task and one more special keyword argument, named result.

For result value, two cases are possible:

• If the task is being reverted because it failed (an exception was raised from its :py~taskflow.atom.Atom.execute method), the result value is an instance of a :py~taskflow.types.failure.Failure object that holds the exception information.
• If the task is being reverted because some other task failed, and this task finished successfully, result value is the result fetched from storage: ie, what the :py~taskflow.atom.Atom.execute method returned.

All other arguments are fetched from storage in the same way it is done for :py~taskflow.atom.Atom.execute method.

To determine if a task failed you can check whether result is instance of :py~taskflow.types.failure.Failure:

from taskflow.types import failure

class RevertingTask(task.Task):

    def execute(self, spam, eggs):
        return do_something(spam, eggs)

    def revert(self, result, spam, eggs):
        if isinstance(result, failure.Failure):
            print("This task failed, exception: %s"
                  % result.exception_str)
        else:
            print("do_something returned %r" % result)

If this task failed (ie do_something raised an exception) it will print "This task failed, exception:" and a exception message on revert. If this task finished successfully, it will print "do_something returned" and a representation of the do_something result.

Retry arguments

A :py~taskflow.retry.Retry controller works with arguments in the same way as a :pyTask <taskflow.task.Task>. But it has an additional parameter 'history' that is itself a :py~taskflow.retry.History object that contains what failed over all the engines attempts (aka the outcomes). The history object can be viewed as a tuple that contains a result of the previous retries run and a table/dict where each key is a failed atoms name and each value is a :py~taskflow.types.failure.Failure object.

Consider the following implementation:

class MyRetry(retry.Retry):

    default_provides = 'value'

    def on_failure(self, history, *args, **kwargs):
        print(list(history))
        return RETRY

    def execute(self, history, *args, **kwargs):
        print(list(history))
        return 5

    def revert(self, history, *args, **kwargs):
        print(list(history))

Imagine the above retry had returned a value '5' and then some task 'A' failed with some exception. In this case on_failure method will receive the following history (printed as a list):

[('5', {'A': failure.Failure()})]

At this point (since the implementation returned RETRY) the :py~taskflow.retry.Retry.execute method will be called again and it will receive the same history and it can then return a value that subsequent tasks can use to alter their behavior.

If instead the :py~taskflow.retry.Retry.execute method itself raises an exception, the :py~taskflow.retry.Retry.revert method of the implementation will be called and a :py~taskflow.types.failure.Failure object will be present in the history object instead of the typical result.

Note

After a :py~taskflow.retry.Retry has been reverted, the objects history will be cleaned.