In TaskFlow, all flow and task state goes to (potentially persistent) storage. That includes all the information that atoms (e.g. tasks) in the flow need when they are executed, and all the information task produces (via serializable task results). A developer who implements tasks or flows can specify what arguments a task 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.
There are different ways to specify the task argument requires set.
Task arguments can be inferred from arguments of the execute() method of the task.
>>> class MyTask(task.Task):
... def execute(self, spam, eggs):
... return spam + eggs
...
>>> MyTask().requires
set(['eggs', 'spam'])
Inference from the method signature is the ‘’simplest’’ way to specify task arguments. Optional arguments (with default values), and special arguments like self, *args and **kwargs are ignored on inference (as these names have special meaning/usage in python).
>>> class MyTask(task.Task):
... def execute(self, spam, eggs=()):
... return spam + eggs
...
>>> MyTask().requires
set(['spam'])
>>>
>>> class UniTask(task.Task):
... def execute(self, *args, **kwargs):
... pass
...
>>> UniTask().requires
set([])
Why: There are cases when the value you want to pass to a task is stored with a name other then the corresponding task arguments name. That’s when the rebind task 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 execute() method with another name. There are two possible ways of accomplishing this.
The first is to pass a dictionary that maps the task 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 task required parameters. For example, you can achieve the same effect as the previous example with:
SpawnVMTask(rebind_args=('name', 'vm_image_id'))
which is equivalent to a more elaborate:
SpawnVMTask(rebind=dict(vm_name='name',
vm_image_id='vm_image_id'))
In both cases, if your task accepts arbitrary arguments with **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 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.
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 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 the 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']
In python, function results are not named, so we can not infer what a task returns. This is important since the complete task result (what the execute() method returns) is saved in (potentially persistent) storage, and it is typically (but not always) desirable to make those results accessible to other tasks. To accomplish this the task specifies names of those values via its provides task constructor parameter or other method (see below).
If task returns just one value, provides should be string – the name of the value.
>>> class TheAnswerReturningTask(task.Task):
... def execute(self):
... return 42
...
>>> TheAnswerReturningTask(provides='the_answer').provides
set(['the_answer'])
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 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 NotFound exception is raised.
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 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 NotFound exception is raised.
As mentioned above, the default task base class provides nothing, which means task results are not accessible to other tasks in the flow.
The task author can override this and specify default value for provides using default_provides class 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 whole 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=())
To revert a task engine calls its revert() method. This method should accept same arguments as execute() method of the task and one more special keyword argument, named result.
For result value, two cases are possible:
All other arguments are fetched from storage in the same way it is done for execute() method.
To determine if task failed you can check whether result is instance of taskflow.utils.misc.Failure:
from taskflow.utils import misc
class RevertingTask(task.Task):
def execute(self, spam, eggs):
return do_something(spam, eggs)
def revert(self, result, spam, eggs):
if isinstance(result, misc.Failure):
print("This task failed, exception: %s"
% result.exception_str)
else:
print("do_something returned %r" % result)
If this task failed (do_something raised exception) it will print "This task failed, exception:" and exception message on revert. If this task finished successfully, it will print "do_something returned" and representation of result.
A Retry controller works with arguments in the same way as a Task. But it has an additional parameter ‘history’ that is a list of tuples. Each tuple contains a result of the previous Retry run and a table where a key is a failed task and a value is a taskflow.utils.misc.Failure.
Consider the following Retry:
class MyRetry(retry.Retry):
default_provides = 'value'
def on_failure(self, history, *args, **kwargs):
print history
return RETRY
def execute(self, history, *args, **kwargs):
print history
return 5
def revert(self, history, *args, **kwargs):
print history
Imagine the following 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:
[('5', {'A': misc.Failure()})]
Then the execute() method will be called again and it’ll receive the same history.
If the execute() method raises an exception, the revert() method of Retry will be called and taskflow.utils.misc.Failure object will be present in the history instead of Retry result:
[('5', {'A': misc.Failure()}), (misc.Failure(), {})]
After the Retry has been reverted, the Retry history will be cleaned.