TaskFlow/Task Arguments and Results
Contents
Overview
In TaskFlow, all flow state should go to storage. That includes all the information that task needs when it is executed (task arguments), and all the information task produces (task results). Developer who implements task or flow can specify what arguments task accepts and what result it returns in several ways.
Set of names of task arguments is available as requires
property
of the task instance. When task is about to be executed values with this names
are retrieved from storage and passed to execute
method of the task
as keyword arguments.
Set of names of task results (what task provides) is available as
provides
property of task instance. After task finishes
successfully, it's result(s) (what task execute
method returns) are
available by these names from storage (there will be examples below).
Arguments Specification
There are different way to specify task argument set.
Arguments Inference
Task arguments can be inferred from arguments of execute
method of
the task. For example:
>>> class MyTask(task.Task): ... def execute(self, spam, eggs): ... return spam + eggs ... >>> MyTask().requires set(['eggs', 'spam'])
Inference from signature is simplest way to specify task arguments.
Optional arguments (with default values), and special arguments like
self
, *args
and **kwargs
are
ignored on iferrence:
>>> 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([])
Rebind
There are cases when value you want to pass to task is stored with name other
then corresponding task argument. That's when rebind
task
constructor parameter comes handy. Using it flow author can instruct engine
to fetch a value from storage by one name, but pass it to task's
execute
method with another.
There are two possible way of using it. First is to pass dictionary that maps task argument name to name of 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 paramters to spawn vm
and you saved vm name with 'name' key in storage, you can spawn vm with such name like this:
SpawnVMTask(rebind={'vm_name': 'name'})
Second, you can pass a tuple or list of argument names, and values with that names are passed to task. The length of tuple or list should not be less then number of task required parameters. For example, you can achieve same effect as the previous example with:
SpawnVMTask(rebind_args=('name', 'vm_image_id'))
which is equivalent to 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. For example:
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 stroage, and, 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
.
Manually Specifying Requirements
TODO(imelnikov): describe requires
parameter, optional task
args and **kwargs
.