Simple Flow is a Python library that provides abstractions to write programs in the distributed dataflow paradigm. It relies on futures to describe the dependencies between tasks. It coordinates the execution of distributed tasks with Amazon SWF.

A Future object models the asynchronous execution of a computation that may end.

It tries to mimics the interface of the Python concurrent.futures library.

• Provides a Future abstraction to define dependencies between tasks.
• Define asynchronous tasks from callables.
• Handle workflows with Amazon SWF.
• Implement replay behavior like the Amazon Flow framework.
• Handle retry of tasks that failed.
• Automatically register decorated tasks.
• Handle the completion of a decision with more than 100 tasks.
• Provides a local executor to check a workflow without Amazon SWF (see simpleflow --local command).
• Provides decider and activity worker process for execution with Amazon SWF.
• Ships with the simpleflow command. simpleflow --help for more information about the commands it supports.

Let's take a simple example that computes the result of (x + 1) * 2. You will find this example in examples/basic.py.

We need to declare the functions as activities to make them available:

from simpleflow import (
    activity,
    Workflow,
    futures,
)

@activity.with_attributes(task_list='quickstart', version='example')
def increment(x):
    return x + 1

@activity.with_attributes(task_list='quickstart', version='example')
def double(x):
    return x * 2

@activity.with_attributes(task_list='quickstart', version='example')
def delay(t, x):
    time.sleep(t)
    return x

And then define the workflow itself in a example.py file:

class BasicWorkflow(Workflow):
    name = 'basic'
    version = 'example'
    task_list = 'example'

    def run(self, x, t=30):
        y = self.submit(increment, x)
        yy = self.submit(delay, t, y)
        z = self.submit(double, y)

        print '({x} + 1) * 2 = {result}'.format(
            x=x,
            result=z.result)
        futures.wait(yy, z)
        return z.result

Now check that the workflow works locally with an integer "x" and a wait value "t": :

$ simpleflow workflow.start --local examples.basic.BasicWorkflow --input '[1, 5]'
(1 + 1) * 2 = 4

input is encoded in JSON format and can contain the list of positional arguments such as '[1, 1] or a dict with the args and kwargs keys such as {"args": [1], "kwargs": {}}, {"kwargs": {"x": 1}}, or '{"args": [1], "kwargs": {"t": 5}}'`.

Now that you are confident that the workflow should work, you can run it on Amazon SWF with the standalone command : :

$ simpleflow standalone --domain TestDomain examples.basic.BasicWorkflow --input '[1, 5]'

The standalone command sets an unique task list and manage all the processes that are needed to execute the workflow: decider, activity worker, and a client that starts the workflow. It is very convenient for testing a workflow by executing it with SWF during the development steps or integration tests.

Let's take a closer look to the workflow definition.

It is a class that inherits from simpleflow.Workflow:

class BasicWorkflow(Workflow):

It defines 3 class attributes:

• name, the name of the SWF workflow type.
• version, the version of the SWF workflow type. It is currently provided only for labeling a workflow.
• task_list, the default task list (see it as a dynamically created queue) where decision tasks for this workflow will be sent. Any decider that listens on this task list can handle this workflow. This value can be overrided by the simpleflow commands and objects.

It also implements the run method that takes two arguments: x and t=30 (i.e. t is optional and has the default value 30). These arguments are passed with the --input option. The run method describes the workflow and how its tasks should execute.

Each time a decider takes a decision task, it executes again the run from the start. When the workflow execution starts, it evaluates y = self.submit(increment, x) for the first time. y holds a future in state PENDING. The execution continues with the line yy = self.submit(delay, t, y). yy holds another future in state PENDING. This state means the task has not been scheduled. Now execution still continue in the run method with the line z = self.submit(double, y). Here it needs the value of the y future to evaluate the double activity. As the execution cannot continues, the decider schedules the task increment. yy is not a dependency for any task so it is not scheduled.

Once the decider has scheduled the task for y, it sleeps and waits for an event to be waken up. This happens when the increment task completes. SWF schedules a decision task. A decider takes it and executes the BasicWorkflow.run method again from the start. It evalues the line y = self.submit(increment, x). The task associated with the y future has completed. Hence y is in state FINISHED and contains the value 2 in y.result. The execution continues until it blocks. It goes by yy = self.submit(delay, t, y) that stays the same. Then it reaches z = self.submit(double, y). It gets the value of y.result and z now holds a future in state PENDING. Execution reaches the line with the print. It blocks here because z.result is not available. The decider schedules the task backs by the z future: double(y). The workflow execution continues so forth by evaluating the BasicWorkflow.run again from the start until it finishes.

Please read and even run the demo script to have a quick glance of simpleflow commands. To run the demo you will need to start decider and activity worker processes.

Start a decider with: :

$ simpleflow decider.start --domain TestDomain --task-list test examples.basic.BasicWorkflow

Start an activity worker with: :

$ simpleflow worker.start --domain TestDomain --task-list quickstart examples.basic.BasicWorkflow

Then execute ./demo.

The SWF region is controlled by the environment variable AWS_DEFAULT_REGION. This variable comes from the legacy "simple-workflow" project. The option might be exposed through a --region option in the future (if you want that, please open an issue).

The SWF domain is controlled by the --domain on most simpleflow commands. It can also be set via the SWF_DOMAIN environment variable. In case both are supplied, the command-line value takes precedence over the environment variable.

Note that some simpleflow commands expect the domain to be passed as a positionnal argument. In that case the environment variable has no effect for now.

The number of retries for accessing SWF can be controlled via SWF_CONNECTION_RETRIES (defaults to 5).

$ simpleflow workflow.list TestDomain basic-example-1438722273 basic OPEN

$ simpleflow --header workflow.info TestDomain basic-example-1438722273 domain workflow_type.name workflow_type.version task_list workflow_id run_id tag_list execution_time input TestDomain basic example basic-example-1438722273 22QFVi362TnCh6BdoFgkQFlocunh24zEOemo1L12Yl5Go= 1.70 {u'args': [1], u'kwargs': {}}

You can check the status of the workflow execution with: :

$ simpleflow --header workflow.tasks DOMAIN WORKFLOW_ID [RUN_ID] --nb-tasks 3
$ simpleflow --header workflow.tasks TestDomain basic-example-1438722273
Tasks                     Last State    Last State Time             Scheduled Time
examples.basic.increment  scheduled     2015-08-04 23:04:34.510000  2015-08-04 23:04:34.510000
$ simpleflow --header workflow.tasks TestDomain basic-example-1438722273
Tasks                     Last State    Last State Time             Scheduled Time
examples.basic.double     completed     2015-08-04 23:06:19.200000  2015-08-04 23:06:17.738000
examples.basic.delay      completed     2015-08-04 23:08:18.402000  2015-08-04 23:06:17.738000
examples.basic.increment  completed     2015-08-04 23:06:17.503000  2015-08-04 23:04:34.510000

You can profile the execution of the workflow with: :

$ simpleflow --header workflow.profile TestDomain basic-example-1438722273
Task                                 Last State    Scheduled           Time Scheduled  Start               Time Running  End                 Percentage of total time
activity-examples.basic.double-1     completed     2015-08-04 23:06              0.07  2015-08-04 23:06            1.39  2015-08-04 23:06                        1.15
activity-examples.basic.increment-1  completed     2015-08-04 23:04            102.20  2015-08-04 23:06            0.79  2015-08-04 23:06                        0.65

You can control log verbosity via the LOG_LEVEL environment variable. Default is INFO. For instance, the following command will start a decider with DEBUG logs:

$ LOG_LEVEL=DEBUG simpleflow decider.start --domain TestDomain --task-list test examples.basic.BasicWorkflow

Full documentation (work-in-progress) is available at https://simpleflow.readthedocs.org/.

• Python 2.6.x or 2.7.x
• Python 3.x compatibility is NOT guaranteed for now: botify-labs#87

In order to release a new version, you'll need credentials on pypi.python.org for this software, as long as write access to this repository. Ask via an issue if needed. Rough process:

git checkout master git pull --rebase v=0.10.0 vi simpleflow/__init__.py git add . && git commit -m "Bump version to $v" git tag $v git push --tags python setup.py sdist upload -r pypi

MIT licensed. See the bundled LICENSE file for more details.