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Python Workflow Library built on the AWS SWF service

Project description

pyflow

A Python workflow framework based on the AWS Simple Workflow Service.

Summary

Pyflow is a Python library which supports defining distributed asynchronous workflow applications using ordinary procedural python code. It is implemented using SWF. Workflow components can can be implemented as AWS Lambda functions, or activity functions implemented in Python, Ruby or Java which run on any computing resource capable of connecting to SWF.

Pyflow is heavily inspired by the AWS Flow Framework for Java and Flow Framework for Ruby, but makes no attempt to be compatible with either of those frameworks.

Programming Model

This page gives a good overview of the concepts used in a pyflow application: AWS Flow Framework Basic Concepts. It's about Java Flow but the concepts are the same for pyflow. The diagram on that page doesn't mention Lambda functions, but if they were added to the diagram, the Lambda service would be another activity worker, with Lambda functions being the activity methods.

Implementing a Workflow

To implement a workflow, you subclass the pyflow.Workflow class, and implement the run method to define the workflow's behavior.

import pyflow

class MyWorkflow(pyflow.Workflow):
    NAME = 'MyWorkflow'
    VERSION = '1.0'
    
    some_func = pyflow.LambdaDescriptor('some_lambda_func')
    other_func = pyflow.LambdaDescriptor('other_lambda_func')
    
    def run(self, input_arg):
       if input_arg == 'bad_input':
           raise pyflow.WorkflowFailedException('BAD_INPUT', 'Received bad input')
           
       future1 = self.some_func(input_arg)
       
       x = future1.result() + 2
       
       future2 = self.other_func(x)
       
       return future2.result()

The input_arg argument to the run method is an arbitrary value that can be passed to the workflow when invoking it. The workflow instance has an swf attribute, which is a WorkflowInvocationHelper object that provides the interface for invoking remote tasks and retrieving information about the workflow execution context.

The example above demonstrates invoking lambda functions. You first define a class attribute for each lambda function you want to invoke, and then use it like a method inside the run method. There are similar descriptor classes for invoking SWF activities, and other SWF workflows. These methods are asynchronous. They immediately return a Future object, which can be used to retrieve the result of the invocation when it is done. Calling the result() method on a future "blocks" until the result is ready. If the invocation succeeded, its result will be returned. If the invocation failed, the result method will raise an InvocationException.

Blocking methods such as Future.result() don't actually block the python process, but rather allow control to transfer back to the Workflow Worker process so it can process other SWF events. I'll explain later in this document how the context switching is implemented, as well as some rules you need to follow in your workflow implementation code as a result of this implementation.

The code above also demonstrates how to signal a workflow failure. Raise the WorkflowFailedException with two arguments. The first is a short reason string, and the second is a longer details string.

Executing a Workflow

To execute a worklow you need to do two things. First you need to start a Workflow Worker process, which will manage the execution of one or more workflow definitions, and then you need to tell SWF to invoke a workflow. Here is how to create a workflow worker with SWF.

domain = 'SWFSampleDomain'
task_list = 'my-workflow-tasklist'


# Will poll indefinitely for events
pyflow.poll_for_executions([MyWorkflow], domain=domain, task_list=task_list,
    identity='My Workflow Worker')

Executing the above will first ensure that the workflow type is registered with SWF, and then enter an endless loop waiting to receive events from the SWF service and executing workflow instances.

Optionally, a max_time parameter can be passed to poll_for_executions to make it only perform max_time seconds before returning. The workflow runner is stateless; execution state of workflow instances is maintained by the SWF service. This means it's possible to have poll_for_executions process several events, then exit the python process and start it again with the same arguments, and have it pick up workflow executions where it left off.

To actually start a workflow instance, you can run code like this:

domain = 'SWFSampleDomain'
task_list = 'my-workflow-tasklist'
workflow_name = 'MyWorkflow'
workflow_version = '1.0'
lambda_role='arn:aws:iam::528461152743:role/swf-lambda'

workflow_id = pyflow.start_workflow(
    domain=domain,
    workflow_name=workflow_name,
    workflow_version=workflow_version,
    task_list=task_list,
    lambda_role=lambda_role,
    input='"Hello"')

print "Workflow started with workflow_id {}".format(workflow_id)

Or using the AWS CLI:

aws swf start-workflow-execution --domain SWFSampleDomain \
    --workflow-id my-unique-workflow-id \
    --workflow-type name=MyWorkflow,version=1.0 \
    --task-list name=string-transformer-decider \
    --lambda-role arn:aws:iam::528461152743:role/swf-lambda \
    --input '"Hello"'

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