Note

You are viewing the documentation for an older version of boto (boto2).

Boto3, the next version of Boto, is now stable and recommended for general use. It can be used side-by-side with Boto in the same project, so it is easy to start using Boto3 in your existing projects as well as new projects. Going forward, API updates and all new feature work will be focused on Boto3.

For more information, see the documentation for boto3.

Amazon Simple Workflow Tutorial

This tutorial focuses on boto’s interface to AWS SimpleWorkflow service.

What is a workflow?

A workflow is a sequence of multiple activities aimed at accomplishing a well-defined objective. For instance, booking an airline ticket as a workflow may encompass multiple activities, such as selection of itinerary, submission of personal details, payment validation and booking confirmation.

Except for the start and completion of a workflow, each step has a well-defined predecessor and successor. With that
  • on successful completion of an activity the workflow can progress with its execution,
  • when one of workflow’s activities fails it can be retried,
  • and when it keeps failing repeatedly the workflow may regress to the previous step to gather alternative inputs or it may simply fail at that stage.

Why use workflows?

Modelling an application on a workflow provides a useful abstraction layer for writing highly-reliable programs for distributed systems, as individual responsibilities can be delegated to a set of redundant, independent and non-critical processing units.

How does Amazon SWF help you accomplish this?

Amazon SimpleWorkflow service defines an interface for workflow orchestration and provides state persistence for workflow executions.

Amazon SWF applications involve communication between the following entities:
  • The Amazon Simple Workflow Service - providing centralized orchestration and workflow state persistence,
  • Workflow Executors - some entity starting workflow executions, typically through an action taken by a user or from a cronjob.
  • Deciders - a program codifying the business logic, i.e. a set of instructions and decisions. Deciders take decisions based on initial set of conditions and outcomes from activities.
  • Activity Workers - their objective is very straightforward: to take inputs, execute the tasks and return a result to the Service.

The Workflow Executor contacts SWF Service and requests instantiation of a workflow. A new workflow is created and its state is stored in the service. The next time a decider contacts SWF service to ask for a decision task, it will be informed about a new workflow execution is taking place and it will be asked to advise SWF service on what the next steps should be. The decider then instructs the service to dispatch specific tasks to activity workers. At the next activity worker poll, the task is dispatched, then executed and the results reported back to the SWF, which then passes them onto the deciders. This exchange keeps happening repeatedly until the decider is satisfied and instructs the service to complete the execution.

Prerequisites

You need a valid access and secret key. The examples below assume that you have exported them to your environment, as follows:

bash$ export AWS_ACCESS_KEY_ID=<your access key>
bash$ export AWS_SECRET_ACCESS_KEY=<your secret key>

Before workflows and activities can be used, they have to be registered with SWF service:

# register.py
import boto.swf.layer2 as swf
from boto.swf.exceptions import SWFTypeAlreadyExistsError, SWFDomainAlreadyExistsError
DOMAIN = 'boto_tutorial'
VERSION = '1.0'

registerables = []
registerables.append(swf.Domain(name=DOMAIN))
for workflow_type in ('HelloWorkflow', 'SerialWorkflow', 'ParallelWorkflow', 'SubWorkflow'):
    registerables.append(swf.WorkflowType(domain=DOMAIN, name=workflow_type, version=VERSION, task_list='default'))

for activity_type in ('HelloWorld', 'ActivityA', 'ActivityB', 'ActivityC'):
    registerables.append(swf.ActivityType(domain=DOMAIN, name=activity_type, version=VERSION, task_list='default'))

for swf_entity in registerables:
    try:
        swf_entity.register()
        print swf_entity.name, 'registered successfully'
    except (SWFDomainAlreadyExistsError, SWFTypeAlreadyExistsError):
        print swf_entity.__class__.__name__, swf_entity.name, 'already exists'

Execution of the above should produce no errors.

bash$ python -i register.py
Domain boto_tutorial already exists
WorkflowType HelloWorkflow already exists
SerialWorkflow registered successfully
ParallelWorkflow registered successfully
ActivityType HelloWorld already exists
ActivityA registered successfully
ActivityB registered successfully
ActivityC registered successfully
>>>

HelloWorld

This example is an implementation of a minimal Hello World workflow. Its execution should unfold as follows:

  1. A workflow execution is started.
  2. The SWF service schedules the initial decision task.
  3. A decider polls for decision tasks and receives one.
  4. The decider requests scheduling of an activity task.
  5. The SWF service schedules the greeting activity task.
  6. An activity worker polls for activity task and receives one.
  7. The worker completes the greeting activity.
  8. The SWF service schedules a decision task to inform about work outcome.
  9. The decider polls and receives a new decision task.
  10. The decider schedules workflow completion.
  11. The workflow execution finishes.

Workflow logic is encoded in the decider:

# hello_decider.py
import boto.swf.layer2 as swf

DOMAIN = 'boto_tutorial'
ACTIVITY = 'HelloWorld'
VERSION = '1.0'
TASKLIST = 'default'

class HelloDecider(swf.Decider):

    domain = DOMAIN
    task_list = TASKLIST
    version = VERSION

    def run(self):
        history = self.poll()
        if 'events' in history:
            # Find workflow events not related to decision scheduling.
            workflow_events = [e for e in history['events']
                if not e['eventType'].startswith('Decision')]
            last_event = workflow_events[-1]

            decisions = swf.Layer1Decisions()
            if last_event['eventType'] == 'WorkflowExecutionStarted':
                decisions.schedule_activity_task('saying_hi', ACTIVITY, VERSION, task_list=TASKLIST)
            elif last_event['eventType'] == 'ActivityTaskCompleted':
                decisions.complete_workflow_execution()
            self.complete(decisions=decisions)
            return True

The activity worker is responsible for printing the greeting message when the activity task is dispatched to it by the service:

import boto.swf.layer2 as swf

DOMAIN = 'boto_tutorial'
VERSION = '1.0'
TASKLIST = 'default'

class HelloWorker(swf.ActivityWorker):

    domain = DOMAIN
    version = VERSION
    task_list = TASKLIST

    def run(self):
        activity_task = self.poll()
        if 'activityId' in activity_task:
            print 'Hello, World!'
            self.complete()
            return True

With actors implemented we can spin up a workflow execution:

$ python
>>> import boto.swf.layer2 as swf
>>> execution = swf.WorkflowType(name='HelloWorkflow', domain='boto_tutorial', version='1.0', task_list='default').start()
>>>

From separate terminals run an instance of a worker and a decider to carry out a workflow execution (the worker and decider may run from two independent machines).

$ python -i hello_decider.py
>>> while HelloDecider().run(): pass
...

$ python -i hello_worker.py
>>> while HelloWorker().run(): pass
...
Hello, World!

Great. Now, to see what just happened, go back to the original terminal from which the execution was started, and read its history.

>>> execution.history()
[{'eventId': 1,
  'eventTimestamp': 1381095173.2539999,
  'eventType': 'WorkflowExecutionStarted',
  'workflowExecutionStartedEventAttributes': {'childPolicy': 'TERMINATE',
                                              'executionStartToCloseTimeout': '3600',
                                              'parentInitiatedEventId': 0,
                                              'taskList': {'name': 'default'},
                                              'taskStartToCloseTimeout': '300',
                                              'workflowType': {'name': 'HelloWorkflow',
                                                               'version': '1.0'}}},
 {'decisionTaskScheduledEventAttributes': {'startToCloseTimeout': '300',
                                           'taskList': {'name': 'default'}},
  'eventId': 2,
  'eventTimestamp': 1381095173.2539999,
  'eventType': 'DecisionTaskScheduled'},
 {'decisionTaskStartedEventAttributes': {'scheduledEventId': 2},
  'eventId': 3,
  'eventTimestamp': 1381095177.5439999,
  'eventType': 'DecisionTaskStarted'},
 {'decisionTaskCompletedEventAttributes': {'scheduledEventId': 2,
                                           'startedEventId': 3},
  'eventId': 4,
  'eventTimestamp': 1381095177.855,
  'eventType': 'DecisionTaskCompleted'},
 {'activityTaskScheduledEventAttributes': {'activityId': 'saying_hi',
                                           'activityType': {'name': 'HelloWorld',
                                                            'version': '1.0'},
                                           'decisionTaskCompletedEventId': 4,
                                           'heartbeatTimeout': '600',
                                           'scheduleToCloseTimeout': '3900',
                                           'scheduleToStartTimeout': '300',
                                           'startToCloseTimeout': '3600',
                                           'taskList': {'name': 'default'}},
  'eventId': 5,
  'eventTimestamp': 1381095177.855,
  'eventType': 'ActivityTaskScheduled'},
 {'activityTaskStartedEventAttributes': {'scheduledEventId': 5},
  'eventId': 6,
  'eventTimestamp': 1381095179.427,
  'eventType': 'ActivityTaskStarted'},
 {'activityTaskCompletedEventAttributes': {'scheduledEventId': 5,
                                           'startedEventId': 6},
  'eventId': 7,
  'eventTimestamp': 1381095179.6989999,
  'eventType': 'ActivityTaskCompleted'},
 {'decisionTaskScheduledEventAttributes': {'startToCloseTimeout': '300',
                                           'taskList': {'name': 'default'}},
  'eventId': 8,
  'eventTimestamp': 1381095179.6989999,
  'eventType': 'DecisionTaskScheduled'},
 {'decisionTaskStartedEventAttributes': {'scheduledEventId': 8},
  'eventId': 9,
  'eventTimestamp': 1381095179.7420001,
  'eventType': 'DecisionTaskStarted'},
 {'decisionTaskCompletedEventAttributes': {'scheduledEventId': 8,
                                           'startedEventId': 9},
  'eventId': 10,
  'eventTimestamp': 1381095180.026,
  'eventType': 'DecisionTaskCompleted'},
 {'eventId': 11,
  'eventTimestamp': 1381095180.026,
  'eventType': 'WorkflowExecutionCompleted',
  'workflowExecutionCompletedEventAttributes': {'decisionTaskCompletedEventId': 10}}]

Serial Activity Execution

The following example implements a basic workflow with activities executed one after another.

The business logic, i.e. the serial execution of activities, is encoded in the decider:

# serial_decider.py
import time
import boto.swf.layer2 as swf

class SerialDecider(swf.Decider):

    domain = 'boto_tutorial'
    task_list = 'default_tasks'
    version = '1.0'

    def run(self):
        history = self.poll()
        if 'events' in history:
            # Get a list of non-decision events to see what event came in last.
            workflow_events = [e for e in history['events']
                               if not e['eventType'].startswith('Decision')]
            decisions = swf.Layer1Decisions()
            # Record latest non-decision event.
            last_event = workflow_events[-1]
            last_event_type = last_event['eventType']
            if last_event_type == 'WorkflowExecutionStarted':
                # Schedule the first activity.
                decisions.schedule_activity_task('%s-%i' % ('ActivityA', time.time()),
                   'ActivityA', self.version, task_list='a_tasks')
            elif last_event_type == 'ActivityTaskCompleted':
                # Take decision based on the name of activity that has just completed.
                # 1) Get activity's event id.
                last_event_attrs = last_event['activityTaskCompletedEventAttributes']
                completed_activity_id = last_event_attrs['scheduledEventId'] - 1
                # 2) Extract its name.
                activity_data = history['events'][completed_activity_id]
                activity_attrs = activity_data['activityTaskScheduledEventAttributes']
                activity_name = activity_attrs['activityType']['name']
                # 3) Optionally, get the result from the activity.
                result = last_event['activityTaskCompletedEventAttributes'].get('result')

                # Take the decision.
                if activity_name == 'ActivityA':
                    decisions.schedule_activity_task('%s-%i' % ('ActivityB', time.time()),
                        'ActivityB', self.version, task_list='b_tasks', input=result)
                if activity_name == 'ActivityB':
                    decisions.schedule_activity_task('%s-%i' % ('ActivityC', time.time()),
                        'ActivityC', self.version, task_list='c_tasks', input=result)
                elif activity_name == 'ActivityC':
                    # Final activity completed. We're done.
                    decisions.complete_workflow_execution()

            self.complete(decisions=decisions)
            return True

The workers only need to know which task lists to poll.

# serial_worker.py
import time
import boto.swf.layer2 as swf

class MyBaseWorker(swf.ActivityWorker):

    domain = 'boto_tutorial'
    version = '1.0'
    task_list = None

    def run(self):
        activity_task = self.poll()
        if 'activityId' in activity_task:
            # Get input.
            # Get the method for the requested activity.
            try:
                print 'working on activity from tasklist %s at %i' % (self.task_list, time.time())
                self.activity(activity_task.get('input'))
            except Exception as error:
                self.fail(reason=str(error))
                raise error

            return True

    def activity(self, activity_input):
        raise NotImplementedError

class WorkerA(MyBaseWorker):
    task_list = 'a_tasks'
    def activity(self, activity_input):
        self.complete(result="Now don't be givin him sambuca!")

class WorkerB(MyBaseWorker):
    task_list = 'b_tasks'
    def activity(self, activity_input):
        self.complete()

class WorkerC(MyBaseWorker):
    task_list = 'c_tasks'
    def activity(self, activity_input):
        self.complete()

Spin up a workflow execution and run the decider:

$ python
>>> import boto.swf.layer2 as swf
>>> execution = swf.WorkflowType(name='SerialWorkflow', domain='boto_tutorial', version='1.0', task_list='default_tasks').start()
>>>

$ python -i serial_decider.py
>>> while SerialDecider().run(): pass
...

Run the workers. The activities will be executed in order:

$ python -i serial_worker.py
>>> while WorkerA().run(): pass
...
working on activity from tasklist a_tasks at 1382046291

$ python -i serial_worker.py
>>> while WorkerB().run(): pass
...
working on activity from tasklist b_tasks at 1382046541

$ python -i serial_worker.py
>>> while WorkerC().run(): pass
...
working on activity from tasklist c_tasks at 1382046560

Looks good. Now, do the following to inspect the state and history of the execution:

>>> execution.describe()
{'executionConfiguration': {'childPolicy': 'TERMINATE',
  'executionStartToCloseTimeout': '3600',
  'taskList': {'name': 'default_tasks'},
  'taskStartToCloseTimeout': '300'},
 'executionInfo': {'cancelRequested': False,
  'closeStatus': 'COMPLETED',
  'closeTimestamp': 1382046560.901,
  'execution': {'runId': '12fQ1zSaLmI5+lLXB8ux+8U+hLOnnXNZCY9Zy+ZvXgzhE=',
   'workflowId': 'SerialWorkflow-1.0-1382046514'},
  'executionStatus': 'CLOSED',
  'startTimestamp': 1382046514.994,
  'workflowType': {'name': 'SerialWorkflow', 'version': '1.0'}},
 'latestActivityTaskTimestamp': 1382046560.632,
 'openCounts': {'openActivityTasks': 0,
  'openChildWorkflowExecutions': 0,
  'openDecisionTasks': 0,
  'openTimers': 0}}
>>> execution.history()
...

Parallel Activity Execution

When activities are independent from one another, their execution may be scheduled in parallel.

The decider schedules all activities at once and marks progress until all activities are completed, at which point the workflow is completed.

# parallel_decider.py

import boto.swf.layer2 as swf
import time

SCHED_COUNT = 5

class ParallelDecider(swf.Decider):

    domain = 'boto_tutorial'
    task_list = 'default'
    def run(self):
        decision_task = self.poll()
        if 'events' in decision_task:
            decisions = swf.Layer1Decisions()
            # Decision* events are irrelevant here and can be ignored.
            workflow_events = [e for e in decision_task['events']
                               if not e['eventType'].startswith('Decision')]
            # Record latest non-decision event.
            last_event = workflow_events[-1]
            last_event_type = last_event['eventType']
            if last_event_type == 'WorkflowExecutionStarted':
                # At start, kickoff SCHED_COUNT activities in parallel.
                for i in range(SCHED_COUNT):
                    decisions.schedule_activity_task('activity%i' % i, 'ActivityA', '1.0',
                                                     task_list=self.task_list)
            elif last_event_type == 'ActivityTaskCompleted':
                # Monitor progress. When all activities complete, complete workflow.
                completed_count = sum([1 for a in decision_task['events']
                                       if a['eventType'] == 'ActivityTaskCompleted'])
                print '%i/%i' % (completed_count, SCHED_COUNT)
                if completed_count == SCHED_COUNT:
                    decisions.complete_workflow_execution()
            self.complete(decisions=decisions)
            return True

Again, the only bit of information a worker needs is which task list to poll.

# parallel_worker.py
import time
import boto.swf.layer2 as swf

class ParallelWorker(swf.ActivityWorker):

    domain = 'boto_tutorial'
    task_list = 'default'

    def run(self):
        """Report current time."""
        activity_task = self.poll()
        if 'activityId' in activity_task:
            print 'working on', activity_task['activityId']
            self.complete(result=str(time.time()))
            return True

Spin up a workflow execution and run the decider:

$ python -i parallel_decider.py
>>> execution = swf.WorkflowType(name='ParallelWorkflow', domain='boto_tutorial', version='1.0', task_list='default').start()
>>> while ParallelDecider().run(): pass
...
1/5
2/5
4/5
5/5

Run two or more workers to see how the service partitions work execution in parallel.

$ python -i parallel_worker.py
>>> while ParallelWorker().run(): pass
...
working on activity1
working on activity3
working on activity4

$ python -i parallel_worker.py
>>> while ParallelWorker().run(): pass
...
working on activity2
working on activity0

As seen above, the work was partitioned between the two running workers.

Sub-Workflows

Sometimes it’s desired or necessary to break the process up into multiple workflows.

Since the decider is stateless, it’s up to you to determine which workflow is being used and which action you would like to take.

import boto.swf.layer2 as swf

class SubWorkflowDecider(swf.Decider):

    domain = 'boto_tutorial'
    task_list = 'default'
    version = '1.0'

    def run(self):
        history = self.poll()
        events = []
        if 'events' in history:
            events = history['events']
            # Collect the entire history if there are enough events to become paginated
            while 'nextPageToken' in history:
                history = self.poll(next_page_token=history['nextPageToken'])
                if 'events' in history:
                    events = events + history['events']

            workflow_type = history['workflowType']['name']

            # Get all of the relevent events that have happened since the last decision task was started
            workflow_events = [e for e in events
                    if e['eventId'] > history['previousStartedEventId'] and
                    not e['eventType'].startswith('Decision')]

            decisions = swf.Layer1Decisions()

            for event in workflow_events:
                last_event_type = event['eventType']
                if last_event_type == 'WorkflowExecutionStarted':
                    if workflow_type == 'SerialWorkflow':
                        decisions.start_child_workflow_execution('SubWorkflow', self.version,
                            "subworkflow_1", task_list=self.task_list, input="sub_1")
                    elif workflow_type == 'SubWorkflow':
                        for i in range(2):
                            decisions.schedule_activity_task("activity_%d" % i, 'ActivityA', self.version, task_list='a_tasks')
                    else:
                        decisions.fail_workflow_execution(reason="Unknown workflow %s" % workflow_type)
                        break

                elif last_event_type == 'ChildWorkflowExecutionCompleted':
                    decisions.schedule_activity_task("activity_2", 'ActivityB', self.version, task_list='b_tasks')

                elif last_event_type == 'ActivityTaskCompleted':
                    attrs = event['activityTaskCompletedEventAttributes']
                    activity = events[attrs['scheduledEventId'] - 1]
                    activity_name = activity['activityTaskScheduledEventAttributes']['activityType']['name']

                    if activity_name == 'ActivityA':
                        completed_count = sum([1 for a in events if a['eventType'] == 'ActivityTaskCompleted'])
                        if completed_count == 2:
                            # Complete the child workflow
                            decisions.complete_workflow_execution()
                    elif activity_name == 'ActivityB':
                        # Complete the parent workflow
                        decisions.complete_workflow_execution()

            self.complete(decisions=decisions)
        return True

Misc

Some of these things are not obvious by reading the API documents, so hopefully they help you avoid some time-consuming pitfalls.

Decision Tasks

When first running deciders and activities, it may seem that the decider gets called for every event that an activity triggers; however, this is not the case. More than one event can happen between decision tasks. The decision task will contain a key previousStartedEventId that lets you know the eventId of the last DecisionTaskStarted event that was processed. Your script will need to handle all of the events that have happened since then, not just the last activity.

workflow_events = [e for e in events if e['eventId'] > decision_task['previousStartedEventId']]

You may also wish to still filter out tasks that start with ‘Decision’ or filter it in some other way that fulfills your needs. You will now have to iterate over the workflow_events list and respond to each event, as it may contain multiple events.

Filtering Events

When running many tasks in parallel, a common task is searching through the history to see how many events of a particular activity type started, completed, and/or failed. Some basic list comprehension makes this trivial.

def filter_completed_events(self, events, type):
    completed = [e for e in events if e['eventType'] == 'ActivityTaskCompleted']
    orig = [events[e['activityTaskCompletedEventAttributes']['scheduledEventId']-1] for e in completed]
    return [e for e in orig if e['activityTaskScheduledEventAttributes']['activityType']['name'] == type]