The Python Workflows SDK supports DAG workflows in a declarative way, using step.do and parameter names to define dependencies (other steps that must complete before a step can run).
from workers import Response, WorkflowEntrypoint, WorkerEntrypoint
class PythonWorkflowStarter(WorkflowEntrypoint):
async def run(self, event, step):
async def await_step(fn):
try:
return await fn()
except TypeError as e:
print(f"Successfully caught {type(e).__name__}: {e}")
await step.sleep('demo sleep', '10 seconds')
@step.do()
async def dep_1():
# does stuff
print('executing dep1')
return 'dep1'
@step.do()
async def dep_2():
# does stuff
print('executing dep2')
return 'dep2'
@step.do(concurrent=True)
async def final_step(dep_1, dep_2):
# does stuff
print(f'{dep_1} {dep_2}')
await await_step(final_step)
class Default(WorkerEntrypoint):
async def fetch(self, request):
await self.env.MY_WORKFLOW.create()
return Response("Hello world!")In this example, dep_1 and dep_2 are run concurrently before execution of final_step, which depends on both of them.
Having concurrent=True allows dependencies to be resolved concurrently. If a dependency has already completed, it will be skipped and its return value will be reused.
This pattern is useful for diamond shaped workflows, where a step depends on two or more other steps that can run concurrently.