Using Prefect Client and Prefect Context for event driven workflows

Here is some example code to help you get started using prefect context and prefect client to trigger flow runs programmatically .

from prefect import flow, get_run_logger, task
from prefect.client import get_client
from prefect.context import get_run_context
from prefect.orion.schemas.states import Scheduled

Line 85 needs to be updated with the relevant deployment ID

# -- Build a Subflow to demonstrate get_run_context() and return_state argument --

def task_that_logs_context():

    task_run_context_dict = get_run_context().task_run.dict()

    # check out the availble keys
    logger = get_run_logger()"INFO I am a task, check out my task run context below:")"INFO Task Run Keys: {task_run_context_dict.keys()}")

    # Provide a return value to for .result() example
    return "Hello Result"

def flow_that_logs_context():

    # Run task with return_state=True to get a Prefect State returned
    task_state = task_that_logs_context(return_state=True)

    # To get the actual value of the tasks output from a Prefect state,
    # use the .result() method
    task_result = task_state.result()
    logger = get_run_logger()"INFO Task Result: {task_result}")

    # The availble keys for flow run context work like the task run context.
    flow_run_context_dict = get_run_context().flow_run.dict()"INFO I am a flow, check out my flow run context below:")"INFO Flow Run Keys: {flow_run_context_dict.keys()}")

    # Now we will raise an artificial error that will prompt us to schedule
    # a different 'reactive' flow x minutes in the future
    raise Exception("Deliberate Failure for Example.")

# -- Build a Task that adds a schedule for a reactive flow to run --
async def add_new_scheduled_run(depl_id, original_start_time, delta_minutes=0):
    This task adds a scheduled flow run to the deployment of a reactive flow
    x minutes from the start time of the currently executing flow.
    # Get the time x minutes from now.
    scheduled_time = original_start_time.add(minutes=delta_minutes)

    # Use Prefect get_client() to schedule a new flow run x minutes from now
    async with get_client() as client:
        # Pro Tip: create_flow_run_from_deployment has MANY useful argument in addition
        # to adding a schedule, you can also add specific flow parameter values, etc.
        response = await client.create_flow_run_from_deployment(
            deployment_id=depl_id, state=Scheduled(scheduled_time=scheduled_time)
    logger = get_run_logger()"INFO get client response: {response}")"INFO Scheduled a flow run for {scheduled_time}!")

# -- Build a flow that dynamically schedules a reactive flow upon subflow failure --
def main_flow():

    # Run the Sub-Flow with return_state=True
    flow_state = flow_that_logs_context(return_state=True)

    # We'll use the state.is_completed method to check the status of the subflow
    logger = get_run_logger()"INFO In complete state? {flow_state.is_completed()}")

    # Lets schedule a different reactive flow to run in a few minutes
    # from now if the subflow failed
    if not flow_state.is_completed():
        # Specify Deployment ID for Reactive Flow
        depl_id = "deployment-id-goes-here-4242"

        # Use Context to get original scheduled start time of current flow.
        original_start_time = get_run_context().flow_run.expected_start_time

        # Schedule Reactive Flow to run 5 Minutes from
        # Current Flow's Scheduled Start Time
        add_new_scheduled_run.submit(depl_id, original_start_time, delta_minutes=5)

if __name__ == "__main__":