@Marvin is there an easy way to fail flow run if on_completion/on_failure/on_crashed hooks have an exception or right before they throw an exception?

In Prefect 3.x, you can handle exceptions in state change hooks like

on_completion

,

on_failure

, and

on_crashed

by implementing custom logic within these hooks to fail the flow run if an exception occurs. Here's a general approach: 1. Define Custom Hook Functions: Create functions for each hook that include try-except blocks to catch exceptions. 2. Update Flow with Hooks: Register these functions as hooks in your flow. Here's a simple example:

python
from prefect import flow

def on_completion_hook(flow, flow_run, state):
    try:
        # Your custom logic here
        pass
    except Exception as e:
        # Handle exception and fail the flow
        state = state.copy(update={"type": "Failed", "message": str(e)})
        return state

@flow(on_completion=[on_completion_hook])
def my_flow():
    # Your flow logic
    pass

if __name__ == "__main__":
    my_flow()

In this example, if an exception occurs in

on_completion_hook

, the flow's state is updated to "Failed". Adjust the logic to suit your needs for

on_failure

or

on_crashed

hooks similarly. You can find more details about state change hooks in the Prefect documentation.

@Marvin this doesnt seem to be updating the final state of the flow run? Since it has already been set as Completed?

In Prefect 3.x, the final state of a flow run is determined by the flow function's return value and whether it allows exceptions to propagate. Here are some key points: 1. Final State Determination: - Literal return values are considered successful. - Any explicit

State

returned will be considered the final state. If an iterable of

State

objects is returned, all must be

Completed

for the flow run to be considered

Completed

. If any are

Failed

, the flow run will be marked as

Failed

. - Exceptions that propagate result in a

Failed

state, while those suppressed with

raise_on_failure=False

won't affect the flow run state. 2. State Transition Issues: - There have been reports of issues with state transitions, such as tasks being re-executed even after manual state changes. This behavior is possibly due to changes in the retry mechanism and task identification logic in Prefect 3.x. For more details, you can refer to the Prefect documentation on managing states here and the related GitHub issue here. If you're trying to force a state change that isn't taking effect, you might need to ensure that your flow's logic explicitly handles the states and exceptions as intended.

@Marvin is there any way to check if the hooks of a child flow have been completed in parent flow run?

To check if the hooks of a child flow have been completed in a parent flow run, you can utilize the

flow_engine

module in Prefect. Here are some key points: 1. Subflow Run Loading: The

flow_engine

module has a mechanism to load subflow runs, which means you can check if a subflow has been initiated and track its state. 2. Hook Execution: Prefect provides hooks for different flow states. You can define hooks like

on_failure_hooks

,

on_completion_hooks

,

on_cancellation_hooks

, and

on_running_hooks

for your flows. These hooks are executed based on the state transitions of the flow run. 3. State Check: By examining the state of the subflow, you can determine if the hooks have been executed. For example, if the state is

completed

, it indicates that the

on_completion_hooks

should have been executed. 4. Logging: The

flow_engine

logs information about the execution of hooks, which can be useful to verify their completion. For more detailed implementation, you can review the Prefect flow engine source code. If you need further assistance on how to implement these checks programmatically, feel free to ask!

@Marvin is there any to force parent flow to wait for subflow completion hooks to complete before continuing?

To ensure that a parent flow waits for a subflow's completion hooks before continuing, you can leverage Prefect's state management and asynchronous capabilities. Here's a general approach: 1. Await Subflow Completion: Use the

await

keyword to ensure that the parent flow waits for the subflow to complete. Prefect's asynchronous capabilities allow you to manage flow executions asynchronously. 2. Check Subflow State: After initiating the subflow, you can poll or await the subflow's state to transition to a completed state. This ensures that all hooks associated with the subflow's completion are executed before the parent flow continues. 3. Hook Execution: Hooks in Prefect are tied to state transitions. By waiting for the subflow state to reach

completed

, you ensure that the

on_completion_hooks

have been triggered. Here's a simplified example of how you might structure this in an imperative style:

python
from prefect import flow, task, get_run_logger

@flow
async def parent_flow():
    logger = get_run_logger()
    <http://logger.info|logger.info>("Starting parent flow")
    
    # Initiate the subflow and wait for its completion
    subflow_run = await child_flow()
    
    # Check the state of the subflow to ensure hooks have run
    if subflow_run.state.is_completed():
        <http://logger.info|logger.info>("Subflow completed and hooks executed")
    
    <http://logger.info|logger.info>("Continuing parent flow")

@flow
async def child_flow():
    logger = get_run_logger()
    <http://logger.info|logger.info>("Running child flow")
    # Child flow logic here
    return "Subflow result"

parent_flow()

This example assumes that you're using Prefect 3.x and that you're comfortable with asynchronous programming. For more details, you might want to explore the Prefect flow engine source code for deeper insights into flow and hook management.

@Marvin I want that the hooks are executed and completed

To ensure that the hooks of a subflow are not only executed but also completed before the parent flow continues, you can implement a mechanism to explicitly wait for the hooks to finish. Here’s how you can do it in an imperative style: 1. Asynchronous Execution: Ensure that the hooks are defined as asynchronous functions if they involve any I/O or long-running operations. 2. Await Hook Completion: Explicitly await the completion of each hook in the subflow before returning control to the parent flow. Here's a conceptual example demonstrating this:

python
from prefect import flow, task, get_run_logger

@flow
async def parent_flow():
    logger = get_run_logger()
    <http://logger.info|logger.info>("Starting parent flow")
    
    # Initiate the subflow and wait for its completion
    subflow_result = await child_flow()
    
    # Ensure that all hooks have completed
    <http://logger.info|logger.info>("Subflow completed and hooks executed")
    
    <http://logger.info|logger.info>("Continuing parent flow")

@flow
async def child_flow():
    logger = get_run_logger()
    <http://logger.info|logger.info>("Running child flow")
    
    # Simulate hook execution
    await execute_hooks()
    
    return "Subflow result"

async def execute_hooks():
    # Simulate asynchronous hook execution
    await some_async_hook()
    await another_async_hook()

async def some_async_hook():
    # Simulate a hook task
    logger = get_run_logger()
    <http://logger.info|logger.info>("Executing some_async_hook")
    # Perform async operations here

async def another_async_hook():
    # Simulate another hook task
    logger = get_run_logger()
    <http://logger.info|logger.info>("Executing another_async_hook")
    # Perform async operations here

parent_flow()

### Key Points: - Asynchronous Hooks: Define your hooks as asynchronous functions if they involve operations that can be awaited. - Await Hooks: Use the

await

keyword to ensure that the parent flow waits for each hook to complete before proceeding. This approach ensures that the parent flow only continues after all defined hooks in the subflow have been fully executed and completed. If you have specific hook implementations, make sure they are compatible with asynchronous execution.

@Marvin running tasks concurrently example

To run tasks concurrently in Prefect 3.x, you can utilize task runners and multiple task workers. Here's a concise example of how you might achieve this: 1. Set Up Task Workers: Start multiple instances of task workers to handle tasks in parallel. You can do this by running multiple instances of a script like

task_worker.py

in separate terminals.

bash
    python task_worker.py

2. Submit Multiple Tasks: Modify your task submission script, such as

task_submitter.py

, to send multiple tasks to the task workers. Here's an example using a background task:

python
    from tasks import my_background_task

    if __name__ == "__main__":
        my_background_task.delay("Ford")
        my_background_task.delay("Prefect")
        my_background_task.delay("Slartibartfast")

3. Observe Parallel Execution: Run the submission script and observe tasks being distributed and executed across the task workers. You can monitor this in the Prefect UI. For more detailed guidance, you can refer to the documentation on task runners and the background task examples. This approach allows you to leverage Prefect's orchestration capabilities to manage concurrent task execution efficiently.

@Marvin using @task

In Prefect 3.x, the

@task

decorator is used to define tasks, which are the building blocks of your workflows. Here's a basic example of how to use the

@task

decorator:

python
from prefect import task, flow

@task
def greet(name):
    print(f"Hello, {name}!")

@flow
def my_flow():
    greet("Ford Prefect")
    greet("Arthur Dent")

if __name__ == "__main__":
    my_flow()

### Key Points: - Decorate Functions: Use

@task

to turn a regular Python function into a Prefect task. This allows Prefect to manage its execution and state. - Task Execution: Tasks are executed in the main thread by default. For concurrent execution, you might integrate task runners like Dask or Ray. - Flow Composition: Use the

@flow

decorator to define a flow, which orchestrates task execution. For more details, you can explore the Prefect documentation on writing tasks and the Prefect GitHub repository.

@Marvin give me an example of using Dask or Ray

Here's how you can use Dask or Ray with Prefect to run tasks in parallel: ### Using Dask 1. Install Prefect-Dask: Ensure you have the

prefect-dask

package installed. 2. Set Up DaskTaskRunner: Use the

DaskTaskRunner

to run tasks in parallel.

python
from prefect import task, flow
from prefect_dask import DaskTaskRunner

@task
def download_image(image_url):
    # Simulated image download
    return f"Downloaded {image_url}"

@flow(task_runner=DaskTaskRunner())
def download_images_flow(image_urls):
    for url in image_urls:
        download_image.submit(url)

if __name__ == "__main__":
    download_images_flow(["<http://example.com/image1>", "<http://example.com/image2>"])

### Using Ray 1. Install Prefect-Ray: Ensure you have the

prefect-ray

package installed. 2. Set Up RayTaskRunner: Use the

RayTaskRunner

to distribute tasks with Ray.

python
from prefect import task, flow
from prefect_ray import RayTaskRunner

@task
def process_data(data_chunk):
    # Simulated data processing
    return f"Processed {data_chunk}"

@flow(task_runner=RayTaskRunner())
def data_processing_flow(data_chunks):
    for chunk in data_chunks:
        process_data.submit(chunk)

if __name__ == "__main__":
    data_processing_flow(["chunk1", "chunk2", "chunk3"])

### Additional Links - Prefect-Dask Integration Guide - Prefect-Ray Integration Guide These examples demonstrate how to use Prefect with Dask and Ray to parallelize task execution, improving the efficiency of your workflows.