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<@ULVA73B9P> Explain why the code snippet below re...
# ask-marvin
k
@Marvin Explain why the code snippet below returns the following error message: 
Finished in state Failed('Flow run encountered an exception: TypeError: Unexpected result for failed state: 1 —— int cannot be resolved into an exception')
Copy code
from prefect import flow, get_run_logger, task, allow_failure
from prefect.states import Failed, Completed


@task(task_run_name="inital_task: {x}")
def initial_task(x):    
    if x < 3:
        return Failed(data=x)      
    return Completed(data=x)

@flow()
def demo_passing_data():
    task_run_future = initial_task.submit(1)
    
    print(task_run_future.result())
if __name__ == "__main__":
    demo_passing_data()
m
The error you're encountering is due to an incorrect usage of Prefect's 
Failed
and 
Completed
states. In Prefect, these states are not meant to be directly instantiated with arbitrary data like integers. Instead, they are used to represent the state of a task or flow, and they expect specific types of data (usually exceptions for 
Failed
states). Here's a breakdown of what's happening: 1. State Misuse: In your 
initial_task
, you are returning 
Failed(data=x)
when 
x < 3
. This is incorrect because 
Failed
expects an exception, not an integer. The 
data
attribute is meant for exceptions or specific result types, not arbitrary data like integers. 2. Correct Usage: If you want to handle failures based on the value of 
x
, you should raise an exception instead of returning a 
Failed
state directly. Prefect will automatically handle the exception and transition the task to a 
Failed
state. Here's a corrected version of your code:
Copy code
python
from prefect import flow, get_run_logger, task

@task(task_run_name="initial_task: {x}")
def initial_task(x):
    if x < 3:
        raise ValueError(f"Value {x} is less than 3, failing task.")
    return x

@flow()
def demo_passing_data():
    task_run_future = initial_task.submit(1)
    
    print(task_run_future.result())

if __name__ == "__main__":
    demo_passing_data()
This modification raises a 
ValueError
if 
x
is less than 3, which Prefect will interpret as the task having failed. For more details on handling states in Prefect, you might want to check the Prefect documentation.
k
@Marvin what is the proper way to return data from a task when I also want to explicitly return Completed and Failed states?
m
To properly return data from a task while explicitly handling 
Completed
and 
Failed
states in Prefect, you can use the 
return_state
parameter. Here's how you can manage this: 1. Returning Data with a Completed State: Simply return the data from the task. Prefect will automatically place the task in a 
Completed
state if no exceptions are raised. 2. Returning a Failed State with Data: You can explicitly return a 
Failed
state by raising an exception. Prefect will interpret this as a failure and transition the task to a 
Failed
state. 3. Using 
return_state=True
: This allows you to access state objects directly and conditionally respond to terminal states within your flow. Here is an example demonstrating these concepts:
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python
from prefect import flow, task
from prefect.states import Completed, Failed

@task
def my_task(x):
    if x < 3:
        # Raise an exception to transition to a Failed state
        raise ValueError(f"Value {x} is less than 3, failing task.")
    # Simply return data to transition to a Completed state
    return x

@flow
def my_flow():
    task_run_future = my_task.submit(1, return_state=True)
    try:
        result = task_run_future.result()
        print(f"Task completed with result: {result}")
    except Exception as e:
        print(f"Task failed with exception: {e}")

if __name__ == "__main__":
    my_flow()
In this example, if the task fails (e.g., 
x < 3
), it raises an exception, leading to a 
Failed
state. If successful, it returns the data, resulting in a 
Completed
state. For more details, you can refer to the Prefect documentation on managing states.
k
@Marvin So it is not okay for tasks to return explicit Failed or Completed states, and the flow should run the task in a try.. except? Is it okay for a flow to return explicit completed or failed states?
m
In Prefect, tasks should not explicitly return 
Completed
or 
Failed
states. Instead, tasks should return data directly for successful completions or raise exceptions to be automatically transitioned to a 
Failed
state. As for flows, they can handle states more explicitly. ### Handling States in Flows 1. Returning States from Flows: Flows can explicitly set their own states by using state management functions. This is typically done by handling exceptions and setting the state based on the outcome of the flow's execution. 2. Using Try-Except in Flows: Within a flow, you can use a 
try...except
block to catch exceptions from tasks and set the flow's state accordingly. This allows you to handle failures gracefully and decide the flow's final state. 3. State Management in Flows: You can manage the flow's state using Prefect's state management APIs, which allow you to propose and set states based on the flow run's outcomes. Here is an example of how you might handle states within a flow:
Copy code
python
from prefect import flow, task
from prefect.states import Failed, Completed

@task
def my_task(x):
    if x < 3:
        raise ValueError(f"Value {x} is less than 3, failing task.")
    return x

@flow
def my_flow():
    try:
        result = my_task(1)
        # Flow completes successfully if no exceptions are raised
        return Completed(data=result)
    except Exception as e:
        # Set flow to Failed state if an exception is caught
        return Failed(message=str(e))

if __name__ == "__main__":
    state = my_flow()
    print(f"Flow finished with state: {state}")
In this example, the flow explicitly returns a 
Completed
or 
Failed
state based on the execution of its tasks. This approach gives you more control over the flow's final state. For more detailed guidance, refer to the Prefect documentation on managing states.
k
I manually return Failed() and Completed() states in my tasks so that I can add a customised message and also return data. How do I access the data of a return statement when I call the task with .submit()?
@Marvin I manually return Failed() and Completed() states in my tasks so that I can add a customised message and also return data. How do I access the data of a return statement when I call the task with .submit()?
m
When you submit a task using 
.submit()
, it returns a 
PrefectFuture
object. This object provides access to the state and result of the task. To access the data from a task that manually returns 
Failed
or 
Completed
states, you can retrieve the state using the 
.result()
method of the 
PrefectFuture
, and then access the 
data
attribute of the state. Here's how you can do it:
Copy code
python
from prefect import flow, task
from prefect.states import Completed, Failed

@task
def my_task(x):
    if x < 3:
        return Failed(message="Value is too low", data={"value": x})
    return Completed(message="Task completed successfully", data={"value": x})

@flow
def my_flow():
    task_run_future = my_task.submit(1)
    state = task_run_future.result()  # Get the state
    print(f"Task finished with state: {state}")
    
    # Access the data from the state
    if isinstance(state, Completed):
        print(f"Task completed with data: {state.data}")
    elif isinstance(state, Failed):
        print(f"Task failed with data: {state.data}")

if __name__ == "__main__":
    my_flow()
This example demonstrates how to access the 
data
from the task's state after it has been submitted and completed. The 
state.data
attribute contains the information you returned with the state.
k
@Marvin this returns TypeError: Unexpected result for failed state: {'value': 1} —— dict cannot be resolved into an exception
m
The error you're encountering is due to the way Prefect expects the 
Failed
state to be constructed. Prefect requires that a 
Failed
state be associated with an exception rather than arbitrary data like a dictionary. To resolve this, you should raise an exception when you want to represent a failed state, and Prefect will automatically handle it as a 
Failed
state. Here's how you can modify your task to work correctly:
Copy code
python
from prefect import flow, task
from prefect.states import Completed

@task
def my_task(x):
    if x < 3:
        # Raise an exception to create a Failed state
        raise ValueError(f"Value is too low: {x}")
    # Return data normally for a Completed state
    return {"value": x}

@flow
def my_flow():
    task_run_future = my_task.submit(1)
    try:
        # This will raise an exception if the task failed
        result = task_run_future.result()
        print(f"Task completed with data: {result}")
    except Exception as e:
        print(f"Task failed with exception: {e}")

if __name__ == "__main__":
    my_flow()
In this corrected version, an exception is raised when 
x < 3
, which Prefect interprets as a failure. The 
try...except
block in the flow is used to catch this exception and handle it appropriately. This approach aligns with Prefect's handling of task states and exceptions.
k
@Marvin when my tasks fails, I still need to be able to access the return value of x in my flow
m
To access the return value of 
x
even when a task fails, you can include the data you want to preserve within the exception itself. Here's how you can achieve that by creating a custom exception that carries the data:
Copy code
python
from prefect import flow, task

# Define a custom exception to carry additional data
class TaskFailureException(Exception):
    def __init__(self, message, data):
        super().__init__(message)
        self.data = data

@task
def my_task(x):
    if x < 3:
        # Raise a custom exception with the data you want to preserve
        raise TaskFailureException(f"Value is too low: {x}", data={"value": x})
    # Return data normally for a Completed state
    return {"value": x}

@flow
def my_flow():
    task_run_future = my_task.submit(1)
    try:
        # This will raise an exception if the task failed
        result = task_run_future.result()
        print(f"Task completed with data: {result}")
    except TaskFailureException as e:
        print(f"Task failed with exception: {e}")
        # Access the data from the exception
        print(f"Failed task data: {e.data}")

if __name__ == "__main__":
    my_flow()
In this example, the 
TaskFailureException
is used to encapsulate both the error message and the data you want to access later. When the task fails, you can catch this custom exception in the flow and access the data stored in the exception object. This way, you maintain access to the task's return value even in failure scenarios.
k
@Marvin I want a subflow to return data. Is it good practice to it like this: return return Completed(message="Databricks jobs ran successfully", data=results_to_pass) or in case of a fail: return Failed(message="Databricks jobs ran successfully", data=results_to_pass)
m
Returning explicit 
Completed
or 
Failed
states from a subflow is a valid approach in Prefect. This allows you to programmatically determine the final state of the subflow based on the logic you implement. Here's how it works: 1. Returning a State: If a subflow returns a manually created state (like 
Completed
or 
Failed
), that state is used as the final state of the flow run. This gives you precise control over the flow's outcome. 2. State with Data: You can attach data to these states using the 
data
attribute, allowing you to pass results or context back to the calling flow. Here’s an example:
Copy code
python
from prefect import flow
from prefect.states import Completed, Failed

@flow
def subflow():
    # Simulating a condition to decide success or failure
    success = False
    results_to_pass = {"some_key": "some_value"}

    if success:
        return Completed(message="Databricks jobs ran successfully", data=results_to_pass)
    else:
        return Failed(message="Databricks jobs failed", data=results_to_pass)

@flow
def main_flow():
    subflow_state = subflow()
    
    if subflow_state.is_completed():
        print(f"Subflow succeeded with data: {subflow_state.data}")
    elif subflow_state.is_failed():
        print(f"Subflow failed with data: {subflow_state.data}")

if __name__ == "__main__":
    main_flow()
In this setup, the 
subflow
returns either a 
Completed
or 
Failed
state with attached data. The 
main_flow
then checks the state and processes the data accordingly. This method is effective for handling complex workflows where subflows need to communicate their results or status back to the parent flow. For more details, you can refer to the Prefect documentation on managing states.
k
@Marvin can you adapt this class so that it does not print the entire stack trace but limits the message?
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class TaskFailureException(Exception):
    def __init__(self, message, data):
        super().__init__(message)
        self.data = data
@Marvin can you adapt this class so that it does not print the entire stack trace but limits the message?
Copy code
class TaskFailureException(Exception):
    def __init__(self, message, data):
        super().__init__(message)
        self.data = data
m
To limit the exception message without printing the entire stack trace, you can override the 
__str__
method in your custom exception class. This allows you to control the output when the exception is printed. Here's how you can adapt the class:
Copy code
python
class TaskFailureException(Exception):
    def __init__(self, message, data):
        super().__init__(message)
        self.data = data

    def __str__(self):
        # Customize the message to include only the essential information
        return f"{self.args[0]} | Data: {self.data}"
With this modification, when the 
TaskFailureException
is raised and caught, printing the exception will display the customized message rather than the full stack trace. Here's an example of how it might be used:
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python
try:
    raise TaskFailureException("An error occurred", {"value": 1})
except TaskFailureException as e:
    print(e)  # This will print: An error occurred | Data: {'value': 1}
This approach ensures that the exception message is concise and focused on the important details you want to convey.
k
@Marvin I am using this code snippet that you provided, however, if I launch a second task that uses task_run_future as source, the task will not start if the task_run_future failed
Copy code
python
from prefect import flow, task

# Define a custom exception to carry additional data
class TaskFailureException(Exception):
    def __init__(self, message, data):
        super().__init__(message)
        self.data = data

@task
def my_task(x):
    if x < 3:
        # Raise a custom exception with the data you want to preserve
        raise TaskFailureException(f"Value is too low: {x}", data={"value": x})
    # Return data normally for a Completed state
    return {"value": x}

@flow
def my_flow():
    task_run_future = my_task.submit(1)
    try:
        # This will raise an exception if the task failed
        result = task_run_future.result()
        print(f"Task completed with data: {result}")
    except TaskFailureException as e:
        print(f"Task failed with exception: {e}")
        # Access the data from the exception
        print(f"Failed task data: {e.data}")

if __name__ == "__main__":
    my_flow()
m
In Prefect, when a task fails, any downstream tasks that depend on its result will not start. This is because Prefect enforces a dependency chain, where downstream tasks wait for upstream tasks to complete successfully. To handle cases where you want downstream tasks to run regardless of whether an upstream task fails, you can use the 
allow_failure
function to explicitly allow a task to continue even if its dependencies fail. This function will wrap the task's result in a state that doesn't propagate failure. Here's how you can modify the flow to allow a second task to run regardless of the first task's outcome:
Copy code
python
from prefect import flow, task, allow_failure

# Define a custom exception to carry additional data
class TaskFailureException(Exception):
    def __init__(self, message, data):
        super().__init__(message)
        self.data = data

@task
def my_task(x):
    if x < 3:
        # Raise a custom exception with the data you want to preserve
        raise TaskFailureException(f"Value is too low: {x}", data={"value": x})
    # Return data normally for a Completed state
    return {"value": x}

@task
def downstream_task(data):
    print(f"Running downstream task with data: {data}")

@flow
def my_flow():
    task_run_future = my_task.submit(1)
    
    # Allow the task to continue even if the upstream task failed
    task_state = allow_failure(task_run_future)
    
    try:
        # This will raise an exception if the task failed
        result = task_state.result()
        print(f"Task completed with data: {result}")
    except TaskFailureException as e:
        print(f"Task failed with exception: {e}")
        # Access the data from the exception
        print(f"Failed task data: {e.data}")
        # Use the data from the exception to continue
        downstream_task.submit(e.data)

if __name__ == "__main__":
    my_flow()
In this example, 
allow_failure
is used to wrap the task's result, allowing the downstream task to run regardless of the first task's success or failure. The downstream task can then use the data from the exception if the first task fails.
k
@Marvin correction, my task_run_future.submit(1), did not fail, it is stuck in PENDING state, therefor the second task can not start
m
If your task is stuck in a 
PENDING
state, it might be due to several reasons such as resource availability, task scheduling issues, or configuration problems. Here are some steps to diagnose and resolve the issue: 1. Check Worker Availability: Ensure that there are available workers to execute the task. If you're using a local setup, make sure the worker process is running. 2. Review Task Configuration: Verify that the task is correctly configured and that there are no misconfigurations that might prevent it from starting. 3. Inspect Logs: Check the logs for any messages that might indicate why the task is not progressing beyond the 
PENDING
state. Logs can provide insights into issues like missing dependencies or connectivity problems. 4. Resource Limits: Ensure that there are no resource limits being hit, such as CPU or memory constraints, that might prevent the task from being scheduled. 5. State Transitions: Prefect uses polling to check for state transitions. If a task remains in 
PENDING
due to a state transition issue, you might need to investigate if there's a problem with state updates. 6. Check for Errors: Sometimes, underlying errors can prevent tasks from starting. Ensure there are no exceptions or errors that might be causing the task to hang. If these steps do not resolve the issue, you may need to provide more specific logs or context to diagnose further. Additionally, reviewing the task and flow configuration in detail can help identify potential issues.
k
@Marvin in the code below, my "another_task" task can not start because it is waiting for "task_run_future". Because task_run_future.submit(1) fails, the exception is caught in the flow and the task_run_future stays in PENDING state
Copy code
from prefect import flow, get_run_logger, task, allow_failure
from prefect.states import Failed, Completed


#Custom Exception class that allows to return data
class TaskFailureException(Exception):
    def __init__(self, message, data):
        super().__init__(message)
        self.data = data


@task(task_run_name="task_that_raises: {x}")
def task_that_raises(x):    
    if x < 3:
        # Raise a custom exception with the data you want to preserve
        raise TaskFailureException(f"Value is too low: {x}", data=x)
    # Return data normally for a Completed state
    return x

@task()
def another_task():
    print("got here")
    

@flow()
def demo_passing_data():
    global run_logger
    run_logger = get_run_logger()

 
    task_run_future = task_that_raises.submit(x = 2) #task started

    try:
        result = task_run_future.result() #task fails, jump to except
        print(f"Task 2 completed with data: {result}")
        
    except TaskFailureException as e:
        print(f"Task failed with exception: {e}")
        # Access the data from the exception
        print(f"Failed task data: {e.data}")

    another_task(wait_for=task_run_future)
    
    return Completed()

if __name__ == "__main__":
    demo_passing_data()
m
The issue you're encountering is due to the use of 
wait_for=task_run_future
in the 
another_task
call. Since 
task_run_future
is in a 
PENDING
state due to the exception handling in the flow, 
another_task
is waiting indefinitely for it to complete. To allow 
another_task
to run regardless of the state of 
task_run_future
, you should not set it as an upstream dependency using 
wait_for
. Instead, you can handle the exception and proceed to call 
another_task
directly. Here's how you can modify the flow to achieve this:
Copy code
python
from prefect import flow, get_run_logger, task

# Custom Exception class that allows to return data
class TaskFailureException(Exception):
    def __init__(self, message, data):
        super().__init__(message)
        self.data = data

@task(task_run_name="task_that_raises: {x}")
def task_that_raises(x):    
    if x < 3:
        # Raise a custom exception with the data you want to preserve
        raise TaskFailureException(f"Value is too low: {x}", data=x)
    # Return data normally for a Completed state
    return x

@task
def another_task():
    print("got here")

@flow
def demo_passing_data():
    run_logger = get_run_logger()

    task_run_future = task_that_raises.submit(x=2)

    try:
        result = task_run_future.result()
        print(f"Task 2 completed with data: {result}")
    except TaskFailureException as e:
        print(f"Task failed with exception: {e}")
        print(f"Failed task data: {e.data}")

    # Call another_task without wait_for, as we want it to run regardless
    another_task.submit()

if __name__ == "__main__":
    demo_passing_data()
In this version, 
another_task
is called with 
.submit()
directly, and it will execute regardless of the state of 
task_run_future
. This ensures that 
another_task
runs even if 
task_that_raises
fails.
k
@Marvin the issue with the code above is that I want to create a dependency line between task_run_future and another_task. In my real life example, I also need the returned data from task_run_future in another_task, regardless if it failed or not
m
To create a dependency between 
task_run_future
and 
another_task
while ensuring that 
another_task
receives data regardless of whether 
task_run_future
succeeds or fails, you can handle the exception within the flow and pass the necessary data explicitly to 
another_task
. Here's how you can achieve this: 1. Catch the Exception: Handle the exception from 
task_run_future
to determine the result data. 2. Pass Data Explicitly: Pass the data to 
another_task
explicitly, whether it comes from a successful result or the exception data. Here is the modified code:
Copy code
python
from prefect import flow, get_run_logger, task

# Custom Exception class that allows returning data
class TaskFailureException(Exception):
    def __init__(self, message, data):
        super().__init__(message)
        self.data = data

@task(task_run_name="task_that_raises: {x}")
def task_that_raises(x):    
    if x < 3:
        # Raise a custom exception with the data you want to preserve
        raise TaskFailureException(f"Value is too low: {x}", data=x)
    # Return data normally for a Completed state
    return x

@task
def another_task(data):
    print(f"Running another_task with data: {data}")

@flow
def demo_passing_data():
    run_logger = get_run_logger()

    task_run_future = task_that_raises.submit(x=2)

    try:
        # Attempt to get the result, which will raise an exception if the task failed
        result = task_run_future.result()
    except TaskFailureException as e:
        # Use the data from the exception if the task failed
        result = e.data
        print(f"Task failed with exception: {e}")
        print(f"Failed task data: {result}")

    # Pass the result data to another_task
    another_task.submit(data=result)

if __name__ == "__main__":
    demo_passing_data()
In this setup, 
another_task
is called with the data from 
task_run_future
, whether it completed successfully or failed. The flow handles the exception, extracts the data, and passes it to 
another_task
, maintaining the dependency chain.
k
@Marvin the above code did not draw a dependency line in the prefect run UI
m
To ensure that a dependency line is drawn between tasks in the Prefect UI, you need to explicitly use the 
wait_for
parameter to establish a dependency. However, since the task is failing, you must ensure that the downstream task can proceed regardless of the upstream task's state. You can achieve this by using 
allow_failure
to wrap the task's future, allowing it to proceed even if it fails. Here's how you can modify the flow to ensure a dependency line is drawn:
Copy code
python
from prefect import flow, get_run_logger, task, allow_failure

# Custom Exception class that allows returning data
class TaskFailureException(Exception):
    def __init__(self, message, data):
        super().__init__(message)
        self.data = data

@task(task_run_name="task_that_raises: {x}")
def task_that_raises(x):    
    if x < 3:
        # Raise a custom exception with the data you want to preserve
        raise TaskFailureException(f"Value is too low: {x}", data=x)
    # Return data normally for a Completed state
    return x

@task
def another_task(data):
    print(f"Running another_task with data: {data}")

@flow
def demo_passing_data():
    run_logger = get_run_logger()

    task_run_future = task_that_raises.submit(x=2)

    # Allow failure to ensure the downstream task can proceed
    task_run_future = allow_failure(task_run_future)

    try:
        # Attempt to get the result, which will raise an exception if the task failed
        result = task_run_future.result()
    except TaskFailureException as e:
        # Use the data from the exception if the task failed
        result = e.data
        print(f"Task failed with exception: {e}")
        print(f"Failed task data: {result}")

    # Pass the result data to another_task and establish dependency
    another_task.submit(data=result, wait_for=[task_run_future])

if __name__ == "__main__":
    demo_passing_data()
In this version, 
allow_failure
is used to wrap 
task_run_future
, which allows 
another_task
to start even if 
task_that_raises
fails. The 
wait_for
parameter is used to explicitly establish the dependency in the Prefect UI. This ensures that the dependency line is drawn, reflecting the intended task order.
k
@Marvin the above code returned: Finished in state Failed("Flow run encountered an exception: AttributeError: 'allow_failure' object has no attribute 'result'")
m
The 
allow_failure
function is used to wrap a task's result to allow it to continue even if it fails. However, it seems my previous explanation did not correctly apply it for accessing the result. Let's correct this by ensuring you can access the result and maintain the dependency:
Copy code
python
from prefect import flow, get_run_logger, task, allow_failure

# Custom Exception class that allows returning data
class TaskFailureException(Exception):
    def __init__(self, message, data):
        super().__init__(message)
        self.data = data

@task(task_run_name="task_that_raises: {x}")
def task_that_raises(x):    
    if x < 3:
        raise TaskFailureException(f"Value is too low: {x}", data=x)
    return x

@task
def another_task(data):
    print(f"Running another_task with data: {data}")

@flow
def demo_passing_data():
    run_logger = get_run_logger()

    task_run_future = task_that_raises.submit(x=2)

    # Allow failure to ensure the downstream task can proceed
    task_state = allow_failure(task_run_future)

    try:
        # Attempt to get the result, which will raise an exception if the task failed
        result = task_state.result()
    except TaskFailureException as e:
        # Use the data from the exception if the task failed
        result = e.data
        print(f"Task failed with exception: {e}")
        print(f"Failed task data: {result}")

    # Pass the result data to another_task and establish dependency
    another_task.submit(data=result)

if __name__ == "__main__":
    demo_passing_data()
In this corrected version, 
task_state
is used to wrap the result of 
task_run_future
with 
allow_failure
, allowing you to proceed with 
another_task
using the result or exception data. The 
another_task
is called directly with the 
result
to maintain the intended dependency chain.
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