@Marvin I am using this code block. Why am I getting the warning "WARNING | EventsWorker - Still processing items: 6 items remaining..."

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(data):
    return (f"got here and showing data from previous task: {data}")
    

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

 
    task_run_future = task_that_raises.submit(x = 1) #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}")

        result = e.data
        task_run_future.wait()


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


    thistasksresult = future.result()
    print(thistasksresult)
    
    return Completed()

if __name__ == "__main__":
    demo_passing_data()

The warning "WARNING | EventsWorker - Still processing items: 6 items remaining..." typically indicates that there are pending tasks or events that the worker has not yet completed processing. This can happen if the worker is still handling asynchronous tasks or if there is a delay in processing due to resource constraints or misconfiguration. From the gathered context, here are a few potential causes and solutions: 1. Asynchronous Processing: Prefect workers or agents (now replaced by workers in Prefect 3.x) handle tasks asynchronously. If tasks are not awaited properly, they might remain in the queue. Make sure that any asynchronous tasks are properly awaited or resolved. 2. Resource Constraints: Ensure that the environment running Prefect has adequate resources (CPU, memory) to handle the tasks. The worker might be overloaded, causing delays in processing. 3. Configuration Issues: Double-check your Prefect configuration, especially related to workers and infrastructure. Ensure that the infrastructure is correctly set up to handle the flow runs. 4. Prefect Version Differences: If you have recently upgraded from Prefect 2.x to 3.x, ensure that you have adapted your code and infrastructure to match the changes in Prefect 3.x, such as using workers instead of agents. For more detailed guidance, you might want to explore Prefect's documentation on workers and infrastructure and check the task worker source code. Additionally, reviewing the common gotchas and differences in Prefect 3.x might help identify other potential issues in your setup.

@Marvin this is in prefect 3. I expected the "task_run_future.wait()" to make sure that the asynchronous task is properly awaited or resolved, but it seems like this is not the case. Can you explain why that is and how I can do this instead?

In Prefect 3.x, the

task_run_future.wait()

method might not be functioning as you expect because Prefect Futures have a synchronous interface and may not inherently wait for asynchronous tasks to complete. Here are some key points and suggestions for handling task futures properly: 1. Futures Interface: Prefect Futures in version 3.x provide a synchronous interface, and an asynchronous one is planned for future releases. This means that methods like

.wait()

might not behave as expected in an asynchronous context. 2. Handling Task Completion: - Use

task_run_future.result()

to retrieve the result of a task. This call will block until the task is completed, effectively waiting for the task to finish. - If you need to handle asynchronous tasks, ensure you are within an asynchronous context and use

await

with any asynchronous methods provided by Prefect, once available. 3. Error Handling: If you are dealing with task failures, consider using try/except blocks to handle exceptions and determine the flow's behavior based on task success or failure. For more details, you can explore the Prefect documentation on resolving task futures and the futures source code. Here's a small adjustment to your code to ensure the task result is properly awaited:

python
try:
    result = task_run_future.result() # This will block until the task completes
    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}")
    result = e.data

This approach ensures that the task's result is awaited synchronously, which should resolve the warning about pending items.