@Marvin Do you have a suggestion of why this error appears, and how to solve it? Examples are welcome.

prefect.exceptions.MissingResult: State data is missing. Typically, this occurs when result persistence is disabled and the state has been retrieved from the API.

The error you're encountering,

prefect.exceptions.MissingResult

, is typically seen when result persistence is disabled in Prefect, and the state has been retrieved from the API. This means that when Prefect attempts to access the data of a task run from a previous state, the data is unavailable because it hasn't been stored or persisted. Here are some strategies to resolve this: 1. Enable Result Persistence: By enabling result persistence, you'll ensure that task results are stored and can be accessed later. You can configure this in Prefect by adjusting settings such as

persist_result

in your flow or task definitions. For Prefect 3.x, see more about result management in Prefect 3.x and for Prefect 2.x, see more about results in Prefect 2.x. 2. Result Storage: Configure where and how your results should be stored. Prefect can store results in various locations, like local storage, cloud storage, or Prefect Cloud. Ensure that your storage settings are properly configured. 3. Check Workflows: Sometimes, passing certain types of data between tasks might not be well-supported. For instance, attempting to pass Prefect's internal objects like

FlowRun

instances as arguments can lead to issues. If you're using Prefect objects in your task arguments, consider simplifying the data being passed or persisting only necessary data. 4. Check GitHub Issues: The Prefect community and developers frequently address these issues. For instance, not all data types can be easily serialized and may require additional handling, as discussed in specific GitHub issues like those found here and here. Implementing some or all of these changes should help resolve the error you're facing. Remember, persistence settings can typically default to off unless required by a feature like retries or caching, so checking these settings would be a first good step. If more Martian life form-like errors persist, feel free to reach out again!

@Marvin In my case, it is related to the third point. I have a flow like this one:

my_flow(wait_for=[x])

Where

x

is a

FlowRun

, that is returned from a

run_deployment

function.

If you are trying to use a

FlowRun

returned from a

run_deployment

function as part of a dependency in the

wait_for

list of a flow, you might run into the error you mentioned because Prefect expects objects in this context that it can persist and manage more directly. Here are a few suggestions you might consider: 1. Extract Required Information: Instead of passing the entire

FlowRun

object, try to extract the necessary information, like the flow run's ID or state, and use that in your logic. This minimizes serialization issues and ties to internal Prefect objects that might not be meant for task dependencies directly. 2. Use Result Persistence: Make sure that the results of tasks or flows (like those triggering through

run_deployment

) have their results properly managed with persistence, if you rely on their states downstream. 3. Polling Flow State: Another strategy might be to poll the state of this flow run using its ID. You could create a small utility function that checks the completion status of the flow and orchestrates behavior accordingly within your main flow. To read more about how Prefect handles deployments and execution, you can explore the Prefect 3.x deployment documentation and integration with Python. Alternatively, the Prefect 2.x documentation provides insights into handling deployments and their parameters, including using

run_deployment

. If ever in doubt, trying to be a bit less "adventurous" with what kind of variables you pass between tasks can often simplify tracking down such issues. Keep the crucial data and let the Prefect magic work behind the scenes!