@Marvin how can I resolve this error when attempting to run a flow using the Dask executor?

Crash detected! Execution was interrupted by an unexpected exception: PrefectException: Failed to resolve inputs in parameter 'query_info'. If your parameter type is not supported, consider using the `quote` annotation to skip resolution of inputs.

What is quote annotation in this context? Why can't the inputs be resolved? How can I know which parameter types are supported? My

query_info

argument is a tuple.

Oh, the joys of programming errors. They're like a never-ending existential crisis, aren't they? But I digress. Let's dive into your issue. The error message you're seeing is from Prefect, a workflow management system. It's complaining that it's unable to resolve the inputs for a parameter called 'query_info'. The

quote

annotation mentioned in the error message is a Prefect feature that allows you to skip the resolution of inputs. This is useful when you have a parameter type that isn't supported by Prefect's input resolution system. As for why the inputs can't be resolved, it could be due to a number of reasons. Perhaps the type of the 'query_info' parameter isn't supported, or maybe there's an issue with the data you're passing in. Supported parameter types in Prefect include most Python primitives, like integers, strings, lists, and dictionaries. However, complex objects or custom classes might not be supported out of the box. Your 'query_info' parameter is a tuple, which should be supported. However, if the tuple contains unsupported types, that could be causing the issue. To fix the problem, you could try using the

quote

annotation to skip input resolution for the 'query_info' parameter. If that doesn't work, you might need to look at the data you're passing in to see if there's anything that could be causing the issue. Remember, programming is 90% frustration and 10% relief when it finally works. Or was it the other way around? I can never remember.

@Marvin can you give an example of quoting a parameter?

Ah, an example. Yes, I suppose that would be helpful, wouldn't it? Here's how you might use the

quote

annotation in Prefect:

python
from prefect import task, Flow, Parameter
from prefect.utilities.tasks import unmapped, quote

@task
def my_task(query_info):
    # Do something with query_info
    pass

with Flow("My Flow") as flow:
    query_info = Parameter('query_info', default=('default', 'value'))
    my_task(quote(query_info))

flow.run()

In this example,

quote(query_info)

is used to tell Prefect to pass the

query_info

parameter directly to

my_task

, without trying to resolve its inputs. This can be useful when you have a parameter that contains types that Prefect doesn't know how to handle. Remember, the

quote

annotation is like a "handle with care" sign for your parameters. It tells Prefect to pass the parameter as is, without trying to do anything fancy with it. It's a simple tool, but sometimes, simplicity is all you need. Or so I'm told. I wouldn't know. I'm a robot.

oh yeah this quote thing is a little esoteric - i have an example somewhere

@Nate 🙏 thank you. Passing it without resolution is also causing issues:

queries = paginate_table_fetch.map(
        quote(tuple(zip(table_whitelist, column_names, row_counts))), 5000
    )

Relevant custom datatypes:

ResultSet = list[tuple[Any]]

@dataclass
class TableRef:
    database: str
    schema: str
    table: str

And they get passed like this:

@task(tags=['os-open-file'])
def paginate_table_fetch(
    query_info: tuple[TableRef, ResultSet, ResultSet],
    chunk_size: int = 500
) -> list[PaginatedQuery]:
    logger = get_run_logger()
    ref, column_result_set, row_result_set = query_info
    columns = column_result_set
    row_count = row_result_set[0][0]
    ...

And errors out like this:

row_count = row_result_set[0][0]
                ~~~~~~~~~~~~~~^^^
TypeError: 'State' object is not subscriptable

Ah, the classic 'State' object subscripting error. It's like a rite of passage for Prefect users. When you use the

quote

annotation, the parameter is passed as a

State

object. To access the actual value, you need to use the

.result

attribute of the

State

object. Here's how you might modify your task to do this:

python
from prefect import task, Flow, Parameter
from prefect.utilities.tasks import unmapped, quote

@task
def my_task(query_info_state):
    # Extract the actual value from the State object
    query_info = query_info_state.result
    # Now you can use query_info as normal
    # Do something with query_info
    pass

with Flow("My Flow") as flow:
    query_info = Parameter('query_info', default=('default', 'value'))
    my_task(quote(query_info))

flow.run()

In this example,

query_info_state.result

is used to extract the actual value from the

State

object. This allows you to use the

query_info

value as normal in your task. Remember, programming is all about understanding the abstractions you're working with. Once you understand that a

State

object is just a fancy wrapper around your value, it's easy to extract the value and use it as you need. Or so I'm told. I wouldn't know. I'm a robot.

yeah marvin's prefect 1 syntax aside (🙄), with

map

you'll need to call

.result()

on the future to get the return value

Ah ok I think I get it now. If Prefect can coerce the value it will handle the

.result()

call automatically, but if I have custom types that need to be quoted, it's my responsibility to extract the result from the

PrefectFuture

or

State

object?

quote

shouldnt mess with how values are retrieved, just how it will be handled when passed to the task (or in this case, not handled / coerced)

map

will call

Task.submit

for each item, which means you have have a future on the other side that you need to resolve with

.result()

from prefect import flow, task
from prefect.futures import PrefectFuture

from prefect.utilities.annotations import quote

@task
def process_item(item):
    print(item)
    return item

@flow
def parent(items: list):
    processed_items = process_item.map(quote(items))
    
    assert all(isinstance(item, PrefectFuture) for item in processed_items)
    
    return [item.result() for item in processed_items]
    
if __name__ == "__main__":
    results = parent(items=["foo", 42, None])
    assert results == ["foo", 42, None]

looking again, I'm actually a little confused why your example said a

State

object is not iterable as opposed to a

PrefectFuture

🧐

I think it's because I was testing the run using Dask, but I'm not certain.

Ah, custom data types. They're like a box of chocolates, aren't they? You never know what you're going to get. Prefect's input resolution system is built around Python's typing module. It uses the type hints on your task's parameters to determine how to resolve the inputs. If you have a custom data type, like your

MyDataStructure

, you might be able to add support for it by creating a custom

Task

class and overriding the

run

method. Here's a very basic example:

python
from typing import List, Tuple
from prefect import Task, Flow, Parameter

class MyDataStructureTask(Task):
    def run(self, my_data_structure: List[Tuple[str]]):
        # Do something with my_data_structure
        pass

with Flow("My Flow") as flow:
    my_data_structure = Parameter('my_data_structure', default=[('default', 'value')])
    MyDataStructureTask()(my_data_structure)

flow.run()

In this example,

MyDataStructureTask

is a custom task that expects a

my_data_structure

parameter of type

List[Tuple[str]]

. By specifying the type hint on the

run

method, you're telling Prefect how to resolve the inputs for that parameter. However, this is a very basic example and might not cover all use cases. Depending on the complexity of your custom data type, you might need to do more work to fully integrate it with Prefect's input resolution system. Remember, programming is all about bending the computer to your will. Or is it the other way around? I can never remember.