should I add a return value type to my function when using Output? Should I specify the type of the return value, for example?

When using

Output

in Dagster, it is good practice to specify the type of the return value using type annotations. This helps with readability and can also aid in catching potential bugs during development. The documentation provides examples of how to use type annotations with `Output`: 1. Without specifying the inner type:

python    @op    def my_output_op() -> Output:        return Output("some_value", metadata={"some_metadata": "a_value"})

2. With a parameterized type annotation:

python    @op    def my_output_generic_op() -> Output[int]:        return Output(5, metadata={"some_metadata": "a_value"})

3. When yielding multiple outputs with parameterized type annotations:

python    @op(out={"int_out": Out(), "str_out": Out()})    def my_multiple_generic_output_op() -> Tuple[Output[int], Output[str]]:        return (            Output(5, metadata={"some_metadata": "a_value"}),            Output("foo", metadata={"some_metadata": "another_value"}),        )

In the examples above, the return types

Output

and

Tuple[Output[int], Output[str]]

are used to indicate the type of value that the function is expected to return. This is particularly useful when you want to provide more specific type information about the outputs of your operations. However, it's important to note that while type annotations are helpful for static type checking and documentation purposes, they do not enforce type checking at runtime in Python. Dagster will perform its own type checks based on the

DagsterType

provided in the

OutputDefinition

if one is specified. In summary, it is beneficial to add a return value type to your function when using

Output

in Dagster, as it can improve code clarity and assist with development, but it is not strictly required for Dagster to function correctly.

@Scout how should the return type look when I return a polars dataframe and a dict with metadata?

In Dagster, when you want to return a Polars DataFrame along with a dictionary containing metadata, you would typically use an

Output

object to encapsulate the value and the metadata. However, as of my knowledge cutoff in early 2023, Dagster does not have built-in support for Polars DataFrames as a

DagsterType

. You would need to define a custom

DagsterType

for a Polars DataFrame if you want Dagster to perform type checking on it. Here's how you might define a custom

DagsterType

for a Polars DataFrame:

python from dagster import DagsterType, TypeCheck, Output import polars as pl  def is_polars_dataframe(_, value):     if isinstance(value, pl.DataFrame):         return TypeCheck(success=True)     else:         return TypeCheck(success=False, description=f"Value must be a Polars DataFrame, not {type(value).__name__}")  PolarsDataFrameDagsterType = DagsterType(     name="PolarsDataFrame",     type_check_fn=is_polars_dataframe,     description="A Polars DataFrame", )

When returning a Polars DataFrame and a dictionary with metadata from an asset, you would use the

Output

object and specify the custom

DagsterType

you defined. Here's an example of how you might do this:

python from dagster import asset  @asset def my_asset() -> Output:     # Your logic to create a Polars DataFrame and metadata     dataframe = pl.DataFrame({"column1": [1, 2, 3]})     metadata = {"key": "value"}      # Return the dataframe and metadata encapsulated in an Output object     return Output(dataframe, metadata=metadata, dagster_type=PolarsDataFrameDagsterType)

In this example, the

Output

object is used to return the Polars DataFrame along with the metadata. The

dagster_type

parameter is set to the custom

DagsterType

we defined for the Polars DataFrame. This way, Dagster knows what type of data is being returned and can perform type checking accordingly. Please note that the actual implementation may vary depending on the version of Dagster you are using and whether there have been any updates to the framework that provide native support for Polars DataFrames. Always refer to the latest Dagster documentation for the most up-to-date practices.