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Aasim Sani

02/09/2024, 9:20 PM
@Scout How do I pass a dataclass as an asset output?
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class S3DatasetItem:
    """Simple dataclass for the s3 csv file"""
    data: pd.core.frame.DataFrame
    filename: str
s

Scout

02/09/2024, 9:20 PM
To pass a dataclass as an asset output in Dagster, you would typically define an asset function that returns an instance of your dataclass. However, the documentation does not provide a specific example of using a dataclass as the output of an asset. Instead, it provides examples of assets that return common data types like Pandas DataFrames or lists. Here's a general approach based on the principles outlined in the documentation: 1. Define your dataclass, as you have done. 2. Create an asset function that performs the necessary computation and returns an instance of your dataclass. 3. Ensure that the IOManager associated with the asset can handle the custom data type. You may need to implement custom serialization and deserialization logic if you're using a dataclass that isn't natively supported by the IOManager you're using. Here's a conceptual example:
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python from dataclasses import dataclass import pandas as pd from dagster import asset  @dataclass class S3DatasetItem:     """Simple dataclass for the s3 csv file"""     data: pd.DataFrame     filename: str  @asset def my_asset() -> S3DatasetItem:     # Perform some computation to get a DataFrame     data = pd.DataFrame(...)     filename = "my_data.csv"          # Return an instance of S3DatasetItem     return S3DatasetItem(data=data, filename=filename)
In this example,
my_asset
is an asset function that returns an
S3DatasetItem
instance. The IOManager responsible for storing the output of this asset would need to know how to serialize and deserialize
S3DatasetItem
objects. If you're using a custom IOManager, you would implement the
handle_output
method to serialize the
S3DatasetItem
to persistent storage and the
load_input
method to deserialize it when used as an input to another asset or operation. Please note that the above example is conceptual and does not include the implementation details for the IOManager, which would be necessary for this to work in a real Dagster pipeline. If you need to implement a custom IOManager for your dataclass, you would refer to the documentation on IO Managers for guidance on how to do so.
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