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Title
# ask-ai
g

geoHeil

02/28/2024, 10:19 AM
@Scout how can I create an asset with a factory method where pythonic configuration is passed. How can this be extended to serve in the context of dagster pipes
s

Scout

02/28/2024, 10:19 AM
Creating an asset with a factory method where pythonic configuration is passed can be done by defining a function that generates an asset, taking configuration parameters as arguments. This factory function can then use these parameters to configure the asset's behavior. To extend this to work with Dagster Pipes, you would need to ensure that the asset can communicate with the Dagster orchestration layer, typically by using the
open_dagster_pipes
context manager. Here's a step-by-step guide based on the documentation: 1. Define a Factory Method: Create a factory method that accepts configuration parameters and returns an asset. Use the
@asset
decorator to define the asset within the factory method. 2. Pythonic Configuration: Pass the configuration as arguments to the factory method. These arguments can be used to configure the asset's behavior or to pass to the asset's execution context. 3. Dagster Pipes Integration: To integrate with Dagster Pipes, you would typically use the
open_dagster_pipes
context manager within the asset's code to establish communication with the Dagster orchestration layer. This allows you to log messages, report asset materializations, and pass metadata back to Dagster. 4. Report Asset Materialization: Use the
report_asset_materialization
method from the
PipesContext
to report asset materialization metadata and data version back to Dagster. Here's an example of how you might define such a factory method, based on the documentation:
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python
from dagster import asset
from dagster_pipes import open_dagster_pipes

def create_asset_with_config(param_1, param_2):
    @asset
    def my_configured_asset(context):
        with open_dagster_pipes() as pipes:
            # Use the configuration parameters within your asset's logic
            result = some_computation_based_on_params(param_1, param_2)
            
            # Log messages and report materializations as needed
            <http://pipes.log.info|pipes.log.info>("Computation completed")
            pipes.report_asset_materialization(
                metadata={"param_1": param_1, "param_2": param_2},
                data_version="some_version"
            )
            return result
    
    return my_configured_asset

# Create an asset instance with specific configuration
my_asset = create_asset_with_config("value1", "value2")
Please note that the above code is a conceptual example to illustrate the process. You would need to replace
some_computation_based_on_params
with your actual computation logic and provide the necessary imports and context for
open_dagster_pipes
to work within your environment. For extending this to work in the context of Kubernetes with Dagster Pipes, you would use the
PipesK8sClient
to run the asset within a Kubernetes env
ironment. The documentation provides an example of how to use
PipesK8sClient
with an asset:
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python from dagster import AssetExecutionContext, asset from dagster_k8s import PipesK8sClient  @asset def k8s_pipes_asset(context: AssetExecutionContext, k8s_pipes_client: PipesK8sClient):     return k8s_pipes_client.run(         context=context,         image="pipes-example:v1",     ).get_materialize_result()
In this example,
k8s_pipes_client.run
is used to execute the asset within Kubernetes, and
get_materialize_result
is called to obtain the result of the execution. Remember to consult the documentation for the most up-to-date and detailed instructions, as the examples provided here are based on the documentation's guidance and may not cover all use cases or configurations.