geoHeil
02/28/2024, 10:19 AMScout
02/28/2024, 10:19 AMopen_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:
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 envScout
02/28/2024, 10:20 AMPipesK8sClient
with an asset: 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.