Josh
03/12/2024, 7:10 AMScout
03/12/2024, 7:10 AMDefinitions
object, which is used to construct the repository. However, if you're looking to define assets programmatically or dynamically, you might want to use functions that can create asset definitions and then include them in your Dagster setup. The documentation provides an example of how to define external assets using the external_assets_from_specs
function, which can be used to create asset definitions from AssetSpec
objects: python from dagster import AssetSpec, Definitions, external_assets_from_specs raw_logs = AssetSpec("raw_logs") processed_logs = AssetSpec("processed_logs", deps=[raw_logs]) defs = Definitions(assets=external_assets_from_specs([raw_logs, processed_logs]))
In this example, external_assets_from_specs
is used to create asset definitions from a list of AssetSpec
objects, which are then added to the Definitions
object. If you're looking to define assets dynamically, you might create a function that generates AssetSpec
objects based on some logic and then use external_assets_from_specs
to create the asset definitions. These definitions can then be included in your Dagster repository. Remember that external assets can't be executed by Dagster, as indicated by the disabled "Materialize" button in the Dagster UI for such assets. External assets are typically used to represent data produced by systems outside of Dagster's control. For more complex scenarios or if you need further guidance on dynamically creating assets, you might want to consult the Dagster community or the documentation for additional patterns and best practices.Josh
03/12/2024, 7:12 AMScout
03/12/2024, 7:12 AMDefinitions
object, which is a common pattern for organizing your assets, pipelines, and other components. However, if you want to create assets outside of the Definitions
object, you can still do so by defining them with the @asset
decorator and then including them in your repository in other ways. For instance, you can define assets using the @asset
decorator directly in your code: python from dagster import asset @asset def my_asset(): # Your asset computation logic here pass
Once you have defined your assets using the @asset
decorator, you can include them in your repository by collecting them into a list and passing them to the repository
decorator: python from dagster import repository from my_assets_module import my_asset, another_asset @repository def my_repository(): return [my_asset, another_asset]
This approach allows you to define assets wherever it makes sense in your codebase and then bring them together in your repository definition. If you need to create assets dynamically based on some runtime information or external configuration, you would typically use a factory pattern where you define a function that generates @asset
-decorated functions or AssetsDefinition
objects based on your dynamic criteria. Keep in mind that the assets still need to be included in a repository to be recognized by Dagster's tooling, such as Dagit and the CLI. The repository serves as the entry point for Dagster to discover and execute your assets. If you have a specific use case or pattern in mind that doesn't fit these common approaches, you might want to consult the Dagster community or the documentation for additional guidance.