https://dagster.io/ logo
#ask-ai
Title
# ask-ai
j

Joram Sbrisny

02/27/2024, 10:04 PM
@Scout: how i can use config_schema with dbt_assets decorator
s

Scout

02/27/2024, 10:04 PM
The
dbt_assets
decorator in Dagster is used to create assets from a dbt project. When using
dbt_assets
, you typically specify the path to the dbt project's manifest file and optionally provide a custom
DagsterDbtTranslator
for customizing how dbt assets are represented in Dagster. However, the
dbt_assets
decorator itself does not directly accept a
config_schema
argument for defining configuration schemas like you would with individual ops or assets. Instead, the configuration for dbt assets is generally derived from the dbt project configuration and the
DbtCliResource
that is used to execute dbt commands. If you need to pass specific configuration to dbt when using
dbt_assets
, you would configure the
DbtCliResource
that is passed to the
dbt_assets
function. This resource can be configured with the necessary dbt project directory, profiles directory, and other dbt-related settings. Here's an example from the documentation on how to use the
dbt_assets
decorator:
Copy code
python from dagster import AssetExecutionContext, dbt_assets from dagster_dbt import DbtCliResource  @dbt_assets(     manifest_path="path/to/manifest.json",     # Additional arguments like io_manager_key can be specified here ) def my_dbt_assets(context: AssetExecutionContext, dbt: DbtCliResource):     # Execute dbt commands using the dbt CLI resource     yield from dbt.cli(["build"], context=context).stream()
In this example, the
DbtCliResource
is implicitly used by the
dbt_assets
function, and you can configure it separately in your job or repository definition to include the necessary dbt configuration. If you need to customize the configuration of the
DbtCliResource
, you can do so when defining your job or repository. Here's an example of how to configure the `DbtCliResource`:
Copy code
python from dagster import job from dagster_dbt import dbt_cli_resource  my_job = job(     resource_defs={         "dbt": dbt_cli_resource.configured(             {                 "project_dir": "path/to/dbt/project",                 "profiles_dir": "path/to/dbt/profiles",                 # Other dbt CLI configurations can be specified here             }         )     } )
In this job definition, the
dbt_cli_resource
is configured with the necessary dbt project and profiles directories. You can add additional configuration options as needed for your dbt project. Remember that the configuration for dbt assets is primarily driven by the dbt project's own configuration files (
dbt_project.yml
and
profiles.yml
) and the settings provided to the
DbtCliResource
. The
dbt_assets
decorator leverages these configurations to execute dbt commands and create assets in Dagster.