For a given run of a dbt asset, how can I supply config so that dbt performs a full refresh

To perform a full refresh of a dbt asset during a run, you can supply custom configuration to the underlying op that backs your software-defined assets. This configuration can be passed through Dagster's run configuration system. Here's an example from the documentation that demonstrates how to use the

--full-refresh

flag with

@dbt_assets

by adding a custom configuration class:

python from pathlib import Path from dagster import AssetExecutionContext, Config from dagster_dbt import dbt_assets, DbtCliResource  class MyDbtConfig(Config):     full_refresh: bool  @dbt_assets(manifest=Path("target", "manifest.json")) def my_dbt_assets(context: AssetExecutionContext, dbt: DbtCliResource, config: MyDbtConfig):     dbt_build_args = ["build"]     if config.full_refresh:         dbt_build_args += ["--full-refresh"]      yield from dbt.cli(dbt_build_args, context=context).stream()

When you define a job that includes these dbt assets, you can specify the

full_refresh

configuration to be

True

in the

RunConfig

. This will ensure that the dbt models are fully refreshed during the run:

python from dagster import define_asset_job, AssetSelection, RunConfig  dbt_incremental_full_refresh_job = define_asset_job(     "dbt_incremental_full_refresh_job",     selection=AssetSelection(my_dbt_assets),     config=RunConfig({"my_dbt_assets": MyDbtConfig(full_refresh=True)}), )

When you execute this job, Dagster will pass the

--full-refresh

flag to the dbt CLI command, triggering a full refresh of the dbt models.