@Scout:can i pass parameter du @dbt_asset

Yes, you can pass parameters to the

@dbt_assets

decorator. The documentation provides examples of how to use

@dbt_assets

and the

DbtCliResource

to execute commands like

dbt run

or

dbt build

on your dbt project. It also mentions that you can customize your dbt assets using

DagsterDbtTranslator

and generate a dbt manifest for

@dbt_assets

at runtime using

dbt parse

. Parameters such as

project_dir

,

profiles_dir

,

target_dir

,

select

,

exclude

, and

dagster_dbt_translator

can be specified when using

@dbt_assets

. These parameters allow you to define the directory containing the dbt project, the profiles directory, the target directory where dbt will place compiled artifacts, and selection strings for the models in a project that you want to include or exclude. The

dagster_dbt_translator

parameter allows customizing how to map dbt models, seeds, etc., to asset keys and asset metadata. For example, if you want to use the

select

parameter to filter the set of models that are loaded as assets, you can specify it when decorating a function with

@dbt_assets

. This is useful when you want to run a subset of dbt assets based on dbt selection syntax. Here is an example from the documentation on how to use

@dbt_assets

with parameters:

python @dbt_assets(     project_dir="path/to/dbt/project",     profiles_dir="path/to/profiles",     target_dir="path/to/target",     select="fqn:*",     exclude="",     dagster_dbt_translator=CustomDagsterDbtTranslator(), ) def my_dbt_assets():     ...

In this example,

CustomDagsterDbtTranslator

would be a class you define to customize the mapping of dbt resources to Dagster asset keys and metadata. The

select

parameter is used to include all models in the dbt project, and the

exclude

parameter is currently set to exclude none. Please refer to the documentation for more details and examples on how to use

@dbt_assets

and customize your dbt assets within Dagster.

@Scout: an how i can do this with

dbt.cli

The

dbt.cli

method from the

dagster_dbt

package allows you to execute dbt commands programmatically within a Dagster job or op. You can pass parameters directly to the

dbt.cli

method to customize the dbt command that gets executed. Here's an example of how you might use

dbt.cli

with parameters:

python from dagster import job from dagster_dbt import dbt_cli_resource  @job(resource_defs={"dbt": dbt_cli_resource}) def my_dbt_job():     dbt_cli_resource.cli(["run", "--models", "my_model+", "--full-refresh"])

In this example, the

dbt_cli_resource.cli

method is called with a list of command-line arguments that you would normally pass to dbt in the terminal. The

--models

argument is used to specify which models to run (in this case,

my_model

and its dependencies), and

--full-refresh

is used to indicate that a full refresh should be performed. You can pass any valid dbt CLI arguments to the

cli

method in this list format. This allows you to dynamically construct dbt commands based on the context of your Dagster job or op. Please note that the

dbt_cli_resource

is a resource definition that needs to be provided to the job, and it should be configured with the necessary project directory, profiles directory, and other settings that dbt needs to run. For more advanced use cases, you can also use the

DbtCli

class to execute dbt commands and interact with the dbt CLI programmatically. This class provides methods for running dbt commands and retrieving artifacts associated with the dbt command. Here's an example using `DbtCli`:

python from dagster import op from dagster_dbt import DbtCli  @op(required_resource_keys={"dbt"}) def my_dbt_op(context):     dbt_cli = context.resources.dbt     dbt_cli.run(["run", "--models", "my_model+"])

In this example,

DbtCli.run

is used to execute the

dbt run

command with specific models. The

DbtCli

instance is retrieved from the context's resources, assuming that

DbtCli

has been provided as a resource under the key "dbt". Please refer to the documentation for the most up-to-date examples and usage patterns for

dbt.cli

and

DbtCli

within Dagster.

@Scout: wenn i use dbt.clie with --vars i get the following error message Couldn't find key AssetKey(['parameter_key'])

The error message "Couldn't find key AssetKey(['parameter_key'])" suggests that Dagster is unable to locate an asset with the specified key. This could happen for a few reasons, such as a mismatch between the asset keys defined in your Dagster code and the asset keys expected by the dbt project, or if there's an issue with how the asset keys are being generated or referenced. When using

--vars

with

dbt.cli

, you're typically passing variables to your dbt project that can be used within your dbt models. For example:

python dbt_cli_resource.cli(["run", "--vars", '{"key": "value"}'])

Here are a few steps you can take to troubleshoot the issue: 1. Check Asset Key Definitions: Ensure that the asset keys you're using in your Dagster code match the asset keys that are generated by your dbt project. If you're using a custom

DagsterDbtTranslator

, make sure it's correctly translating dbt node names to Dagster asset keys. 2. Review dbt Project Configuration: Verify that your dbt project is correctly configured to use the variables you're passing with

--vars

. The variables should be referenced in your dbt models or project configuration files where necessary. 3. Validate Asset Dependencies: If the asset key in question is supposed to be an input to another asset, ensure that the dependencies are correctly defined in your Dagster asset definitions. 4. Check for Typos: A common issue could be a simple typo in the asset key name or in the variable name passed to

--vars

. Double-check for any discrepancies. 5. Review dbt Run Context: Make sure that the dbt run is being executed in the correct context, with the appropriate profiles and target directories specified, so that dbt can find and use the variables as expected. 6. Examine Dagster Logs: Look at the logs generated by Dagster during the execution of the dbt command. They might provide additional information about why the asset key could not be found. 7. Consult Documentation: Review the documentation for both Dagster and dbt to ensure that you're using the

--vars

parameter correctly and that your asset keys are being defined and referenced as expected. If you continue to experience issues, you may want to provide more detailed information about your setup, including how you're defining your assets and how you're invoking

dbt.cli

with

--vars

. This will help in diagnosing the specific cause of the error you're encountering.