How do I run a dbt command using dbtResourceConfig.cli method?

To run a dbt command using the

dbtResourceConfig.cli

method, you would typically use the

DbtCliResource

within a Dagster job or op. The

DbtCliResource

provides a

cli

method that allows you to execute dbt CLI commands. Here's a general example based on the documentation:

python from dagster import job from dagster_dbt import dbt_cli_resource  @job(resource_defs={"dbt": dbt_cli_resource}) def my_dbt_job():     # Here you would invoke the dbt CLI command using the `cli` method     # For example, to run `dbt run`, you might do something like this:     dbt_cli_resource.cli(["run"])

In this example,

dbt_cli_resource

is a predefined resource that represents the dbt CLI. The

cli

method takes a list of strings that represent the dbt command and its arguments that you want to execute. Please note that the exact usage may vary depending on your specific setup and the version of Dagster and dagster-dbt you are using. Always refer to the latest documentation for the most accurate and up-to-date examples and usage instructions.

@Scout how do I run a dbt test command ?

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@Scout I have been getting this error when using the dbt cli command. Any idea about the root cause ?

dagster_dbt.errors.DagsterDbtCliRuntimeError: The dbt CLI process failed with exit code 2. Check the stdout in the Dagster compute logs for the full information about the error.

The

DagsterDbtCliRuntimeError

with exit code 2 indicates that the dbt CLI process encountered a fatal error during its execution. This error is not specific and can be caused by a variety of issues within your dbt project or environment. Here are some common reasons why you might encounter this error: 1. Project Configuration Issues: There might be a problem with your

dbt_project.yml

configuration. This could be due to syntax errors, incorrect configurations, or other issues that prevent dbt from running successfully. 2. Model Errors: There could be errors in your dbt models, such as SQL syntax errors, reference to undefined models, or issues with model dependencies. 3. Environment Issues: The environment in which dbt is running might not be set up correctly. This could include missing environment variables, incorrect paths to profiles or project directories, or issues with the installed dbt version. 4. Dependency Issues: If your dbt project has dependencies on other packages or modules, there might be issues resolving or installing these dependencies. 5. Permissions Issues: The dbt CLI might not have the necessary permissions to read from or write to certain directories or files. 6. Resource Constraints: The execution environment might have insufficient resources, such as memory or CPU, which could lead to the dbt process being killed. To diagnose the root cause of the error, you should: - Check the Compute Logs: Look at the detailed logs in the Dagster compute logs to see the full output from the dbt CLI. This output will often contain detailed error messages that can help you identify the specific issue. - Run dbt Manually: Try running the dbt command manually in the same environment where your Dagster job runs. This can help you determine if the issue is related to Dagster or the dbt project itself. - Validate dbt Project: Use commands like

dbt debug

or

dbt run --dry

to validate your dbt project configuration and connectivity to the data warehouse. - Review dbt Project Files: Ensure that all necessary files, such as

dbt_project.yml

and

profiles.yml

, are correctly configured and accessible. - Check Environment Variables: If your dbt project relies on environment variables, make sure they are set correctly in the Dagster environment. By following these steps, you should be able to pinpoint the cause of the

DagsterDbtCliRuntimeError

and take the appropriate action to resolve it. If the issue persists, you may want to seek further assistance from the Dagster community or support channels with specific details from your logs and project configuration.