Warnings

Use the --warn-error flag to promote all warnings to errors or --warn-error-options for granular control through options.

Use --warn-error to promote all warnings to errors

Enabling WARN_ERROR config or setting the --warn-error flag will convert all dbt warnings into errors. Any time dbt would normally warn, it will instead raise an error. Examples include --select criteria that selects no resources, deprecations, configurations with no associated models, invalid test configurations, or tests and freshness checks that are configured to return warnings.

Usage

dbt --warn-error run

Use --warn-error-options for targeted warnings

In some cases, you may want to convert all warnings to errors. However, when you want some warnings to stay as warnings and only promote or silence specific warnings you can instead use --warn-error-options. The WARN_ERROR_OPTIONS config or --warn-error-options flag gives you more granular control over exactly which types of warnings are treated as errors.

WARN_ERROR and WARN_ERROR_OPTIONS are mutually exclusive

WARN_ERROR and WARN_ERROR_OPTIONS are mutually exclusive. You can only specify one, even when you're specifying the config in multiple places (like env var or a flag), otherwise, you'll see a usage error.

Configuration

You can configure warnings as errors or which warnings to silence, by warn error options through command flag, environment variable, or dbt_project.yml.

Examples

Here are some examples that show you how to configure warn_error_options using flags or file-based configuration.

Target specific warnings

Some of the examples use NoNodesForSelectionCriteria, which is a specific warning that occurs when your --select flag doesn't match any nodes/resources in your dbt project:

Promote all warnings to errors

Some examples of how to promote all warnings to errors:

using dbt command flags

using environment variables

caution

Note, as mentioned earlier, using will treat all current and future warnings as errors.

This means that if a new warning is introduced in a future version of dbt Core, your production job may start failing unexpectedly. We recommend proceeding with caution when doing this in production environments, and explicitly listing only the warnings you want to treat as errors in production.