dbt-autofix 0.7.0

CLI to autofix deprecations in dbt projects

dbt-autofix

dbt-autofix automatically scans your dbt project for deprecated configurations and updates them to align with the latest best practices. This makes it easier to resolve deprecation warnings introduced in dbt v1.10 as well as prepare for migration to the dbt Fusion engine.

Deprecation Code in dbt Core	Files	Handling	Support
CustomKeyInObjectDeprecation / PropertyMovedToConfigDeprecation	YAML files	Move all models configs under config: in YAML files	Full
CustomKeyInObjectDeprecation	YAML files	Move all models extra config (not valid or custom) under meta: and meta under config:	Full
DuplicateYAMLKeysDeprecation	YAML files	Remove duplicate keys in YAML files, keeping the second one to keep the same behaviour	Full
PropertyMovedToConfigDeprecation	YAML files	Only allow email and name as properties for groups and exposures owners	Full
UnexpectedJinjaBlockDeprecation	SQL files	Remove extra {% endmacro %} and {% endif %} that don't have corresponding opening statements	Full
GenericJSONSchemaValidationDeprecation	dbt_project.yml	Prefix all configs for models/tests etc... with a +	Partial
ConfigDataPathDeprecation	dbt_project.yml	Remove deprecated config for data path (now seed)	Full
ConfigLogPathDeprecation	dbt_project.yml	Remove deprecated config for log path	Full
ConfigSourcePathDeprecation	dbt_project.yml	Remove deprecated config for source path	Full
ConfigTargetPathDeprecation	dbt_project.yml	Remove deprecated config for target path	Full

Installation

From PyPi

With uv (recommended)

We recommend using uv/uvx to run the package. If you don't have uv installed, you can install uv and uvx, following the instructions on the offical website.

• to run the latest version of the tool: uvx dbt-autofix
• to run a specific version of the tool: uvx dbt-autofix@0.1.2
• to install the tool as a dedicated CLI: uv tool install dbt-autofix

With pip

You can also use pip if you prefer, but we then recommend installing the tool in its own Python virtual environment. Once in a venv, install the tool with pip install dbt-autofix and then run dbt-autofix ...

From the source repo

To run it from the git repo directly, install uv following those instructions and then:

run the tool directly

uvx --from git+https://github.com/dbt-labs/dbt-autofix.git dbt-autofix --help

or install it so that it can be run with dbt-cleanup in the future

uv tool install --from git+https://github.com/dbt-labs/dbt-autofix.git dbt-autofix

Usage

deprecations - the main one

• dbt-autofix deprecations: refactor YAML and SQL files to fix some deprecations
  • add --path <mypath> to configure the path of the dbt project (defaults to .)
  • add --dry-run for running in dry run mode
  • add --json to get resulting data in a JSONL format
  • add --json-schema-version v2.0.0-beta.4 to get the JSON schema from a specific Fusion release (by default we pick the latest)
  • add --select <path> to only select files in a given path (by default the tool will look at all files of the dbt project)

Each JSON object will have the following keys:

• "mode": "applied" or "dry_run"
• "file_path": the full path of the file modified. Each file will appear only once
• "refactors": the list of refactoring rules applied

Calling deprecations without --dry-run should be safe if your dbt code is part of a git repo.

Please review the suggested changes to your dbt project before merging to main and make those changes go through your typical CI/CD process.

jobs

dbt-autofix jobs: update dbt platform jobs steps to use -s/--select selectors instead of -m/--models/--model which are deprecated in the Fusion engine

Run dbt-autofix jobs --help to see the required parameters and supported arguments.

This tool requires connecting to the dbt Admin API to retrieve and update jobs which means that the user token or service token used need to have Read and Write access to jobs

Running with --dry-run/d will output what changes would have been triggered without triggering them