dbt-review-assistant 2.0.1

Pre-commit hooks for dbt projects

dbt Review Assistant

A collection of CLI tools designed to make reviewing dbt projects quicker and easier

dbt-review-assistant is a Python-based CLI tool which helps dbt developers with ensuring their projects are well documented, comprehensively tested and consistent.

Maintaining dbt projects can be challenging, especially when the projects get large, complex and have lots of contributors. dbt-review-assistant aims to help developers and reviewers to focus on what matters, by taking care of the most boring checklist items automatically.

Below are all the checks available in this package, which are available as both standalone CLI commands or as pre-commit hooks:

Model checks:

• models-have-descriptions: Check if models have descriptions
• models-have-tags: Check if models have tags. Optionally specify a set from which models must have all tags, or from which they must have at least one tag
• models-have-contracts: Check if models have contracts enabled
• models-have-constraints: Check if models have constraints configured
• models-have-data-tests: Check if models have data tests (generic or singular tests)
• models-have-unit-tests: Check if models have unit tests
• models-have-properties-file: Check if models have a corresponding properties YAML file
• models-have-columns: Check if a model has columns listed in a properties YAML file.
• models-have-specific-materialization: Check if models are materialized with one of a set of allowed materializations
• model-columns-have-descriptions: Check if model columns have descriptions. Models without columns listed at all will always pass this check, so it's recommend to run models-have-columns as a complementary check.
• model-columns-have-types: Check if model columns have data types documented. Models without columns listed at all will always pass this check, so it's recommend to run models-have-columns as a complementary check.
• model-column-names-match-manifest-vs-catalog: Check if model column names match between the manifest.json and the catalog.json
• model-column-types-match-manifest-vs-catalog: Check if model column data types match between the manifest.json and the catalog.json
• model-column-descriptions-are-consistent: Check if all instances of a column have the same description across different models
• model-names-match-pattern: Check if model names match a regex pattern
• model-column-names-match-pattern: Check if model column names match a regex pattern. Only documented columns will be included in this check

Source checks:

• sources-have-descriptions: Check if sources have descriptions
• sources-have-data-tests: Check if sources have data tests (generic or singular tests)
• sources-have-columns: Check if sources have columns listed
• source-columns-have-descriptions: Check if source columns have descriptions. Sources without columns listed at all will always pass this check, so it's recommend to run sources-have-columns as a complementary check.
• source-columns-have-types: Check if source columns have data types documented. Sources without columns listed at all will always pass this check, so it's recommend to run sources-have-columns as a complementary check.
• source-column-names-match-manifest-vs-catalog: Check if source column names match between the manifest.json and the catalog.json
• source-column-types-match-manifest-vs-catalog: Check if source column data types match between the manifest.json and the catalog.json

Macro checks:

• macros-have-descriptions: Check if macros have descriptions
• macros-have-types: Check if macros have data types
• macro-arguments-have-descriptions: Check if macro arguments have descriptions
• macro-arguments-match-manifest-vs-sql: Check if macro arguments match between the manifest.json and the macro SQL code
• macro-names-match-pattern: Check if macro names match a regex pattern

Installing as a stand-alone package

To install the package to be used without pre-commit, run the following:

pip install dbt-review-assistant

Usage

Run the following command:

dbt-review-assistant

Or the abbreviated command:

dbtra

Supported Check Arguments

The following arguments may be used globally, or per-check:

--project-dir: Optional - path to the dbt project directory (where the dbt_project.yml file is located). Defaults to the current working directory.

--manifest-dir: Optional - path to the dbt manifest.json file (usually in the dbt project's target directory). Defaults to the target directory underneath the dbt project directory.

--catalog-dir: path to the dbt catalog.json file (usually in the dbt project's target directory). Defaults to the target directory underneath the dbt project directory.

--include-materializations: Optional - list of materializations to include models by. Only models materialized as one of these values will be considered in-scope for the check(s).

--exclude-materializations: Optional - list of materializations to exclude models by. Only models not materialized as one of these values will be considered in-scope for the check(s).

--include-packages: Optional - list of dbt package names to include nodes by. Only nodes in one of these packages will be considered in-scope for the check(s).

--exclude-packages: Optional - list of dbt package names to exclude nodes by. Only nodes not in one of these packages will be considered in-scope for the check(s).

--include-tags: Optional - list of tags to include nodes by. Only nodes having at least one of these tags will be considered in-scope for the check(s).

--exclude-tags: Optional - list of tags to exclude nodes by. Nodes which have at least one of these tags will be considered out-of-scope for the check(s).

--include-node-paths: Optional - list of node paths to include nodes by. Nodes not under any of these paths will be considered out-of-scope for the check(s).

--exclude-node-paths: Optional - list of node paths to exclude nodes by. Nodes under any of these paths will be considered out-of-scope for the check(s).

--include-name-patterns: Optional - list of name regex patterns to include nodes by. Nodes names not matching any of these regex patterns will be considered out-of-scope for the check(s).

--exclude-name-patterns: Optional - list of name regex patterns to exclude nodes by. Nodes names matching any of these regex patterns will be considered out-of-scope for the check(s).

--must-have-all-constraints-from: Optional - List of constraint names, from which objects must have the full set.

--must-have-any-constraint-from: Optional - List of constraint names, from which objects must have at least one value.

--must-have-all-data-tests-from: Optional - List of data test names, from which objects must have the full set. This option can include generic test or singular tests.

--must-have-any-data-test-from: Optional - List of data test names, from which objects must have at least one value. This option can include generic test or singular tests.

--must-have-all-tags-from: Optional - List of tags, from which objects must have the full set.

--must-have-any-tag-from: Optional - List of tags, from which objects must have at least one value.

--name-must-match-pattern: Optional - Regex pattern to match object names against.

--must-be-materialized-as-one-of: Optional - Specific materialization names from which models must use one

Running checks individually

To run individual checks using the CLI, run the dbt-review-assistant command followed by the name of a check, and any arguments required, for example:

dbt-review-assistant all-models-have-descriptions --include-packages my_dbt_project

Running several checks together

The intended usage of this tool is running several checks all together. This way users ensure the integrity of their dbt whole project with one single command, and several checks can be written to complement each other and give wide coverage. There may also be a performance advantage, as dbt-review-assistant can cache data between checks to avoid having to re-load data from the manifest and catalog files.

The config file allows users to configure any number of checks in YAML. Simply create a file named .dbt-review-assistant.yaml and place it anywhere in the repo. Here is an example of a basic config file, defining two checks:

# .dbt-review-assistant.yaml

global_arguments:
  arguments: [
    "--project-dir",
    "my_dbt_project",
    "--include-packages",
    "my_dbt_project",
  ]

per_check_arguments:
  - check_id: models-have-descriptions
    description: We love descriptions! Everything should have descriptions
  - check_id: models-have-constraints
    description: >
      Primary Key constraints are great, but we only want them on tables
    arguments: [
      "--must-have-all-constraints-from",
      "primary_key",
      "--include-materializations",
      "table",
      "incremental"
    ]

To run all the checks specified in the config file, use all-checks as the check id, and include the --config-dir or -c argument, to tell dbt-review-assistant where the config file is:

dbt-review-assistant all-checks --config-dir ./my_dbt_project

Note - if using all-checks then any arguments other than --config-dir are ignored, in favour of arguments specified in the config file.

global_arguments

The global_arguments section sets default arguments which will be passed to every check. These arguments will be overridden by individual checks, if they also define the same arguments with different values.

per_check_arguments

The per_check_arguments section sets the arguments for each check instance. Each check instance must specify a check-id, and may optionally set an array of string arguments. Note that check-id does not need to be unique - the same check can be used any number of times, to allow them to be used with different arguments. The description key is completely optional, but it is suggested to add a description to tell other developers what the specific check is and why it is needed, so all contributors to your project know what the expectations are.

Running checks using pre-commit

All checks may be run as pre-commit hooks, either individually, or as one single entry encompassing one or more checks.

For example, to run all hooks, use the all-checks hook and point it to the config file directory:

repos:
  - repo: https://github.com/sambloom92/dbt-review-assistant
    rev: <latest tag>
    hooks:
      - id: all-checks
        args: [ "--config-dir", "my_dbt_project" ]

Or to run individual checks as standalone hooks:

# .pre-commit-config.yaml
repos:
  - repo: https://github.com/sambloom92/dbt-review-assistant
    rev: <latest tag>
    hooks:
      - id: all-models-have-descriptions
        args: [ "--include-packages", "my_dbt_project" ]
      - id: all-models-have-constraints
        args: [
          "--must-have-all-constraints-from",
          "primary_key",
          "--include-materializations",
          "table",
          "incremental"
        ]

Note that the recommended option is to use the single entry version, because this can benefit from improved performance by allowing dbt-review-assistant to cache data in memory between checks. Running checks individually forces them to be run in separate environments, so they cannot share cached data.

Using pass_filenames

pre-commit hooks have an option called pass_filenames, which defaults to true. This instructs pre-commit to pass all filenames that are staged for commit into the hook entry command as positional arguments.

As of v2.0.0 dbt-review-assistant now supports pass_filenames: true. When filenames are passed to each check, they will automatically filter objects from the manifest, based on whether the patch_path or original_file_path of the object matches one of the set of filenames passed to them. This means, for example, that changes to either a model's definition in its .sql file, or its properties in its .yml file will cause it to be checked by pre-commit. If neither have changed then the model will not be included in the check. This allows a more 'slim' CI setup where only resources which have changed since the last commit are checked.

If you do not want this behaviour in your pre-commit checks, simply set pass_filenames: false on the hooks, and they will run on all files in your project every time.

Refreshing dbt artifacts

All checks rely on the data in the dbt manifest.json file, and some checks have an additional dependency on the dbt catalog.json file. As such, these files need to be refreshed whenever any change is made to the dbt project, otherwise dbt-review-assistant will not have the most up-to-date view of your project. dbt-review-assistant does not look at any SQL or YAML files in your project at all, or connect to your database, or even run any dbt commands - the manifest and catalog JSON files are its only source of truth.

This table shows which checks require which dbt artifacts:

check-id	manifest	catalog
models-have-descriptions	✅	❌
models-have-tags	✅	❌
models-have-contracts	✅	❌
models-have-constraints	✅	❌
models-have-data-tests	✅	❌
models-have-unit-tests	✅	❌
models-have-properties-file	✅	❌
model-names-match-pattern	✅	❌
models-have-specific-materialization	✅	❌
model-column-names-match-pattern	✅	❌
model-columns-have-descriptions	✅	❌
model-columns-have-types	✅	❌
model-column-names-match-manifest-vs-catalog	✅	✅
model-column-types-match-manifest-vs-catalog	✅	✅
model-column-descriptions-are-consistent	✅	❌
sources-have-descriptions	✅	❌
sources-have-data-tests	✅	❌
source-columns-have-descriptions	✅	❌
source-columns-have-types	✅	❌
source-column-names-match-manifest-vs-catalog	✅	✅
source-column-types-match-manifest-vs-catalog	✅	✅
macros-have-descriptions	✅	❌
macro-arguments-have-descriptions	✅	❌
macro-arguments-have-types	✅	❌
macro-arguments-match-manifest-vs-sql	✅	❌
macro-names-match-pattern	✅	❌

These JSON files are typically in the .gitignore, so they are not tracked in git, and are often cleaned up when running dbt clean, so knowing how to generate them is important.

To refresh the manifest, run:

dbt parse

To refresh the catalog, run:

dbt docs generate --no-compile

To ensure the manifest and/or catalog are refreshed automatically by pre-commit, simply add dbt commands as locally installed entries to your existing pre-commit configuration, before the checks:

# .pre-commit-config.yaml
repos:
  - repo: local
    hooks:
      # for any checks requiring the manifest.json
      - id: refresh-manifest
        name: Refresh dbt Manifest
        entry: dbt parse
        args: [
          "--project-dir",
          "./my_dbt_project",
          "--profiles-dir",
          "./my_dbt_project"
        ]
        language: python
        pass_filenames: true
        require_serial: true
        types_or: [sql,yaml]
        files: "my_dbt_project/"
        exclude: "(my_dbt_project/target/|my_dbt_project/dbt_packages/)"
      # for any checks requiring the catalog.json
      - id: refresh-catalog
        name: Refresh dbt Catalog
        entry: dbt docs generate
        args: [
          "--project-dir",
          "./my_dbt_project",
          "--profiles-dir",
          "./my_dbt_project",
          "--no-compile"
        ]
        language: python
        pass_filenames: true
        require_serial: true
        types_or: [sql,yaml]
        files: "my_dbt_project/"
        exclude: "(my_dbt_project/target/|my_dbt_project/dbt_packages/)"
  - repo: https://github.com/sambloom92/dbt-review-assistant
    rev: <latest tag>
    hooks:
      - id: all-models-have-descriptions
        args: [ "--include-packages", "my_dbt_project" ]
      - id: all-models-have-constraints
        args: [
          "--must-have-all-constraints-from",
          "primary_key",
          "--include-materializations",
          "table",
          "incremental"
        ]

The refresh-manifest and refresh-catalog hooks demonstrated above are not part of dbt-review-assistant, and rely on your project's own local dbt installation. Add whichever arguments you would normally include when running dbt commands within your project. To use these in a CI environment such as GitHubActions, ensure that the worker has the dbt adapter installed and, if refreshing the catalog, has permission to connect to your database.

GitHub Actions

This tool can be used as part of a GitHub Actions Workflow, however this repository does not provide a ready-made GitHub Action. This is because the implementation details would be heavily dependent on the project's unique dbt setup, including the build system and method of authentication to connect to the data platform.

To write your own GitHub Actions Workflow, the following steps should be included:

1. Run the dbt parse command. Note that this requires dbt to be installed.
2. Run the dbt docs generate --no-compile command (Optional - can be skipped if not required - see table of check requirements above). Note that this requires a connection, and therefore authentication with your data plaform.
3. Run the dbt-review-assistant command

Here is an example of a GitHub Actions Workflow for a BigQuery project, using service account authentication, with the keyfile stored as a GitHub secret:

# .github/workflows/ci.yml

name: run dbt review assistant
on:
  pull_request:
    branches:
      - main
jobs:
  run_bigquery:
    name: dbt-review-assistant
    runs-on: ubuntu-latest

    env:
      DBT_PROFILES_DIR: ./
      DBT_GOOGLE_PROJECT: ${{ vars.DBT_GOOGLE_PROJECT }}
      DBT_GOOGLE_DATASET: ${{ vars.DBT_GOOGLE_DATASET }}
      DBT_GOOGLE_KEYFILE: /tmp/google/google-service-account.json
      KEYFILE_CONTENTS: ${{secrets.KEYFILE_CONTENTS}}

    steps:
      - run: mkdir -p "$(dirname $DBT_GOOGLE_KEYFILE)"
      - run: echo "$KEYFILE_CONTENTS" > $DBT_GOOGLE_KEYFILE
      - uses: "actions/checkout@v4"
      - uses: "actions/setup-python@v5"
        with:
          python-version: "3.12"
      - name: Install uv
        run: python3 -m pip install uv
      - name: Install python deps
        run: uv pip install -r requirements.txt --system
      - name: Run dbt deps
        run: dbt deps
      - name: Run dbt parse
        run: dbt parse
      - name: Run dbt docs generate
        run: dbt docs generate --no-compile
      - name: Run dbt-review-assistant
        run: dbt-review-assistant all-checks --config ./

With the following profiles.yml contents:

# .profiles.yml

jaffle_shop:
  outputs:
    dev:
      job_execution_timeout_seconds: 300
      job_retries: 1
      location: europe-west2
      method: service-account
      keyfile: "{{ env_var('DBT_GOOGLE_KEYFILE') }}"
      project: "{{ env_var('DBT_GOOGLE_PROJECT') }}"
      dataset: "{{ env_var('DBT_GOOGLE_DATASET') }}"
      priority: interactive
      threads: 1
      type: bigquery
  target: dev

This example requires a GitHub secret called KEYFILE_CONTENTS, with the contents of a service account keyfile, and the following environment variables:

• DBT_GOOGLE_KEYFILE: filepath to the service account JSON keyfile
• DBT_GOOGLE_PROJECT: GCP project ID for the dbt project
• DBT_GOOGLE_DATASET: GCP dataset ID for the dbt project

Your data platform may require a different authentication method, and your dbt project may require different steps for installing dependencies, so adjust this example accordingly.

Acknowledgements

This tool was inspired by the popular dbt-checkpoint pre-commit hooks by DataCoves (formerly pre-commit-dbt). I have found these hooks immensely useful for my own dbt projects, and I am very grateful to them for contributing it. That said, there were a number of ways in which I believed it could be improved and simplified, so I decided to try writing my own tool. While there may be similarities in some of the checks, all code in this repository is written by myself, with nothing taken from any other projects or AI tools.