dbt-checkpoint

Sponsors

Hosted VS Code, dbt-core, SqlFluff, and Airflow, find out more at Datacoves.com.

Montreal Analytics is a full-stack data consultancy servicing North America, and are both a dbt Preferred Consulting Partner and Platinum dbt Certification Award winner.

Goal

dbt-checkpoint provides pre-commit hooks to ensure the quality of your dbt projects.

dbt is awesome, but when the number of models, sources, and macros in a project grows, it becomes challenging to maintain the same level of quality across developers.. Users forget to update columns in property(yml) files or add table and column add descriptions. Without automation the reviewer workload increases and unintentional errors may be missed. dbt-checkpoint allows organizations to add automated validations improving your code review and release process.

Telemetry

dbt-checkpoint has telemetry built into some of its hooks to help the maintainers from Datacoves and Montreal Analytics understand which hooks are being used and which are not to prioritize future development of dbt-checkpoint. We do not track credentials nor details of your dbt execution such as model names. We also do not track any of the dbt hooks, such as for generating documentation. The one detail we do use related to dbt is the anonymous user_id generated by dbt to help us identify distinct projects.

By default this is turned on – you can opt out of event tracking at any time by adding the following to your .dbt-checkpoint.yaml file:

version: 1
disable-tracking: true

Setting dbt project root

You can specify a dbt project root directory for all hooks. This is particularly useful when your dbt project is not located at the root of your repository but in a sub-directory of it.

In that situation, you previously had to specify a --manifest flag in each hook.

Now, you can avoid repeating yourself by adding the dbt-project-dir key to your .dbt-checkpoint.yaml config file:

version: 1
dbt-project-dir: my_dbt_project

This way, we will automatically look for the required manifest/catalog inside your my_dbt_project project folder.

General exclude and per-hook excluding

Since dbt-checkpoint 1.1.0, certain hooks implement an implicit logic that "discover" their sql/yml equivalent for checking.

For a complete background please refer to #118.

Since the root-level exclude statement is handled by pre-commit, when those hooks discover their related sql/yml files, this root exclusion is ommitted (dbt-checkpoint re-includes files that may have been excluded). To exclude files from being discovered by this logic, the exclude path/regex must be provided in each hook (#119)

List of dbt-checkpoint hooks

💡 Click on hook name to view the details.

Model checks:

• check-column-desc-are-same: Check column descriptions are the same.
• check-column-name-contract: Check column name abides to contract.
• check-model-columns-have-desc: Check the model columns have description.
• check-model-has-all-columns: Check the model has all columns in the properties file.
• check-model-has-description: Check the model has description.
• check-model-has-meta-keys: Check the model has keys in the meta part.
• check-model-has-properties-file: Check the model has properties file.
• check-model-has-tests-by-name: Check the model has a number of tests by test name.
• check-model-has-tests-by-type: Check the model has a number of tests by test type.
• check-model-has-tests-by-group: Check the model has a number of tests from a group of tests.
• check-model-has-tests: Check the model has a number of tests.
• check-model-name-contract: Check model name abides to contract.
• check-model-parents-and-childs: Check the model has a specific number (max/min) of parents or/and childs.
• check-model-parents-database: Check the parent model has a specific database.
• check-model-parents-schema: Check the parent model has a specific schema.
• check-model-tags: Check the model has valid tags.

Script checks:

• check-script-semicolon: Check the script does not contain a semicolon.
• check-script-has-no-table-name: Check the script has not table name (is not using source() or ref() macro for all tables).
• check-script-ref-and-source: Check the script has only existing refs and sources.

Source checks:

• check-source-columns-have-desc: Check for source column descriptions.
• check-source-has-all-columns: Check the source has all columns in the properties file.
• check-source-table-has-description: Check the source table has description.
• check-source-has-freshness: Check the source has the freshness.
• check-source-has-loader: Check the source has loader option.
• check-source-has-meta-keys: Check the source has keys in the meta part.
• check-source-has-tests-by-group: Check the source has a number of tests from a group of tests.
• check-source-has-tests-by-name: Check the source has a number of tests by test name.
• check-source-has-tests-by-type: Check the source has a number of tests by test type.
• check-source-has-tests: Check the source has a number of tests.
• check-source-tags: Check the source has valid tags.
• check-source-childs: Check the source has a specific number (max/min) of childs.

Macro checks:

• check-macro-has-description: Check the macro has description.
• check-macro-arguments-have-desc: Check the macro arguments have description.

Modifiers:

• generate-missing-sources: If any source is missing this hook tries to create it.
• generate-model-properties-file: Generate model properties file.
• unify-column-description: Unify column descriptions across all models.
• replace-script-table-names: Replace table names with source() or ref() macros in the script.
• remove-script-semicolon: Remove the semicolon at the end of the script.

dbt commands:

• dbt-clean: Run dbt clean command.
• dbt-compile: Run dbt compile command.
• dbt-deps: Run dbt deps command.
• dbt-docs-generate: Run dbt docs generate command.
• dbt-run: Run dbt run command.
• dbt-test: Run dbt test command.

---

❗If you have a suggestion for a new hook or you find a bug, let us know❗

Install

For detailed installation and usage, instructions see pre-commit.com site.

pip install pre-commit

Setup

1. Create a file named .pre-commit-config.yaml in your project root folder.
2. Add list of hooks you want to run befor every commit. E.g.:

repos:
- repo: https://github.com/dbt-checkpoint/dbt-checkpoint
  rev: v1.1.0
  hooks:
  - id: check-script-semicolon
  - id: check-script-has-no-table-name
  - id: dbt-test
  - id: dbt-docs-generate
  - id: check-model-has-all-columns
    name: Check columns - core
    files: ^models/core
  - id: check-model-has-all-columns
    name: Check columns - mart
    files: ^models/mart
  - id: check-model-columns-have-desc
    files: ^models/mart

3. Optionally, run pre-commit install to set up the git hook scripts. With this, pre-commit will run automatically on git commit! You can also manually run pre-commit run after you stage all files you want to run. Or pre-commit run --all-files to run the hooks against all of the files (not only staged).

Run as Github Action

Unfortunately, you cannot natively use dbt-checkpoint if you are using dbt Cloud. But you can run checks after you push changes into Github.

dbt-checkpoint for the most of the hooks needs manifest.json (see requirements section in hook documentation), that is in the target folder. Since this target folder is usually in .gitignore, you need to generate it. For that you need to run dbt-compile (or dbt-run) command. To be able to compile dbt, you also need profiles.yml file with your credentials. To provide passwords and secrets use Github Secrets (see example).

Say you want to check that a model contains at least two tests, you would use this configuration:

repos:
- repo: https://github.com/dbt-checkpoint/dbt-checkpoint
 rev: v1.1.0
 hooks:
 - id: check-model-has-tests
   args: ["--test-cnt", "2", "--"]

To be able to run this in Github actions you need to modified it to:

repos:
- repo: https://github.com/dbt-checkpoint/dbt-checkpoint
 rev: v1.1.0
 hooks:
 - id: dbt-compile
   args: ["--cmd-flags", "++profiles-dir", "."]
 - id: check-model-has-tests
   args: ["--test-cnt", "2", "--"]

Create profiles.yml

First step is to create profiles.yml. E.g.

# example profiles.yml file
jaffle_shop:
  target: dev
  outputs:
    dev:
      type: postgres
      host: localhost
      user: alice
      password: "{{ env_var('DB_PASSWORD') }}"
      port: 5432
      dbname: jaffle_shop
      schema: dbt_alice
      threads: 4

and store this file in project root ./profiles.yml.

Create new workflow

• inside your Github repository create folder .github/workflows (unless it already exists).
• create new file e.g. pr.yml
• specify your workflow e.g.:

name: pre-commit

on:
  push:
  pull_request:
    branches:
      - main

jobs:
  pre-commit:
  runs-on: ubuntu-latest
  steps:
  - uses: actions/checkout@v2
  - uses: actions/setup-python@v2
  - id: file_changes
    uses: trilom/file-changes-action@v1.2.4
    with:
      output: ' '
  - uses: dbt-checkpoint/dbt-checkpoint@v1.1.0
    env:
      DB_PASSWORD: ${{ secrets.SuperSecret }}
    with:
      args: run --files ${{ steps.file_changes.outputs.files}}

Acknowledgements

Thank you to Radek Tomšej for initial development and maintenance of this great package, and for sharing your work with the community!