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dbt-gloss

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

Goal

Quick ensure the quality of your dbt projects.

dbt is awesome, but when a number of models, sources, and macros grow it starts to be challenging to maintain quality. People often forget to update columns in schema files, add descriptions, or test. Besides, with the growing number of objects, dbt slows down, users stop running models/tests (because they want to deploy the feature quickly), and the demands on reviews increase.

If this is the case, dbt-gloss is here to help you!

List of dbt-gloss hooks

:bulb: Click on hook name to view the details.

Model checks:

Script checks:

Source checks:

Macro checks:

Modifiers:

dbt commands:


:exclamation:If you have an idea for a new hook or you found a bug, let us know:exclamation:

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 dbt root folder.
  2. Add list of hooks you want to run befor every commit. E.g.:
repos:
- repo: https://github.com/Montreal-Analytics/dbt-gloss
  rev: v1.0.0
  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
  1. 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-gloss if you are using dbt Cloud. But you can run checks after you push changes into Github.

dbt-gloss 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).

So you want to e.g. run chach on number of tests:

repos:
- repo: https://github.com/Montreal-Analytics/dbt-gloss
 rev: v1.0.0
 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/Montreal-Analytics/dbt-gloss
 rev: v1.0.0
 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. main.yml
  • specify your workflow e.g.:
name: pre-commit

on:
  pull_request:
  push:
  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: Montreal-Analytics/dbt-gloss@v1.0.0
    env:
      DB_PASSWORD: ${{ secrets.SuperSecret }}
    with:
      args: run --files ${{ steps.file_changes.outputs.files}}

Credits

This software is a fork of pre-commit-dbt. We created this fork since the last release of pre-commit-dbt is dated April 2021 and had many unresolved issues with more recent versions of dbt. As of September 2022, dbt-gloss is supported, but not warrantied by Montreal Analytics. Issues and feature requests can be reported via https://github.com/Montreal-Analytics/dbt-gloss/issues, which will be regularly monitored and prioritized by Montreal Analytics, a preferred dbt consulting partner. Major thanks to Radek Tomsej who originally created this tool.

Release files for dbt-gloss 1.0.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for dbt-gloss 1.0.0
File Size Uploaded
dbt-gloss-1.0.0.tar.gz 26.7 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for dbt-gloss 1.0.0
File Interpreter ABI Platform
dbt_gloss-1.0.0-py2.py3-none-any.whl Python 3, Python 2 none any Details

Total release size: 77.6 kB

Release files / dbt-gloss-1.0.0.tar.gz

Download URL dbt-gloss-1.0.0.tar.gz
Size 26.7 kB
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Release files / dbt_gloss-1.0.0-py2.py3-none-any.whl

Download URL dbt_gloss-1.0.0-py2.py3-none-any.whl
Size 50.8 kB
Tags Python 2 Python 3
SHA-256 checksum
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