dbt Cloud action

This action lets you trigger a job run on dbt Cloud, fetches the run_results.json artifact, and git checkouts the branch that was ran by dbt Cloud.

Example usage at fal-ai/fal_bike_example

Inputs

Credentials

• dbt_cloud_token - dbt Cloud API token
• dbt_cloud_account_id - dbt Cloud Account ID
• dbt_cloud_job_id - dbt Cloud Job ID

We recommend passing sensitive variables as GitHub secrets. Example usage.

Action configuration

• failure_on_error - Boolean to make the action report a failure when dbt-cloud runs. Mark this as false to run fal after the dbt-cloud job.
• interval - The interval between polls in seconds (Default: 30)

dbt Cloud Job configuration

Use any of the documented options for the dbt API.

• cause (Default: Triggered by a Github Action)
• git_sha
• git_branch
• schema_override
• dbt_version_override
• threads_override
• target_name_override
• generate_docs_override
• timeout_seconds_override
• steps_override: pass a YAML-parseable string. (e.g. steps_override: '["dbt seed", "dbt run"]')

Create your workflow

name: Run dbt cloud
on:
  workflow_dispatch:

jobs:
  deploy:
    runs-on: ubuntu-latest

    steps:
      - uses: fal-ai/dbt-cloud-action@main
        id: dbt_cloud_run
        with:
          dbt_cloud_token: ${{ secrets.DBT_CLOUD_API_TOKEN }}
          dbt_cloud_account_id: ${{ secrets.DBT_CLOUD_ACCOUNT_ID }}
          dbt_cloud_job_id: ${{ secrets.DBT_CLOUD_JOB_ID }}
          failure_on_error: true
          steps_override: |
            - dbt seed
            - dbt run

Use with fal

You can trigger a dbt Cloud run and it will download the artifacts to be able to run your fal run command easily in GitHub Actions.

You have to do certain extra steps described here:

name: Run dbt cloud and fal scripts
on:
  workflow_dispatch:

jobs:
  deploy:
    runs-on: ubuntu-latest

    steps:
      # Checkout before downloading artifacts or setting profiles.yml
      - uses: actions/checkout@v3
        with:
          fetch-depth: 0

      - uses: fal-ai/dbt-cloud-action@main
        id: dbt_cloud_run
        with:
          dbt_cloud_token: ${{ secrets.DBT_CLOUD_API_TOKEN }}
          dbt_cloud_account_id: ${{ secrets.DBT_ACCOUNT_ID }}
          dbt_cloud_job_id: ${{ secrets.DBT_CLOUD_JOB_ID }}
          failure_on_error: false

      - name: Setup profiles.yml
        shell: python
        env:
          contents: ${{ secrets.PROFILES_YML }}
        run: |
          import yaml
          import os
          import io

          profiles_string = os.getenv('contents')
          profiles_data = yaml.safe_load(profiles_string)

          with io.open('profiles.yml', 'w', encoding='utf8') as outfile:
            yaml.dump(profiles_data, outfile, default_flow_style=False, allow_unicode=True)

      - uses: actions/setup-python@v2
        with:
          python-version: "3.9.x"

      - name: Install dependencies
        # Normally would use a `requirements.txt`.
        run: |
          pip install dbt-bigquery
          pip install fal[bigquery]

      - name: Run fal scripts
        env:
          SLACK_BOT_TOKEN: ${{ secrets.SLACK_BOT_TOKEN }}
          SLACK_BOT_CHANNEL: ${{ secrets.SLACK_BOT_CHANNEL }}
        run: |
          # Move to the same code state of the dbt Cloud Job
          git checkout ${{ steps.dbt_cloud_run.outputs.git_sha }}
          # TODO: review target in passed profiles.yaml contents
          fal run --profiles-dir .

Getting the correct artifacts from dbt-cloud

fal relies on the generated artifacts from a dbt run step to get model statuses. dbt-cloud only makes these artifacts available after the last step finished running.

In order to get the status information that you need for fal, make sure to run the step you are interested in last.

For example, this dbt job will provide the run_results.json of dbt docs generate, which is probably not what you want fal to report about:

So, you would make dbt docs generate run before dbt run and leave dbt run as the last step.