github-actions.md

GitHub Actions

This guide provides documentation for setting up continuous integration with GitHub Actions for different types of projects.

Contents

• Running containerized tests
• Deploying a Python package
• Deploying a legacy application to CodeDeploy
• Setting up Google Lighthouse for accessibility testing
• Resources

Running containerized tests

N.b., we recommend running containerized tests when the application is containerized in deployment, i.e., deployed to Heroku. For legacy applications deployed on AWS EC2 infrastructure, we recommend a service-based test run. Skip to deploying a legacy application to CodeDeploy for an example of this pattern.

To initialize a workflow for running containerized tests, start by creating a new feature branch in your repo. Then, create a directory called .github/workflows/ at the root of your repo. GitHub Actions will read any workflows from this directory and run them automatically.

Next, define a file called main.yml in the workflows directory, and paste in the following configuration:

name: CI

on:
  push:
    branches:
    - main
    - deploy
  pull_request:
    branches:
    - main

jobs:
  test:
    name: Run tests
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v1
    - name: Build containers and run tests
      run: docker-compose -f docker-compose.yml -f tests/docker-compose.yml run --rm app

Due to the on block, this workflow will run the test job on all commits to main and all commits to pull requests that have been opened against main.

If your tests need any additional configurations, such as a .env file or a local settings file, add a step to your test job to create or copy the necessary files, prior to running the tests. For example:

jobs:
  test:
    name: Run tests
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v1
    - shell: bash
      run: |
        cp .env.example .env
    - name: Build containers and run tests
      run: docker-compose -f docker-compose.yml -f tests/docker-compose.yml run --rm app

Commit this file to your feature branch and open up a pull request. You should be able to confirm that your workflow runs the tests for your pull request.

Examples

This workflow is in use in the following projects:

• Minnesota Election Archive (visible to DataMade developers only)

Deploying a Python package

GitHub Actions can run arbitrary code, so we can use it to deploy Python packages to PyPI in addition to testing them.

As above, start by making a new feature branch and creating the directory .github/workflows/ at the root of your repo if it doesn't yet exist. Then, define two workflow files, one for each of the production and test PyPI instances:

📄.github/workflows/publish-to-pypi.yml

name: Publish to PyPI

on: push

jobs:
  build-and-publish:
    name: Publish to PyPI
    if: startsWith(github.event.ref, 'refs/tags')
    runs-on: ubuntu-18.04
    steps:
    - uses: actions/checkout@main
    - name: Set up Python 3.7
      uses: actions/setup-python@v1
      with:
        python-version: 3.7
    - name: Publish to PyPI
      env:
        TWINE_USERNAME: '__token__'
        TWINE_PASSWORD: ${{ secrets.pypi_token }}
      run: |
        pip install twine wheel
        pip wheel -w dist --no-deps .
        twine upload --skip-existing dist/*
      continue-on-error: true

📄.github/workflows/publish-to-test-pypi.yml

name: Publish to Test PyPI

on:
  push:
    branches:
      - main

jobs:
  build-and-publish:
    name: Publish to Test PyPI
    runs-on: ubuntu-18.04
    steps:
    - uses: actions/checkout@main
    - name: Set up Python 3.7
      uses: actions/setup-python@v1
      with:
        python-version: 3.7
    - name: Publish to Test PyPI
      env:
        TWINE_USERNAME: '__token__'
        TWINE_PASSWORD: ${{ secrets.test_pypi_token }}
      run: |
        pip install twine wheel
        pip wheel -w dist --no-deps .
        twine upload --skip-existing --repository-url https://test.pypi.org/legacy/ dist/*
      continue-on-error: true

Taken together, these two workflows will publish any branch merged into main to test PyPI and publish tagged commits to PyPI. This structure parallels our practice of keeping a staging site consistent with main and pushing tagged commits to production. If the active package version has already been deployed to either instance, the workflows will skip the upload to that instance; this means that only commits that bump the package version in setup.py will result in a newly deployed package.

Next, follow the instructions for creating encrypted secrets and save values for pypi_token and test_pypi_token.

To test that this configuration works, you can alter the on.push.branches array of publish-to-test-pypi.yml to include the name of your feature branch, and you can push a test tag to your feature branch. Make sure to undo these two changes once you've confirmed that your configuration can properly deploy to PyPI and the test PyPI instance.

Examples

Versions of this workflow are in use in the following packages:

• django-geomultiplechoice
• pytest-flask-sqlalchemy (no deployment to test PyPI, tests different versions of Python)

Deploying a legacy application to CodeDeploy

As above, start by making a new feature branch and creating the directory .github/workflows/ at the root of your repo if it doesn't yet exist. Then, define a workflow file:

📄.github/workflows/main.yml

name: CI

on:
  push:
    branches:
      - main
      - deploy
  pull_request:
    branches:
      - main

jobs:
  test:
    name: Run tests
    # 🚨 Update the Ubuntu version to match the server you're deploying to. See
    # https://docs.github.com/en/actions/reference/workflow-syntax-for-github-actions#jobsjob_idruns-on
    # for supported versions.
    runs-on: ubuntu-${UBUNTU_VERSION}
    services:
      # 🚨 Update the Postgres version and database name to match your app environment and test config
      postgres:
        image: postgres:${POSTGRES_VERSION}
        env:
          POSTGRES_DB: ${POSTGRES_DATABASE}
          POSTGRES_PASSWORD: postgres
        options: >-
          --health-cmd pg_isready
          --health-interval 10s
          --health-timeout 5s
          --health-retries 5
        ports:
          - 5432:5432
    steps:
    - uses: actions/checkout@v2
    # 🚨 Update the Python version to match your app environment
    - name: Set up Python ${PYTHON_VERSION}
      uses: actions/setup-python@v2
      with:
        # Semantic version range syntax or exact version of a Python version
        python-version: '${PYTHON_VERSION}'
    - name: Install dependencies
      run: |
        # 🚨 Need PostGIS? Uncomment these lines to install GDAL.
        # sudo apt update
        # sudo apt install -y gdal-bin
        python -m pip install --upgrade pip
        pip install -r requirements.txt
    - name: Test with pytest
      # 🚨 Swap in the correct filenames for your test and application configs
      run: |
        mv ${TEST_CONFIG} ${APP_CONFIG}
        pytest -sv
  deploy:
    needs: test
    name: Deploy to AWS
    runs-on: ubuntu-latest
    steps:
      - uses: aws-actions/configure-aws-credentials@v1
        with:
          aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
          aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
          aws-region: us-east-1
      - uses: actions/checkout@v2
      - id: deploy
        uses: webfactory/create-aws-codedeploy-deployment@0.2.2
        with:
          # 🚨 Swap in your CodeDeploy application name, which is usually the same as the repository name
          application: ${CODEDEPLOY_APPLICATION_NAME}

Update the values indicated inline with the 🚨 emoji. Then, append the following to .appspec.yml.

📄.appspec.yml

# ... deployment lifecycle config ...
branch_config:
  main:
    deploymentGroupName: staging
  deploy:
    deploymentGroupName: production

This workflow runs your tests, then creates a deployment conditional on your tests passing. The deploy action determines which deployment group, if any, it should create a deployment for using the branch_config you added to .appspec.yml. In effect, commits to main are deployed to staging and commits to deploy are deployed to production; commits to all other branches result in a no-op.

Next, follow the instructions for creating encrypted secrets and save values for AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY, using the developer credentials in our team LastPass.

To test that this configuration works, you can update the on.push.branches array of main.yml to include the name of your feature branch, and point your branch to the staging deployment group in the branch_config block of .appspec.yml. The result should be a staging deployment of your app on the next commit to your branch. Don't forget to revert these changes once you've tested the integration.

Examples

This workflow is in use in the following applications:

• la-metro-councilmatic
• la-metro-dashboard

Setting Up Google Lighthouse

Google Lighthouse is a tool that provides information on 5 different areas of performance for websites. We only use it to run accessibility audits on our sites prior to deploying to production, and using Lighthouse is the first step in the Accessibility section of our site launch checklist.

The documentation for Google's Lighthouse can be found here, along with some basic configuration. Since we only use Lighthouse for accessibility audits, our configuration files are set up a bit differently.

1. List the audits you would like to run in a file named lighthouserc.js in the root of the application directory:

module.exports = {
  ci: {
    collect: {
        staticDistDir: './cms_app/templates/',
      },
      assert: {
        preset: 'lighthouse:recommended',
        assertions: {
          'categories:accessibility': ['error', {'minScore': 0.9}],
          'apple-touch-icon': 'off',
          'content-width': 'off',
          'csp-xss': 'off',
          'doctype': 'off',
          'document-title': 'off',
          'errors-in-console': 'off',
          'font-size': 'off',
          'heading-order': 'off',
          'html-has-lang': 'off',
          'installable-manifest': 'off',
          'interactive': 'off',
          'is-on-https': 'off',
          'legacy-javascript': 'off',
          'maskable-icon': 'off',
          'meta-description': 'off',
          'no-vulnerable-libraries': 'off',
          'render-blocking-resources': 'off',
          'service-worker': 'off',
          'splash-screen': 'off',
          'tap-targets': 'off',
          'themed-omnibox': 'off',
          'unminified-javascript': 'off',
          'unused-javascript': 'off',
          'uses-long-cache-ttl': 'off',
          'viewport': 'off',
        },
      },
    upload: {
      target: 'temporary-public-storage',
    },
  },
};

2. Configure GitHub Actions in .github/workflows/main.yml

...
jobs:
  lhci:
    name: Lighthouse
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      - name: Use Node.js 12.x
        uses: actions/setup-node@v1
        with:
          node-version: 12.x
      - name: run Lighthouse CI
        run: |
          npm install -g @lhci/cli@0.8.x
          lhci autorun
...

See Running Containerized Tests for what additional features of a .github/workflows/main.yml file.

For an example of Lighthouse built into a CI pipeline, see the lighthouse-ci branch of the CPS app.

Resources

Our workflows for deploying Python packages to PyPI were adapted from the Python guide to publishing package distribution releases using GitHub Actions.

Our workflow for deploying to CodeDeploy via GitHub Actions comes from Webfactory's create-aws-codedeploy-deployment action, which includes further details on configuration and usage.

For more detailed information on using GitHub Actions, refer to the documentation.