docker-compose.yml

# This file is for local testing only, see:
# https://devcenter.heroku.com/articles/local-development-with-docker-compose
# Deployment is configured in `heroku.yml` & `app.json`

version: '3'
services:
  web:
    # For platform spec, see https://stackoverflow.com/a/70238851
    platform: linux/amd64
    build:
      context: .
      dockerfile: Dockerfile.heroku
    ports:
      - '3000:8000'
    depends_on:
      - db
    environment:
      # Nearly the same config as for postgres service in `.github/workflows/main.yaml`,
      # with exception of hostname. For hostname, see https://docs.docker.com/compose/networking/
      DATABASE_URL: 'postgres://postgres_user:postgres_password@db:5432/postgres_db'
      # Get the project_id dynamically after dataflow key file is decrypted in entrypoint.
      # Python command for parsing JSON is set as env var because putting it directly in
      # entrypoint is too many layers of nested quotation marks.
      GET_PROJECT_ID: "import sys, json; print(json.load(sys.stdin)['project_id'].strip())"
      DATAFLOW_CREDS: './secrets/dataflow-job-submission.json'
    # Note on this entrypoint:
    # - Sleep at start to allow postgres enought time to start. There are some more involved
    #   solutions to this problem at https://docs.docker.com/compose/startup-order/, but because
    #   this is just for testing, I think this should be enough, and is much lighter weight.
    # - The commands following sleep combine the `release` and `web` directives from `heroku.yml`,
    #   with the following small differences:
    #     - docker-compose spec requires double $$ for string interpolation (vs. single $)
    #     - Only running one gunicorn worker here (as opposed to 4 in production). Specifying more
    #       than 1 worker fails in this context.
    #     - We bind to 0.0.0.0 (as opposed to default 127.0.0.1) to make port binding to host work
    entrypoint: >
      /bin/sh -c 'sleep 1
      && python3.9 -m alembic upgrade head
      && aws configure set aws_access_key_id $AWS_ACCESS_KEY_ID
      && aws configure set aws_secret_access_key $AWS_SECRET_ACCESS_KEY
      && sops -d -i ./secrets/bakery-args.pangeo-ldeo-nsf-earthcube.yaml
      && sops -d -i ./secrets/config.$${PANGEO_FORGE_DEPLOYMENT}.yaml
      && sops -d -i $${DATAFLOW_CREDS}
      && gcloud auth activate-service-account --key-file=$${DATAFLOW_CREDS}
      && cat $${DATAFLOW_CREDS} | python3.9 -c "$$GET_PROJECT_ID" | xargs -I{} gcloud config set project {}
      && export GOOGLE_APPLICATION_CREDENTIALS=$${DATAFLOW_CREDS}
      && gunicorn -w 1 -t 300 -k uvicorn.workers.UvicornWorker pangeo_forge_orchestrator.api:app -b 0.0.0.0:8000'
  db:
    platform: linux/amd64
    image: postgres:latest
    ports:
      - '5432:5432'
    environment:
      # Identical config as for postgres service in `.github/workflows/main.yaml`
      POSTGRES_DB: postgres_db
      POSTGRES_PASSWORD: postgres_password
      POSTGRES_PORT: 5432
      POSTGRES_USER: postgres_user