Create a python virtual environment for this project: python3 -m venv venv

Activate your environment: source venv/bin/activate

Install dependencies: pip install -r requirements.txt

Run tests: pytest tests

Running databases docker-compose up -d

(venv) liamconsidine@CQQ27KJ4GL ports-and-adapters-framework % docker ps
CONTAINER ID   IMAGE          COMMAND                  CREATED              STATUS              PORTS                      NAMES
268fd24362f4   mongo:latest   "docker-entrypoint.s…"   About a minute ago   Up About a minute   0.0.0.0:27017->27017/tcp   ports-and-adapters-framework-mongodb-1
1c810913ab04   redis:6        "docker-entrypoint.s…"   About a minute ago   Up About a minute   0.0.0.0:6379->6379/tcp     ports-and-adapters-framework-redis-1
5f7dff5ac287   postgres:13    "docker-entrypoint.s…"   About a minute ago   Up About a minute   0.0.0.0:5432->5432/tcp     ports-and-adapters-framework-postgres-1

To get into the database manually:

docker exec -it CONTAINER_ID psql -U dev_user -d dev_db

docker-compose logs postgres_dev

Helpful alias

alias dev_db_exec='docker exec -it $(docker ps --filter "name=ports-and-adapters-framework-postgres" -q) psql -U dev_user -d dev_db'
alias test_db_exec='docker exec -it $(docker ps --filter "name=ports-and-adapters-framework-postgres" -q) psql -U test_user -d test_db'

Basic postgres inspection: \l list databases \c dev_db connect to the dev_db \dt list tables

Do we want Alembic create migrations based on ORM models?

    alembic revision --autogenerate -m "create sensors table"
    alembic upgrade head

Run the application uvicorn sensor_app.main:app --reload

Some type checking:

mypy sensor_app

Success: no issues found in 20 source files

Swagger Documentation:

http://localhost:8000/docs

With Celery connected to postgres it creates these tables need to make tests work...

                 List of relations
 Schema |        Name         |   Type   |  Owner
--------+---------------------+----------+----------
 public | alembic_version     | table    | dev_user
 public | celery_taskmeta     | table    | dev_user
 public | celery_tasksetmeta  | table    | dev_user
 public | sensors             | table    | dev_user
 public | sensors_id_seq      | sequence | dev_user
 public | task_id_sequence    | sequence | dev_user
 public | taskset_id_sequence | sequence | dev_user

Flower:

Visit it at http://0.0.0.0:5555

 FastAPI background tasks are a good fit for lightweight applications that need to perform simple background tasks, such as logging or updating a database. They are simple to set up and integrate seamlessly with FastAPI web applications, making them a good choice for developers who want a simple and lightweight solution.

On the other hand, Celery is a good choice for more complex applications that need to handle a large volume of tasks, or require more advanced scheduling features. Celery’s distributed architecture allows it to handle large volumes of tasks, and its Celery Beat scheduling functionality makes it a powerful tool for scheduling periodic tasks.

To test the docker build:

docker build -t ports-and-adapters-framework .

docker run -p 7777:8000 ports-and-adapters-framework