Bootstrap python service

This is an example implementation of a python application applying
concepts from Clean Architecture
and SOLID principles.

The repository classes are isolated behind interfaces, enforcing the Interface Segregation principle
and the Inversion of Control design pattern
The application frameworks are decoupled from the domain logic
The storage layer is decoupled from the domain logic

Sync and Async API Documentation using FastAPI and AsyncAPI
Async tasks execution using Dramatiq
Websocket application using Socket.io
Repository pattern for databases using SQLAlchemy and SQLAlchemy bind manager
Database migrations using Alembic (configured supporting both sync and async SQLAlchemy engines)
Database fixtures support using customized Alembic configuration
Authentication and Identity Provider using ORY Zero Trust architecture
Full observability setup using OpenTelemetry (Metrics, Traces and Logs)
Example CI/CD deployment pipeline for GitLab (The focus for this repository is still GitHub but, in case you want to use GitLab 🤷)
[TODO] Producer and consumer to emit and consume events using CloudEvents format using HTTP, to be used with Knative Eventing

Online on GitHub pages
Offline by running make docs after installing dependencies with make dev-dependencies

make containers: Build containers
docker compose up dev-http: Run HTTP application with hot reload
docker compose up dev-socketio: Run HTTP application with hot reload
docker compose up dramatiq-worker: Run the dramatiq worker
docker compose run --rm test: Run test suite
docker compose run --rm migrate: Run database migrations
docker compose run --rm autogenerate-migration: Generate a new migration file

Using Make (you still need Docker for most of them):

make install-dependencies: Install requirements
make dev-dependencies: Install dev requirements
make update-dependencies: Updates requirements
make dev-http: Run HTTP application with hot reload
make dev-socketio: Run HTTP application with hot reload
make test: Run test suite
make migrate: Run database migrations
make autogenerate-migration: Generate a new migration file

Other commands for development

make check: Run tests, code style and lint checks
make fix: Run tests, code style and lint checks with automatic fixes (where possible)

Multistage dockerfile configuration

Python docker image tend to become large after installing the application requirements
(the slim base is ~150 MB uncompressed), therefore it's important to spend efforts
to minimise the image size, even if it produces a slightly more complex multistage
Dockerfile.

The following setup makes sure the production image will keep to a minimal size ("only" 360MB):
150MB base image
210MB python installed dependencies

Using the following pipeline the "test" image is instead ~850MB, more than 400MB that would
end up as a cost in traffic on each image pull.