A django-graphql microservice to model simple store operations

This microservice is deployed in kubernetes cluster at http://174.138.123.164/ with a CI/CD pipeline configured

OIDC Authentication: The microservice uses OpenID Connect (OIDC) for authentication and authorization. It is implemented in a stateless way: instead of storing session data or issuing its own JWTs, the service validates incoming tokens directly against the OIDC provider’s public resources (such as the .well-known configuration and JSON Web Keys). This ensures that only users with valid OIDC-issued tokens can access the service.This exists at this endpoint

Products & Product Categories: The microservice exposes an API endpoint that allow authenticated users to create products and organize them into categories. Categories are structured as a hierarchical tree and can be nested beyond three levels, enabling complex product taxonomies.

Unauthenticated users can query all products using this API endpoint

Average Category Price: The microservice provides an API endpoint that allows all users to calculate the average price of products within a given category.

Order Creation: The microservice provides an API endpoint for authenticated users to create orders for selected products.

Order Alerts: The microservice notifies admins of new orders via email to support dispatch, and sends order confirmations to customers via SMS.

These features can be viewed with annotated screen shots here

1. Run git clone on your local directory
2. Create a .env file in your root directory and populate per .env.example
3. Run docker compose up --build to spin up the docker containers

• We have the following services:
  • web for our django/graphql microservice
  • pgdb for the postgres database
  • nginx for our reverse proxy
  • huey for background asynchronous task
  • redis for storing task queues

4. Navigate to http://127.0.0.1:8080/ to confirm it is running successfully
5. All python commands to the web service will be prepended with docker compose exec web e.g docker compose exec web python manage.py shell
6. Run prepend manage.py runscript init_db to populate db with products

• Root directory refers to the location where manage.py file is

1. Run git clone on your local directory
2. Install uv tool from this link https://docs.astral.sh/uv/getting-started/installation/
3. Create a new virtual environment by running uv venv .venv in your root directory
4. Activate the environment by running source .venv/bin/activate
5. Install packages using the command uv sync
6. Create a .env file in your root directory and populate per .env.example
7. Create a local postgres db for app functioning and update DATABASE_URL in .env file accordingly

• We use dj-database-url to configure django database values

8. Run migrations python manage.py migrate
9. Run python manage.py runscript init_db to populate db with products
10. Run python manage.py runserver to run the backend
11. Install redis on your system and run python manage.py run_huey to run the background task process
12. Navigate to http://127.0.0.1:8000/ to confirm it is running successfully

• Installation
• Coding Conventions
• Code Quality Enforcement
• Tests
• Deployment
• Graphql Api Documentation
• Results As Per Project Requirements
• Monitoring & Observability