As part of the "Microservice Scaffolder" workflow, this deliverable provides a complete, production-ready microservice scaffold. This includes the core application code, Docker setup for containerization, a comprehensive testing suite, a CI/CD pipeline configuration, and basic deployment scripts.
The microservice is designed around a common "Product Service" example, demonstrating CRUD (Create, Read, Update, Delete) operations for products. It utilizes Python with FastAPI for the API, PostgreSQL as the database, and SQLAlchemy for ORM.
This section details the generated code and configurations for your new microservice.
The generated project follows a standard, organized structure to enhance maintainability and scalability.
. ├── .github/ │ └── workflows/ │ └── ci.yml # GitHub Actions CI/CD pipeline configuration ├── app/ │ ├── api/ │ │ └── v1/ │ │ └── endpoints/ │ │ └── products.py # API endpoints for product CRUD operations │ ├── crud/ │ │ └── product.py # CRUD operations for database interaction │ ├── models/ │ │ └── product.py # SQLAlchemy ORM model for Product │ ├── schemas/ │ │ └── product.py # Pydantic schemas for data validation │ ├── __init__.py # Python package init │ ├── config.py # Application configuration │ └── database.py # Database connection and session management ├── scripts/ │ └── deploy.sh # Basic deployment script ├── tests/ │ ├── __init__.py │ ├── conftest.py # Pytest fixtures for database and client setup │ ├── test_main.py # Basic application health check tests │ └── test_products.py # API integration tests for product endpoints ├── .dockerignore # Files to ignore when building Docker image ├── Dockerfile # Docker configuration for the application ├── docker-compose.yml # Docker Compose for local development (app + db) ├── main.py # Main FastAPI application entry point ├── requirements.txt # Python dependencies └── README.md # Project README and setup instructions
This document outlines the architectural plan for a robust microservice and provides a comprehensive study plan for understanding and implementing such services. This output serves as a foundational deliverable for the "Microservice Scaffolder" workflow, specifically detailing the design principles and technology choices for the generated microservice, followed by a learning path to master these concepts.
This section details the proposed architecture for a high-performance, scalable, and maintainable microservice. This plan will guide the scaffolding process, ensuring all generated components adhere to industry best practices.
Target Technology Stack:
The generated microservice will follow a clean, modular project structure designed for clarity and maintainability.
/): Contains configuration files, Dockerfiles, CI/CD workflows, and top-level scripts.src/: The main application source code. * main.py: FastAPI application entry point, defining global dependencies and routers.
* api/: Contains API routers, each representing a logical resource or domain.
* v1/: Versioned API endpoints.
* items/: router.py for /items endpoints.
* users/: router.py for /users endpoints.
* services/: Business logic layer, orchestrating data access and external interactions.
* item_service.py: Logic for item-related operations.
* user_service.py: Logic for user-related operations.
* models/: Database models (SQLAlchemy ORM definitions).
* base.py: Base class for declarative models.
* item.py: Item model definition.
* user.py: User model definition.
* schemas/: Pydantic models for request/response validation and serialization.
* item_schema.py: Pydantic models for Item requests/responses.
* user_schema.py: Pydantic models for User requests/responses.
* dependencies/: Common dependencies (e.g., database session, authentication).
* core/: Core utilities, settings, exceptions, and logging configuration.
* config.py: Centralized application settings (using Pydantic's BaseSettings).
* database.py: Database engine and session management.
* exceptions.py: Custom application exceptions.
* tests/: Unit and integration tests.
* conftest.py: Pytest fixtures.
* unit/: Unit tests for services, models, schemas.
* integration/: Integration tests for API endpoints.
migrations/: Alembic scripts for database schema migrations.docs/: Additional documentation (e.g., API usage examples, architectural decisions)./items, /items/{id}, using standard HTTP methods like GET, POST, PUT, DELETE)./api/v1/items) to allow for backward compatibility and graceful evolution./docs). This will be enhanced with descriptive summaries and examples.* Declarative Models: Database tables will be defined as Python classes.
* Session Management: Database sessions will be managed using FastAPI's dependency injection system, ensuring proper connection handling and transaction isolation.
* Base Image: Official Python slim image.
* Dependencies: poetry for dependency management, or pip with requirements.txt.
* Application Code: Copying application code.
* Entrypoint: Uvicorn with Gunicorn for production-grade ASGI server.
docker-compose.yml file will be generated for local development, enabling quick setup of the microservice along with a local PostgreSQL database and potentially other services (e.g., adminer for database GUI).A comprehensive testing suite is crucial for microservice reliability.
pytest-cov) will be integrated into the CI/CD pipeline to ensure adequate test coverage.A /.github/workflows/main.yml file will define the CI/CD pipeline.
* Triggers: On push to main and feature branches, and pull requests.
* Steps:
1. Checkout code.
2. Set up Python environment.
3. Install dependencies.
4. Run linters (e.g., Black, Flake8, MyPy).
5. Run unit and integration tests.
6. Generate test coverage report.
7. Build Docker image.
8. Push Docker image to container registry (e.g., GitHub Container Registry, Docker Hub, ECR).
main or specific tags)* Steps:
1. Pull the latest Docker image.
2. Update Kubernetes deployment (via Helm or kubectl).
3. Run database migrations (if applicable, using a dedicated job).
4. Health checks.
* Deployment (for the application pods).
* Service (for internal/external access).
* Ingress (for external HTTP/HTTPS access).
* Horizontal Pod Autoscaler (HPA) definition.
* Resource limits and requests.
python-json-logger) to stdout/stderr, allowing collection by external log aggregators (e.g., ELK Stack, Splunk, CloudWatch Logs). Log levels will be configurable. * Metrics: Exposing Prometheus-compatible metrics (e.g., using prometheus-client or fastapi-prometheus) for request rates, error rates, latency, and custom business metrics.
* Health Checks: Standard /health and /ready endpoints for Kubernetes liveness and readiness probes.
app/database.py - Database Connection and SessionWe are pleased to present the complete microservice scaffold, meticulously generated to provide a robust, scalable, and production-ready foundation for your new service. This deliverable includes the core application code, Dockerization, a comprehensive testing suite, CI/CD pipeline configuration, and initial deployment scripts, all designed for efficiency and ease of use.
This package provides a fully functional microservice template, developed using Python 3.10+ and the FastAPI framework, backed by PostgreSQL (via SQLAlchemy). It adheres to modern best practices for development, testing, and deployment, ensuring a solid starting point for your project.
Key Features:
The application is structured for clarity and maintainability, separating concerns into distinct modules.
├── my_microservice/
│ ├── api/
│ │ ├── __init__.py
│ │ └── endpoints/
│ │ ├── __init__.py
│ │ ├── items.py # Example API routes for 'items'
│ │ └── users.py # Example API routes for 'users'
│ ├── crud/ # Create, Read, Update, Delete operations
│ │ ├── __init__.py
│ │ ├── items.py
│ │ └── users.py
│ ├── database/
│ │ ├── __init__.py
│ │ ├��─ base.py # Base for SQLAlchemy models
│ │ ├── session.py # Database session management
│ │ └── models.py # SQLAlchemy ORM models
│ ├── schemas/ # Pydantic models for request/response validation
│ │ ├── __init__.py
│ │ ├── items.py
│ │ └── users.py
│ ├── core/
│ │ ├── __init__.py
│ │ ├── config.py # Application configuration settings
│ │ └── security.py # Security utilities (e.g., JWT, password hashing)
│ ├── main.py # FastAPI application entry point
│ └── dependencies.py # Common dependencies for API routes
├── alembic/ # Database migration scripts
│ ├── versions/
│ └── env.py
│ └── script.py.mako
├── tests/
│ ├── __init__.py
│ ├── unit/
│ │ ├── test_models.py
│ │ └── test_crud.py
│ ├── integration/
│ │ └── test_api_items.py
│ │ └── test_api_users.py
├── .github/
│ └── workflows/
│ └── ci-cd.yml # GitHub Actions pipeline
├── kubernetes/
│ ├── deployment.yml
│ ├── service.yml
│ └── ingress.yml (optional)
├── scripts/
│ ├── deploy_to_cloud.sh # Generic cloud deployment script
│ └── build_and_push.sh # Script to build Docker image and push to registry
├── .env.example # Example environment variables
├── Dockerfile
├── docker-compose.yml
├── requirements.txt
├── README.md
└── alembic.ini
The application includes example API routes for items and users, demonstrating standard CRUD operations.
Example Endpoints:
* POST /api/v1/items/ - Create a new item.
* GET /api/v1/items/ - Retrieve all items.
* GET /api/v1/items/{item_id} - Retrieve a specific item.
* PUT /api/v1/items/{item_id} - Update an item.
* DELETE /api/v1/items/{item_id} - Delete an item.
* POST /api/v1/users/ - Register a new user.
* GET /api/v1/users/ - Retrieve all users.
* GET /api/v1/users/{user_id} - Retrieve a specific user.
* PUT /api/v1/users/{user_id} - Update a user.
* DELETE /api/v1/users/{user_id} - Delete a user.
* POST /api/v1/login/access-token - User authentication (JWT token generation).
Pydantic Schemas (my_microservice/schemas/):
These define the data structures for API requests and responses, ensuring robust data validation and clear API contracts.
ItemBase, ItemCreate, ItemUpdate, ItemInDB, ItemUserBase, UserCreate, UserUpdate, UserInDB, UserToken, TokenData for authentication.The my_microservice/database/models.py file defines the SQLAlchemy ORM models that map to your PostgreSQL database tables.
Example Models:
* id (UUID, Primary Key)
* email (String, Unique)
* hashed_password (String)
* is_active (Boolean, default True)
* created_at (DateTime)
* updated_at (DateTime)
* items (Relationship to Item model)
* id (UUID, Primary Key)
* title (String)
* description (String, Optional)
* owner_id (UUID, Foreign Key to User)
* created_at (DateTime)
* updated_at (DateTime)
* owner (Relationship to User model)
Database Migrations (Alembic):
Alembic is configured for managing database schema changes.
alembic revision --autogenerate -m "Add new table"alembic upgrade headmy_microservice/core/config.py)Application settings are managed using Pydantic's BaseSettings, allowing for environment variable overrides.
Key configurations include:
PROJECT_NAMEDATABASE_URLSECRET_KEY for JWTACCESS_TOKEN_EXPIRE_MINUTESAPI_V1_STR (e.g., /api/v1)The project includes all necessary files to containerize your microservice, providing a consistent environment across development, testing, and production.
DockerfileDefines the steps to build a Docker image for your application.
docker-compose.ymlFacilitates local development by orchestrating the application and its dependencies (e.g., PostgreSQL database).
Services Defined:
app: Your FastAPI microservice.db: A PostgreSQL database instance.adminer (optional): A web-based database management tool for easy access during development.Usage:
docker-compose up --builddocker-compose exec app alembic upgrade headhttp://localhost:8000 (or configured port)http://localhost:8000/docshttp://localhost:8000/redocA comprehensive testing suite is provided using pytest to ensure the reliability and correctness of your microservice.
tests/)unit/: Contains tests for individual components, functions, and ORM models, isolated from external dependencies (e.g., database). * test_models.py: Tests ORM model definitions and methods.
* test_crud.py: Tests CRUD operations logic in my_microservice/crud/.
integration/: Contains tests that verify the interaction between different components, including API endpoints and database interactions. * test_api_items.py: Tests the /api/v1/items endpoints.
* test_api_users.py: Tests the /api/v1/users and authentication endpoints.
pytest)pytest is configured with fixtures for database setup (e.g., an in-memory SQLite for unit tests or a dedicated test PostgreSQL for integration tests via docker-compose).
How to Run Tests:
pip install -r requirements.txt).pytestpytest tests/unit/test_models.pypytest --cov=my_microservice --cov-report=term-missingA pre-configured CI/CD pipeline using GitHub Actions is provided to automate the build, test, and deployment process for your microservice.
.github/workflows/ci-cd.yml)Stages:
* Triggers on push to main branch and pull_request to main.
* Sets up Python environment.
* Installs dependencies.
* Lints code (flake8, black).
* Runs pytest with coverage.
* Builds the Docker image.
* Triggers on push to main branch.
* Logs into a specified container registry (e.g., Docker Hub, AWS ECR, GCP GCR).
* Pushes the built Docker image with appropriate tags (e.g., latest, commit SHA).
* Actionable: Configure DOCKER_USERNAME, DOCKER_PASSWORD (or cloud provider credentials) as GitHub Secrets.
* Triggers on successful push to main branch.
* Uses kubectl to apply the Kubernetes manifests from the kubernetes/ directory.
* Actionable: Configure KUBECONFIG or cloud provider authentication (e.g., AWS IAM roles, GCP Workload Identity) as GitHub Secrets. This step is a placeholder and may require customization based on your specific Kubernetes cluster setup and cloud provider.
Initial deployment scripts are provided to assist with pushing your container image and deploying to a target environment.
scripts/build_and_push.shA simple shell script to build your Docker image and push it to a specified container registry.
Usage:
./scripts/build_and_push.sh my-registry/my-microservice:v1.0.0
kubernetes/ ManifestsContains basic Kubernetes manifests for deploying your microservice to a Kubernetes cluster.
deployment.yml: Defines the desired state for your application (number of replicas, container image, ports, environment variables).service.yml: Defines how to access your application within the cluster (e.g., ClusterIP, NodePort, LoadBalancer).ingress.yml (optional): Defines external access to your service via an Ingress controller.Usage (assuming kubectl is configured):
kubectl apply -f kubernetes/deployment.yml
kubectl apply -f kubernetes/service.yml
# kubectl apply -f kubernetes/ingress.yml
scripts/deploy_to_cloud.sh (Generic Example)This is a placeholder script for a generic cloud deployment. It demonstrates the conceptual steps.
Example Conceptual Steps:
README.mdA comprehensive README.md is included at the project root, covering: