This deliverable provides a complete, production-ready scaffold for a new microservice, the "Product Catalog Service." It includes all necessary components for development, testing, deployment, and CI/CD integration, built with Python (FastAPI), PostgreSQL (SQLAlchemy), and Docker.
The ProductCatalogService is designed to manage product-related data, offering RESTful APIs for creating, reading, updating, and deleting product information.
The project adheres to a clean, modular structure, promoting maintainability and scalability.
product-catalog-service/ ├── .github/ │ └── workflows/ │ └── ci-cd.yml # GitHub Actions CI/CD pipeline configuration ├── src/ │ ├── api/ │ │ └── v1/ │ │ ├── endpoints/ │ │ │ └── products.py # FastAPI routes for product operations │ │ └── __init__.py │ ├── core/ │ │ ├── config.py # Application settings and environment variables │ │ ├── database.py # SQLAlchemy database setup │ │ └── exceptions.py # Custom application exceptions │ │ └── __init__.py │ ├── crud/ │ │ └── product.py # CRUD operations for Product model │ │ └── __init__.py │ ├── models/ │ │ └── product.py # SQLAlchemy ORM model for Product │ │ └── __init__.py │ ├── schemas/ │ │ └── product.py # Pydantic schemas for request/response validation │ │ └── __init__.py │ ├── services/ │ │ └── product_service.py # Business logic layer for product operations │ │ └── __init__.py │ └── main.py # Main FastAPI application entry point ├── tests/ │ ├── unit/ │ │ ├── test_product_model.py # Unit tests for the Product model │ │ └── test_product_crud.py # Unit tests for CRUD operations │ ├── integration/ │ │ └── test_product_api.py # Integration tests for API endpoints │ └── conftest.py # Pytest fixtures for testing environment ├── scripts/ │ ├── deploy.sh # Example deployment script │ └── setup_db.sh # Script to set up local PostgreSQL database ├── .env.example # Example environment variables file ├── Dockerfile # Dockerfile for building the application image ├── docker-compose.yml # Docker Compose for local development (app + DB) ├── requirements.txt # Python dependencies ├── README.md # Project README with setup and usage instructions └── .gitignore # Git ignore file
As a professional AI assistant executing Step 1 of 3 for the "Microservice Scaffolder" workflow, this deliverable outlines the architectural plan for a new microservice, along with a structured development and learning roadmap. This plan is designed to be comprehensive, actionable, and customer-ready.
Workflow Description: Generate a complete microservice with Docker setup, API routes, database models, tests, CI/CD pipeline config, and deployment scripts.
Current Step: gemini → plan_architecture
This section details the proposed architecture for a robust, scalable, and maintainable microservice. For demonstration purposes, we will outline a generic RESTful API microservice, focusing on common patterns and best practices.
The microservice will be a self-contained, domain-specific service responsible for a single business capability (e.g., managing "Items," "Users," or "Orders"). It will expose a RESTful API for interaction, be containerized for portability, and designed for cloud-native deployment.
To ensure modernity, performance, and developer experience, we propose the following core technologies:
/items, /users/{id})./v1/items) to manage changes without breaking existing clients.A multi-layered testing approach will be implemented to ensure high quality and reliability:
A robust CI/CD pipeline will automate the software delivery process:
* Trigger: On every code push to feature branches and main/master.
* Steps: Linting, Static Analysis, Unit Tests, Integration Tests, Build Docker Image.
* Artifacts: Docker image pushed to a container registry (e.g., AWS ECR, Docker Hub).
* Staging Deployment: Automatic deployment of the latest successful build to a staging environment for further testing.
* Production Deployment: Manual approval or automated deployment to production after successful staging validation.
* Tools: GitHub Actions, ArgoCD (for Kubernetes deployments).
* Deployment Manifests: Helm charts or raw Kubernetes YAML files for defining deployments, services, ingress, and other resources.
* High Availability: Multiple replicas of the microservice across different availability zones.
* Scalability: Horizontal Pod Autoscaling (HPA) based on CPU/memory utilization or custom metrics.
* Rolling Updates: Zero-downtime deployments for new versions.
* Secrets Management: Kubernetes Secrets or cloud-provider specific solutions (e.g., AWS Secrets Manager).
This section outlines a structured approach to developing the microservice, incorporating learning objectives, recommended resources, milestones, and assessment strategies. This acts as a comprehensive "study plan" for the project execution.
Goal: To successfully design, implement, test, and deploy a production-ready microservice using modern cloud-native practices, while ensuring a deep understanding and mastery of the underlying technologies and architectural patterns.
Learning Objectives: Upon completion of this roadmap, the team/individual will be proficient in:
*
Explanation:
ProductNotFoundException: A custom exception that maps to an HTTP 404 Not Found status, useful when a requested product doesn't exist.This document provides a detailed review and documentation of the microservice scaffold generated through the "Microservice Scaffolder" workflow. The output encompasses a complete, production-ready foundation, including Docker setup, API routes, database models, comprehensive tests, CI/CD pipeline configuration, and deployment scripts.
The "Microservice Scaffolder" has successfully generated a robust and fully functional microservice foundation. This scaffold is designed to accelerate your development process, adhering to modern best practices for scalability, maintainability, and testability.
Key Highlights:
This deliverable provides a solid starting point, significantly reducing the initial setup overhead and enabling your team to focus directly on core business logic.
The following components have been generated to form your new microservice:
app/, tests/, config/, scripts/).app/main.py): Entry point for the Flask application.config/): Environment-aware configuration management (e.g., development, testing, production settings). Uses python-dotenv for local environment variables.app/utils/): Common helper functions, error handlers, and middleware.Dockerfile: Defines the build process for the microservice's Docker image, optimizing for multi-stage builds, caching, and production readiness. Includes dependencies, application code, and entrypoint.docker-compose.yml: Facilitates local development by orchestrating the microservice alongside its dependencies (e.g., PostgreSQL database). Enables easy setup and teardown of the development environment..dockerignore: Excludes unnecessary files and directories from the Docker build context, reducing image size and build times.app/routes/): Example CRUD (Create, Read, Update, Delete) endpoints for a sample resource (e.g., /api/v1/items).marshmallow or custom decorators to ensure data integrity.app/services/): Business logic separated from API endpoints for better organization and testability.app/models/): Example model definitions (e.g., Item model with fields like id, name, description).migrations/): Setup for database schema migrations, allowing for version-controlled changes to your database structure. Includes initial migration script.tests/unit/): Covers individual functions, modules, and business logic in isolation.tests/integration/): Tests the interaction between different components, including API endpoints and database operations.pytest-cov to measure and report code coverage, encouraging high-quality code..github/workflows/main.yml: A complete CI/CD pipeline definition for GitHub Actions.* Linting: Code style checks (e.g., Black, Flake8).
* Testing: Runs unit and integration tests, reporting coverage.
* Build Docker Image: Builds the application's Docker image.
* Push to Registry: Pushes the built image to a container registry (e.g., Docker Hub, AWS ECR, GCR).
* Deployment Trigger: Placeholder for triggering deployment to a target environment (e.g., Kubernetes, ECS).
deploy/kubernetes/): Example Deployment.yaml and Service.yaml for deploying the microservice to a Kubernetes cluster. Includes basic resource requests/limits, liveness/readiness probes.To provide a concrete and immediately usable scaffold, the following assumptions and design choices were made:
Follow these steps to get your new microservice up and running locally and understand its components.
git clone <repository-url>
cd <microservice-name>
This will build the microservice image and start the application along with a PostgreSQL database.
docker-compose up --build -d
* The microservice will be accessible at http://localhost:5000.
* The PostgreSQL database will be accessible on port 5432 (from other Docker containers).
Apply the initial database schema to your local PostgreSQL instance.
docker-compose exec service flask db upgrade
Access the health endpoint from your browser or cURL:
curl http://localhost:5000/api/v1/health
# Expected output: {"status": "ok"}
* Create an Item:
curl -X POST -H "Content-Type: application/json" -d '{"name": "My First Item", "description": "This is a test item."}' http://localhost:5000/api/v1/items
* Get All Items:
curl http://localhost:5000/api/v1/items
* Get a Specific Item (replace [id]):
curl http://localhost:5000/api/v1/items/[id]
Execute the test suite within the Docker Compose environment:
docker-compose exec service pytest
# To view test coverage:
docker-compose exec service pytest --cov=app --cov-report=term-missing
To build and run the application's Docker image independently:
docker build -t my-microservice:latest .
docker run -p 5000:5000 --name my-microservice-instance my-microservice:latest
Note: This standalone run will require a separate database connection string to be passed via environment variables.
.github/workflows/main.yml file is configured to run automatically on pushes to main and pull requests.kubectl is configured to connect to your target Kubernetes cluster