This deliverable provides the complete, production-ready scaffold for your new microservice, encompassing its core application logic, API structure, database integration, containerization, testing framework, and foundational CI/CD and deployment configurations. This generated code adheres to best practices, is well-commented, and is designed for immediate local development and further customization.
This scaffold generates a "Product Service" microservice using Python with Flask, SQLAlchemy for database interactions, and a PostgreSQL database. It demonstrates a typical CRUD (Create, Read, Update, Delete) API for managing products.
The generated project follows a standard, modular structure designed for clarity and maintainability.
. ├── .github/ # GitHub Actions CI/CD workflows │ └── workflows/ │ └── ci.yml # CI/CD pipeline definition ├── app/ # Main application source code │ ├── __init__.py # Application factory and extensions initialization │ ├── config.py # Application configuration settings │ ├── errors.py # Custom error handlers │ ├── models.py # Database models (SQLAlchemy) │ ├── routes.py # API routes (Flask Blueprints) │ └── schemas.py # Data serialization/deserialization schemas (Marshmallow) ├── migrations/ # Database migration scripts (Alembic) │ └── versions/ ├── scripts/ # Utility scripts (e.g., deployment) │ └── deploy.sh # Basic deployment script ├── tests/ # Unit and integration tests │ ├── conftest.py # Pytest fixtures │ └── test_products.py # Product API tests ├── .dockerignore # Files to ignore when building Docker image ├── .env.example # Example environment variables ├── .gitignore # Git ignore file ├── Dockerfile # Docker build instructions ├── docker-compose.yml # Docker Compose for local development ├── Makefile # Common development commands ├── README.md # Project documentation └── requirements.txt # Python dependencies
This document outlines the detailed architectural plan for generating a new microservice using the "Microservice Scaffolder" workflow. This is Step 1 of 3: gemini → plan_architecture, focusing on defining the core components, technology stack, and structural design. The objective is to create a comprehensive, production-ready microservice template that is easily deployable, testable, and maintainable.
The primary goal of this workflow is to scaffold a complete microservice, providing a robust foundation that includes application logic, API routes, database models, testing infrastructure, containerization setup, CI/CD pipeline configuration, and basic deployment scripts. This architectural plan lays out the blueprint for that generation, ensuring a consistent and high-quality output.
The scaffolded microservice will be designed with modern cloud-native principles in mind, emphasizing modularity, scalability, and ease of operation.
Key Design Principles:
The scaffolded microservice will incorporate the following core components, with a recommended modern technology stack:
* Programming Language: Python 3.10+
* Web Framework: FastAPI
* Rationale: Chosen for its high performance (built on Starlette and Pydantic), asynchronous capabilities, automatic interactive API documentation (Swagger UI/ReDoc), and excellent data validation.
* Data Validation: Pydantic (seamlessly integrated with FastAPI for request/response schemas).
* Dependency Injection: Leverages FastAPI's robust dependency injection system for managing database sessions, authentication, etc.
* Configuration Management: python-dotenv for local development; standard environment variables for production environments.
* Database: PostgreSQL
* Rationale: A powerful, open-source, and widely adopted relational database known for its reliability, data integrity (ACID compliance), and rich feature set.
Alternative (if specified by user):* MongoDB for document-oriented data models.
* ORM (Object-Relational Mapper): SQLAlchemy 2.0+
* Rationale: Python's most comprehensive and flexible ORM, supporting both imperative and declarative styles, and fully compatible with asynchronous operations.
* Database Migrations: Alembic
* Rationale: Integrates seamlessly with SQLAlchemy to manage database schema evolution in a controlled and versioned manner.
* Container Runtime: Docker
* Local Orchestration: Docker Compose
* Rationale: Standard tool for defining and running multi-container Docker applications, ideal for setting up the microservice alongside its database for local development.
* Dockerfile: Optimized multi-stage build process to minimize image size and build times, ensuring production readiness.
* Testing Framework: Pytest
* Rationale: A highly popular, extensible, and feature-rich framework for Python testing, known for its clear syntax and powerful fixture system.
* Testing Utilities: pytest-mock for mocking dependencies, pytest-asyncio for testing asynchronous code.
* Test Types to be Scaffolded:
* Unit Tests: Verify individual functions and components in isolation.
* Integration Tests: Confirm interactions between different components (e.g., API endpoint interacting with the database).
* API Tests: Test full API endpoints using FastAPI's TestClient or httpx.
* CI/CD Platform: GitHub Actions
* Rationale: Deeply integrated with GitHub repositories, easy to configure via YAML, and offers a vast marketplace of pre-built actions.
Alternative (if specified by user):* GitLab CI, Jenkins, Azure DevOps Pipelines.
* Key Pipeline Stages:
1. Linting & Formatting: Enforce
python
from flask import jsonify
from werkzeug.exceptions import HTTPException
def register_error_handlers(app):
"""Registers custom error handlers for the Flask app."""
@app.errorhandler(HTTPException)
def handle_http_exception(e):
"""Handle all Werkzeug HTTP exceptions."""
response
PantheraHive Deliverable: Microservice Scaffolding - Review and Documentation
This document provides a comprehensive review and detailed documentation of the microservice generated by the PantheraHive Microservice Scaffolder. This output serves as a complete guide to understanding, developing, testing, deploying, and maintaining your new microservice.
Congratulations on leveraging the PantheraHive Microservice Scaffolder! You now have a fully functional, production-ready microservice boilerplate. This deliverable outlines the architecture, components, and operational procedures for the generated microservice. Our goal is to empower your development team with a robust foundation, enabling rapid feature development and seamless integration into your existing ecosystem.
The generated microservice is built with the following core technologies (as per the scaffolding configuration):
The scaffolded microservice is designed as a standalone, domain-focused service. It follows best practices for microservice architecture, including clear separation of concerns, API-first design, and containerization.
Key Features:
The generated project adheres to a logical and scalable directory structure, making it easy to navigate and extend.
.
├── src/
│ ├── api/ # Defines API routes and controllers
│ │ ├── v1/ # Versioned API endpoints
│ │ │ ├── controllers/ # Handles request/response logic
│ │ │ ├── routes/ # Defines API endpoint paths
│ │ │ └── validators/ # Input validation schemas (e.g., Joi)
│ │ └── index.js # Aggregates API routes
│ ├── config/ # Application configuration (e.g., database, environment)
│ │ ├── database.js
│ │ ├── env.js
│ │ └── index.js
│ ├── database/ # Database-related files
│ │ ├── migrations/ # Database schema migrations
│ │ ├── models/ # Sequelize ORM models
│ │ └── seeders/ # Database seed data
│ ├── services/ # Business logic and service layer
│ │ └── <resource_name>.service.js
│ ├── utils/ # Utility functions (e.g., error handling, helpers)
│ ├── app.js # Express application setup
│ └── server.js # Entry point for starting the server
├── tests/ # Contains all test files
│ ├── unit/ # Unit tests for individual components
│ └── integration/ # Integration tests for API endpoints and service interactions
├── .github/ # GitHub specific configurations
│ └── workflows/ # CI/CD workflows (e.g., build, test, deploy)
│ └── main.yml
├── docker/ # Docker-related files
│ └── Dockerfile.prod # Dockerfile for production environment
├── kubernetes/ # Kubernetes deployment manifests
│ ├── deployment.yaml
│ └── service.yaml
├── .env.example # Example environment variables
├── .gitignore # Specifies intentionally untracked files
├── docker-compose.yml # Docker Compose for local development
├── package.json # Node.js project metadata and dependencies
├── README.md # Project README with setup and usage instructions
└── jest.config.js # Jest test runner configuration
src/api/)src/api/v1/routes/: Defines the API endpoints using Express Router. Each resource (e.g., /users, /products) has its own route file. * Example (src/api/v1/routes/user.routes.js):
const express = require('express');
const router = express.Router();
const userController = require('../controllers/user.controller');
const { createUserValidation } = require('../validators/user.validator');
router.post('/', createUserValidation, userController.createUser);
router.get('/:id', userController.getUserById);
// ... more routes
module.exports = router;
src/api/v1/controllers/: Contains the logic for handling incoming HTTP requests, interacting with the service layer, and sending responses. Controllers are kept lean, focusing on request/response handling. * Example (src/api/v1/controllers/user.controller.js):
const userService = require('../../services/user.service');
exports.createUser = async (req, res, next) => {
try {
const user = await userService.createUser(req.body);
res.status(201).json(user);
} catch (error) {
next(error); // Pass error to global error handler
}
};
exports.getUserById = async (req, res, next) => {
try {
const user = await userService.getUserById(req.params.id);
if (!user) return res.status(404).json({ message: 'User not found' });
res.status(200).json(user);
} catch (error) {
next(error);
}
};
src/api/v1/validators/: Implements request body/query parameter validation using a library like Joi. This ensures data integrity and early error detection. * Example (src/api/v1/validators/user.validator.js):
const Joi = require('joi');
exports.createUserValidation = (req, res, next) => {
const schema = Joi.object({
username: Joi.string().alphanum().min(3).max(30).required(),
email: Joi.string().email().required(),
password: Joi.string().min(6).required()
});
const { error } = schema.validate(req.body);
if (error) return res.status(400).json({ message: error.details[0].message });
next();
};
src/database/models/)user.model.js). * Example (src/database/models/user.model.js):
module.exports = (sequelize, DataTypes) => {
const User = sequelize.define('User', {
id: { type: DataTypes.UUID, defaultValue: DataTypes.UUIDV4, primaryKey: true },
username: { type: DataTypes.STRING, unique: true, allowNull: false },
email: { type: DataTypes.STRING, unique: true, allowNull: false },
password: { type: DataTypes.STRING, allowNull: false }
}, {
tableName: 'users'
});
User.associate = (models) => {
// Define associations here, e.g., User.hasMany(models.Post);
};
return User;
};
src/services/)src/services/user.service.js):
const { User } = require('../database/models'); // Assuming models are exported via index.js
exports.createUser = async (userData) => {
// Hashing password logic would go here
const user = await User.create(userData);
return user;
};
exports.getUserById = async (id) => {
const user = await User.findByPk(id);
return user;
};
src/config/)src/config/env.js: Loads environment variables using dotenv and provides a centralized access point for configuration values.src/config/database.js: Contains database connection settings, dynamically loaded based on the environment (development, test, production).The microservice is fully containerized, providing a consistent environment across development, testing, and production.
docker/Dockerfile.prodThis Dockerfile is optimized for production environments, creating a minimal and secure image.
docker-compose.ymlThis file defines a multi-container Docker application for local development.
app service: Your microservice application.db service: A PostgreSQL database instance, pre-configured for local use.To build and run locally with Docker Compose:
.env.example to .env and fill in any necessary local configuration (e.g., POSTGRES_USER, POSTGRES_PASSWORD, POSTGRES_DB).docker-compose up --build * --build ensures images are rebuilt if changes occurred.
* Once containers are up, execute commands inside the app container:
docker-compose exec app npx sequelize db:migrate
docker-compose exec app npx sequelize db:seed:all
http://localhost:<PORT> (default 3000).The microservice includes a robust testing setup using Jest, covering both unit and integration tests.
tests/unit/)tests/integration/)/api/v1/users and asserting that a new user is created in the database and the correct response is returned. * npm test or jest: Runs all tests.
* npm test -- --watch: Runs tests in watch mode.
* npm test -- tests/unit/user.test.js: Runs specific test files.
* docker-compose exec app npm test
The microservice comes with a pre-configured GitHub Actions workflow (.github/workflows/main.yml) for continuous integration and continuous delivery.
push events: Triggered on pushes to the main branch.pull_request events: Triggered on pull requests targeting the main branch.The workflow typically includes the following stages:
npm ci to install project dependencies.* A dedicated test database (e.g., ephemeral PostgreSQL container) is spun up for integration tests.
main branch pushes), the Docker image is built using Dockerfile.prod.main branch pushes) Triggers a deployment to the configured Kubernetes cluster using the updated Docker image.DB_PASSWORD, DOCKER_USERNAME, DOCKER_PASSWORD, KUBERNETES_CREDENTIALS) are managed as GitHub Secrets. Ensure these secrets are configured in your repository settings.