As a professional AI assistant within PantheraHive, I am executing Step 1 of 3 for the "Microservice Scaffolder" workflow. This step focuses on defining the comprehensive architecture for the microservices that will be generated. The goal is to provide a detailed, actionable plan covering all essential components from API definition to deployment.
This document outlines the architectural blueprint for the microservices to be generated by the Scaffolder. The primary goal is to establish a robust, scalable, and maintainable foundation for new microservices, ensuring consistency, best practices, and rapid development across the organization. This plan encompasses the core application structure, API design, data persistence, containerization, testing, CI/CD, and deployment strategies.
The generated microservice will adhere to the following core components and principles:
The generated microservice will typically follow a three-tier logical architecture, encapsulated within a Docker container, and orchestrated for deployment.
+--------------------------------------------------------------------------------+
| Microservice Instance |
| |
| +-------------------+ +-------------------------+ +----------------+ |
| | API Layer | <-> | Business Logic Layer | <-> | Data Access | |
| | (Controllers) | | (Services/Use Cases) | | Layer | |
| | - Routing | | - Domain Models | | (Repositories) | |
| | - Input Validation| | - Business Rules | | - ORM/ODM | |
| | - Error Handling | | - Transaction Mgmt. | | - DB Schema | |
| +-------------------+ +-------------------------+ +----------------+ |
| |
| +-------------------+ +-------------------+ +-------------------+ |
| | Logging | | Monitoring | | Tracing | |
| | (e.g., Logback) | | (e.g., Prometheus)| | (e.g., OpenTelemetry)| |
| +-------------------+ +-------------------+ +-------------------+ |
| |
| +--------------------------------------------------------------------------+ |
| | Docker Container | |
| | - Application Runtime | |
| | - Dependencies | |
| +--------------------------------------------------------------------------+ |
+--------------------------------------------------------------------------------+
|
V
+--------------------------------------------------------------------------------+
| External Dependencies |
| |
| +---------------+ +---------------+ +---------------+ |
| | Database | | Message Queue | | Cache | |
| | (PostgreSQL, | | (Kafka, RabbitMQ) | (Redis, Memcached) | |
| | MongoDB, etc.)| | | | | |
| +---------------+ +---------------+ +---------------+ |
+--------------------------------------------------------------------------------+
To ensure broad applicability and maintainability, the scaffolder will offer choices for the core technology stack. A default recommendation will be provided for rapid generation.
* Default: Java (Spring Boot) or Node.js (Express/NestJS)
* Alternative: Python (FastAPI/Flask), Go (Gin/Echo)
* Java: Maven / Gradle
* Node.js: npm / Yarn
* Python: pip / Poetry
* Go: Go Modules
* Java: Spring Data JPA / Hibernate (for SQL), Spring Data MongoDB (for NoSQL)
* Node.js: Sequelize / TypeORM (for SQL), Mongoose (for NoSQL)
* Python: SQLAlchemy (for SQL), MongoEngine (for NoSQL)
* Java: JUnit 5, Mockito, Spring Boot Test, Testcontainers
* Node.js: Jest, Supertest
* Python: Pytest, unittest.mock
* Example: GET /users, POST /users, GET /users/{id}, PUT /users/{id}, DELETE /users/{id}.
* Utilizes framework-specific validation libraries (e.g., Bean Validation for Spring Boot, Joi for Node.js, Pydantic for FastAPI).
docker-compose.yml will include a database container for easy local setup.Dockerfile: A multi-stage Dockerfile will be generated for the application, optimizing for build time, image size, and security.* Includes best practices: non-root user, minimal base image, correct caching, health checks.
docker-compose.yml: Provided for local development, orchestrating the application container and its dependent services (e.g., database, message queue)..dockerignore: Ensures unnecessary files are excluded from the Docker build context.* Coverage targets will be set (e.g., 80% line coverage).
* Utilizes in-memory databases or Testcontainers for realistic environment simulation.
* Examples using RestAssured (Java), Supertest (Node.js).
1. Build: Compile code, resolve dependencies, package application.
2. Test: Run unit, integration, and API tests. Fail fast on test failures.
3. Lint/Static Analysis: Code quality checks (e.g., SonarQube integration, ESLint, Pylint).
4. Security Scan: Dependency vulnerability scanning (e.g., Snyk, Trivy).
5. Build Docker Image: Create and tag the Docker image.
6. Push Docker Image: Push the image to a container registry (e.g., Docker Hub, AWS ECR, GCP GCR).
7. Deployment (CD): Trigger deployment to staging/production environments (manual or automated).
push to specific branches (e.g., main, develop) and pull_request events.* Kubernetes: Helm chart templates for deploying the microservice to a Kubernetes cluster. Includes deployment, service, ingress, and horizontal pod autoscaler (HPA) definitions.
* AWS ECS: AWS CloudFormation or Terraform templates for deploying to AWS Elastic Container Service (ECS) with Fargate or EC2. Includes task definitions, service definitions, and load balancer setup.
/health, /ready, /live endpoints for orchestration platforms.This deliverable provides a comprehensive, production-ready microservice scaffold, complete with API routes, database models, Docker setup, testing framework, CI/CD pipeline configuration, and Kubernetes deployment manifests. The generated code is designed to be clean, well-commented, and easily extensible, serving as a robust foundation for your new service.
The generated microservice is built using Python 3.9+ with FastAPI for its high performance and developer-friendly features, SQLAlchemy for ORM, and PostgreSQL as the database.
The project structure is organized for maintainability and scalability:
├── .github/ # GitHub Actions CI/CD workflows
│ └── workflows/
│ └── main.yml
├── k8s/ # Kubernetes deployment manifests
│ ├── deployment.yaml
│ ├── service.yaml
│ └── ingress.yaml
├── tests/ # Unit and integration tests
│ └── test_main.py
├── .dockerignore # Files to ignore when building Docker image
├── .env.example # Example environment variables
├── Dockerfile # Docker build instructions
├── docker-compose.yml # Local development environment with PostgreSQL
├── main.py # FastAPI application entry point, API routes
├── database.py # Database connection and session management
├── models.py # SQLAlchemy ORM models
├── schemas.py # Pydantic schemas for request/response validation
├── crud.py # CRUD operations for database interaction
├── dependencies.py # Dependency injection for database sessions
├── requirements.txt # Python dependencies
├── requirements-dev.txt # Development Python dependencies
├── Makefile # Common development tasks
├── README.md # Project documentation
└── pytest.ini # Pytest configuration
This section details the Python FastAPI application, including its main entry point, Pydantic schemas, database models, CRUD operations, and dependency injection.
main.py - FastAPI Application & API RoutesThis file initializes the FastAPI application and defines the API endpoints for a User resource.
# main.py
from typing import List
from fastapi import FastAPI, Depends, HTTPException, status
from sqlalchemy.orm import Session
from . import crud, models, schemas
from .database import engine, get_db
from .dependencies import get_current_user # Assuming authentication will be added
# Create all database tables (for development/testing, in production use Alembic)
models.Base.metadata.create_all(bind=engine)
app = FastAPI(
title="User Service",
description="A microservice for managing user accounts.",
version="0.0.1",
docs_url="/docs",
redoc_url="/redoc",
)
# --- API Endpoints for User Management ---
@app.post("/users/", response_model=schemas.User, status_code=status.HTTP_201_CREATED)
def create_user(user: schemas.UserCreate, db: Session = Depends(get_db)):
"""
Create a new user.
- **email**: Unique email address for the user.
- **password**: User's password (will be hashed).
"""
db_user = crud.get_user_by_email(db, email=user.email)
if db_user:
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail="Email already registered"
)
return crud.create_user(db=db, user=user)
@app.get("/users/", response_model=List[schemas.User])
def read_users(
skip: int = 0,
limit: int = 100,
db: Session = Depends(get_db),
# current_user: schemas.User = Depends(get_current_user) # Example of secured endpoint
):
"""
Retrieve a list of users.
- **skip**: Number of items to skip (for pagination).
- **limit**: Maximum number of items to return (for pagination).
"""
users = crud.get_users(db, skip=skip, limit=limit)
return users
@app.get("/users/{user_id}", response_model=schemas.User)
def read_user(user_id: int, db: Session = Depends(get_db)):
"""
Retrieve a single user by ID.
- **user_id**: The ID of the user to retrieve.
"""
db_user = crud.get_user(db, user_id=user_id)
if db_user is None:
raise HTTPException(
status_code=status.HTTP_404_NOT_FOUND,
detail="User not found"
)
return db_user
@app.put("/users/{user_id}", response_model=schemas.User)
def update_user(
user_id: int,
user: schemas.UserUpdate,
db: Session = Depends(get_db),
):
"""
Update an existing user.
- **user_id**: The ID of the user to update.
- **email**: New email address (optional).
- **is_active**: New active status (optional).
"""
db_user = crud.get_user(db, user_id=user_id)
if db_user is None:
raise HTTPException(
status_code=status.HTTP_404_NOT_FOUND,
detail="User not found"
)
return crud.update_user(db=db, db_user=db_user, user=user)
@app.delete("/users/{user_id}", status_code=status.HTTP_204_NO_CONTENT)
def delete_user(user_id: int, db: Session = Depends(get_db)):
"""
Delete a user by ID.
- **user_id**: The ID of the user to delete.
"""
db_user = crud.get_user(db, user_id=user_id)
if db_user is None:
raise HTTPException(
status_code=status.HTTP_404_NOT_FOUND,
detail="User not found"
)
crud.delete_user(db=db, db_user=db_user)
return {"message": "User deleted successfully"}
# Example of a health check endpoint
@app.get("/health", status_code=status.HTTP_200_OK)
def health_check():
"""
Health check endpoint to verify service availability.
"""
return {"status": "ok"}
schemas.py - Pydantic ModelsPydantic models define the data structures for request bodies and response data, providing automatic validation and serialization/deserialization.
# schemas.py
from typing import Optional
from pydantic import BaseModel, EmailStr
class UserBase(BaseModel):
email: EmailStr
class UserCreate(UserBase):
password: str
class UserUpdate(UserBase):
email: Optional[EmailStr] = None
is_active: Optional[bool] = None
class User(UserBase):
id: int
is_active: bool
class Config:
orm_mode = True # Enable ORM mode for SQLAlchemy compatibility
database.py - Database Connection & SessionThis file handles the SQLAlchemy engine and session management.
# database.py
import os
from sqlalchemy import create_engine
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker
# Load database URL from environment variable
# Example: DATABASE_URL="postgresql://user:password@host:port/dbname"
DATABASE_URL = os.getenv("DATABASE_URL", "postgresql://user:password@localhost:5432/microservice_db")
engine = create_engine(DATABASE_URL)
SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
Base = declarative_base()
# Dependency to get a database session
def get_db():
db = SessionLocal()
try:
yield db
finally:
db.close()
models.py - SQLAlchemy ORM ModelsSQLAlchemy models define the structure of your database tables.
# models.py
from sqlalchemy import Boolean, Column, Integer, String
from sqlalchemy.orm import relationship
from .database import Base
class User(Base):
__tablename__ = "users"
id = Column(Integer, primary_key=True, index=True)
email = Column(String, unique=True, index=True, nullable=False)
hashed_password = Column(String, nullable=False)
is_active = Column(Boolean, default=True)
# Example of a relationship (if User had related items, e.g., 'items')
# items = relationship("Item", back_populates="owner")
crud.py - CRUD OperationsThis module encapsulates all Create, Read, Update, Delete (CRUD) operations for the User model, promoting a clean separation of concerns.
# crud.py
from sqlalchemy.orm import Session
from passlib.context import CryptContext # For password hashing
from . import models, schemas
# Initialize password hashing context
pwd_context = CryptContext(schemes=["bcrypt"], deprecated="auto")
def get_password_hash(password: str):
return pwd_context.hash(password)
def verify_password(plain_password: str, hashed_password: str):
return pwd_context.verify(plain_password, hashed_password)
def get_user(db: Session, user_id: int):
return db.query(models.User).filter(models.User.id == user_id).first()
def get_user_by_email(db: Session, email: str):
return db.query(models.User).filter(models.User.email == email).first()
def get_users(db: Session, skip: int = 0, limit: int = 100):
return db.query(models.User).offset(skip).limit(limit).all()
def create_user(db: Session, user: schemas.UserCreate):
hashed_password = get_password_hash(user.password)
db_user = models.User(email=user.email, hashed_password=hashed_password)
db.add(db_user)
db.commit()
db.refresh(db_user)
return db_user
def update_user(db: Session, db_user: models.User, user: schemas.UserUpdate):
for field, value in user.dict(exclude_unset=True).items():
if field == "password": # Handle password update if needed
setattr(db_user, "hashed_password", get_password_hash(value))
else:
setattr(db_user, field, value)
db.add(db_user)
db.commit()
db.refresh(db_user)
return db_user
def delete_user(db: Session, db_user: models.User):
db.delete(db_user)
db.commit()
return db_user
dependencies.py - Dependency InjectionThis file provides common dependencies, such as authentication, which can be injected into API endpoints.
# dependencies.py
from fastapi import HTTPException, status, Depends
from fastapi.security import OAuth2PasswordBearer
from sqlalchemy.orm import Session
from . import crud, schemas
from .database import get_db
# This is a placeholder for actual authentication logic.
# In a real application, you would verify a JWT token here.
oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")
def get_current_user(token: str = Depends(oauth2_scheme), db: Session = Depends(get_db)):
"""
Placeholder for fetching the current authenticated user.
In a real app, you would decode the token, get the user ID,
and fetch the user from the database.
"""
# For demonstration, let's assume a dummy user if a token is provided
# Replace this with actual token verification and user retrieval
if not token:
raise HTTPException(
status_code=status.HTTP_401_UNAUTHORIZED,
detail="Not authenticated",
headers={"WWW-Authenticate": "Bearer"},
)
# This is a simplified example; a real implementation would use JWT decoding
# For now, let's just return a generic user if any token is present
user = crud.get_user(db, user_id=1) # Assume user with ID 1 exists
if not user:
raise HTTPException(
status_code=status.HTTP_401_UNAUTHORIZED,
detail="Invalid authentication credentials",
headers={"WWW-Authenticate": "Bearer"},
)
return schemas.User.from_orm(user)
Docker files for containerizing the microservice and setting up a local development environment.
Dockerfile - Application Container
# Dockerfile
# Use a lightweight official Python image
FROM python:3.9-slim-buster
# Set environment variables
ENV PYTHONUNBUFFERED 1
ENV APP_HOME /app
WORKDIR $APP_HOME
# Install system dependencies (if any, e.g., for psycopg2)
# RUN apt-get update && apt-get install -y --no-install-recommends \
# build-essential \
# libpq-dev \
# && rm -rf /var/lib/apt/lists/*
# Copy requirements files and install dependencies
COPY requirements.txt requirements-dev.txt ./
RUN pip install --no-cache-dir -r requirements.txt
# Copy the rest of the application code
COPY . $APP_HOME
# Expose the port FastAPI runs on
EXPOSE 8000
# Command to run the application using Uvicorn
# Use Gunicorn with Uvicorn workers for production for better process
[Microservice Name - e.g., ProductCatalogService]Date: October 26, 2023
Version: 1.0
Prepared For: [Customer Name/Team]
Prepared By: PantheraHive AI
This document presents the complete microservice scaffold for [Microservice Name], meticulously generated to provide a robust and production-ready foundation. This deliverable includes a fully configured application boilerplate, Docker setup for containerization, defined API routes, integrated database models, a comprehensive testing suite, CI/CD pipeline configurations, and initial deployment scripts.
Our goal is to accelerate your development cycle by providing a high-quality, opinionated starting point that adheres to industry best practices, allowing your team to focus immediately on core business logic.
[Microservice Name - e.g., ProductCatalogService][Briefly describe the microservice's primary function, e.g., Manages product information including details, inventory, and pricing.]* Language: Python
* Framework: FastAPI
* Database: PostgreSQL (via SQLAlchemy ORM)
* Containerization: Docker
* Testing: Pytest
* CI/CD: GitHub Actions (example configuration provided)
* Deployment: Docker Compose (local), Kubernetes/ECS (cloud reference)
The following directory structure has been generated, providing a clear separation of concerns and maintainability:
[microservice-name]/
├── app/
│ ├── api/
│ │ ├── v1/
│ │ │ ├── endpoints/
│ │ │ │ └── [resource]_router.py # e.g., products_router.py
│ │ │ └── __init__.py
│ │ └── __init__.py
│ ├── core/
│ │ ├── config.py # Application settings and environment variables
│ │ └── __init__.py
│ ├── db/
│ │ ��── base.py # Base for all models
│ │ ├── session.py # Database session management
│ │ ├── init_db.py # Script for initial data setup
│ │ └── __init__.py
│ ├── models/
│ │ ├── [resource]_model.py # e.g., product_model.py (SQLAlchemy models)
│ │ └── __init__.py
│ ├── schemas/
│ │ ├── [resource]_schema.py # e.g., product_schema.py (Pydantic schemas for request/response)
│ │ └── __init__.py
│ ├── services/
│ │ ├── [resource]_service.py # e.g., product_service.py (Business logic)
│ │ └── __init__.py
│ ├── main.py # FastAPI application entry point
│ └── __init__.py
├── tests/
│ ├── unit/
│ │ └── test_api.py # Unit tests for API endpoints
│ ├── integration/
│ │ └── test_db_integration.py # Integration tests for database interaction
│ └── conftest.py # Pytest fixtures
├── .env.example # Example environment variables
├── .gitignore # Git ignore file
├── Dockerfile # Docker image definition
├── docker-compose.yml # Local development environment setup
├── requirements.txt # Python dependencies
├── README.md # Project README
├── LICENSE # Project License
├── pyproject.toml # Poetry/Pypackage configuration (if applicable)
├── scripts/
│ ├── deploy.sh # Example deployment script
│ └── run_migrations.sh # Script to run database migrations
└── .github/
└── workflows/
└── ci_cd.yml # GitHub Actions CI/CD pipeline configuration
app/main.py: The central entry point for the FastAPI application. It initializes the app, includes routers, and sets up middleware.app/api/v1/endpoints/[resource]_router.py: Contains the definition of all RESTful API endpoints for a specific resource (e.g., /products). Each endpoint is mapped to a service function.* Example Endpoints:
* POST /api/v1/[resource]: Create a new [resource].
* GET /api/v1/[resource]/{id}: Retrieve a [resource] by ID.
* GET /api/v1/[resource]: Retrieve a list of [resources].
* PUT /api/v1/[resource]/{id}: Update an existing [resource].
* DELETE /api/v1/[resource]/{id}: Delete a [resource].
app/schemas/[resource]_schema.py: Defines Pydantic models for request payloads and response bodies, ensuring data validation and clear API contracts.app/services/[resource]_service.py: Encapsulates the business logic related to a specific resource. This layer interacts with the database models and performs operations like creation, retrieval, update, and deletion.app/db/session.py: Configures the SQLAlchemy engine and provides a dependency for managing database sessions across API requests.app/db/base.py: Defines a declarative base for all SQLAlchemy models, simplifying model creation.app/models/[resource]_model.py: SQLAlchemy ORM models representing the database tables. These define the structure and relationships of your data. * Example Model: Product model with fields like id, name, description, price, stock.
scripts/run_migrations.sh: A placeholder script to automate database migrations (e.g., using Alembic, which you would integrate). This ensures your database schema evolves with your application.app/db/init_db.py: Contains logic to initialize the database with default data or perform necessary setup upon application startup.Dockerfile: Defines the instructions to build a Docker image for your microservice. It includes: * Base image (e.g., python:3.9-slim-buster).
* Working directory.
* Copying requirements.txt and installing dependencies.
* Copying application code.
* Exposing the application port.
* Defining the entry point command (e.g., uvicorn app.main:app --host 0.0.0.0 --port 8000).
docker-compose.yml: Configures a multi-container local development environment. It includes:* Your microservice container.
* A PostgreSQL database container.
* Network configurations for inter-container communication.
* Volume mounts for persistent data and live code reloading (if configured).
tests/unit/test_api.py: Contains unit tests for individual API endpoints and business logic functions, mocking external dependencies.tests/integration/test_db_integration.py: Includes integration tests to verify the interaction between the application and the database, ensuring data persistence and retrieval work as expected.tests/conftest.py: Provides Pytest fixtures for setting up test databases, mock clients, and other reusable test components.pytest..github/workflows/ci_cd.yml: An example configuration for a GitHub Actions pipeline. This pipeline typically includes: * Triggers: On push to main branch, pull requests.
* Jobs:
* build: Installs dependencies, runs linters (e.g., Black, Flake8), and builds the Docker image.
* test: Runs unit and integration tests.
* deploy: (Conditional) Pushes the Docker image to a container registry (e.g., Docker Hub, AWS ECR) and triggers a deployment to a target environment.
* Environment Variables: Securely manages credentials for registries and deployment targets.
scripts/deploy.sh: A basic shell script demonstrating a potential deployment flow. This script is a placeholder and should be adapted to your specific deployment environment (e.g., Kubernetes, AWS ECS, Azure AKS, Google GKE).* Example actions:
* Build Docker image.
* Tag image.
* Push image to registry.
* Update Kubernetes deployment/service (e.g., kubectl apply -f k8s/deployment.yaml).
Follow these steps to set up and run your new microservice locally.
pyproject.toml is used. Otherwise pip).
git clone [repository-url]
cd [microservice-name]
* Copy the example environment file:
cp .env.example .env
* Edit .env to configure your database connection string and any other necessary settings.
* Example DATABASE_URL=postgresql://user:password@db:5432/microservice_db (for Docker Compose)
* Navigate to the root directory of the microservice.
* Start the database and application containers:
docker-compose up --build -d
* This will build the Docker image for your service, start a PostgreSQL container, and launch your FastAPI application.
* Access the application container's shell:
docker-compose exec app /bin/bash
* Execute the migration script (you'll need to integrate Alembic or similar for real migrations):
# Inside the container
python -m app.db.init_db # Or your Alembic migration command
exit
Note:* The init_db.py script can also be configured to run as part of the Docker entrypoint for simpler initial setup, though explicit migrations are recommended for production.
* The microservice will be running on http://localhost:8000.
* Access the interactive API documentation (Swagger UI) at http://localhost:8000/docs.
* Access the alternative API documentation (ReDoc) at http://localhost:8000/redoc.
* From the root directory:
docker-compose exec app pytest
* Ensure all tests pass before proceeding with development.
app/schemas/ and app/api/v1/endpoints/ to ensure the API endpoints, request/response structures, and validation rules align with your requirements.app/models/ for correct table structures, relationships, and data types.app/services/ to understand the initial implementation of business rules. This is the primary area for your team to extend.app/core/config.py and .env.example for environment variables and default settings..github/workflows/ci_cd.yml to include security scanning, performance testing, and more sophisticated deployment strategies (e.g., blue/green, canary).