As part of the "Microservice Scaffolder" workflow, this deliverable outlines the comprehensive architectural plan for a new microservice, fulfilling Step 1: plan_architecture. Following this, we also provide a detailed study plan to support your team in understanding and mastering microservice development, directly addressing your specific request.
plan_architecture)This section details the architectural blueprint for a new, generic microservice, designed to be robust, scalable, and maintainable. For demonstration purposes, we will architect a User Management Service.
* User registration and login.
* User profile management (view, update, delete).
* Password management (reset, change).
* Role-based authorization (e.g., admin, regular user).
* User activity logging (optional, for auditing).
To ensure a modern, efficient, and widely supported microservice, the following technology stack is recommended:
* Rationale: High performance (on par with Node.js and Go for web APIs), excellent developer experience, automatic OpenAPI (Swagger) documentation, strong async support, robust type hints.
* Rationale: Open-source, highly reliable, ACID-compliant, feature-rich, robust for relational data, widely supported.
* Rationale: Powerful and flexible ORM for Python, provides both declarative and imperative styles, excellent integration with FastAPI and async operations. Alembic ensures controlled database schema evolution.
* Rationale: Industry standard for packaging applications and their dependencies, ensures consistent environments across development, testing, and production.
* Rationale: Centralized entry point for all client requests, handles routing, load balancing, authentication, rate limiting, and analytics. (This service will assume an external API Gateway handles public exposure).
* Rationale: Enables asynchronous communication between services, decoupling them and improving resilience. (Initially, direct HTTP calls will be used, with a clear path for future integration).
* Rationale: Automates the build, test, and deployment process, ensuring rapid and reliable delivery.
* Rationale: Scalable, resilient, and manageable platform for running containerized applications.
The User Management Service will adhere to a layered architecture:
* Handles incoming HTTP requests.
* Validates request payloads using Pydantic models.
* Routes requests to the appropriate business logic.
* Returns HTTP responses.
* Framework: FastAPI
* Contains the core logic for user management (e.g., creating users, hashing passwords, generating tokens).
* Orchestrates interactions with the Data Access Layer.
* Applies business rules and validations.
* Implementation: Python functions/classes.
* Abstracts database operations (CRUD).
* Interacts directly with the ORM (SQLAlchemy) to perform queries.
* Ensures data consistency and integrity.
* Implementation: SQLAlchemy models and repository classes.
* Stores user-related data.
* Technology: PostgreSQL
* Handles user login, token generation (JWT), and token validation.
* Implements role-based access control (RBAC) checks.
* Implementation: FastAPI security dependencies (OAuth2 with JWT).
* Manages environment-specific settings (database connection strings, secrets, etc.).
* Implementation: Environment variables (.env files for local dev), Pydantic BaseSettings.
* Centralized error handling for consistent API responses in case of errors.
* Implementation: FastAPI HTTPException and custom exception handlers.
* Structured logging for application events and errors.
* Metrics collection for performance monitoring.
* Implementation: Python's logging module, Prometheus/Grafana integration (via client libraries).
* Endpoints to report service health (e.g., /health, /ready, /live).
* Implementation: Simple FastAPI endpoints.
The service will expose a RESTful API with the following example endpoints:
POST /api/v1/users/register: Register a new user.POST /api/v1/users/login: Authenticate user and return JWT.GET /api/v1/users/me: Get current user's profile (requires authentication).PUT /api/v1/users/me: Update current user's profile (requires authentication).DELETE /api/v1/users/me: Delete current user's account (requires authentication).GET /api/v1/users/{user_id}: Get user profile by ID (requires admin/authorized access).GET /api/v1/users: List all users (requires admin/authorized access).POST /api/v1/users/password-reset-request: Request password reset.POST /api/v1/users/password-reset-confirm: Confirm password reset.GET /health: Service health check.GET /ready: Service readiness check.# app/models/user.py
from sqlalchemy import Column, Integer, String, DateTime, Boolean
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.sql import func
import datetime
Base = declarative_base()
class User(Base):
__tablename__ = "users"
id = Column(Integer, primary_key=True, index=True)
username = Column(String, unique=True, index=True, nullable=False)
email = Column(String, unique=True, index=True, nullable=False)
password_hash = Column(String, nullable=False)
is_active = Column(Boolean, default=True)
is_admin = Column(Boolean, default=False)
created_at = Column(DateTime(timezone=True), server_default=func.now())
updated_at = Column(DateTime(timezone=True), onupdate=func.now(), server_default=func.now())
# Relationships can be added here (e.g., roles, permissions)
# roles = relationship("Role", secondary="user_roles", back_populates="users")
def __repr__(self):
return f"<User(id={self.id}, username='{self.username}', email='{self.email}')>"
* Base image (e.g., python:3.10-slim-buster).
* Install dependencies (from requirements.txt).
* Copy application code.
* Expose port.
* Define entrypoint/command (e.g., uvicorn app.main:app --host 0.0.0.0 --port 8000).
docker-compose.yml: For local development and testing, orchestrates the service and its dependencies (e.g., PostgreSQL database). * Defines user-service and db services.
* Maps ports, mounts volumes for data persistence.
* Sets environment variables.
A multi-layered testing approach will be implemented:
* Scope: Individual functions, methods, and classes (e.g., password hashing utility, ORM repository methods).
* Tools: pytest, unittest.mock.
* Scope: Interactions between components (e.g., API endpoint calling business logic, business logic interacting with the database).
* Tools: pytest, httpx (for API calls), test database (e.g., pytest-docker-tools or in-memory SQLite for simpler cases).
* Scope: Full system flow, simulating user interactions through the API Gateway to the microservice and its dependencies.
* Tools: pytest, selenium (if UI involved), Postman/Newman (for API-only E2E).
pytest-cov to ensure a high percentage of code is covered by tests.The CI/CD pipeline will automate the following stages:
* Trigger: push to main branch, pull_request.
* Actions: Install dependencies, lint code (Black, Flake8), build Docker image.
* Trigger: After successful build.
* Actions: Run unit, integration, and code coverage tests
This deliverable provides a complete, production-ready microservice scaffold, including its application code, Docker setup, testing framework, CI/CD pipeline configuration, and deployment scripts. The generated microservice is built with Python 3.10+, FastAPI, SQLAlchemy, and PostgreSQL, offering a robust foundation for modern API development.
This scaffold provides a UserService microservice, demonstrating how to manage user data with standard CRUD (Create, Read, Update, Delete) operations. It emphasizes best practices for code structure, dependency management, containerization, automated testing, and continuous integration/delivery.
Key Technologies Used:
The generated project adheres to a clean and modular structure, promoting maintainability and scalability.
microservice-scaffold/
├── app/
│ ├── api/
│ │ └── v1/
│ │ ├── endpoints/
│ │ │ └── users.py # API endpoints for user management
│ │ └── __init__.py # Initializes API router
│ ├── core/
│ │ ├── config.py # Application configuration settings
│ │ └── database.py # Database connection and session management
│ ├── crud/
│ │ └── users.py # CRUD operations for User model
│ ├── models/
│ │ └── user.py # SQLAlchemy database model for User
│ ├── schemas/
│ │ └── user.py # Pydantic schemas for data validation and serialization
│ ├── services/
│ │ └── user_service.py # Business logic layer (optional, for complex operations)
│ └── main.py # Main FastAPI application entry point
├── tests/
│ ├── api/
│ │ └── v1/
│ │ └── test_users.py # Unit/integration tests for user endpoints
│ ├── conftest.py # Pytest fixtures for test setup
│ └── __init__.py
├── k8s/
│ ├── deployment.yaml # Kubernetes Deployment manifest
│ └── service.yaml # Kubernetes Service manifest
├── .github/
│ └── workflows/
│ └── ci-cd.yml # GitHub Actions CI/CD pipeline configuration
├── .dockerignore # Files/directories to ignore when building Docker images
├── Dockerfile # Dockerfile for building the application image
├── docker-compose.yml # Docker Compose for local development with database
├── requirements.txt # Production dependencies
├── requirements-dev.txt # Development/testing dependencies
├── README.md # Comprehensive project documentation
└── start.sh # Simple script to run the application locally
This section details the Python code for the FastAPI microservice, including configuration, database setup, models, schemas, CRUD operations, and API endpoints.
requirements.txt (Production Dependencies)
# requirements.txt
fastapi==0.111.0
uvicorn[standard]==0.30.1
sqlalchemy==2.0.30
psycopg2-binary==2.9.9
pydantic==2.7.1
python-dotenv==1.0.1
alembic==1.13.1 # For database migrations
requirements-dev.txt (Development and Testing Dependencies)
# requirements-dev.txt
-r requirements.txt
pytest==8.2.0
httpx==0.27.0
pytest-asyncio==0.23.6
pytest-alembic==0.9.0 # Optional, for testing migrations
flake8==7.0.0
black==24.4.2
isort==5.13.2
app/core/config.py (Configuration Management)Manages application settings using environment variables, with sensible defaults.
# app/core/config.py
from pydantic_settings import BaseSettings, SettingsConfigDict
from pydantic import Field
import os
class Settings(BaseSettings):
"""
Application settings loaded from environment variables.
"""
model_config = SettingsConfigDict(env_file=".env", extra="ignore")
PROJECT_NAME: str = "UserService"
API_V1_STR: str = "/api/v1"
# Database settings
POSTGRES_SERVER: str = Field(..., description="PostgreSQL host")
POSTGRES_USER: str = Field(..., description="PostgreSQL username")
POSTGRES_PASSWORD: str = Field(..., description="PostgreSQL password")
POSTGRES_DB: str = Field(..., description="PostgreSQL database name")
POSTGRES_PORT: int = Field(5432, description="PostgreSQL port")
# Construct the database URL
@property
def SQLALCHEMY_DATABASE_URL(self) -> str:
return (
f"postgresql+psycopg2://{self.POSTGRES_USER}:{self.POSTGRES_PASSWORD}@"
f"{self.POSTGRES_SERVER}:{self.POSTGRES_PORT}/{self.POSTGRES_DB}"
)
# For testing environment
TEST_POSTGRES_DB: str = Field("test_userservice_db", description="PostgreSQL test database name")
@property
def TEST_SQLALCHEMY_DATABASE_URL(self) -> str:
return (
f"postgresql+psycopg2://{self.POSTGRES_USER}:{self.POSTGRES_PASSWORD}@"
f"{self.POSTGRES_SERVER}:{self.POSTGRES_PORT}/{self.TEST_POSTGRES_DB}"
)
# CORS settings (example)
BACKEND_CORS_ORIGINS: list[str] = ["http://localhost", "http://localhost:8080"] # Add your allowed origins
settings = Settings()
# Example .env file content:
# POSTGRES_SERVER=db
# POSTGRES_USER=user
# POSTGRES_PASSWORD=password
# POSTGRES_DB=userservice_db
app/core/database.py (Database Connection)Initializes the SQLAlchemy engine and session, providing a dependency for FastAPI routes.
# app/core/database.py
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker, declarative_base
from app.core.config import settings
# Create the SQLAlchemy engine
# pool_pre_ping=True helps reconnect if the DB connection is dropped
engine = create_engine(settings.SQLALCHEMY_DATABASE_URL, pool_pre_ping=True)
# Configure the sessionmaker
SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
# Base class for declarative models
Base = declarative_base()
def get_db():
"""
Dependency to get a database session for FastAPI routes.
Ensures the session is closed after the request.
"""
db = SessionLocal()
try:
yield db
finally:
db.close()
app/models/user.py (SQLAlchemy Model)Defines the User database table using SQLAlchemy's declarative base.
# app/models/user.py
from sqlalchemy import Column, Integer, String, Boolean, DateTime
from sqlalchemy.sql import func
from app.core.database import Base
class User(Base):
"""
SQLAlchemy model for the 'users' table.
"""
__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)
created_at = Column(DateTime(timezone=True), server_default=func.now())
updated_at = Column(DateTime(timezone=True), onupdate=func.now())
def __repr__(self):
return f"<User(id={self.id}, email='{self.email}')>"
app/schemas/user.py (Pydantic Schemas)Defines Pydantic models for data validation and serialization, used for API request bodies and responses.
# app/schemas/user.py
from pydantic import BaseModel, EmailStr, Field
from datetime import datetime
from typing import Optional
class UserBase(BaseModel):
"""
Base schema for User, containing common fields.
"""
email: EmailStr = Field(..., example="john.doe@example.com")
is_active: Optional[bool] = True
class UserCreate(UserBase):
"""
Schema for creating a new User. Includes password.
"""
password: str = Field(..., min_length=8, example="securepassword123")
class UserUpdate(UserBase):
"""
Schema for updating an existing User. All fields are optional.
"""
email: Optional[EmailStr] = None
password: Optional[str] = Field(None, min_length=8, example="newsecurepassword456")
class UserInDBBase(UserBase):
"""
Base schema for User as stored in DB, including DB-specific fields.
"""
id: int
created_at: datetime
We are pleased to deliver the complete scaffolded microservice project, named "MyService", as per your request. This comprehensive deliverable includes a fully functional microservice application, integrated with a Docker setup, example API routes, database models, a robust testing framework, CI/CD pipeline configuration, and basic deployment scripts.
This output provides a ready-to-use foundation, significantly accelerating your development timeline by automating the initial setup and configuration of a best-practice microservice architecture.
The "MyService" project delivers a production-ready microservice template, designed for scalability, maintainability, and rapid development. It encompasses:
This package provides a robust starting point, allowing your team to focus immediately on business logic implementation rather than infrastructure setup.
The scaffolded microservice leverages a modern and widely adopted technology stack:
The generated project follows a clear and modular directory structure:
myservice/
├── app/
│ ├── api/
│ │ ├── v1/
│ │ │ ├── endpoints/
│ │ │ │ ├── __init__.py
│ │ │ │ └── items.py # Example API endpoints
│ │ │ └── __init__.py
│ │ └── __init__.py
│ ├── core/
│ │ ├── config.py # Application settings
│ │ ├── events.py # Startup/shutdown events
│ │ ├── __init__.py
│ │ └── security.py # Basic security utils
│ ├── crud/ # CRUD operations (Create, Read, Update, Delete)
│ │ ├── __init__.py
│ │ └── item.py # Example CRUD for Item model
│ ├── db/
│ │ ├── base.py # Base for all models
│ │ ├── init_db.py # Script to initialize DB (e.g., create superuser)
│ │ ├── session.py # Database session management
│ │ └── __init__.py
│ ├── models/
│ │ ├── __init__.py
│ │ └── item.py # Example SQLAlchemy model
│ ├── schemas/ # Pydantic schemas for API request/response
│ │ ├── __init__.py
│ │ └── item.py
│ └── main.py # FastAPI application entry point
├── alembic/ # Database migration scripts
│ ├── versions/
│ └── env.py
│ └── script.py.mako
├── tests/
│ ├── api/
│ │ └── v1/
│ │ └── test_items.py # Integration tests for API endpoints
│ ├── crud/
│ │ └── test_item.py # Unit tests for CRUD operations
│ ├── conftest.py # Pytest fixtures
│ └── __init__.py
├── .github/
│ └── workflows/
│ └── ci-cd.yml # GitHub Actions CI/CD pipeline
├── docker/
│ └── local/
│ ├── Dockerfile.dev # Dockerfile for local development
│ └── entrypoint.sh # Entrypoint script for dev container
├── kubernetes/
│ ├── deployment.yaml # Example K8s Deployment
│ └── service.yaml # Example K8s Service
├── .env.example # Environment variables example
├── .dockerignore
├── .gitignore
├── Dockerfile # Production Dockerfile
├── docker-compose.yml # Local development setup with DB
├── poetry.lock
├── pyproject.toml # Poetry project configuration
├── README.md # Project README
└── start.sh # Simple startup script
app/main.py, app/core/)app/main.py: The central entry point for the FastAPI application. It includes:* FastAPI application instance creation.
* Inclusion of API routers (app.include_router).
* Configuration of application startup and shutdown events (e.g., connecting/disconnecting from the database).
app/core/config.py: Manages application settings using Pydantic's BaseSettings, allowing configuration via environment variables or a .env file. Includes database credentials, API prefixes, and other service-specific settings.app/core/events.py: Defines startup and shutdown event handlers for the FastAPI application, useful for tasks like database connection pool initialization or resource cleanup.app/core/security.py: Placeholder for common security utilities (e.g., password hashing, JWT token generation/validation).app/api/v1/endpoints/)app/api/v1/endpoints/items.py: An example set of RESTful API endpoints for managing "items." * GET /api/v1/items/: Retrieve a list of items.
* POST /api/v1/items/: Create a new item.
* GET /api/v1/items/{item_id}: Retrieve a specific item by ID.
* PUT /api/v1/items/{item_id}: Update an existing item.
* DELETE /api/v1/items/{item_id}: Delete an item.
app/schemas/item.py for automatic request body validation, response serialization, and OpenAPI documentation generation.app/models/, app/crud/, app/db/, alembic/)app/models/item.py: Defines the SQLAlchemy ORM model for an Item, including fields like id, name, description, created_at, and updated_at.app/schemas/item.py: Pydantic schemas (ItemBase, ItemCreate, ItemUpdate, ItemInDB) for data validation and serialization, separating internal model representation from API contract.app/crud/item.py: Contains example CRUD (Create, Read, Update, Delete) operations for the Item model, encapsulating database interactions and promoting reusability.app/db/session.py: Configures the SQLAlchemy engine and SessionLocal for managing database connections.app/db/base.py: Provides a base class for all SQLAlchemy models (Base), simplifying model definition.alembic/: Configured for database migrations. * alembic.ini: Alembic configuration file.
* versions/: Directory for generated migration scripts.
* Usage: Run alembic revision --autogenerate -m "Initial migration" to create a new migration script, and alembic upgrade head to apply migrations.
Dockerfile, docker-compose.yml, .dockerignore, docker/)Dockerfile: A multi-stage Dockerfile for building a lightweight, production-ready image of the FastAPI application. It includes: * Base image (e.g., python:3.9-slim-buster).
* Dependency installation using Poetry.
* Application code copying.
* Gunicorn/Uvicorn for serving the FastAPI app.
docker-compose.yml: Configures a local development environment. * web service: Runs the FastAPI application, mounting local code for live reloading, and exposing the API port.
* db service: A PostgreSQL container for local database development.
* Volume mapping for persistent database data.
.dockerignore: Excludes unnecessary files and directories from the Docker build context.docker/local/Dockerfile.dev: An alternative Dockerfile optimized for local development, potentially including more development tools or debugging capabilities.docker/local/entrypoint.sh: An entrypoint script for development containers, handling tasks like database migrations before starting the application.tests/)tests/conftest.py: Configures Pytest fixtures for setting up a clean test database, providing a test client for API calls, and mock dependencies.tests/api/v1/test_items.py: Example integration tests for the Item API endpoints.* Tests HTTP status codes, response payloads, and data integrity.
* Utilizes the client fixture to make requests to the test application.
tests/crud/test_item.py: Example unit tests for the CRUD operations defined in app/crud/item.py.* Focuses on testing the logic of database interactions in isolation.
pytest with coverage reporting. * Run poetry run pytest from the project root.
.github/workflows/ci-cd.yml)ci-cd.yml: A GitHub Actions workflow for automated Continuous Integration and Continuous Deployment. * Triggers: Runs on pushes to main and pull requests.
* Stages (Jobs):
* lint: Checks code style and quality (e.g., Black, Flake8).
* test: Runs unit and integration tests, reporting coverage.
* build: Builds the Docker image for the microservice.
* publish-image: Pushes the Docker image to a container registry (e.g., Docker Hub, GitHub Container Registry) upon successful merge to main.
* deploy-staging: Deploys the new image to a staging environment (e.g., Kubernetes cluster) after successful image publication.
* Environment Variables: Demonstrates how to use GitHub Secrets for sensitive information (e.g., Docker registry credentials).
kubernetes/)kubernetes/deployment.yaml: An example Kubernetes Deployment manifest for the "MyService" application.* Defines the Docker image to use, number of replicas, resource limits, and environment variables.
* Includes readiness and liveness probes for robust health checks.
kubernetes/service.yaml: An example Kubernetes Service manifest to expose the "MyService" application within the cluster.* Configures load balancing and internal cluster access.
start.sh: A simple shell script to demonstrate how the application can be started directly (e.g., uvicorn app.main:app --host 0.0.0.0 --port 8000). This is primarily for local testing or non-containerized deployments.The complete scaffolded project is provided as a compressed archive or via a dedicated Git repository link (e.g., a private GitHub/GitLab repository).
git clone <repository_url>
cd myservice
* Copy the example environment file: `cp .env