Python design patterns including KISS, Separation of Concerns, Single Responsibility, and composition over inheritance. Use when making architecture decisions, refactoring code structure, or evaluating when abstractions are appropriate.
apm install @wshobson/python-design-patterns
[](https://apm-p1ls2dz87-atlamors-projects.vercel.app/packages/@wshobson/python-design-patterns)---
name: python-design-patterns
description: Python design patterns including KISS, Separation of Concerns, Single Responsibility, and composition over inheritance. Use when making architecture decisions, refactoring code structure, or evaluating when abstractions are appropriate.
---
# Python Design Patterns
Write maintainable Python code using fundamental design principles. These patterns help you build systems that are easy to understand, test, and modify.
## When to Use This Skill
- Designing new components or services
- Refactoring complex or tangled code
- Deciding whether to create an abstraction
- Choosing between inheritance and composition
- Evaluating code complexity and coupling
- Planning modular architectures
## Core Concepts
### 1. KISS (Keep It Simple)
Choose the simplest solution that works. Complexity must be justified by concrete requirements.
### 2. Single Responsibility (SRP)
Each unit should have one reason to change. Separate concerns into focused components.
### 3. Composition Over Inheritance
Build behavior by combining objects, not extending classes.
### 4. Rule of Three
Wait until you have three instances before abstracting. Duplication is often better than premature abstraction.
## Quick Start
```python
# Simple beats clever
# Instead of a factory/registry pattern:
FORMATTERS = {"json": JsonFormatter, "csv": CsvFormatter}
def get_formatter(name: str) -> Formatter:
return FORMATTERS[name]()
```
## Fundamental Patterns
### Pattern 1: KISS - Keep It Simple
Before adding complexity, ask: does a simpler solution work?
```python
# Over-engineered: Factory with registration
class OutputFormatterFactory:
_formatters: dict[str, type[Formatter]] = {}
@classmethod
def register(cls, name: str):
def decorator(formatter_cls):
cls._formatters[name] = formatter_cls
return formatter_cls
return decorator
@classmethod
def create(cls, name: str) -> Formatter:
return cls._formatters[name]()
@OutputFormatterFactory.register("json")
class JsonFormatter(Formatter):
...
# Simple: Just use a dictionary
FORMATTERS = {
"json": JsonFormatter,
"csv": CsvFormatter,
"xml": XmlFormatter,
}
def get_formatter(name: str) -> Formatter:
"""Get formatter by name."""
if name not in FORMATTERS:
raise ValueError(f"Unknown format: {name}")
return FORMATTERS[name]()
```
The factory pattern adds code without adding value here. Save patterns for when they solve real problems.
### Pattern 2: Single Responsibility Principle
Each class or function should have one reason to change.
```python
# BAD: Handler does everything
class UserHandler:
async def create_user(self, request: Request) -> Response:
# HTTP parsing
data = await request.json()
# Validation
if not data.get("email"):
return Response({"error": "email required"}, status=400)
# Database access
user = await db.execute(
"INSERT INTO users (email, name) VALUES ($1, $2) RETURNING *",
data["email"], data["name"]
)
# Response formatting
return Response({"id": user.id, "email": user.email}, status=201)
# GOOD: Separated concerns
class UserService:
"""Business logic only."""
def __init__(self, repo: UserRepository) -> None:
self._repo = repo
async def create_user(self, data: CreateUserInput) -> User:
# Only business rules here
user = User(email=data.email, name=data.name)
return await self._repo.save(user)
class UserHandler:
"""HTTP concerns only."""
def __init__(self, service: UserService) -> None:
self._service = service
async def create_user(self, request: Request) -> Response:
data = CreateUserInput(**(await request.json()))
user = await self._service.create_user(data)
return Response(user.to_dict(), status=201)
```
Now HTTP changes don't affect business logic, and vice versa.
### Pattern 3: Separation of Concerns
Organize code into distinct layers with clear responsibilities.
```
┌─────────────────────────────────────────────────────┐
│ API Layer (handlers) │
│ - Parse requests │
│ - Call services │
│ - Format responses │
└─────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────┐
│ Service Layer (business logic) │
│ - Domain rules and validation │
│ - Orchestrate operations │
│ - Pure functions where possible │
└─────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────┐
│ Repository Layer (data access) │
│ - SQL queries │
│ - External API calls │
│ - Cache operations │
└─────────────────────────────────────────────────────┘
```
Each layer depends only on layers below it:
```python
# Repository: Data access
class UserRepository:
async def get_by_id(self, user_id: str) -> User | None:
row = await self._db.fetchrow(
"SELECT * FROM users WHERE id = $1", user_id
)
return User(**row) if row else None
# Service: Business logic
class UserService:
def __init__(self, repo: UserRepository) -> None:
self._repo = repo
async def get_user(self, user_id: str) -> User:
user = await self._repo.get_by_id(user_id)
if user is None:
raise UserNotFoundError(user_id)
return user
# Handler: HTTP concerns
@app.get("/users/{user_id}")
async def get_user(user_id: str) -> UserResponse:
user = await user_service.get_user(user_id)
return UserResponse.from_user(user)
```
### Pattern 4: Composition Over Inheritance
Build behavior by combining objects rather than inheriting.
```python
# Inheritance: Rigid and hard to test
class EmailNotificationService(NotificationService):
def __init__(self):
super().__init__()
self._smtp = SmtpClient() # Hard to mock
def notify(self, user: User, message: str) -> None:
self._smtp.send(user.email, message)
# Composition: Flexible and testable
class NotificationService:
"""Send notifications via multiple channels."""
def __init__(
self,
email_sender: EmailSender,
sms_sender: SmsSender | None = None,
push_sender: PushSender | None = None,
) -> None:
self._email = email_sender
self._sms = sms_sender
self._push = push_sender
async def notify(
self,
user: User,
message: str,
channels: set[str] | None = None,
) -> None:
channels = channels or {"email"}
if "email" in channels:
await self._email.send(user.email, message)
if "sms" in channels and self._sms and user.phone:
await self._sms.send(user.phone, message)
if "push" in channels and self._push and user.device_token:
await self._push.send(user.device_token, message)
# Easy to test with fakes
service = NotificationService(
email_sender=FakeEmailSender(),
sms_sender=FakeSmsSender(),
)
```
## Advanced Patterns
### Pattern 5: Rule of Three
Wait until you have three instances before abstracting.
```python
# Two similar functions? Don't abstract yet
def process_orders(orders: list[Order]) -> list[Result]:
results = []
for order in orders:
validated = validate_order(order)
result = process_validated_order(validated)
results.append(result)
return results
def process_returns(returns: list[Return]) -> list[Result]:
results = []
for ret in returns:
validated = validate_return(ret)
result = process_validated_return(validated)
results.append(result)
return results
# These look similar, but wait! Are they actually the same?
# Different validation, different processing, different errors...
# Duplication is often better than the wrong abstraction
# Only after a third case, consider if there's a real pattern
# But even then, sometimes explicit is better than abstract
```
### Pattern 6: Function Size Guidelines
Keep functions focused. Extract when a function:
- Exceeds 20-50 lines (varies by complexity)
- Serves multiple distinct purposes
- Has deeply nested logic (3+ levels)
```python
# Too long, multiple concerns mixed
def process_order(order: Order) -> Result:
# 50 lines of validation...
# 30 lines of inventory check...
# 40 lines of payment processing...
# 20 lines of notification...
pass
# Better: Composed from focused functions
def process_order(order: Order) -> Result:
"""Process a customer order through the complete workflow."""
validate_order(order)
reserve_inventory(order)
payment_result = charge_payment(order)
send_confirmation(order, payment_result)
return Result(success=True, order_id=order.id)
```
### Pattern 7: Dependency Injection
Pass dependencies through constructors for testability.
```python
from typing import Protocol
class Logger(Protocol):
def info(self, msg: str, **kwargs) -> None: ...
def error(self, msg: str, **kwargs) -> None: ...
class Cache(Protocol):
async def get(self, key: str) -> str | None: ...
async def set(self, key: str, value: str, ttl: int) -> None: ...
class UserService:
"""Service with injected dependencies."""
def __init__(
self,
repository: UserRepository,
cache: Cache,
logger: Logger,
) -> None:
self._repo = repository
self._cache = cache
self._logger = logger
async def get_user(self, user_id: str) -> User:
# Check cache first
cached = await self._cache.get(f"user:{user_id}")
if cached:
self._logger.info("Cache hit", user_id=user_id)
return User.from_json(cached)
# Fetch from database
user = await self._repo.get_by_id(user_id)
if user:
await self._cache.set(f"user:{user_id}", user.to_json(), ttl=300)
return user
# Production
service = UserService(
repository=PostgresUserRepository(db),
cache=RedisCache(redis),
logger=StructlogLogger(),
)
# Testing
service = UserService(
repository=InMemoryUserRepository(),
cache=FakeCache(),
logger=NullLogger(),
)
```
### Pattern 8: Avoiding Common Anti-Patterns
**Don't expose internal types:**
```python
# BAD: Leaking ORM model to API
@app.get("/users/{id}")
def get_user(id: str) -> UserModel: # SQLAlchemy model
return db.query(UserModel).get(id)
# GOOD: Use response schemas
@app.get("/users/{id}")
def get_user(id: str) -> UserResponse:
user = db.query(UserModel).get(id)
return UserResponse.from_orm(user)
```
**Don't mix I/O with business logic:**
```python
# BAD: SQL embedded in business logic
def calculate_discount(user_id: str) -> float:
user = db.query("SELECT * FROM users WHERE id = ?", user_id)
orders = db.query("SELECT * FROM orders WHERE user_id = ?", user_id)
# Business logic mixed with data access
# GOOD: Repository pattern
def calculate_discount(user: User, order_history: list[Order]) -> float:
# Pure business logic, easily testable
if len(order_history) > 10:
return 0.15
return 0.0
```
## Best Practices Summary
1. **Keep it simple** - Choose the simplest solution that works
2. **Single responsibility** - Each unit has one reason to change
3. **Separate concerns** - Distinct layers with clear purposes
4. **Compose, don't inherit** - Combine objects for flexibility
5. **Rule of three** - Wait before abstracting
6. **Keep functions small** - 20-50 lines (varies by complexity), one purpose
7. **Inject dependencies** - Constructor injection for testability
8. **Delete before abstracting** - Remove dead code, then consider patterns
9. **Test each layer** - Isolated tests for each concern
10. **Explicit over clever** - Readable code beats elegant code