python-design-patterns

name

python-design-patterns

description

Python design patterns including KISS, Separation of Concerns, Single Responsibility, and composition over inheritance. Use this skill when designing a new service or component from scratch and choosing how to layer responsibilities, when refactoring a God class or monolithic function that has grown too large, when deciding whether to add a new abstraction or live with duplication, when evaluating a pull request for structural issues like tight coupling or leaking internal types, when choosing between inheritance and composition for a new class hierarchy, or when a codebase is becoming hard to test because of entangled I/O and business logic.

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

# 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?

# 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.

# 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:

# 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.

# 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.

# 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)

# 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.

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:

# 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:

# 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

Troubleshooting

A class is growing and seems to have multiple responsibilities, but splitting it feels wrong. Apply the "reason to change" test: list every change that could require editing this class. If the list has items from different domains (e.g., HTTP parsing AND business rules AND formatting), split it. If all changes stem from the same domain concern, the class may be appropriately sized.

Injecting all dependencies through the constructor is producing constructors with 7+ parameters. This is a sign of too many responsibilities in one class, not a problem with dependency injection. Split the class into smaller units first, then each constructor naturally becomes smaller.

Composition is producing deeply nested wrapper objects that are hard to trace. Keep the composition shallow (2-3 levels). If wrapping is the only mechanism, consider whether a Protocol-based approach or simple function composition would be cleaner than a chain of decorator objects.

The rule of three says not to abstract yet, but the duplication is causing bugs when one copy is updated but not the other. Duplication that diverges in dangerous ways should be abstracted sooner. The rule of three is a heuristic, not a law. If the copies are already diverging incorrectly, extract immediately and add a test that exercises the shared behavior.

A service layer is importing from the API layer, breaking the dependency direction. This is a layering violation. The service layer must not import from handlers. Introduce a shared types/models layer that both can import from, keeping the dependency arrow pointing downward (API → Service → Repository).

Related Skills

• python-testing-patterns — Test each layer in isolation using the dependency injection structure established here
• python-project-setup — Set up project structure and tooling that enforces layer boundaries from the start