ai-riksarkivet 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
- Reviewing code before merge (anti-patterns checklist)
- Debugging issues that might stem from known bad practices
Core Concepts
- KISS — simplest solution that works; complexity must be justified
- Single Responsibility — each unit has one reason to change
- Composition over Inheritance — combine objects, don't extend classes
- High Cohesion, Low Coupling — modules do one thing well; changes don't ripple
- Rule of Three — wait for three instances before abstracting
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: Guard Clauses (Early Returns)
Eliminate deep nesting by handling edge cases and errors first, then proceeding with the happy path.
# BAD: Deeply nested — hard to follow the happy path
def process_order(order: Order) -> Result:
if order is not None:
if order.status == "pending":
if order.items:
if order.payment_method:
# Finally the actual logic, 4 levels deep
total = sum(item.price for item in order.items)
charge(order.payment_method, total)
return Result(success=True)
else:
return Result(error="No payment method")
else:
return Result(error="No items")
else:
return Result(error="Order not pending")
else:
return Result(error="No order")
# GOOD: Guard clauses — reject early, happy path stays flat
def process_order(order: Order) -> Result:
if order is None:
return Result(error="No order")
if order.status != "pending":
return Result(error="Order not pending")
if not order.items:
return Result(error="No items")
if not order.payment_method:
return Result(error="No payment method")
# Happy path — no nesting
total = sum(item.price for item in order.items)
charge(order.payment_method, total)
return Result(success=True)
Guard clauses work the same way with exceptions:
def get_user(user_id: str) -> User:
if not user_id:
raise ValueError("user_id is required")
if not user_id.startswith("usr_"):
raise ValueError(f"Invalid user_id format: {user_id}")
# Happy path
return repo.get_by_id(user_id)
When to use guard clauses:
- Validating function inputs
- Checking preconditions before expensive operations
- Handling error/edge cases that short-circuit the function
- Any time you find yourself nesting
if/elsemore than 2 levels deep
Pattern 8: 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 9: Tell, Don't Ask
Objects should do things, not just report data. Move logic to the object that owns the data.
# BAD: Interrogating the object, then acting externally
if cart.total_price() > user.credit:
raise InsufficientCreditError()
else:
user.debit(cart.total_price())
# GOOD: Tell the object what to do
cart.checkout(user)
# Inside Cart:
class Cart:
def checkout(self, user: User) -> None:
if self.total_price() > user.credit:
raise InsufficientCreditError()
user.debit(self.total_price())
This keeps logic co-located with the data it operates on. If you find yourself calling obj.get_x() then making decisions based on that value, consider moving the decision into the object itself.
Pattern 10: Replace Comments with Functions
If a condition or expression needs a comment to explain it, extract it into a named function instead.
# BAD: Comment explains a complex condition
# Check if user is eligible for premium features
if user.age > 18 and user.subscription == "active" and not user.is_banned:
grant_access(user)
# GOOD: Function name IS the documentation
def is_eligible_for_premium(user: User) -> bool:
return user.age > 18 and user.subscription == "active" and not user.is_banned
if is_eligible_for_premium(user):
grant_access(user)
Comments can go stale. Function names are verified by usage.
Pattern 11: Eliminate Boolean Arguments
Boolean parameters hide meaning at the call site.
# BAD: What does True mean here?
generate_report(data, True)
def generate_report(data: list, include_summary: bool = False):
...
# GOOD: Separate named functions with clear intent
def generate_full_report(data: list): ...
def generate_basic_report(data: list): ...
If a boolean parameter controls which branch of a function runs, the function is doing two things (violating SRP). Split it.
Anti-Patterns
For the full anti-patterns checklist with code examples and quick reference table, see anti-patterns.md.
Covers: exposed internal types, scattered retry logic, double retry, hard-coded config, bare exceptions, ignored partial failures, missing validation, unclosed resources, blocking in async, missing type hints, happy-path-only testing, and over-mocking.
Refactoring Safely
Always write tests before refactoring. Tests are your safety net — they prove the code works before you touch it and catch regressions as you change it.
# Step 1: Write tests that lock in current behavior
def test_calculate_discount_loyal_customer():
assert calculate_discount(orders=15) == 0.15
def test_calculate_discount_new_customer():
assert calculate_discount(orders=2) == 0.0
# Step 2: Run tests — they must pass BEFORE you refactor
# Step 3: Refactor with confidence — tests catch breakage immediately
# Step 4: Run tests again — green means you didn't break anything
If the code has no tests and you're about to refactor it, write characterization tests first: tests that capture what the code currently does, even if you're not sure it's correct. The goal is to detect unintended changes, not to validate correctness.
# Characterization test: "I don't know if this is right,
# but this is what it does today"
def test_legacy_pricing_current_behavior():
result = legacy_calculate_price(item="widget", qty=3, region="EU")
assert result == 47.85 # Captured from current output
Once tests are green, refactor freely. If a test breaks, you know exactly what changed.
Best Practices Summary
- Keep it simple - Choose the simplest solution that works
- Single responsibility - Each unit has one reason to change
- Separate concerns - Distinct layers with clear purposes
- Compose, don't inherit - Combine objects for flexibility
- Rule of three - Wait before abstracting
- Keep functions small - 20-50 lines (varies by complexity), one purpose
- Inject dependencies - Constructor injection for testability
- Test before refactoring - Lock in behavior with tests, then change safely
- Delete before abstracting - Remove dead code, then consider patterns
- Explicit over clever - Readable code beats elegant code
Troubleshooting
For common design dilemmas (growing classes, too many constructor params, composition depth, when to break the rule of three, layering violations), see troubleshooting.md.
Related Skills
- testing-python — Test each layer in isolation using the dependency injection structure established here
- python-type-safety — Type hints, generics, protocols, and strict type checking