python-design-patterns

Python 设计模式

运用基本设计原则编写易维护的 Python 代码。这些模式有助于构建易于理解、测试和修改的系统。

何时使用此技能

• 设计新的组件或服务
• 重构复杂或混乱的代码
• 决定是否创建抽象
• 在继承与组合之间进行选择
• 评估代码的复杂度和耦合度
• 规划模块化架构

核心概念

1. KISS（保持简单）

选择可行方案中最简单的一种。复杂性必须有具体需求作为依据。

2. 单一职责（SRP）

每个单元都应只有一个变更理由。将不同关注点拆分到各自专注的组件中。

3. 组合优于继承

通过组合对象来构建行为，而不是扩展类。

4. 三次法则

等到出现三个实例后再进行抽象。重复代码通常比过早抽象更好。

快速入门

# Simple beats clever
# Instead of a factory/registry pattern:
FORMATTERS = {"json": JsonFormatter, "csv": CsvFormatter}

def get_formatter(name: str) -> Formatter:
    return FORMATTERS[name]()

基本模式

模式 1：KISS - 保持简单

在增加复杂性之前，先问问自己：更简单的方案是否可行？

# 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]()

在这里，工厂模式增加了代码，却没有带来价值。将模式留给真正能解决问题的场景。

模式 2：单一职责原则

每个类或函数都应只有一个变更理由。

# 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)

这样，HTTP 的变更就不会影响业务逻辑，反之亦然。

模式 3：关注点分离

将代码组织为职责明确的不同层。

┌─────────────────────────────────────────────────────┐
│  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                                  │
└─────────────────────────────────────────────────────┘

每一层只依赖其下层：

# 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)

模式 4：组合优于继承

通过组合对象来构建行为，而不是通过继承。

# 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(),
)

高级模式

模式 5：三次法则

等到出现三个实例后再进行抽象。

# 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

模式 6：函数长度指南

让函数职责明确。在以下情况下拆分函数：

• 超过 20-50 行（视复杂度而定）
• 承担多个不同用途
• 逻辑嵌套过深（3 层以上）

# 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)

模式 7：依赖注入

通过构造函数传入依赖，以便进行测试。

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(),
)

模式 8：避免常见反模式

不要暴露内部类型：

# 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)

不要将 I/O 与业务逻辑混在一起：

# 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

最佳实践总结

1. 保持简单 - 选择可行方案中最简单的一种
2. 单一职责 - 每个单元都只有一个变更理由
3. 分离关注点 - 划分职责明确的不同层
4. 组合而非继承 - 组合对象以提高灵活性
5. 三次法则 - 等待后再进行抽象
6. 保持函数简短 - 20-50 行（视复杂度而定），且只承担一种用途
7. 注入依赖 - 通过构造函数注入以便测试
8. 先删除，再抽象 - 移除无用代码，然后再考虑模式
9. 测试每一层 - 针对每个关注点进行隔离测试
10. 明确胜过巧妙 - 可读代码胜过优雅代码

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注意事项

• 通过 __new__ 实现的单例无法适用于子类 — 每个子类都会获得自己的实例。如果需要支持子类的单例，请使用 __init_subclass__ 或元类。
• Dataclass 的 frozen=True 不会冻结嵌套的可变字段 — 带有 list 字段的冻结 dataclass 仍允许对该列表调用 .append。
• 观察者模式使用强引用会导致内存泄漏：当订阅者超出作用域，但发布者仍持有它们时，就会发生这种情况。请使用 weakref.WeakSet 存储订阅者。
• 在实例方法上使用 functools.lru_cache 会让实例一直保留在内存中（缓存持有 self）— 应用于模块级函数，或使用 methodtools.lru_cache。
• 抽象基类（ABC）：@abstractmethod 在实例化时强制执行，而不是在定义子类时 — 缺少抽象方法的子类会通过类型检查，直到尝试构造该子类时才会报错。