在 Python 中使用 Pydantic 的最佳实践

Pydantic 是一个使用类型提示简化数据验证的 Python 库。它确保数据完整性，并提供一种通过自动类型检查和验证创建数据模型的简单方法。

在软件应用程序中，可靠的数据验证对于防止错误、安全问题和不可预测的行为至关重要。

本指南提供了在 Python 项目中使用 Pydantic 的最佳实践，涵盖模型定义、数据验证、错误处理和性能优化。

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安装 Pydantic

要安装 Pydantic，请使用 Python 包安装程序 pip，并使用以下命令：

pip install pydantic

此命令安装 Pydantic 及其依赖项。

基本用法

通过创建继承自 BaseModel 的类来创建 Pydantic 模型。使用Python类型注释来指定每个字段的类型：

from pydantic import BaseModel

class User(BaseModel):
    id: int
    name: str
    email: str

Pydantic 支持各种字段类型，包括 int、str、float、bool、list 和 dict。您还可以定义嵌套模型和自定义类型：

from typing import List, Optional
from pydantic import BaseModel

class Address(BaseModel):
    street: str
    city: str
    zip_code: Optional[str] = None

class User(BaseModel):
    id: int
    name: str
    email: str
    age: Optional[int] = None
    addresses: List[Address]

定义 Pydantic 模型后，通过提供所需数据来创建实例。如果任何字段不满足指定要求，Pydantic 将验证数据并引发错误：

user = User(
    id=1,
    name="John Doe",
    email="john.doe@example.com",
    addresses=[{"street": "123 Main St", "city": "Anytown", "zip_code": "12345"}]
)

print(user)

# Output:
# id=1 name='John Doe' email='john.doe@example.com' age=None addresses=[Address(street='123 Main St', city='Anytown', zip_code='12345')]

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定义 Pydantic 模型

Pydantic 模型使用 Python 类型注释来定义数据字段类型。

它们支持各种内置类型，包括：

• 基本类型：str、int、float、bool
• 集合类型：列表、元组、集合、字典
• 可选类型：对于可以为 None 的字段，输入模块中可选
• 联合类型：来自类型模块的联合用于指定字段可以是多种类型之一

示例：

from typing import List, Dict, Optional, Union
from pydantic import BaseModel

class Item(BaseModel):
    name: str
    price: float
    tags: List[str]
    metadata: Dict[str, Union[str, int, float]]

class Order(BaseModel):
    order_id: int
    items: List[Item]
    discount: Optional[float] = None

自定义类型

除了内置类型之外，您还可以使用 Pydantic 的 conint、constr 和其他约束函数定义自定义类型。

这些允许您添加额外的验证规则，例如字符串的长度限制或整数的值范围。

示例：

from pydantic import BaseModel, conint, constr

class Product(BaseModel):
    name: constr(min_length=2, max_length=50)
    quantity: conint(gt=0, le=1000)
    price: float

product = Product(name="Laptop", quantity=5, price=999.99)

必填字段与可选字段

默认情况下，Pydantic 模型中的字段是必需的，除非明确标记为可选。

如果模型实例化期间缺少必填字段，Pydantic 将引发 ValidationError。

示例：

from pydantic import BaseModel

class User(BaseModel):
    id: int
    name: str
    email: str

user = User(id=1, name="John Doe")


# Output
#  Field required [type=missing, input_value={'id': 1, 'name': 'John Doe'}, input_type=dict]

具有默认值的可选字段

通过使用输入模块中的Optional并提供默认值，可以将字段设为可选。

示例：

from pydantic import BaseModel
from typing import Optional

class User(BaseModel):
    id: int
    name: str
    email: Optional[str] = None

user = User(id=1, name="John Doe")

在此示例中，电子邮件是可选的，如果未提供，则默认为 None。

嵌套模型

Pydantic 允许模型相互嵌套，从而实现复杂的数据结构。

嵌套模型被定义为其他模型的字段，确保多个级别的数据完整性和验证。

示例：

from pydantic import BaseModel
from typing import Optional, List


class Address(BaseModel):
    street: str
    city: str
    zip_code: Optional[str] = None

class User(BaseModel):
    id: int
    name: str
    email: str
    addresses: List[Address]

user = User(
    id=1,
    name="John Doe",
    email="john.doe@example.com",
    addresses=[{"street": "123 Main St", "city": "Anytown"}]
)

管理嵌套数据的最佳实践

使用嵌套模型时，重要的是：

• 在每个级别验证数据：确保每个嵌套模型都有自己的验证规则和约束。
• 使用清晰一致的命名约定：这使数据结构更具可读性和可维护性。
• 保持模型简单：避免过于复杂的嵌套结构。如果模型变得过于复杂，请考虑将其分解为更小、更易于管理的组件。

---

数据验证

Pydantic 包含一组内置验证器，可以自动处理常见的数据验证任务。

这些验证器包括：

• 类型验证：确保字段匹配指定的类型注释（例如 int、str、list）。
• 范围验证：使用 conint、constr、confloat 等约束强制执行值范围和长度。
• 格式验证：检查特定格式，例如用于验证电子邮件地址的 EmailStr。
• 集合验证：确保集合中的元素（例如列表、字典）符合指定的类型和约束。

这些验证器简化了确保模型内数据完整性和一致性的过程。

以下是一些演示内置验证器的示例：

来自 pydantic 导入 BaseModel、EmailStr、conint、constr

class User(BaseModel):
    id: conint(gt=0)  # id must be greater than 0
    name: constr(min_length=2, max_length=50)  # name must be between 2 and 50 characters
    email: EmailStr  # email must be a valid email address
    age: conint(ge=18)  # age must be 18 or older

user = User(id=1, name="John Doe", email="john.doe@example.com", age=25)

在此示例中，用户模型使用内置验证器来确保 id 大于 0，名称介于 2 到 50 个字符之间，电子邮件是有效的电子邮件地址，并且年龄为 18 岁或以上。
为了能够使用电子邮件验证器，您需要安装 pydantic 扩展：

pip install pydantic[email]

Custom Validators

Pydantic allows you to define custom validators for more complex validation logic.

Custom validators are defined using the @field_validator decorator within your model class.

Example of a custom validator:

from pydantic import BaseModel, field_validator


class Product(BaseModel):
    name: str
    price: float

    @field_validator('price')
    def price_must_be_positive(cls, value):
        if value <= 0:
            raise ValueError('Price must be positive')
        return value

product = Product(name="Laptop", price=999.99)

Here, the price_must_be_positive validator ensures that the price field is a positive number.

Custom validators are registered automatically when you define them within a model using the @field_validator decorator. Validators can be applied to individual fields or across multiple fields.

Example of registering a validator for multiple fields:

from pydantic import BaseModel, field_validator


class Person(BaseModel):
    first_name: str
    last_name: str

    @field_validator('first_name', 'last_name')
    def names_cannot_be_empty(cls, value):
        if not value:
            raise ValueError('Name fields cannot be empty')
        return value

person = Person(first_name="John", last_name="Doe")

In this example, the names_cannot_be_empty validator ensures that both the first_name and last_name fields are not empty.

Using Config Classes

Pydantic models can be customized using an inner Config class.

This class allows you to set various configuration options that affect the model's behavior, such as validation rules, JSON serialization, and more.

Example of a Config class:

from pydantic import BaseModel

class User(BaseModel):
    id: int
    name: str
    email: str

    class Config:
        str_strip_whitespace = True  # Strip whitespace from strings
        str_min_length = 1  # Minimum length for any string field

user = User(id=1, name="  John Doe  ", email="john.doe@example.com")

print(user)

# Output:
# id=1 name='John Doe' email='john.doe@example.com'

In this example, the Config class is used to strip whitespace from string fields and enforce a minimum length of 1 for any string field.

Some common configuration options in Pydantic's Config class include:

• str_strip_whitespace: Automatically strip leading and trailing whitespace from string fields.
• str_min_length: Set a minimum length for any string field.
• validate_default: Validate all fields, even those with default values.
• validate_assignment: Enable validation on assignment to model attributes.
• use_enum_values: Use the values of enums directly instead of the enum instances.
• json_encoders: Define custom JSON encoders for specific types.

---

Error Handling

When Pydantic finds data that doesn't conform to the model's schema, it raises a ValidationError.

This error provides detailed information about the issue, including the field name, the incorrect value, and a description of the problem.

Here's an example of how default error messages are structured:

from pydantic import BaseModel, ValidationError, EmailStr

class User(BaseModel):
    id: int
    name: str
    email: EmailStr

try:
    user = User(id='one', name='John Doe', email='invalid-email')
except ValidationError as e:
    print(e.json())

# Output:
# [{"type":"int_parsing","loc":["id"],"msg":"Input should be a valid integer, unable to parse string as an integer","input":"one","url":"https://errors.pydantic.dev/2.8/v/int_parsing"},{"type":"value_error","loc":["email"],"msg":"value is not a valid email address: An email address must have an @-sign.","input":"invalid-email","ctx":{"reason":"An email address must have an @-sign."},"url":"https://errors.pydantic.dev/2.8/v/value_error"}]

In this example, the error message will indicate that id must be an integer and email must be a valid email address.

Customizing Error Messages

Pydantic allows you to customize error messages for specific fields by raising exceptions with custom messages in validators or by setting custom configurations.

Here’s an example of customizing error messages:

from pydantic import BaseModel, ValidationError, field_validator

class Product(BaseModel):
    name: str
    price: float

    @field_validator('price')
    def price_must_be_positive(cls, value):
        if value <= 0:
            raise ValueError('Price must be a positive number')
        return value

try:
    product = Product(name='Laptop', price=-1000)
except ValidationError as e:
    print(e.json())

# Output:
# [{"type":"value_error","loc":["price"],"msg":"Value error, Price must be a positive number","input":-1000,"ctx":{"error":"Price must be a positive number"},"url":"https://errors.pydantic.dev/2.8/v/value_error"}]

In this example, the error message for price is customized to indicate that it must be a positive number.

Best Practices for Error Reporting

Effective error reporting involves providing clear, concise, and actionable feedback to users or developers.

Here are some best practices:

• Log errors: Use logging mechanisms to record validation errors for debugging and monitoring purposes.
• Return user-friendly messages: When exposing errors to end-users, avoid technical jargon. Instead, provide clear instructions on how to correct the data.
• Aggregate errors: When multiple fields are invalid, aggregate the errors into a single response to help users correct all issues at once.
• Use consistent formats: Ensure that error messages follow a consistent format across the application for easier processing and understanding.

Examples of best practices in error reporting:

from pydantic import BaseModel, ValidationError, EmailStr
import logging

logging.basicConfig(level=logging.INFO)

class User(BaseModel):
    id: int
    name: str
    email: EmailStr

def create_user(data):
    try:
        user = User(**data)
        return user
    except ValidationError as e:
        logging.error("Validation error: %s", e.json())
        return {"error": "Invalid data provided", "details": e.errors()}

user_data = {'id': 'one', 'name': 'John Doe', 'email': 'invalid-email'}
response = create_user(user_data)
print(response)

# Output:
# ERROR:root:Validation error: [{"type":"int_parsing","loc":["id"],"msg":"Input should be a valid integer, unable to parse string as an integer","input":"one","url":"https://errors.pydantic.dev/2.8/v/int_parsing"},{"type":"value_error","loc":["email"],"msg":"value is not a valid email address: An email address must have an @-sign.","input":"invalid-email","ctx":{"reason":"An email address must have an @-sign."},"url":"https://errors.pydantic.dev/2.8/v/value_error"}]
# {'error': 'Invalid data provided', 'details': [{'type': 'int_parsing', 'loc': ('id',), 'msg': 'Input should be a valid integer, unable to parse string as an integer', 'input': 'one', 'url': 'https://errors.pydantic.dev/2.8/v/int_parsing'}, {'type': 'value_error', 'loc': ('email',), 'msg': 'value is not a valid email address: An email address must have an @-sign.', 'input': 'invalid-email', 'ctx': {'reason': 'An email address must have an @-sign.'}}]}

In this example, validation errors are logged, and a user-friendly error message is returned, helping maintain application stability and providing useful feedback to the user.

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Performance Considerations

Lazy initialization is a technique that postpones the creation of an object until it is needed.

In Pydantic, this can be useful for models with fields that are costly to compute or fetch. By delaying the initialization of these fields, you can reduce the initial load time and improve performance.

Example of lazy initialization:

from pydantic import BaseModel
from functools import lru_cache

class DataModel(BaseModel):
    name: str
    expensive_computation: str = None

    @property
    @lru_cache(maxsize=1)
    def expensive_computation(self):
        # Simulate an expensive computation
        result = "Computed Value"
        return result

data_model = DataModel(name="Test")
print(data_model.expensive_computation)

In this example, the expensive_computation field is computed only when accessed for the first time, reducing unnecessary computations during model initialization.

Redundant Validation

Pydantic models automatically validate data during initialization.

However, if you know that certain data has already been validated or if validation is not necessary in some contexts, you can disable validation to improve performance.

This can be done using the model_construct method, which bypasses validation:

Example of avoiding redundant validation:

from pydantic import BaseModel

class User(BaseModel):
    id: int
    name: str
    email: str

# Constructing a User instance without validation
data = {'id': 1, 'name': 'John Doe', 'email': 'john.doe@example.com'}
user = User.model_construct(**data)

In this example, User.model_construct is used to create a User instance without triggering validation, which can be useful in performance-critical sections of your code.

Efficient Data Parsing

When dealing with large datasets or high-throughput systems, efficiently parsing raw data becomes critical.

Pydantic provides the model_validate_json method, which can be used to parse JSON or other serialized data formats directly into Pydantic models.

Example of efficient data parsing:

from pydantic import BaseModel

class User(BaseModel):
    id: int
    name: str
    email: str

json_data = '{"id": 1, "name": "John Doe", "email": "john.doe@example.com"}'
user = User.model_validate_json(json_data)
print(user)

In this example, model_validate_json is used to parse JSON data into a User model directly, providing a more efficient way to handle serialized data.

Controlling Validation

Pydantic models can be configured to validate data only when necessary.

The validate_default and validate_assignment options in the Config class control when validation occurs, which can help improve performance:

• validate_default: When set to False, only fields that are set during initialization are validated.
• validate_assignment: When set to True, validation is performed on field assignment after the model is created.

Example configuration:

from pydantic import BaseModel

class User(BaseModel):
    id: int
    name: str
    email: str

    class Config:
        validate_default = False  # Only validate fields set during initialization
        validate_assignment = True  # Validate fields on assignment

user = User(id=1, name="John Doe", email="john.doe@example.com")
user.email = "new.email@example.com"  # This assignment will trigger validation

In this example, validate_default is set to False to avoid unnecessary validation during initialization, and validate_assignment is set to True to ensure that fields are validated when they are updated.

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Settings Management

Pydantic's BaseSettings class is designed for managing application settings, supporting environment variable loading and type validation.

This helps in configuring applications for different environments (e.g., development, testing, production).

Consider this .env file:

database_url=db
secret_key=sk
debug=False

Example of using BaseSettings:

from pydantic_settings import BaseSettings

class Settings(BaseSettings):
    database_url: str
    secret_key: str
    debug: bool = False

    class Config:
        env_file = ".env"

settings = Settings()
print(settings.model_dump())

# Output:
# {'database_url': 'db', 'secret_key': 'sk', 'debug': False}

In this example, settings are loaded from environment variables, and the Config class specifies that variables can be loaded from a .env file.

For using BaseSettings you will need to install an additional package:

pip install pydantic-settings

Managing settings effectively involves a few best practices:

• Use environment variables: Store configuration values in environment variables to keep sensitive data out of your codebase.
• Provide defaults: Define sensible default values for configuration settings to ensure the application runs with minimal configuration.
• Separate environments: Use different configuration files or environment variables for different environments (e.g., .env.development, .env.production).
• Validate settings: Use Pydantic's validation features to ensure all settings are correctly typed and within acceptable ranges.

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Common Pitfalls and How to Avoid Them

One common mistake when using Pydantic is misapplying type annotations, which can lead to validation errors or unexpected behavior.

Here are a few typical mistakes and their solutions:

• Misusing Union Types: Using Union incorrectly can complicate type validation and handling.
• Optional Fields without Default Values: Forgetting to provide a default value for optional fields can lead to None values causing errors in your application.
• Incorrect Type Annotations: Assigning incorrect types to fields can cause validation to fail. For example, using str for a field that should be an int.

Ignoring Performance Implications

Ignoring performance implications when using Pydantic can lead to slow applications, especially when dealing with large datasets or frequent model instantiations.

Here are some strategies to avoid performance bottlenecks:

• Leverage Configuration Options: Use Pydantic's configuration options like validate_default and validate_assignment to control when validation occurs.
• Optimize Nested Models: When working with nested models, ensure that you are not over-validating or duplicating validation logic.
• Use Efficient Parsing Methods: Utilize model_validate_json and model_validate for efficient data parsing.
• Avoid Unnecessary Validation: Use the model_construct method to create models without validation when the data is already known to be valid.

Overcomplicating Models

Overcomplicating Pydantic models can make them difficult to maintain and understand.

Here are some tips to keep models simple and maintainable:

• 记录您的模型：使用文档字符串和注释来解释模型中嵌入的复杂验证规则或业务逻辑。
• 适当封装逻辑：将验证和业务逻辑保留在适当的模型方法或外部实用程序中，以避免模型定义混乱。
• 谨慎使用继承：虽然继承可以促进代码重用，但过度使用会使模型层次结构复杂且难以遵循。
• 避免过度嵌套：深度嵌套的模型可能难以管理。以平衡的嵌套水平为目标。

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结论

在本指南中，我们介绍了在 Python 项目中有效使用 Pydantic 的各种最佳实践。

我们从 Pydantic 入门的基础知识开始，包括安装、基本用法和定义模型。然后，我们深入研究了自定义类型、序列化和反序列化以及设置管理等高级功能。

强调了关键性能考虑因素，例如优化模型初始化和高效数据解析，以确保您的应用程序顺利运行。

我们还讨论了常见的陷阱，例如滥用类型注释、忽略性能影响以及模型过于复杂，并提供了避免这些陷阱的策略。

在实际项目中应用这些最佳实践将帮助您充分利用 Pydantic 的强大功能，使您的代码更加健壮、可维护和高性能。

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