We Should Be Able To Explicitly Set A Field As `pydantic.fields._Unset`

Introduction

This article delves into a specific issue encountered while using Pydantic V2, focusing on the ability to explicitly set a field as pydantic.fields._Unset. This is particularly relevant when dealing with PATCH requests and aiming for robust static type checking. We will explore the context of the problem, analyze the code example provided, pinpoint the potential fix within Pydantic's source code, and discuss the implications of this behavior. Understanding this issue is crucial for developers leveraging Pydantic for data validation and serialization, especially in scenarios involving partial updates and API design.

Understanding the Challenge with PATCH Requests

When designing APIs, PATCH requests are commonly used to update only specific fields of a resource, leaving the rest untouched. This contrasts with PUT requests, which typically replace the entire resource. To effectively handle PATCH requests, it's essential to have a mechanism to distinguish between fields that are explicitly omitted from the request and those that are set to their default values. In Pydantic, the _Unset marker plays a crucial role in this distinction. When a field is set to _Unset, it signals that the field was not provided in the input data and should be treated accordingly. However, the current behavior of Pydantic, as highlighted in the issue, presents a challenge in accurately reflecting this state. The goal is to ensure that when a field is explicitly set to _Unset, Pydantic recognizes and respects this, allowing for proper handling of partial updates and more precise data validation. This article will guide you through the intricacies of this issue, providing a comprehensive understanding and potential solutions.

Static Type Checking and Pydantic's Role

Static type checking is a powerful technique for detecting errors early in the development process, before runtime. It involves analyzing the code and verifying that the types of variables, function arguments, and return values are consistent with their declarations. Pydantic, a popular Python library for data validation and settings management, plays a vital role in static type checking by allowing developers to define data models with type annotations. These models not only enforce data structure but also enable type checkers like MyPy to perform rigorous analysis. However, the issue discussed in this article reveals a specific scenario where Pydantic's behavior deviates from the expected outcome in the context of static type checking. Specifically, when a field is explicitly set to _Unset, Pydantic's internal handling might not fully align with the intention of the developer, potentially leading to discrepancies between the declared type and the actual value. This discrepancy can impact the accuracy of static type checking and the overall robustness of the application. Therefore, understanding and addressing this issue is crucial for leveraging Pydantic effectively in statically typed Python projects.

Problem Description

The core issue revolves around the behavior of Pydantic when a field is explicitly set to pydantic.fields._Unset. While the instance is created correctly using the default values, the model_fields_set attribute does not accurately reflect which fields were explicitly set during instantiation. This discrepancy can lead to unexpected behavior, especially when using features like model_dump(exclude_unset=True), which is intended to exclude fields that were not explicitly set. To illustrate this, consider a scenario where you're building a PATCH request and want to send only the fields that have been modified. If model_fields_set includes fields that were explicitly set to _Unset, these fields might be inadvertently excluded from the request, leading to data loss or incorrect updates. The problem arises from how Pydantic filters data before passing it to the validator, potentially removing _Unset values and thus not registering them as explicitly set fields. This article will further examine the code example provided to demonstrate this issue in detail and propose a potential solution to align Pydantic's behavior with the intended outcome.

Code Example

To illustrate the problem, consider the following Python code snippet:

from pydantic import BaseModel, Field
from pydantic.fields import _Unset
class InnerModel(BaseModel):
inner_field1: float = Field(default=1.0)
inner_field2: float = Field(default=2.0)
class Example(BaseModel):
field1: int
field2: int = Field(default=5)
field3: int | None = Field(default=None)
inner_model: InnerModel = Field(default_factory=InnerModel)
def build_with_optional(field1: int, field2: int = _Unset, field3: int | None = _Unset, inner_model: InnerModel = _Unset):
return Example(field1=field1, field2=field2, field3=field3, inner_model=inner_model)
print("Expected:")
print(Example(field1=0).model_dump(exclude_unset=True))
print(Example(field1=0).model_dump(exclude_unset=True))
print(Example(field1=0).model_dump(exclude_unset=True))
print(Example(field1=0, inner_model=InnerModel(inner_field1=1.0)).model_dump(exclude_unset=True))
print("-"*50)
print("Actual:")
print(build_with_optional(field1=0).model_dump(exclude_unset=True))
print(build_with_optional(field1=0, field2=_Unset).model_dump(exclude_unset=True))
print(build_with_optional(field1=0, field2=_Unset, field3=_Unset).model_dump(exclude_unset=True))
print(build_with_optional(field1=0, field2=_Unset, field3=_Unset, inner_model=InnerModel(inner_field1=1.0)).model_dump(exclude_unset=True))
"""
Expected:
'field1' 0
'field1' 0
'field1' 0
'field1' 0, 'inner_model': {'inner_field1': 1.0}
Actual:
'field1' 0, 'field2': 5, 'field3': None, 'inner_model': {}
'field1' 0, 'field2': 5, 'field3': None, 'inner_model': {}
'field1' 0, 'field2': 5, 'field3': None, 'inner_model': {}
'field1' 0, 'field2': 5, 'field3': None, 'inner_model': {'inner_field1': 1.0}
"""

This code defines two Pydantic models, InnerModel and Example, with various fields and default values. The build_with_optional function demonstrates how fields can be explicitly set to _Unset. The expected output shows that when model_dump(exclude_unset=True) is called, only the explicitly set fields should be included in the dumped dictionary. However, the actual output reveals that fields set to _Unset are not being excluded as expected. This discrepancy highlights the core issue: Pydantic is not correctly tracking fields explicitly set to _Unset, leading to unexpected behavior when using model_dump(exclude_unset=True). The next sections will delve deeper into the potential cause of this behavior and propose a solution.

Potential Fix

The potential fix lies within the pydantic/main.py file, specifically at line 253. The current implementation filters out values that are PydanticUndefined before passing the data to the validator:

# validated_self = self.__pydantic_validator__.validate_python(data, self_instance=self)
validated_self = self.__pydantic_validator__.validate_python({k: v for k, v in data.items() if v is not PydanticUndefined}, self_instance=self)

This filtering step prevents _Unset values from being processed by the validator, which in turn causes Pydantic to not recognize these fields as explicitly set. The proposed fix involves removing this filtering step, allowing _Unset values to be passed to the validator. This change would enable Pydantic to correctly track fields explicitly set to _Unset and ensure that model_dump(exclude_unset=True) behaves as expected. By commenting out the filtering line and using the original validation call, the behavior should align with the expected outcome:

validated_self = self.__pydantic_validator__.validate_python(data, self_instance=self)
# validated_self = self.__pydantic_validator__.validate_python({k: v for k, v in data.items() if v is not PydanticUndefined}, self_instance=self)

This seemingly small change has significant implications for how Pydantic handles partial updates and data serialization. By preserving _Unset values during validation, Pydantic can more accurately represent the state of the model and provide a more consistent and predictable experience for developers. The following sections will discuss the implications of this fix and its potential impact on various use cases.

Implications of the Fix

Implementing the proposed fix, which involves allowing _Unset values to be processed by the Pydantic validator, has several important implications. First and foremost, it ensures that model_dump(exclude_unset=True) functions as intended, correctly excluding fields that were explicitly set to _Unset during model instantiation. This is crucial for scenarios like building PATCH requests, where only the modified fields should be included in the request body. By accurately tracking _Unset fields, Pydantic can prevent unintended data loss or overwrites during partial updates.

Furthermore, this fix enhances the accuracy of static type checking. When a field is explicitly set to _Unset, it signals a specific intent: the field was not provided in the input data and should be treated accordingly. By preserving this information, Pydantic allows type checkers to perform more rigorous analysis and detect potential type mismatches or errors. For example, if a field is declared as non-nullable but is explicitly set to _Unset, a type checker should flag this as a potential issue. This improved type safety contributes to more robust and maintainable code.

In addition to these core benefits, the fix also promotes a more consistent and intuitive API for Pydantic users. By aligning the behavior of _Unset with its intended purpose, Pydantic becomes easier to reason about and use effectively. Developers can rely on model_dump(exclude_unset=True) to accurately reflect the state of the model, simplifying tasks such as data serialization, API request building, and data validation. This consistency ultimately leads to a better developer experience and reduces the likelihood of unexpected behavior or errors.

Conclusion

In conclusion, the ability to explicitly set a field as pydantic.fields._Unset is a crucial aspect of Pydantic's functionality, particularly when dealing with PATCH requests and aiming for robust static type checking. The current behavior, which filters out _Unset values before validation, can lead to inconsistencies and unexpected results when using features like model_dump(exclude_unset=True). The proposed fix, which involves allowing _Unset values to be processed by the validator, addresses this issue and ensures that Pydantic accurately tracks fields explicitly set to _Unset. This change has significant implications for data serialization, API design, and type safety.

By implementing this fix, Pydantic can provide a more consistent and predictable experience for developers, enabling them to leverage the library's full potential for data validation and settings management. The improved behavior of model_dump(exclude_unset=True) simplifies tasks such as building partial updates and serializing data for API requests. Furthermore, the enhanced accuracy in tracking _Unset fields contributes to more robust static type checking, leading to higher quality code and fewer runtime errors. Overall, this fix is a valuable improvement to Pydantic, making it an even more powerful and versatile tool for Python developers.