Custom validators in FastAPI - Step-by-Step Execution

Concept Flow - Custom validators

Define Pydantic Model

↓

Add Custom Validator Method

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Receive Input Data

↓

Pydantic Validates Fields

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Custom Validator Runs

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Pass

↓

Model Created

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Use Validated Data

The flow shows how FastAPI uses Pydantic models with custom validators to check input data, either accepting it or raising errors.

Execution Sample

FastAPI

from pydantic import BaseModel, validator

class User(BaseModel):
    name: str
    age: int

    @validator('age')
    def age_must_be_adult(cls, v):
        if v < 18:
            raise ValueError('Must be at least 18')
        return v

This code defines a User model with a custom validator to ensure age is at least 18.

Execution Table

Step	Input Data	Validator Check	Result	Action
1	{'name': 'Alice', 'age': 20}	age >= 18?	True	Create User instance
2	{'name': 'Bob', 'age': 16}	age >= 18?	False	Raise ValidationError
3	ValidationError caught	-	-	Return error response

💡 Execution stops when input passes validation or raises ValidationError on failure.

Variable Tracker

Variable	Start	After Step 1	After Step 2	Final
input_data	-	{'name': 'Alice', 'age': 20}	{'name': 'Bob', 'age': 16}	-
age	-	20	16	-
validation_result	-	Passed	Failed	-
user_instance	None	User(name='Alice', age=20)	None	-

Key Moments - 2 Insights

Why does the validator raise an error when age is less than 18?

Because the custom validator method explicitly checks if age < 18 and raises ValueError, as shown in execution_table row 2.

What happens if the input data passes the custom validator?

The model instance is created successfully with validated data, as shown in execution_table row 1.

Visual Quiz - 3 Questions

Look at the execution table, what is the validation result for input {'name': 'Alice', 'age': 20}?

A Error raised

B Passed

C Failed

D Skipped

💡 Hint

Check execution_table row 1 under 'Result' column.

At which step does the ValidationError occur?

A Step 2

B Step 1

C Step 3

D No error occurs

💡 Hint

Look at execution_table row 2 where validation fails and error is raised.

If the age validator allowed ages less than 18, how would the user_instance variable change after step 2?

A It would raise an error

B It would be None

C It would hold User instance with age 16

D It would be deleted

💡 Hint

Refer to variable_tracker row for user_instance and consider if validation passes.

Concept Snapshot

Custom validators in FastAPI use Pydantic's @validator decorator.
They check or transform input fields during model creation.
If validation fails, a ValidationError is raised.
If it passes, the model instance is created with clean data.
Use them to enforce rules like minimum age or format.

Full Transcript

This visual execution shows how FastAPI uses Pydantic models with custom validators to check input data. First, a model is defined with fields and a custom validator method. When input data arrives, Pydantic validates each field. The custom validator runs for the specified field, checking conditions like age >= 18. If the condition passes, the model instance is created and can be used safely. If it fails, a ValidationError is raised, stopping the process and returning an error. Variables like input data, age, validation result, and model instance change step by step, showing how data flows and validation controls acceptance or rejection.