Why Custom validators in FastAPI? - Purpose & Use Cases

The Big Idea

Discover how custom validators save you from endless, messy input checks!

The Scenario

Imagine you have a form where users enter their age and email. You try to check if the age is a positive number and the email looks right by writing many if-else checks everywhere in your code.

The Problem

Manually checking each input everywhere is tiring and easy to forget. It leads to bugs, repeated code, and messy logic that is hard to update or reuse.

The Solution

Custom validators let you write clear, reusable rules that automatically check your data. FastAPI runs these checks for you, so your code stays clean and safe.

Before vs After

✗ Before

if age <= 0:
    raise ValueError('Age must be positive')
if '@' not in email:
    raise ValueError('Invalid email')

✓ After

from pydantic import BaseModel, validator

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

    @validator('age')
    def age_positive(cls, v):
        if v <= 0:
            raise ValueError('Age must be positive')
        return v

    @validator('email')
    def email_valid(cls, v):
        if '@' not in v:
            raise ValueError('Invalid email')
        return v

What It Enables

You can ensure data is always correct and meaningful before your app uses it, without cluttering your main code.

Real Life Example

When users sign up, custom validators check their passwords are strong and emails are valid automatically, preventing bad data from entering your system.

Key Takeaways

Manual checks are repetitive and error-prone.

Custom validators centralize and automate data checks.

This keeps your code clean and your app reliable.

Practice

1. What is the main purpose of using @validator in FastAPI models?

easy

A. To connect the model to a database

B. To automatically generate API documentation

C. To add custom rules that check if input data is valid

D. To style the API response format

Solution

1. Step 1: Understand the role of @validator

   The @validator decorator is used to add custom validation logic to model fields in FastAPI.

2. Step 2: Identify the purpose of validation

   Validation ensures that the data meets specific rules before the app processes it, helping keep input correct and safe.

3. Final Answer:

   To add custom rules that check if input data is valid -> Option C

4. Quick Check:

   Custom validation = check input rules [OK]

Hint: Validators check data rules before use [OK]

Common Mistakes:

• Confusing validators with database connectors
• Thinking validators generate docs
• Assuming validators change response style

2. Which of the following is the correct way to define a custom validator for a Pydantic model field named age?

easy

A. @validator('age')\ndef check_age(cls, value):

B. def validate_age(cls, value):

C. @validate('age')\ndef age_validator(cls, value):

D. @validate_field('age')\ndef age_check(cls, value):

Solution

1. Step 1: Recall the correct decorator syntax

   FastAPI uses Pydantic's @validator decorator with the field name as a string argument.

2. Step 2: Match the function definition style

   The validator function must be a class method with parameters cls and value.

3. Final Answer:

   @validator('age')\ndef check_age(cls, value): -> Option A

4. Quick Check:

   Use @validator('field') with cls, value [OK]

Hint: Use @validator('field') decorator for custom checks [OK]

Common Mistakes:

• Using @validate instead of @validator
• Missing the field name in decorator
• Wrong function parameters

3. Given this code snippet, what will be the output if age is set to 15?

from pydantic import BaseModel, validator

class User(BaseModel):
    age: int

    @validator('age')
    def check_age(cls, value):
        if value < 18:
            raise ValueError('Must be 18 or older')
        return value

try:
    user = User(age=15)
except Exception as e:
    print(e)

medium

A. Must be 18 or older

B. TypeError because age is int

C. User object with age 15 is created

D. No output, code runs silently

Solution

1. Step 1: Analyze the validator logic

   The validator checks if age is less than 18 and raises a ValueError if true.

2. Step 2: Check the input value

   Since age=15 is less than 18, the validator raises the error.

3. Final Answer:

   Must be 18 or older -> Option A

4. Quick Check:

   Age < 18 triggers ValueError [OK]

Hint: Validator raises error if condition fails [OK]

Common Mistakes:

• Assuming object creates despite validation error
• Confusing ValueError with TypeError
• Ignoring the exception handling block

4. Identify the error in this custom validator code:

from pydantic import BaseModel, validator

class Product(BaseModel):
    price: float

    @validator('price')
    def check_price(value):
        if value <= 0:
            raise ValueError('Price must be positive')
        return value

medium

A. Validator method must return None

B. Missing cls parameter in validator method

C. Price field should be int, not float

D. Validator decorator should be @validate

Solution

1. Step 1: Check validator method signature

   Validator methods must accept cls as the first parameter, then value.

2. Step 2: Identify missing parameter

   The method check_price only has value, missing cls.

3. Final Answer:

   Missing cls parameter in validator method -> Option B

4. Quick Check:

   Validator needs cls, value parameters [OK]

Hint: Validator methods need cls first, then value [OK]

Common Mistakes:

• Omitting cls parameter
• Using wrong decorator name
• Returning None instead of value

5. You want to create a custom validator that ensures a username is lowercase and at least 3 characters long. Which code correctly implements this in a FastAPI Pydantic model?

from pydantic import BaseModel, validator

class User(BaseModel):
    username: str

    @validator('username')
    def validate_username(cls, value):
        # Fill in validation logic here
        pass

hard

A. if len(value) < 3 and not value.islower():\n raise ValueError('Username must be lowercase and 3+ chars')\nreturn value

B. if len(value) < 3 and value.islower():\n raise ValueError('Username too short')\nreturn value

C. if len(value) > 3 and value.islower():\n return value\nelse:\n raise ValueError('Invalid username')

D. if value.islower() and len(value) >= 3:\n return value\nraise ValueError('Username invalid')

Solution

1. Step 1: Understand validation requirements

   The username must be lowercase and at least 3 characters long.

2. Step 2: Check each option logic

   if value.islower() and len(value) >= 3:\n return value\nraise ValueError('Username invalid') correctly checks both conditions and raises error if not met, returning value otherwise.

3. Final Answer:

   if value.islower() and len(value) >= 3:\n return value\nraise ValueError('Username invalid') -> Option D

4. Quick Check:

   Check both conditions, raise error if false [OK]

Hint: Check all conditions with 'and', raise error if false [OK]

Common Mistakes:

• Using 'or' instead of 'and' causing wrong logic
• Returning value inside else but missing return in if
• Incorrect length comparison operators