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.