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Data Handling

4 topics

How FastAPI validates incoming data, serializes responses, and leverages Pydantic for type-safe data handling.

Tip

Pydantic models are the backbone of FastAPI. They handle validation, serialization, and OpenAPI schema generation — all from a single class definition.

How Data Flows Through FastAPI

Step 1: JSON Body

The client sends a JSON request body to your endpoint.

Request Body
Accept and validate JSON request bodies with Pydantic models.
Pydantic Models
Define data schemas with automatic validation, serialization, and documentation.
Response Model
Control API response shape and filter sensitive fields automatically.
Headers & Cookies
Read request headers, manage cookies, and set custom response headers.

These are the patterns that trip up developers most often. Switch between Wrong and Fixed to compare the code side by side.

1
Mutable default arguments
Using mutable defaults in Pydantic models
Don't do this
schemas.py
from pydantic import BaseModel

class Item(BaseModel):
    tags: list[str] = []  # Shared mutable!
While Pydantic v2 handles mutable defaults correctly by copying them, using Field(default_factory=list) makes the intent explicit and is the recommended pattern.
2
Exposing internal fields
Returning database models directly without a response model
Don't do this
main.py
@app.get("/users/{id}")
async def get_user(id: int):
    user = db.get(id)
    return user  # Exposes password_hash!
Always use response_model to control what fields are returned. Without it, sensitive fields like password hashes or internal IDs could leak to clients.