Python FastAPI: High Latency with Large JSON Payload Processing
I'm working through a tutorial and I've looked through the documentation and I'm still confused about I'm experiencing significant latency when processing large JSON payloads in my FastAPI application... While testing, I've noticed that when I send a JSON object with multiple nested structures (up to 10,000 records), the response time spikes to over 5 seconds, which is unacceptable for my use case. The payload size is around 1.5 MB, and the structure looks something like this: ```json { "users": [ { "id": 1, "name": "John Doe", "attributes": { "age": 30, "email": "john@example.com" } }, ... { "id": 10000, "name": "Jane Smith", "attributes": { "age": 28, "email": "jane@example.com" } } ] } ``` I have tried a few approaches to improve performance, such as using `async` functions in my route handlers and the `pydantic` models for validation. Here's a simplified version of my endpoint: ```python from fastapi import FastAPI from pydantic import BaseModel from typing import List, Dict app = FastAPI() class UserAttributes(BaseModel): age: int email: str class User(BaseModel): id: int name: str attributes: UserAttributes @app.post("/users/") async def create_users(users: List[User]): # Simulate processing time return {"message": "Users processed successfully."} ``` Despite utilizing asynchronous processing, the latency remains a concern, and I'm not sure if there's a more optimal way to handle large payloads. I also receive a `422 Unprocessable Entity` error occasionally when the payload exceeds a certain size. Are there best practices or design patterns I should consider for handling large JSON payloads in FastAPI? Additionally, any recommendations on profiling this to pinpoint bottlenecks would be greatly appreciated. My development environment is Windows. Is there a better approach? The stack includes Python and several other technologies.