implementing Slow Serialization in FastAPI Using Pydantic Models for Large Data Sets
I'm working through a tutorial and I've searched everywhere and can't find a clear answer. I'm currently developing a FastAPI application that serves JSON responses using Pydantic models for data validation and serialization. The scenario arises when I attempt to serialize large datasets, especially when the response size exceeds 1MB. I notice important delays in response times, often exceeding 500ms, which is unacceptable for my use case. I have tried using the `jsonable_encoder` to optimize the serialization process, but the performance improvement was minimal. Here’s a simplified version of my route: ```python from fastapi import FastAPI from pydantic import BaseModel from typing import List app = FastAPI() class Item(BaseModel): id: int name: str class ResponseModel(BaseModel): items: List[Item] @app.get("/items", response_model=ResponseModel) async def get_items(): # Simulating a large dataset items = [Item(id=i, name=f"Item {i}") for i in range(100000)] return ResponseModel(items=items) ``` When I send a request to this endpoint, I’m seeing a noticeable latency. The logs show `INFO: Uvicorn running on http://127.0.0.1:8000`, and I can see the response taking longer than expected. I also tried tweaking Uvicorn settings, but the question still continues. Additionally, I received some warnings regarding JSON serialization limits, specifically: `JSONDecodeError: Expecting value: line 1 column 1 (char 0)` when the response gets extremely large, which makes me suspect that the default JSON encoder might be struggling under the load. Are there any recommended practices or libraries I can explore to enhance the serialization speed in FastAPI while using Pydantic models? Also, would implementing pagination help mitigate this performance scenario? Any suggestions on optimizing large JSON responses would be greatly appreciated. Any help would be greatly appreciated! I'm developing on CentOS with Python. This issue appeared after updating to Python 3.10. Thanks, I really appreciate it! Any help would be greatly appreciated!