Async Image and Document Transformation
Process thumbnails, resizing, and format conversion in background workers to keep request latency low.
Why Offload Media Work
Resizing an image or converting a PDF can take hundreds of milliseconds to several seconds. If you do that work inside the request handler, the client waits and your worker process is blocked.
The pattern for B2-level FastAPI services is:
- Accept the upload, persist the original quickly
- Return a
202 Acceptedwith a job id - Do thumbnails, resizing, and format conversion in a background worker
This keeps request latency low and makes heavy CPU work independently scalable.
BackgroundTasks vs a Real Queue
FastAPI ships BackgroundTasks, which runs a function after the response is sent but still inside the same process. It is fine for cheap, fast follow-ups (sending an email, writing a log).
For CPU-heavy media transforms it is the wrong tool: it competes with your event loop and dies if the process restarts. Prefer a dedicated task queue (Celery, RQ, Dramatiq, or arq) backed by Redis so work survives deploys and scales horizontally.
from fastapi import FastAPI, BackgroundTasks
app = FastAPI()
def log_upload(filename: str) -> None:
# cheap follow-up work only
print(f"received {filename}")
@app.post("/upload")
async def upload(background: BackgroundTasks):
background.add_task(log_upload, "photo.png")
return {"status": "accepted"}All lessons in this course
- Multipart Uploads and Content Validation
- Streaming Responses and Range Requests
- Offloading Storage to S3-Compatible Buckets
- Async Image and Document Transformation