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FastAPI Backend Development Bootcamp · Lezione

FastAPI e operazioni asincrone

Comprenda come FastAPI gestisce naturalmente le funzioni asincrone e come scrivere codice efficiente e non bloccante.

FastAPI e operazioni asincrone è una lezione FastAPI Backend Development Bootcamp gratuita su CoddyKit. Questa è la lezione 2 di 4. Puoi leggere la lezione completa qui gratuitamente — poi esercitati direttamente nel browser con un editor di codice integrato e un tutor IA disponibile 24/7. Fa parte del percorso di apprendimento FastAPI Backend Development Bootcamp, e i tuoi progressi si sincronizzano tra il web e l'app CoddyKit. Il corso FastAPI Backend Development Bootcamp include 4 lezioni in totale.

Parti di questa lezione non sono ancora state tradotte e vengono mostrate in inglese.

FastAPI's Async Foundation

FastAPI is built for speed! It leverages Python's asynchronous features to handle many requests concurrently, especially I/O-bound tasks.

This means your API can stay responsive even when waiting for external resources like databases or other APIs.

Sync vs. Async Endpoints

In FastAPI, you can define two main types of endpoint functions:

  • Synchronous (def): These functions block the event loop while they run. If one request takes long, others might wait.
  • Asynchronous (async def): These functions can 'pause' and let other tasks run while they await an operation (like reading from a database), making your API non-blocking.

Simple Synchronous Endpoint

Here's a standard synchronous endpoint. While simple, if time.sleep() were a real, slow database call, it would block other requests until it completes.

Try running it and observe the delay if you try to make multiple requests quickly.

from fastapi import FastAPI
import uvicorn
import time

app = FastAPI()

@app.get("/sync_hello")
def sync_hello():
    time.sleep(2) # Simulate a blocking I/O operation
    return {"message": "Hello from sync endpoint!"}

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8000)

Defining Asynchronous Endpoints

To make your endpoint non-blocking, use async def. This tells FastAPI (and Python) that this function can be suspended and resumed.

Inside an async def function, you use the await keyword to wait for other asynchronous operations to complete without blocking the entire application.

Your First Async Endpoint

This example uses asyncio.sleep(), which is an asynchronous sleep function. Notice the await keyword before it.

This allows FastAPI to handle other requests while this one 'sleeps', making the server more responsive.

from fastapi import FastAPI
import uvicorn
import asyncio

app = FastAPI()

@app.get("/async_hello")
async def async_hello():
    await asyncio.sleep(2) # Simulate non-blocking I/O
    return {"message": "Hello from async endpoint!"}

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8000)

When to Go Async

async def shines for I/O-bound operations. These are tasks that spend most of their time waiting for something else to happen, such as:

  • Making requests to external APIs (e.g., httpx).
  • Querying a database (e.g., asyncpg, SQLModel).
  • Reading/writing files from disk.
  • Network communication.

For CPU-bound tasks (heavy calculations), async def doesn't speed up the task itself, but it can help keep the server responsive.

Interacting with Async Libraries

When your FastAPI async def endpoint needs to interact with another asynchronous library (like an async HTTP client or an async database driver), you must use await.

Failing to use await will result in the awaitable object being returned directly, not its resolved result, which is usually not what you want!

Conceptual Async API Call

Imagine fetching data from another service. An async HTTP client allows this without blocking. Here's how it conceptually looks within an async def endpoint:

We simulate network latency with asyncio.sleep to show the non-blocking nature.

from fastapi import FastAPI
import uvicorn
import asyncio

app = FastAPI()

@app.get("/fetch_data")
async def fetch_external_data():
    # In a real app, you'd use an async HTTP client like 'httpx'
    # async with httpx.AsyncClient() as client:
    #     response = await client.get("https://api.example.com/data")
    #     data = response.json()

    await asyncio.sleep(1) # Simulate network latency
    return {"data": "Fetched async data!"}

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8000)

Uvicorn: The Async Engine

FastAPI relies on an ASGI server like Uvicorn. Uvicorn is what actually runs your async def functions efficiently.

  • It manages the Python event loop, which orchestrates when different asynchronous tasks get to run.
  • When an await is encountered, Uvicorn can switch to another ready task, making your API highly concurrent.

This 'context switching' is what allows FastAPI to handle many requests without waiting for each one to finish entirely.

Quick Check: Async Use

You are building a FastAPI endpoint that needs to fetch data from a slow external API (an I/O-bound task). Which of the following is the best way to define this endpoint to ensure your FastAPI application remains responsive?

Recap: Async FastAPI

We've explored how FastAPI harnesses Python's asynchronous features:

  • Use async def for endpoint functions that perform I/O-bound operations.
  • Use await when calling other asynchronous functions or libraries within an async def.
  • This non-blocking approach, powered by Uvicorn and the event loop, allows your FastAPI application to handle many concurrent requests efficiently, leading to highly responsive APIs.

Next, we'll look at how to offload truly long-running or CPU-bound tasks to background processes!

Domande Frequenti

La lezione «FastAPI e operazioni asincrone» è gratuita?

Sì — il testo completo di «FastAPI e operazioni asincrone» è gratuito qui sul web. Per esercitarvi in modo interattivo (un editor di codice integrato e un tutor IA 24/7) e sbloccare il resto del corso FastAPI Backend Development Bootcamp, passa a CoddyKit PRO. Il corso FastAPI Backend Development Bootcamp include 4 lezioni in totale.

Cosa imparerò in «FastAPI e operazioni asincrone»?

Comprenda come FastAPI gestisce naturalmente le funzioni asincrone e come scrivere codice efficiente e non bloccante. Eserciti FastAPI Backend Development Bootcamp con codice pratico che esegui direttamente nel browser, e un tutor IA 24/7 risponde alle tue domande mentre lavori sulla lezione.

Ho bisogno di esperienza per iniziare FastAPI Backend Development Bootcamp?

Non è richiesta alcuna esperienza precedente. FastAPI Backend Development Bootcamp su CoddyKit è strutturato per principianti e studenti avanzati, quindi puoi iniziare da qui o dall'inizio e procedere al tuo ritmo. Questa è la lezione 2 di 4.

Quanto tempo richiede la lezione «FastAPI e operazioni asincrone»?

La maggior parte delle lezioni CoddyKit richiede circa 5–10 minuti. Ogni lezione è breve e interattiva, quindi fai progressi costanti e riprendi esattamente da dove hai lasciato su web e app.

Posso scrivere ed eseguire codice in questa lezione FastAPI Backend Development Bootcamp?

Sì. Ogni lezione FastAPI Backend Development Bootcamp include un editor di codice integrato, quindi scrivi ed esegui codice reale direttamente nel tuo browser e ricevi feedback istantaneo dall'IA — nessuna configurazione locale necessaria.

Tutte le lezioni di questo corso

  1. Ripasso di Async/Await in Python
  2. FastAPI e operazioni asincrone
  3. Eseguire attività in background
  4. WebSocket per la comunicazione in tempo reale
← Torna a FastAPI Backend Development Bootcamp