Alright, let’s dive into handling those long-running requests with FastAPI. Anyone who’s worked on APIs knows the headache of sluggish responses, especially when your server is busy crunching numbers or fetching data from a third-party service. But breathe easy; FastAPI’s got your back with a neat set of features to keep things snappy.

Got to say, long-running requests pop up more often than you’d like. Think about data processing, heavy computations, or those dreaded third-party APIs that take their sweet time to get back to you. If left unmanaged, these can clog up your server, making everything else slow to a crawl. Not cool, right?

So, first up: Asynchronous Endpoints. FastAPI is pretty slick because it natively supports asynchronous programming. Instead of having your whole server seize up, it can keep ticking along while waiting for that slowpoke of a task to finish.

from fastapi import FastAPI
import asyncio

app = FastAPI()

async def long_running_task(data):
    # Simulating a long-running task
    await asyncio.sleep(5)
    return data

@app.get("/async-task")
async def async_task():
    result = await long_running_task("some data")
    return {"result": result}

Here, the endpoint async_task does its thing without freezing everything else. It uses an asynchronous function to simulate a long snooze. During this time, your server remains unhindered and free to handle more requests.

Now, for those cases where immediate responses aren’t necessary, Background Tasks come into play. FastAPI’s BackgroundTasks lets you sneakily run tasks in the background while you send a prompt “We got this!” message to your user.

from fastapi import FastAPI, BackgroundTasks

app = FastAPI()

def long_running_background_task(item_id: int):
    # Simulating a long-running background task
    import time
    time.sleep(5)
    print(f"Processed item {item_id}")

@app.post("/process/{item_id}")
async def process_item_background(item_id: int, background_tasks: BackgroundTasks):
    background_tasks.add_task(long_running_background_task, item_id)
    return {"message": "Processing started in the background"}

So, the process_item_background endpoint kicks off the background task, immediately tells your user, “Don’t worry, we’re on it,” and keeps on trucking. Your background task does its thing without dragging your server down.

CPU-bound tasks? Those are a different beast altogether. These heavy computations will drown your event loop in no time, async or not. The trick here is to offload these bad boys to another thread or process.

from fastapi import FastAPI, BackgroundTasks
import asyncio
from fastapi.concurrency import run_in_threadpool

app = FastAPI()

def cpu_bound_task(data):
    # Simulating a CPU-bound task
    import time
    time.sleep(5)
    return data

@app.post("/cpu-bound-task")
async def cpu_bound_task_endpoint(data: str, background_tasks: BackgroundTasks):
    def run_task():
        result = cpu_bound_task(data)
        print(f"Task completed with result: {result}")

    background_tasks.add_task(run_in_threadpool, run_task)
    return {"message": "Task started in the background"}

In this example, the cpu_bound_task_endpoint ensures your heavy computations don’t bog things down by running them in a separate thread. Your main event loop stays free and responsive, serving other requests without breaking a sweat.

Notifying users when the task finishes? That’s an interesting part. Several options are there for keeping your users in the loop and giving them a friendly nudge when their task is done.

First option, Polling. It’s old-school but effective. You give a task ID to the user and let them ping an endpoint to check for updates.

from fastapi import FastAPI, BackgroundTasks

app = FastAPI()

def long_running_task(task_id: int):
    # Simulating a long-running task
    import time
    time.sleep(5)
    print(f"Task {task_id} completed")

@app.post("/start-task")
async def start_task(task_id: int, background_tasks: BackgroundTasks):
    background_tasks.add_task(long_running_task, task_id)
    return {"task_id": task_id}

@app.get("/task-status/{task_id}")
async def task_status(task_id: int):
    # Simulate task status check
    if task_id == 1:
        return {"status": "completed"}
    else:
        return {"status": "in progress"}

Another neat trick is Webhooks. Here, the client provides a callback URL, and you tell them when the job’s done by posting the result to their URL.

from fastapi import FastAPI, BackgroundTasks
import requests

app = FastAPI()

def long_running_task(callback_url: str, result: str):
    # Simulating a long-running task
    import time
    time.sleep(5)
    # POST result to callback URL
    requests.post(callback_url, json={"result": result})

@app.post("/start-task")
async def start_task(callback_url: str, background_tasks: BackgroundTasks):
    background_tasks.add_task(long_running_task, callback_url, "Task completed")
    return {"message": "Task started in the background"}

For some real-time magic, WebSockets come into play. Your server and the client keep a live connection, so you can update them as soon as the task is done.

from fastapi import FastAPI, WebSocket

app = FastAPI()

async def long_running_task(websocket: WebSocket):
    # Simulating a long-running task
    import time
    await asyncio.sleep(5)
    # Send result over WebSocket
    await websocket.send_text("Task completed")

@app.websocket("/ws")
async def websocket_endpoint(websocket: WebSocket):
    await websocket.accept()
    await long_running_task(websocket)

Lastly, we have Server-Sent Events (SSE). This approach lets your server update the client about the task’s progress over a single HTTP connection.

from fastapi import FastAPI, Response
import asyncio

app = FastAPI()

async def long_running_task():
    # Simulating a long-running task
    await asyncio.sleep(5)
    return "Task completed"

@app.get("/task-status")
async def task_status():
    async def event_generator():
        # Simulating task status updates
        yield "data: Task in progress\n\n"
        result = await long_running_task()
        yield f"data: {result}\n\n"

    return Response(event_generator(), media_type="text/event-stream")

In conclusion, managing long-running requests in FastAPI doesn’t have to be a nightmare. By smartly using asynchronous programming, background tasks, and effective user notification strategies, you can maintain a responsive and efficient API. Whether it’s through asynchronous endpoints, background tasks, or clever notification methods like webhooks and WebSockets, FastAPI offers the right tools to keep your API running smoothly.

So there’s nothing to fear; FastAPI’s got this covered. Touching on these techniques not only makes your API more resilient but also keeps your end-users happy. Faster responses, smartly handled background tasks, and clear notifications are the secret sauce for a snappy, responsive API.