Why Does My FastAPI Application Process Concurrent Requests Sequentially Instead of in Parallel?

FastAPI Executes API Calls in Serial Rather Than Parallel Fashion

Problem Statement:

Although FastAPI provides parallel capabilities, API calls made concurrently through multiple browser tabs are processed sequentially instead of in parallel. This behavior occurs when endpoints are defined with def rather than async def.

Analysis and Solution:

FastAPI utilizes an external threadpool to handle endpoints defined with def. When such an endpoint receives a request, FastAPI runs it in a separate thread from the threadpool. However, only one request can be processed at a time, resulting in sequential request processing rather than true parallelism.

In contrast, endpoints defined with async def execute directly in the main event loop, allowing for true parallel request processing. This is because await calls within async def endpoints yield to other tasks in the event loop while waiting for asynchronous operations.

To resolve the issue, ensure that endpoints that do not require blocking I/O-bound operations are defined with async def to leverage FastAPI's parallel capabilities. Here's an example of an endpoint that can execute in parallel:

@app.get("/ping")
async def ping(request: Request):
    print("Hello")
    await asyncio.sleep(5)
    print("bye")
    return {"ping": "pong!"}

Additional Insights:

• Blocking operations, such as time.sleep(), in async def endpoints will block the entire server, rendering the benefits of parallel processing ineffective.
• CPU-bound tasks or blocking I/O operations can be executed in separate threads or processes using techniques such as run_in_threadpool(), loop.run_in_executor(), ThreadPoolExecutor, or ProcessPoolExecutor.
• Increasing the number of workers (processes) can enhance concurrency and serve more requests simultaneously.
• For heavy background computations, consider using tools like Celery or AsyncIOScheduler from apscheduler.

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