I've been troubleshooting some odd behavior in my async data pipeline recently. The pipeline consists of two long-running tasks managed by `asyncio.gather()`. One task retrieves around 6,000 rows via a websocket every 100 ms and saves them into a global dictionary, while the other performs ETL operations on this dictionary and writes the results to a database. However, after about 30 seconds of running, the process slows down dramatically—where it should take around 150 ms, it sometimes spikes to over 5,000 ms. This became apparent when I scaled the process beyond the initial 5,000 rows, which it handled smoothly before the slowdown began. To my surprise, adding `await asyncio.sleep(0)` at the end of my ETL function seems to have alleviated these slow runs, consistently keeping the processing time around 150-160 ms for all 6700 rows. I suspect this might yield control back to the event loop, but it feels like a hack. As we're now in 2025 with Python 3.11, I'm wondering: is there a better way to handle this issue without relying on `sleep(0)`?
5 Answers
You’re probably facing resource exhaustion here. If your tasks are doing synchronous operations for prolonged periods (like deep-copying), using intermediate sleeps will help in allowing the event loop to switch tasks. Still, for heavy CPU-bound tasks, you might want to consider moving those to threads to prevent holding up the event loop for too long.
The `sleep(0)` actually yields control back to the event loop, not the GIL. By doing that, you're likely allowing other tasks to progress, which reduces contention. However, it could still indicate some underlying issues like blocking behavior from your database or potential memory constraints. It might be worthwhile to analyze your resource usage and profile the latency of your async operations.
I have a video processing service that also combines IO and compute-bound tasks. I add `await asyncio.sleep(0)` in lengthy loops to let other coroutines run.
From what you described, it seems one of your tasks might not be yielding correctly, which can happen if one task exhaustively runs while others just progress gradually. Using `await asyncio.sleep(0)` ensures you're explicitly yielding control back to the event loop. Without seeing the full code, it’s tough to diagnose, but this practice is pretty standard for avoiding stalls.
It sounds like you're not managing your resources properly. The slowdown you’re experiencing could be due to many coroutines waiting for resources, especially if you’re holding onto DB connections unnecessarily. Instead of keeping them open, use a connection pool and make sure to release connections back after you're done with them. Also, when dealing with CPU-bound tasks, consider using a `ThreadPoolExecutor` for those parts instead of blocking your event loop with heavy computations.
This really clears up a lot of related issues I was struggling with. I definitely need to get a better handle on how Async works.
Good point! You also need to be careful about coroutines that don’t call `await`, or else they can block the event loop and cause delays. Using `await asyncio.sleep(0)` in such cases can actually help.
It looks like you might be running into a scenario where deep-copying larger data structures is blocking your tasks. If your global dictionary keeps growing, this could translate to increased CPU usage and slower operations. Streamlining how you handle data transformations and minimizing deep-copies could alleviate those spikes.
You might be right about that; organizing your dataset efficiently really helps. I’ve experienced how colossal global structures can drastically slow things down.
I just tried refactoring with a ThreadPoolExecutor, but it still slows down after a while. `asyncio.to_thread()` seems like a better approach to manage CPU-bound work without sacrificing responsiveness!