Memory efficiency of parallel IO operations in Python
Honest question.<p>Can somebody explain to me why async IO is so important and why it is better than using the operating system scheduler?<p>If process A is blocked because of IO, then then thing that needs to be done will need to wait for the IO anyways.<p>Of course, in a server context, process A cannot handle new server requests while it is blocked. But luckily we can run more than one process, so process B will be free to pick it up. I will need to  run a few more worker processes than there are CPU cores, but is there a problem with that?<p>EDIT:<p>I&#x27;m thinking now the problem is maybe that running more workers than there are cores will mean that the server accepts more concurrent connections than it can handle?
If I use async code and run exacly as many workers as I have cores, the workers will never blocked. 
But then, I have the scenario where multiple async callbacks resolve in short sequence, but cannot be picked up by a worker because all workers are busy.<p>So, in both scenarios (no async but more workers than cores VS async with as many workers as cores) it can happen that the server puts too much on its plate and accepts more than it can handle.<p>I have a feeling that this is a fundamental problem that manifests itself differently in both paradigms, but exists notheless?