This seems like a resource allocation issue then? Async python is just starting a bunch of jobs with no regard to how each job claims CPUs. Whereas sync python is using native OS threads which I guess does a much better job of allocating CPUs?
For async python, when you make 1000 requests, does it immediately register 1000 jobs across your CPUs via workers for processing? Does that just mean each job takes a tiny piece (1/1000) of the resource pie resulting in slower performance for all jobs?
Whereas in sync python you are saying you can only perform X number of jobs at a time where X is the number of allocated workers. So resource allocation is roughly divided into X parts.
You also have a db connection pool layer after the server code. Isn't that ultimately your bottleneck? I wonder if your async server is saturating the CPUs making the connection pool slow.
For async python, when you make 1000 requests, does it immediately register 1000 jobs across your CPUs via workers for processing? Does that just mean each job takes a tiny piece (1/1000) of the resource pie resulting in slower performance for all jobs?
Whereas in sync python you are saying you can only perform X number of jobs at a time where X is the number of allocated workers. So resource allocation is roughly divided into X parts.
You also have a db connection pool layer after the server code. Isn't that ultimately your bottleneck? I wonder if your async server is saturating the CPUs making the connection pool slow.