[heavyset_go]

Threads are cheap, you can do N work simultaneously with N threads in one process, without serialization, IPC or process creation overhead.

With multiprocessing, processes are expensive and work hogs each process. You must serialize data twice for IPC, that's expensive and time consuming.

You shouldn't have to break out multiple processes, for example, to do some simple pure-Python math in parallel. It doesn't make sense to use multiple processes for something like that because the actual work you want to do will be overwhelmed by the IPC overhead.

There are also limitations, only some data can be sent to and from multiple processes. Not all of your objects can be serialized for IPC.

[akdev1l]

I think you have a good point on IPC but process creation in Linux is almost as fast as thread creation

Unless the app would constantly be creating and killing processes then the process creation overhead would not be that much but IPC is killer

And also your types aren’t pickable or whatever and now you gotta change a lot of stuff to get it to work lol.

[connorboyle]

It makes sense to me that a program currently written using multiple processes would now be re-written to use multiple truly parallel threads. But it seems very odd to suggest (as your grandparent comment does) that a program currently run in multiple containers would likely be migrated to run on multiple threads.

In other words, I imagine anyone who cares about the overhead from serialization, IPC, or process creation would already be avoiding (as much as possible) using containers to scale in the first place.

[heavyset_go]

Yeah, I somehow glossed over the whole container thing.

The container thing might be horizontal scaling thing where 1 container runs on 1 instance with 1 vCPU, running multiple processes on instances means you need beefier slices of compute to take advantage of the parallelism, and you can't cleanly scale up and then down using only the resources you need.

If you have a queue distributing work, that model makes sense with single-threaded interpreters where consumers instances are spun up and down as needed, versus pushing work to a thread pool, or multiple instances with their own thread pools, that aren't inhibited by the GIL. The latter could be more efficient depending on the work.

[LtWorf]

But… in python threads don't run in parallel, which is the whole problem we are working around here.