> Co-routines are not necessarily faster than threads, but they yield to a performance improvement if one has to spin thousands of them : they have less creation overhead and consume less RAM.
This hardly matters when spinning up a few thousand threads. Only memory that's actually used is committed, one 4k page at a time. What is 10MB these days? And that is main memory, while it's much more interesting what fits in cache. At that point it doesn't matter if your data is in heap objects or on a stack.
Add to that the fact that Python stacks are mostly on the heap, the real stack growing only due to nested calls in extensions. It's rare for a stack in Python to exceed 4k.
Languages that to green threads don't do them for memory savings, but to save on context switches when a thread is blocked and cannot run. System threads are scheduled by the OS, green threads my the language runtime, which saves a context switch.
Green threads are scheduled by the language runtime and by the OS. If the OS switches from one thread to another in the same process, there is no context switch, really, apart from the syscall itself which was happening anyway (the recv that blocks and causes the switch). At least not on Linux, where I've measured the difference.
This hardly matters when spinning up a few thousand threads. Only memory that's actually used is committed, one 4k page at a time. What is 10MB these days? And that is main memory, while it's much more interesting what fits in cache. At that point it doesn't matter if your data is in heap objects or on a stack.
Add to that the fact that Python stacks are mostly on the heap, the real stack growing only due to nested calls in extensions. It's rare for a stack in Python to exceed 4k.