As much as I dislike Java the language, this is somewhere where the difference between CPython and JVM languages (and probably BEAM too) is hugely stark. Want to know if garbage collection or memory allocation is a problem in your long running Python program? I hope you're ready to be disappointed and need to roll a lot of stuff yourself. On the JVM the tooling for all kinds of observability is immensely better. I'm not hopeful that the gap is really going to close.

Generally speaking the optimal horizontal scaling is as little as you have to. You may want a bit of horizontal scaling for redundancy and geo distribution, but past that vertically scaling to fewer larger process tend to be more efficient, easier to load balance and a handful of other benefits.

And for smaller projects it's such an annoyance. Having a simple project running, and having to muck around to get cron jobs, background/async tasks etc. to work in a nice way is one of the reasons I never reach for python in these instances. I hope removing the GIL makes it better, but also afraid it will expose a whole can of worms where lots of apps, tools and frameworks aren't written with this possibility in mind.

Not by much. The cases where you can replace processes with threads and save memory are rather limited.

There's even the multiprocessing module in the stdlib to achieve this.

Imo the GIL was used as an excuse for a long time to avoid building those out.

Measure aggressively and test under real concurrency: use tracemalloc to find memory hotspots, py-spy or perf to profile contention, and fuzz C extension paths with stress tests so bugs surface in the lab not in production. Watch per thread stack overhead and GC behavior, design shared state as immutable or sharded, keep critical sections tiny, and if process level isolation is still required stick with ProcessPoolExecutor or expose large datasets via read only mmap.

Greater power draw though; remember that energy is the integral of power over time.

5.5 depends a lot on the implementation used for locks. High energy consumption due to contention normally indicates bad lock implementations.

In the best implementations, there is no actual contention. A waiting core only reads a private cache line, which consumes very little energy, until the thread that had hold the lock immediately before it modifies the cache line, which causes an exit from the waiting loop. In such implementations there is no global lock variable. There is only a queue associated with a resource and the threads insert themselves in the queue when they want to use the shared resource, providing to the previous thread the address where to signal that it has completed its use of the resource, so the single shared lock variable is replaced with per-thread variables that accomplish its function, without access contention.

While this has been known for several decades, one can still see archaic lock implementations where multiple cores attempt to read or write the same memory locations, which causes data transfers between the caches of various cores, at a very high power consumption.

Moreover, even if you use optimum lock implementations, mutual exclusion is not the best strategy for accessing a shared data resource. Even optimistic access, which is usually called "lock-free", is typically a bad choice.

In my opinion, the best method of cooperation between multiple threads is to use correctly implemented shared buffers or message queues.

By correctly implemented, I mean using neither mutual exclusion nor optimistic access (which may require retries), but using dynamic partitioning of the shared buffers/queues, which is done using an atomic fetch-and-add instruction and which ensures that when multiple threads access simultaneously the shared buffers or queues they access non-overlapping ranges. This is better than mutual exclusion because the threads are never stalled and this is better than "lock-free", i.e. optimistic access, because retries are never needed.