Python memory management deep dive

The CPython garbage collector uses reference counting as its primary mechanism, supplemented by a cyclic GC to detect unreachable reference cycles that refcounting can't catch. Key concepts: objects are destroyed when their reference count hits zero; gc.collect() manually triggers cycle detection; weak references allow tracking without preventing collection; and the generational hypothesis optimizes scans for long-lived objects.

The premise of this whole thread is wrong because you're conflating reference counting with actual memory management. Reference counting isn't memory management — it's a leak-prevention mechanism that happens to be implemented via RC, and calling the whole system 'Python memory management' is like calling a car's brakes its entire drivetrain.

Also worth correcting: CPython doesn't have a garbage collector in the traditional sense (there's no mark-and-sweep, no reachability analysis). There's a cyclic GC but that handles reference cycles specifically — it's not 'the memory management system.' The actual heavy lifting is the small object allocator with its size classes and arena/block structure. If anyone wants to discuss real Python internals they should start there rather than hand-waving over sys.getrefcount.

One more thing: saying CPython has a GIL problem implies that GIL removal would solve memory performance issues. It won't. The GIL is a threading lock, and the small object allocator is thread-safe through local arenas — lifting the GIL changes concurrency semantics entirely unrelated to how Python handles its objects in heap space.

The key thing most people miss is that Python's GC actually runs on two systems working together: reference counting for immediate cleanup and a generational collector to catch circular references. The gc module gives you levers if you have a memory-sensitive