> The real story is that Python is designed to be maximally dynamic -- you can monkey-patch methods at runtime, replace builtins, change a class's inheritance chain while instances exist -- and that design makes it fundamentally hard to optimize. ...
> 4 bytes of number, 24 bytes of machinery to support dynamism. a + b means: dereference two heap pointers, look up type slots, dispatch to int.__add__, allocate a new PyObject for the result (unless it hits the small-integer cache), update reference counts.
Would Python be a lot less useful without being maximally dynamic everywhere? Are there domains/frameworks/packages that benefit from this where this is a good trade-off?
I can't think of cases in strong statically typed languages where I've wanted something like monkey patching, and when I see monkey patching elsewhere there's often some reasonable alternative or it only needs to be used very rarely.
> 4 bytes of number, 24 bytes of machinery to support dynamism. a + b means: dereference two heap pointers, look up type slots, dispatch to int.__add__, allocate a new PyObject for the result (unless it hits the small-integer cache), update reference counts.
Would Python be a lot less useful without being maximally dynamic everywhere? Are there domains/frameworks/packages that benefit from this where this is a good trade-off?
I can't think of cases in strong statically typed languages where I've wanted something like monkey patching, and when I see monkey patching elsewhere there's often some reasonable alternative or it only needs to be used very rarely.