Immutability is common in the programming mainstream: deep immutability is the default in functional languages while imperative languages typically provide opt-in support for shallow immutability, usually enforced through static checking.<\/p>\n\n\n\n
Python is a dynamic imperative language where mutability is inherent: not only are most objects mutable, but programs themselves—modules, classes, functions—are represented by mutable objects at run-time, and libraries routinely rely on this mutability. This makes adding immutability to Python a significant challenge.<\/p>\n\n\n\n
This paper presents the design and implementation of deep immutability for Python. Our primary motivation is to permit multiple sub-interpreters to directly share object references, which currently requires costly serialisation. Sharing via immutability introduces a soundness challenge, as a violation could corrupt the interpreter’s state.<\/p>\n\n\n\n
We identify numerous challenges that stem from decades of design decisions that did not anticipate immutability, and show how they can be overcome through two complementary techniques: detachment, which severs run-time links that would cause immutability to propagate too widely, and freezability, which gives objects run-time control over whether and how they may become immutable. Together, these principles form a general design pattern for deep immutability in dynamic languages. We validate our design with an implementation on CPython 3.15 that is backwards-compatible with existing programs and enables direct, zero-copy sharing of immutable objects across sub-interpreters.<\/p>\n","protected":false},"excerpt":{"rendered":"
Immutability is common in the programming mainstream: deep immutability is the default in functional languages while imperative languages typically provide opt-in support for shallow immutability, usually enforced through static checking. 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