python-immutable-accumulator

You can implement immutable state management using frozen dataclasses with slots and tuple-based storage, where methods return new instances. This pattern eliminates shared mutable state and ensures safer state changes.

Using a frozen dataclass accumulator that returns a new instance from each add() operation is the best way to track state without side effects. This functional pattern naturally enables thread safety and parallel processing.

Yes, Python supports event sourcing using frozen dataclasses with slots and tuple-based storage. Each add() operation yields a new immutable instance, enabling reproducible stateful workflows and functional pipelines.

Yes, an optional Pydantic variant provides immutable state accumulation for environments relying on Pydantic validation. It uses the same pattern of returning new instances from add operations while integrating model-based validation.

Yes, Python 3.8 or later is required because the immutable accumulator pattern relies on the slots=True parameter alongside frozen=True in dataclasses. This ensures tuple-based storage and new instance creation work correctly.

You should use an immutable accumulator for cost tracking because mutable state accumulation causes bugs and unpredictable concurrent execution. Returning new instances from every update eliminates shared state, enabling safer debugging and functional pipelines.