Why Do Multiple Python Objects Created in Quick Succession Sometimes Share the Same ID?

Identical Object IDs in Python: Unveiling the Mystery

When instantiating multiple objects from the same class in rapid succession, it's observed that they share the same ID in Python. This behavior may seem counterintuitive, as one might expect each object to have a unique identifier.

Understanding Object IDs

In Python, the id() function returns the memory address of an object. This address serves as the object's ID and is guaranteed to be unique during its lifetime. However, objects created in quick succession may reside in adjacent memory locations, resulting in identical IDs.

Implementation Details

The CPython implementation of Python uses reference counting for garbage collection. As a result, objects can be immediately deallocated once their reference count reaches zero. In the scenario being discussed, both someClass() objects are created and then immediately destroyed by the garbage collector since they are no longer referenced after the print() call.

Memory Allocation and ID Sharing

Furthermore, CPython assigns IDs based on the value of the underlying pointer to an object. As the first someClass() object is deallocated, its memory location becomes available for reallocation. Consequently, the next object created (the second someClass() object) is likely to be placed in the same location, inheriting the ID of the previous object.

Resolving the Issue

To avoid shared IDs and maintain distinct object identifiers, it's recommended to either:

• Keep objects in memory by storing them in a list or other data structure, ensuring their lifetimes overlap.
• Implement a custom ID generation mechanism within the class using a counter or other logic that guarantees uniqueness.

By understanding these implementation nuances, programmers can avoid relying on identical object IDs while using Python for efficient object management.

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