Why can two distinct objects in CPython have the same ID even though they are not the same object?

Understanding Identical Object IDs in CPython

In CPython, the built-in function id() returns a unique integer representing the object's memory address. Intriguingly, under certain circumstances, two distinct objects can have the same ID, as observed in the following example:

<code class="python">tuple1 = ()
tuple2 = ()
dict1 = {}
dict2 = {}
list1 = []
list2 = []

# Tuples are immutable
assert(id(tuple1) == id(tuple2))

# Dicts are mutable
assert(id(dict1) != id(dict2))

# Lists are mutable too
assert(id(list1) != id(list2))

# Unexpectedly, empty dicts and lists have the same ID
assert(id({}) == id({}))
assert(id([]) == id([]))</code>

The Reason Behind the Behavior

This peculiar phenomenon occurs due to the runtime behavior of CPython's memory allocator and the way it handles the creation and destruction of objects. When id({}) is called, CPython creates a new dictionary object and assigns it to a temporary location in memory. However, this temporary object is destroyed immediately after its ID is returned by the id() function. When the next id({}) is called, the new dictionary may happen to be created in the same memory location as the previous one, resulting in the same ID even though these objects are distinct.

This reasoning is further supported by the behavior observed in the following sequence:

<code class="python">id([])
4330909912

x = []
id(x)
4330909912

id([])
4334243440</code>

Impact of Mutability and Caching

It's worth noting that mutability plays an indirect role in this behavior. While mutable objects (such as lists and dictionaries) are not eligible for code object caching, immutable objects (like empty tuples and certain strings) are. This means that within the same code object, these immutable objects will be re-used, potentially leading to the assignment of the same ID to distinct immutable objects.

Id Uniqueness and Object Lifetime

In summary, an object's ID is unique only for the duration of its lifetime. Once an object is destroyed or before it is created, another object may be assigned the same ID. This behavior underscores the ephemeral nature of object IDs in CPython and the importance of understanding the memory allocation and object creation dynamics in the context of the underlying Python implementation.

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