Скачать или смотреть Understanding Why Python Function Parameters are Stored on the Stack in CPython

Discover the reasons behind how `CPython` manages function parameters on the stack, emphasizing memory management principles that optimize performance and efficiency.
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Understanding Why Python Function Parameters are Stored on the Stack in CPython

Python developers often delve into the intricacies of how the language functions under the hood, particularly regarding memory management. One common question that arises is: Why are references to Python values that are function parameters stored on the stack (frame) in CPython? This inquiry dives into the efficient operation of Python’s reference counting system and how it facilitates seamless memory management during function calls.

The Problem with Reference Counting

To get a clearer understanding, we need to first examine the concept of reference counting in Python. In Python, every object maintains a count of references pointing to it. This mechanism is critical because:

When an object's reference count drops to zero (i.e., no remaining references), the memory allocated for that object is released.

This allows Python to efficiently manage memory, recycling resources as needed.

Reference Counting Explained

To illustrate this concept, let’s consider a function that works with a large object (let’s say an instance of BigObj):

[[See Video to Reveal this Text or Code Snippet]]

In this example, two references are created to BigObj. Notice that even when set to None inside work_with, the original reference in myfn still holds on to the memory until myfn completes execution.

The Mechanics of Parameter Storage in CPython

The primary reason why CPython retains an additional reference on the stack for function parameters is due to its implementation model. Specifically:

When a function is called, the frame for that function is created, temporarily holding references to its parameters. This implementation considers that parameters may be temporary objects that need to persist for the duration of the function call.

The frame remains intact until the function returns, meaning all parameters persist in memory during execution.

Borrowed References

Within CPython, the references to the arguments that are passed to a function are termed borrowed references. When borrowed, the callee (the called function) does not have to manage the lifetime (or decrement the reference count) of these parameters:

This design simplifies the function implementation as the callee can operate on the parameters without worrying about their lifecycle.

When you set the parameter to None within the function, it does not reduce the reference count, thus preventing the memory from being freed prematurely.

Considerations on Alternative Approaches

It is worth noting that an alternative approach could involve having the callee decrement the reference count of the parameters. However, this comes with its own set of trade-offs:

Immediate Destruction: This might allow for more immediate destruction of temporary objects which could be advantageous in certain situations.

Increased Complexity: The drawback would be the increased complexity for the implementers, requiring explicit reference management at the C level every time a function is invoked.

Most often, the concerns about memory management are negligible for typical use cases in Python, but the design decision persists primarily for simplicity and clarity in usage.

Summary of Key Takeaways

In conclusion, CPython's decision to hold an additional reference to function parameters on the stack is a consequence of its implementation choices focusing on memory management efficiency. Here’s a quick recap of the key points:

Reference counting allows Python to manage memory effectively.

Function frames temporarily hold references to parameters during execution.

Borrowed references prevent the need