Hacker News Reader

Comment by Waterluvian | Hacker News Reader

So... Because lists are dynamically typed and heterogeneous, does that mean the underlying C is basically a contiguous segment of memory of python object references? Then in cpython the actual list contents are Python objects sitting on the heap in a non-contiguous manner?

And when the list needs to be resized and doubles itself, it's just doubling the space for object references?

View on HN

> Because lists are dynamically typed and heterogeneous, does that mean the underlying C is basically a contiguous segment of memory of python object references?

Yep, it is an array of pointers: https://github.com/python/cpython/blob/3.7/Include/listobjec...

> And when the list needs to be resized and doubles itself, it's just doubling the space for object references?

Basically yes. The growth pattern is not as aggressive as doubling though: https://github.com/python/cpython/blob/3.7/Objects/listobjec...

I keep forgetting just how readable cpython source is. I'm still mentally stuck in my beginner years, where it was all Greek to me.

Thanks for sharing these links.

Interesting:

"The growth pattern is: 0, 4, 8, 16, 25, 35, 46, 58, 72, 88, ..."

What is the big-o amortized cost of using this pattern? Doubling has a easy proof that it's O(1) amortized.

The growth pattern is still exponential, instead of going x -> 2 * x, you go x -> 9/8 * x + 6. I think it should still be O(1) amortized, you just need to adjust your proof for doubling a bit.

Mathematically the best is not doubling but fibonacci. for the start doubling is used because it's much faster to compute.

> So... Because lists are dynamically typed and heterogeneous, does that mean the underlying C is basically a contiguous segment of memory of python object references?

Yes. Every Python object is held by the interpreter as a pointer to a PyObject struct on the (C) heap.

Not something I would put into production, but had a fun application of this on a side project. I had the `id` of a function (but not the function object itself) and needed to recover the function. In CPython, `id` of a function corresponds to it's memory address (not sure if that can be overridden). By casting the id to a PyObject, I was able to recover the original.

    func = ctypes.cast(int(address), ctypes.py_object).value

Was kinda cool actually seeing that work.

Reply on news.ycombinator.com