Does Python have a stack/heap and how is memory ma

Question

How are variables and memory managed in Python? Does it have a stack and a heap and what algorithm is used to manage memory? Given this knowledge are there any recommendations on memory management for large number/data crunching?

Answer 1

Python doesn't have any such thing.

Python is the language and does not specify how exactly implementations must achieve the semantics defined by Python the language.

Every implementation (CPython, PyPy, IronPython, Stackless, Jython...) is free to do its own thing!

In CPython, all objects live on the heap:

Memory management in Python involves a private heap containing all Python objects and data structures.¹

The CPython virtual machine is stack based:

>>> def g():
    x = 1
    y = 2
    return f(x, y)

>>> import dis
>>> dis.dis(g)
  2           0 LOAD_CONST           1 (1) # Push 1 onto the stack
              3 STORE_FAST           0 (x) # Stores top of stack into local var x

  3           6 LOAD_CONST           2 (2) # Push 2 onto stack
              9 STORE_FAST           1 (y) # Store TOS into local var y

  4          12 LOAD_GLOBAL          0 (f) # Push f onto stack
             15 LOAD_FAST            0 (x) # Push x onto stack
             18 LOAD_FAST            1 (y) # Push y onto stack
             21 CALL_FUNCTION        2     # Execute function with 2 
                                           # f's return value is pushed on stack
             24 RETURN_VALUE               # Return TOS to caller (result of f)

Keep in mind, that this is CPython specific. The stack does not contain the actual values though, it keeps references to those objects.

¹: Source

Answer 2

How are variables and memory managed in Python.

Automagically! No, really, you just create an object and the Python Virtual Machine handles the memory needed and where it shall be placed in the memory layout.

Does it have a stack and a heap and what algorithm is used to manage memory?

When we are talking about CPython it uses a private heap for storing objects. From the CPython C API documentation:

Memory management in Python involves a private heap containing all Python objects and data structures. The management of this private heap is ensured internally by the Python memory manager. The Python memory manager has different components which deal with various dynamic storage management aspects, like sharing, segmentation, preallocation or caching.

Memory reclamation is mostly handled by reference counting. That is, the Python VM keeps an internal journal of how many references refer to an object, and automatically garbage collects it when there are no more references referring to it. In addition, there is a mechanism to break circular references (which reference counting can't handle) by detecting unreachable "islands" of objects, somewhat in reverse of traditional GC algorithms that try to find all the reachable objects.

NOTE: Please keep in mind that this information is CPython specific. Other python implementations, such as pypy, iron python, jython and others may differ from one another and from CPython when it comes to their implementation specifics. To understand that better, it may help to understand that there is a difference between Python the semantics (the language) and the underlying implementation

Given this knowledge are there any recommendations on memory management for large number/data crunching?

Now I can not speak about this, but I am sure that NumPy (the most popular python library for number crunching) has mechanisms that handle memory consumption gracefully.

If you would like to know more about Python's Internals take a look at these resources:

• Stepping through CPython (video)
• A presentation about the internals of the Python Virtual Machine
• In true hacker spirit, the CPython Object Allocator source code