The document discusses garbage collection in Python. It introduces concepts like the heap, mutator, and collector. It then describes the reference counting algorithm used by CPython, which has challenges with cyclic references. It also discusses mark and sweep garbage collection used by PyPy, which can collect cyclic garbage but requires stopping program execution. The document aims to provide an overview of memory management techniques in Python.

1. Knowing your garbage collector
Francisco Fernandez Castano
upclose.me
francisco.fernandez.castano@gmail.com @fcofdezc
April 17, 2015
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2. Overview
1 Introduction
Motivation
Concepts
2 Algorithms
CPython RC
PyPy
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3. Motivation
Managing memory manually is hard.
Who owns the memory?
Should I free these resources?
What happens with double frees?
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4. Dangling pointers
int *func(void)
{
int num = 1234;
/* ... */
return #
}
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5. Ownership
int *func(void)
{
int *num = malloc (10 * sizeof(int ));;
/* ... */
return num;
}
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6. John Maccarthy
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7. Basic concepts
Heap
A data structure in which objects may be allocated or deallocated in any
order.
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8. Basic concepts
Heap
A data structure in which objects may be allocated or deallocated in any
order.
Mutator
The part of a running program which executes application code.
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9. Basic concepts
Heap
A data structure in which objects may be allocated or deallocated in any
order.
Mutator
The part of a running program which executes application code.
Collector
The part of a running program responsible of garbage collection.
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10. Garbage collection
Definition
Garbage collection is automatic memory management. While the
mutator runs , it routinely allocates memory from the heap. If more
memory than available is needed, the collector reclaims unused memory
and returns it to the heap.
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11. CPython GC
CPython implementation has garbage collection.
CPython GC algorithm is Reference counting with cycle detector
It also has a generational GC.
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12. Reference Counting Algorithm
typedef struct _object {
_PyObject_HEAD_EXTRA
Py_ssize_t ob_refcnt;
struct _typeobject *ob_type;
} PyObject;
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13. Reference Counting Algorithm
my_list = []
class A(object ): pass
a = A()
my_list.append(a)
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14. Reference Counting Algorithm
static int
ins1(PyListObject *self , Py_ssize_t where , PyObject
{
.
.
.
items = self ->ob_item;
for (i = n; --i >= where; )
items[i+1] = items[i];
Py_INCREF(v);
items[where] = v;
return 0;
}
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15. Reference Counting Algorithm
#define Py_INCREF(op) (
_Py_INC_REFTOTAL _Py_REF_DEBUG_COMMA
(( PyObject *)(op))-> ob_refcnt ++)
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16. Reference Counting Algorithm
my_list [0] = 1
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17. Reference Counting Algorithm
#define Py_DECREF(op)
do {
if ( _Py_DEC_REFTOTAL _Py_REF_DEBUG_COMMA
--(( PyObject *)(op))-> ob_refcnt != 0)
_Py_CHECK_REFCNT (op)
else
_Py_Dealloc (( PyObject *)(op));
} while (0)
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18. Reference Counting Algorithm
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19. Reference Counting Algorithm
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20. Reference Counting Algorithm
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21. Reference Counting Algorithm
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22. Reference Counting Algorithm
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23. Reference Counting Algorithm
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24. Cycles
l = []
l.append(l)
del l
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25. Cycles
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26. Cycles
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27. PyGC Head
typedef union _gc_head {
struct {
union _gc_head *gc_next;
union _gc_head *gc_prev;
Py_ssize_t gc_refs;
} gc;
double dummy; /* force worst -case alignment */
} PyGC_Head;
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28. Cycles
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29. Cycles
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30. Cycles
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31. Cycles
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32. Cycles
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33. Cycles
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34. Cycles
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35. Cycles
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36. Cycles
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37. Cycles
Wait!!
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38. Cycles
Finalizers
Weak references
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39. Cycles
Objects reachable from finalizers can’t safely be deleted.
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40. Cycles
So, they’re moved to uncollectable set.
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41. Cycles
So, they’re moved to uncollectable set.
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42. Cycles
Only reachable weakref callbacks are invoked.
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43. CPython Memory Allocator
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44. CPython Memory Allocator
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45. Demo
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46. Reference counting
Pros: Is incremental, as it works, it frees memory.
Cons: Detecting Cycles could be hard.
Cons: Size overhead on objects.
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47. PyPy
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48. PyPy GC
Agnostic GC
Different implementations over time
Nowadays it uses incminmark
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49. Young objects
[elem * 2 for elem in elements]
balance = (a / b / c) * 4
’asdadsasd -xxx’.replace(’x’, ’y’). replace(’a’, ’
foo.bar()
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50. PyPy memory model
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51. PyPy GC
Minor and Major collection
Objects are moved only once
Major collection is done incrementally (to avoid long stops)
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52. PyPy memory model
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53. Francisco Fernandez Castano (@fcofdezc) Python GC April 17, 2015 53 / 61

54. Mark and Sweep Algorithm
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55. Mark and Sweep Algorithm
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56. Mark and Sweep Algorithm
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57. Mark and Sweep Algorithm
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58. Mark and sweep
Pros: Can collect cycles.
Cons: Basic implementation stops the world
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59. EuroPython
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60. Questions?
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61. The End
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