Python Garbage collection.

1. Knowing your garbage collector
Francisco Fernandez Castano
upclose.me
francisco.fernandez.castano@gmail.com @fcofdezc
January 31, 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.
Francisco Fernandez Castano (@fcofdezc) Python GC January 31, 2015 8 / 37

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.
Francisco Fernandez Castano (@fcofdezc) Python GC January 31, 2015 9 / 37

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. 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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13. PyObject
typedef struct _object {
_PyObject_HEAD_EXTRA
Py_ssize_t ob_refcnt;
struct _typeobject *ob_type;
} PyObject;
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14. PyTypeObject
typedef struct _typeobject {
PyObject_VAR_HEAD
const char *tp_name;
Py_ssize_t tp_basicsize , tp_itemsize;
destructor tp_dealloc;
printfunc tp_print;
getattrfunc tp_getattr;
setattrfunc tp_setattr;
void *tp_reserved;
.
.
} PyTypeObject;
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15. Reference Counting Algorithm
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16. Reference Counting Algorithm
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17. Reference Counting Algorithm
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18. Reference Counting Algorithm
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19. Reference Counting Algorithm
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20. Cycles
l = []
l.append(l)
del l
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21. Cycles
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22. Cycles
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23. PyObject
typedef struct _object {
_PyObject_HEAD_EXTRA
Py_ssize_t ob_refcnt;
struct _typeobject *ob_type;
} PyObject;
Francisco Fernandez Castano (@fcofdezc) Python GC January 31, 2015 23 / 37

24. PyTypeObject
typedef struct _typeobject {
PyObject_VAR_HEAD
const char *tp_name;
Py_ssize_t tp_basicsize , tp_itemsize;
destructor tp_dealloc;
printfunc tp_print;
getattrfunc tp_getattr;
setattrfunc tp_setattr;
void *tp_reserved;
.
.
} PyTypeObject;
Francisco Fernandez Castano (@fcofdezc) Python GC January 31, 2015 24 / 37

25. 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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26. CPython Memory Allocator
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27. CPython Memory Allocator
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28. Demo
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29. 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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30. PyPy
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31. Mark and Sweep Algorithm
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32. Mark and Sweep Algorithm
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33. Mark and Sweep Algorithm
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34. Mark and Sweep Algorithm
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35. Mark and sweep
Pros: Can collect cycles.
Cons: Basic implementation stops the world
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36. Questions?
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37. The End
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