Talk about garbage collection in CPython and PyPy

1. Knowing your garbage collector
Francisco Fernandez Castano
Rushmore.fm
francisco.fernandez.castano@gmail.com @fcofdezc
October 21, 2014
Francisco Fernandez Castano (@fcofdezc) Python GC October 21, 2014 1 / 37

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 &num ;
}
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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 October 21, 2014 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 October 21, 2014 9 / 37

10. Garbage collection
De

11. nition
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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12. 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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13. Young objects
[ elem * 2 for elem in elements ]
balance = (a / b / c) * 4
'asdadsasd - xxx '. replace ('x', 'y'). replace ('a', 'b')
foo.bar ()
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14. PyObject
typedef struct _object {
_PyObject_HEAD_EXTRA
Py_ssize_t ob_refcnt ;
struct _typeobject * ob_type ;
} PyObject ;
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15. 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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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. Reference Counting Algorithm
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21. Cycles
l = []
l. append (l)
del l
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22. Cycles
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23. Cycles
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24. PyObject
typedef struct _object {
_PyObject_HEAD_EXTRA
Py_ssize_t ob_refcnt ;
struct _typeobject * ob_type ;
} PyObject ;
Francisco Fernandez Castano (@fcofdezc) Python GC October 21, 2014 23 / 37

25. 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 October 21, 2014 24 / 37

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