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55 | <H1><A NAME="SECTION0011100000000000000000"></A><A NAME="memoryOverview"></A> | |
56 | <BR> | |
57 | 9.1 Overview | |
58 | </H1> | |
59 | ||
60 | <P> | |
61 | Memory management in Python involves a private heap containing all | |
62 | Python objects and data structures. The management of this private | |
63 | heap is ensured internally by the <em>Python memory manager</em>. The | |
64 | Python memory manager has different components which deal with various | |
65 | dynamic storage management aspects, like sharing, segmentation, | |
66 | preallocation or caching. | |
67 | ||
68 | <P> | |
69 | At the lowest level, a raw memory allocator ensures that there is | |
70 | enough room in the private heap for storing all Python-related data | |
71 | by interacting with the memory manager of the operating system. On top | |
72 | of the raw memory allocator, several object-specific allocators | |
73 | operate on the same heap and implement distinct memory management | |
74 | policies adapted to the peculiarities of every object type. For | |
75 | example, integer objects are managed differently within the heap than | |
76 | strings, tuples or dictionaries because integers imply different | |
77 | storage requirements and speed/space tradeoffs. The Python memory | |
78 | manager thus delegates some of the work to the object-specific | |
79 | allocators, but ensures that the latter operate within the bounds of | |
80 | the private heap. | |
81 | ||
82 | <P> | |
83 | It is important to understand that the management of the Python heap | |
84 | is performed by the interpreter itself and that the user has no | |
85 | control over it, even if she regularly manipulates object pointers to | |
86 | memory blocks inside that heap. The allocation of heap space for | |
87 | Python objects and other internal buffers is performed on demand by | |
88 | the Python memory manager through the Python/C API functions listed in | |
89 | this document. | |
90 | ||
91 | <P> | |
92 | To avoid memory corruption, extension writers should never try to | |
93 | operate on Python objects with the functions exported by the C | |
94 | library: <tt class="cfunction">malloc()</tt><a id='l2h-895' xml:id='l2h-895'></a>, | |
95 | <tt class="cfunction">calloc()</tt><a id='l2h-896' xml:id='l2h-896'></a>, | |
96 | <tt class="cfunction">realloc()</tt><a id='l2h-897' xml:id='l2h-897'></a> and | |
97 | <tt class="cfunction">free()</tt><a id='l2h-898' xml:id='l2h-898'></a>. This will result in | |
98 | mixed calls between the C allocator and the Python memory manager | |
99 | with fatal consequences, because they implement different algorithms | |
100 | and operate on different heaps. However, one may safely allocate and | |
101 | release memory blocks with the C library allocator for individual | |
102 | purposes, as shown in the following example: | |
103 | ||
104 | <P> | |
105 | <div class="verbatim"><pre> | |
106 | PyObject *res; | |
107 | char *buf = (char *) malloc(BUFSIZ); /* for I/O */ | |
108 | ||
109 | if (buf == NULL) | |
110 | return PyErr_NoMemory(); | |
111 | ...Do some I/O operation involving buf... | |
112 | res = PyString_FromString(buf); | |
113 | free(buf); /* malloc'ed */ | |
114 | return res; | |
115 | </pre></div> | |
116 | ||
117 | <P> | |
118 | In this example, the memory request for the I/O buffer is handled by | |
119 | the C library allocator. The Python memory manager is involved only | |
120 | in the allocation of the string object returned as a result. | |
121 | ||
122 | <P> | |
123 | In most situations, however, it is recommended to allocate memory from | |
124 | the Python heap specifically because the latter is under control of | |
125 | the Python memory manager. For example, this is required when the | |
126 | interpreter is extended with new object types written in C. Another | |
127 | reason for using the Python heap is the desire to <em>inform</em> the | |
128 | Python memory manager about the memory needs of the extension module. | |
129 | Even when the requested memory is used exclusively for internal, | |
130 | highly-specific purposes, delegating all memory requests to the Python | |
131 | memory manager causes the interpreter to have a more accurate image of | |
132 | its memory footprint as a whole. Consequently, under certain | |
133 | circumstances, the Python memory manager may or may not trigger | |
134 | appropriate actions, like garbage collection, memory compaction or | |
135 | other preventive procedures. Note that by using the C library | |
136 | allocator as shown in the previous example, the allocated memory for | |
137 | the I/O buffer escapes completely the Python memory manager. | |
138 | ||
139 | <P> | |
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175 | <span class="release-info">Release 2.4.2, documentation updated on 28 September 2005.</span> | |
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