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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 | |
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| 175 | <span class="release-info">Release 2.4.2, documentation updated on 28 September 2005.</span> |
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