Memory management in Python involves a private heap containing all Python objects and data structures. The management of this private heap is ensured internally by the Python memory manager . The Python memory manager has different components which deal with various dynamic storage management aspects, like sharing, segmentation, preallocation or caching.
At the lowest level, a raw memory allocator ensures that there is enough room in the private heap for storing all Python-related data by interacting with the memory manager of the operating system. On top of the raw memory allocator, several object-specific allocators operate on the same heap and implement distinct memory management policies adapted to the peculiarities of every object type. For example, integer objects are managed differently within the heap than strings, tuples or dictionaries because integers imply different storage requirements and speed/space tradeoffs. The Python memory manager thus delegates some of the work to the object-specific allocators, but ensures that the latter operate within the bounds of the private heap.
It is important to understand that the management of the Python heap is performed by the interpreter itself and that the user has no control over it, even if they regularly manipulate object pointers to memory blocks inside that heap. The allocation of heap space for Python objects and other internal buffers is performed on demand by the Python memory manager through the Python/C API functions listed in this document.
To avoid memory corruption, extension writers should never try to operate on Python objects with the functions exported by the C library:
malloc()
,
calloc()
,
realloc()
and
free()
. This will result in mixed calls between the C allocator and the Python memory manager with fatal consequences, because they implement different algorithms and operate on different heaps. However, one may safely allocate and release memory blocks with the C library allocator for individual purposes, as shown in the following example:
PyObject *res; char *buf = (char *) malloc(BUFSIZ); /* for I/O */ if (buf == NULL) return PyErr_NoMemory(); ...Do some I/O operation involving buf... res = PyBytes_FromString(buf); free(buf); /* malloc'ed */ return res;
In this example, the memory request for the I/O buffer is handled by the C library allocator. The Python memory manager is involved only in the allocation of the bytes object returned as a result.
In most situations, however, it is recommended to allocate memory from the Python heap specifically because the latter is under control of the Python memory manager. For example, this is required when the interpreter is extended with new object types written in C. Another reason for using the Python heap is the desire to inform the Python memory manager about the memory needs of the extension module. Even when the requested memory is used exclusively for internal, highly specific purposes, delegating all memory requests to the Python memory manager causes the interpreter to have a more accurate image of its memory footprint as a whole. Consequently, under certain circumstances, the Python memory manager may or may not trigger appropriate actions, like garbage collection, memory compaction or other preventive procedures. Note that by using the C library allocator as shown in the previous example, the allocated memory for the I/O buffer escapes completely the Python memory manager.
另请参阅
PYTHONMALLOC
environment variable can be used to configure the memory allocators used by Python.
PYTHONMALLOCSTATS
环境变量可用于打印统计信息为
pymalloc 内存分配器
每次创建新 pymalloc 对象 arena 时和关闭时。
All allocating functions belong to one of three different “domains” (see also
PyMemAllocatorDomain
). These domains represent different allocation strategies and are optimized for different purposes. The specific details on how every domain allocates memory or what internal functions each domain calls is considered an implementation detail, but for debugging purposes a simplified table can be found at
here
. There is no hard requirement to use the memory returned by the allocation functions belonging to a given domain for only the purposes hinted by that domain (although this is the recommended practice). For example, one could use the memory returned by
PyMem_RawMalloc()
for allocating Python objects or the memory returned by
PyObject_Malloc()
for allocating memory for buffers.
The three allocation domains are:
Raw domain: intended for allocating memory for general-purpose memory buffers where the allocation must go to the system allocator or where the allocator can operate without the GIL . The memory is requested directly to the system.
“Mem” domain: intended for allocating memory for Python buffers and general-purpose memory buffers where the allocation must be performed with the GIL held. The memory is taken from the Python private heap.
Object domain: intended for allocating memory belonging to Python objects. The memory is taken from the Python private heap.
When freeing memory previously allocated by the allocating functions belonging to a given domain,the matching specific deallocating functions must be used. For example,
PyMem_Free()
must be used to free memory allocated using
PyMem_Malloc()
.
The following function sets are wrappers to the system allocator. These functions are thread-safe, the GIL 不需要保持。
默认原生内存分配器
使用下列函数：
malloc()
,
calloc()
,
realloc()
and
free()
; call
malloc(1)
(或
calloc(1, 1)
) 当请求 0 字节时。
3.4 版新增。
分配
n
字节并返回指针为类型
void
*
到分配的内存，或
NULL
若请求失败。
Requesting zero bytes returns a distinct non-
NULL
pointer if possible, as if
PyMem_RawMalloc(1)
had been called instead. The memory will not have been initialized in any way.
分配
nelem
elements each whose size in bytes is
elsize
and returns a pointer of type
void
*
到分配的内存，或
NULL
if the request fails. The memory is initialized to zeros.
Requesting zero elements or elements of size zero bytes returns a distinct non-
NULL
pointer if possible, as if
PyMem_RawCalloc(1, 1)
had been called instead.
3.5 版新增。
Resizes the memory block pointed to by p to n bytes. The contents will be unchanged to the minimum of the old and the new sizes.
若
p
is
NULL
, the call is equivalent to
PyMem_RawMalloc(n)
；否则若
n
is equal to zero, the memory block is resized but is not freed, and the returned pointer is non-
NULL
.
除非
p
is
NULL
, it must have been returned by a previous call to
PyMem_RawMalloc()
,
PyMem_RawRealloc()
or
PyMem_RawCalloc()
.
若请求失败，
PyMem_RawRealloc()
返回
NULL
and
p
remains a valid pointer to the previous memory area.
Frees the memory block pointed to by
p
, which must have been returned by a previous call to
PyMem_RawMalloc()
,
PyMem_RawRealloc()
or
PyMem_RawCalloc()
。否则，或者若
PyMem_RawFree(p)
has been called before, undefined behavior occurs.
若
p
is
NULL
，没有操作被履行。
The following function sets, modeled after the ANSI C standard, but specifying behavior when requesting zero bytes, are available for allocating and releasing memory from the Python heap.
default memory allocator 使用 pymalloc 内存分配器 .
警告
GIL 必须保持当使用这些函数时。
3.6 版改变：
默认分配器现在是 pymalloc 而不是系统
malloc()
.
分配
n
字节并返回指针为类型
void
*
到分配的内存，或
NULL
若请求失败。
Requesting zero bytes returns a distinct non-
NULL
pointer if possible, as if
PyMem_Malloc(1)
had been called instead. The memory will not have been initialized in any way.
分配
nelem
elements each whose size in bytes is
elsize
and returns a pointer of type
void
*
到分配的内存，或
NULL
if the request fails. The memory is initialized to zeros.
Requesting zero elements or elements of size zero bytes returns a distinct non-
NULL
pointer if possible, as if
PyMem_Calloc(1, 1)
had been called instead.
3.5 版新增。
Resizes the memory block pointed to by p to n bytes. The contents will be unchanged to the minimum of the old and the new sizes.
若
p
is
NULL
, the call is equivalent to
PyMem_Malloc(n)
；否则若
n
is equal to zero, the memory block is resized but is not freed, and the returned pointer is non-
NULL
.
除非
p
is
NULL
, it must have been returned by a previous call to
PyMem_Malloc()
,
PyMem_Realloc()
or
PyMem_Calloc()
.
若请求失败，
PyMem_Realloc()
返回
NULL
and
p
remains a valid pointer to the previous memory area.
Frees the memory block pointed to by
p
, which must have been returned by a previous call to
PyMem_Malloc()
,
PyMem_Realloc()
or
PyMem_Calloc()
。否则，或者若
PyMem_Free(p)
has been called before, undefined behavior occurs.
若
p
is
NULL
，没有操作被履行。
The following type-oriented macros are provided for convenience. Note that TYPE refers to any C type.
如同
PyMem_Malloc()
, but allocates
(n * sizeof(TYPE))
bytes of memory. Returns a pointer cast to
TYPE
*
. The memory will not have been initialized in any way.
如同
PyMem_Realloc()
, but the memory block is resized to
(n *
sizeof(TYPE))
bytes. Returns a pointer cast to
TYPE
*
. On return,
p
will be a pointer to the new memory area, or
NULL
in the event of failure.
This is a C preprocessor macro; p is always reassigned. Save the original value of p to avoid losing memory when handling errors.
如同
PyMem_Free()
.
In addition, the following macro sets are provided for calling the Python memory allocator directly, without involving the C API functions listed above. However, note that their use does not preserve binary compatibility across Python versions and is therefore deprecated in extension modules.
PyMem_MALLOC(size)
PyMem_NEW(type, size)
PyMem_REALLOC(ptr, size)
PyMem_RESIZE(ptr, type, size)
PyMem_FREE(ptr)
PyMem_DEL(ptr)
The following function sets, modeled after the ANSI C standard, but specifying behavior when requesting zero bytes, are available for allocating and releasing memory from the Python heap.
注意
There is no guarantee that the memory returned by these allocators can be successfully cast to a Python object when intercepting the allocating functions in this domain by the methods described in the 定制内存分配器 章节。
默认对象分配器 使用 pymalloc 内存分配器 .
警告
GIL 必须保持当使用这些函数时。
分配
n
字节并返回指针为类型
void
*
到分配的内存，或
NULL
若请求失败。
Requesting zero bytes returns a distinct non-
NULL
pointer if possible, as if
PyObject_Malloc(1)
had been called instead. The memory will not have been initialized in any way.
分配
nelem
elements each whose size in bytes is
elsize
and returns a pointer of type
void
*
到分配的内存，或
NULL
if the request fails. The memory is initialized to zeros.
Requesting zero elements or elements of size zero bytes returns a distinct non-
NULL
pointer if possible, as if
PyObject_Calloc(1, 1)
had been called instead.
3.5 版新增。
Resizes the memory block pointed to by p to n bytes. The contents will be unchanged to the minimum of the old and the new sizes.
若
p
is
NULL
, the call is equivalent to
PyObject_Malloc(n)
；否则若
n
is equal to zero, the memory block is resized but is not freed, and the returned pointer is non-
NULL
.
除非
p
is
NULL
, it must have been returned by a previous call to
PyObject_Malloc()
,
PyObject_Realloc()
or
PyObject_Calloc()
.
若请求失败，
PyObject_Realloc()
返回
NULL
and
p
remains a valid pointer to the previous memory area.
Frees the memory block pointed to by
p
, which must have been returned by a previous call to
PyObject_Malloc()
,
PyObject_Realloc()
or
PyObject_Calloc()
。否则，或者若
PyObject_Free(p)
has been called before, undefined behavior occurs.
若
p
is
NULL
，没有操作被履行。
默认内存分配器：
| 配置 | 名称 | PyMem_RawMalloc | PyMem_Malloc | PyObject_Malloc |
|---|---|---|---|---|
| Release build |
"pymalloc"
|
malloc
|
pymalloc
|
pymalloc
|
| Debug build |
"pymalloc_debug"
|
malloc
+ debug
|
pymalloc
+ debug
|
pymalloc
+ debug
|
| Release build, without pymalloc |
"malloc"
|
malloc
|
malloc
|
malloc
|
| Debug build, without pymalloc |
"malloc_debug"
|
malloc
+ debug
|
malloc
+ debug
|
malloc
+ debug
|
图例：
Name: value for
PYTHONMALLOC
环境变量。
malloc
: system allocators from the standard C library, C functions:
malloc()
,
calloc()
,
realloc()
and
free()
.
pymalloc
:
pymalloc 内存分配器
.
“+ debug”: with debug hooks on the Python memory allocators .
“Debug build”: Python build in debug mode .
3.4 版新增。
Structure used to describe a memory block allocator. The structure has the following fields:
| 字段 | 含义 |
|---|---|
void *ctx
|
传递作为第一自变量的用户上下文 |
void* malloc(void *ctx, size_t size)
|
分配内存块 |
void* calloc(void *ctx, size_t nelem, size_t elsize)
|
分配初始化为 0 的内存块 |
void* realloc(void *ctx, void *ptr, size_t new_size)
|
分配或重置内存块大小 |
void free(void *ctx, void *ptr)
|
释放内存块 |
3.5 版改变：
PyMemAllocator
结构被重命名为
PyMemAllocatorEx
和新的
calloc
字段被添加。
用于标识分配器域的枚举。域：
函数：
函数：
函数：
Get the memory block allocator of the specified domain.
Set the memory block allocator of the specified domain.
The new allocator must return a distinct non-
NULL
pointer when requesting zero bytes.
对于
PYMEM_DOMAIN_RAW
domain, the allocator must be thread-safe: the
GIL
is not held when the allocator is called.
If the new allocator is not a hook (does not call the previous allocator), the
PyMem_SetupDebugHooks()
function must be called to reinstall the debug hooks on top on the new allocator.
另请参阅
PyPreConfig.allocator
and
采用 PyPreConfig 预初始化 Python
.
Setup debug hooks in the Python memory allocators to detect memory errors.
当
Python is built in debug mode
，
PyMem_SetupDebugHooks()
function is called at the
Python 预初始化
to setup debug hooks on Python memory allocators to detect memory errors.
PYTHONMALLOC
environment variable can be used to install debug hooks on a Python compiled in release mode (ex:
PYTHONMALLOC=debug
).
PyMem_SetupDebugHooks()
function can be used to set debug hooks after calling
PyMem_SetAllocator()
.
These debug hooks fill dynamically allocated memory blocks with special, recognizable bit patterns. Newly allocated memory is filled with the byte
0xCD
(
PYMEM_CLEANBYTE
), freed memory is filled with the byte
0xDD
(
PYMEM_DEADBYTE
). Memory blocks are surrounded by “forbidden bytes” filled with the byte
0xFD
(
PYMEM_FORBIDDENBYTE
). Strings of these bytes are unlikely to be valid addresses, floats, or ASCII strings.
运行时校验：
Detect API violations. For example, detect if
PyObject_Free()
is called on a memory block allocated by
PyMem_Malloc()
.
Detect write before the start of the buffer (buffer underflow).
Detect write after the end of the buffer (buffer overflow).
Check that the
GIL
is held when allocator functions of
PYMEM_DOMAIN_OBJ
(ex:
PyObject_Malloc()
) 和
PYMEM_DOMAIN_MEM
(ex:
PyMem_Malloc()
) domains are called.
当出错时，调试挂钩使用
tracemalloc
module to get the traceback where a memory block was allocated. The traceback is only displayed if
tracemalloc
is tracing Python memory allocations and the memory block was traced.
Let
S
=
sizeof(size_t)
.
2*S
bytes are added at each end of each block of
N
bytes requested. The memory layout is like so, where p represents the address returned by a malloc-like or realloc-like function (
p[i:j]
means the slice of bytes from
*(p+i)
inclusive up to
*(p+j)
exclusive; note that the treatment of negative indices differs from a Python slice):
p[-2*S:-S]
Number of bytes originally asked for. This is a size_t, big-endian (easier to read in a memory dump).
p[-S]
API identifier (ASCII character):
'r'
for
PYMEM_DOMAIN_RAW
.
'm'
for
PYMEM_DOMAIN_MEM
.
'o'
for
PYMEM_DOMAIN_OBJ
.
p[-S+1:0]
Copies of PYMEM_FORBIDDENBYTE. Used to catch under- writes and reads.
p[0:N]
The requested memory, filled with copies of PYMEM_CLEANBYTE, used to catch reference to uninitialized memory. When a realloc-like function is called requesting a larger memory block, the new excess bytes are also filled with PYMEM_CLEANBYTE. When a free-like function is called, these are overwritten with PYMEM_DEADBYTE, to catch reference to freed memory. When a realloc- like function is called requesting a smaller memory block, the excess old bytes are also filled with PYMEM_DEADBYTE.
p[N:N+S]
Copies of PYMEM_FORBIDDENBYTE. Used to catch over- writes and reads.
p[N+S:N+2*S]
Only used if the
PYMEM_DEBUG_SERIALNO
macro is defined (not defined by default).
A serial number, incremented by 1 on each call to a malloc-like or realloc-like function. Big-endian
size_t
. If “bad memory” is detected later, the serial number gives an excellent way to set a breakpoint on the next run, to capture the instant at which this block was passed out. The static function bumpserialno() in obmalloc.c is the only place the serial number is incremented, and exists so you can set such a breakpoint easily.
A realloc-like or free-like function first checks that the PYMEM_FORBIDDENBYTE bytes at each end are intact. If they’ve been altered, diagnostic output is written to stderr, and the program is aborted via Py_FatalError(). The other main failure mode is provoking a memory error when a program reads up one of the special bit patterns and tries to use it as an address. If you get in a debugger then and look at the object, you’re likely to see that it’s entirely filled with PYMEM_DEADBYTE (meaning freed memory is getting used) or PYMEM_CLEANBYTE (meaning uninitialized memory is getting used).
3.6 版改变：
PyMem_SetupDebugHooks()
function now also works on Python compiled in release mode. On error, the debug hooks now use
tracemalloc
to get the traceback where a memory block was allocated. The debug hooks now also check if the GIL is held when functions of
PYMEM_DOMAIN_OBJ
and
PYMEM_DOMAIN_MEM
domains are called.
3.8 版改变：
Byte patterns
0xCB
(
PYMEM_CLEANBYTE
),
0xDB
(
PYMEM_DEADBYTE
) 和
0xFB
(
PYMEM_FORBIDDENBYTE
) have been replaced with
0xCD
,
0xDD
and
0xFD
to use the same values than Windows CRT debug
malloc()
and
free()
.
Python 拥有
pymalloc
allocator optimized for small objects (smaller or equal to 512 bytes) with a short lifetime. It uses memory mappings called “arenas” with a fixed size of 256 KiB. It falls back to
PyMem_RawMalloc()
and
PyMem_RawRealloc()
for allocations larger than 512 bytes.
pymalloc
是
default allocator
的
PYMEM_DOMAIN_MEM
(ex:
PyMem_Malloc()
) 和
PYMEM_DOMAIN_OBJ
(ex:
PyObject_Malloc()
) 域。
The arena allocator uses the following functions:
VirtualAlloc()
and
VirtualFree()
在 Windows，
mmap()
and
munmap()
若可用，
malloc()
and
free()
否则。
This allocator is disabled if Python is configured with the
--without-pymalloc
option. It can also be disabled at runtime using the
PYTHONMALLOC
environment variable (ex:
PYTHONMALLOC=malloc
).
3.4 版新增。
Structure used to describe an arena allocator. The structure has three fields:
| 字段 | 含义 |
|---|---|
void *ctx
|
传递作为第一自变量的用户上下文 |
void* alloc(void *ctx, size_t size)
|
allocate an arena of size bytes |
void free(void *ctx, void *ptr, size_t size)
|
释放 arena |
获取 arena 分配器。
设置 arena 分配器。
3.7 版新增。
Track an allocated memory block in the
tracemalloc
模块。
返回
0
当成功时，返回
-1
on error (failed to allocate memory to store the trace). Return
-2
if tracemalloc is disabled.
If memory block is already tracked, update the existing trace.
Untrack an allocated memory block in the
tracemalloc
module. Do nothing if the block was not tracked.
返回
-2
if tracemalloc is disabled, otherwise return
0
.
Here is the example from section 概述 , rewritten so that the I/O buffer is allocated from the Python heap by using the first function set:
PyObject *res; char *buf = (char *) PyMem_Malloc(BUFSIZ); /* for I/O */ if (buf == NULL) return PyErr_NoMemory(); /* ...Do some I/O operation involving buf... */ res = PyBytes_FromString(buf); PyMem_Free(buf); /* allocated with PyMem_Malloc */ return res;
The same code using the type-oriented function set:
PyObject *res; char *buf = PyMem_New(char, BUFSIZ); /* for I/O */ if (buf == NULL) return PyErr_NoMemory(); /* ...Do some I/O operation involving buf... */ res = PyBytes_FromString(buf); PyMem_Del(buf); /* allocated with PyMem_New */ return res;
Note that in the two examples above, the buffer is always manipulated via functions belonging to the same set. Indeed, it is required to use the same memory API family for a given memory block, so that the risk of mixing different allocators is reduced to a minimum. The following code sequence contains two errors, one of which is labeled as fatal because it mixes two different allocators operating on different heaps.
char *buf1 = PyMem_New(char, BUFSIZ); char *buf2 = (char *) malloc(BUFSIZ); char *buf3 = (char *) PyMem_Malloc(BUFSIZ); ... PyMem_Del(buf3); /* Wrong -- should be PyMem_Free() */ free(buf2); /* Right -- allocated via malloc() */ free(buf1); /* Fatal -- should be PyMem_Del() */
In addition to the functions aimed at handling raw memory blocks from the Python heap, objects in Python are allocated and released with
PyObject_New()
,
PyObject_NewVar()
and
PyObject_Del()
.
These will be explained in the next chapter on defining and implementing new object types in C.