对象、类型和引用计数
¶
大多数 Python/C API 函数拥有一个或多个自变量及返回值对于类型
PyObject
*
. This type is a pointer to an opaque data type representing an arbitrary Python object. Since all Python object types are treated the same way by the Python language in most situations (e.g., assignments, scope rules, and argument passing), it is only fitting that they should be represented by a single C type. Almost all Python objects live on the heap: you never declare an automatic or static variable of type
PyObject
, only pointer variables of type
PyObject
*
can be declared. The sole exception are the type objects; since these must never be deallocated, they are typically static
PyTypeObject
对象。
All Python objects (even Python integers) have a
type
和
引用计数
. An object’s type determines what kind of object it is (e.g., an integer, a list, or a user-defined function; there are many more as explained in
标准类型层次结构
). For each of the well-known types there is a macro to check whether an object is of that type; for instance,
PyList_Check(a)
is true if (and only if) the object pointed to by
a
is a Python list.
引用计数
¶
The reference count is important because today’s computers have a finite (and often severely limited) memory size; it counts how many different places there are that have a
强引用
to an object. Such a place could be another object, or a global (or static) C variable, or a local variable in some C function. When the last
强引用
to an object is released (i.e. its reference count becomes zero), the object is deallocated. If it contains references to other objects, those references are released. Those other objects may be deallocated in turn, if there are no more references to them, and so on. (There’s an obvious problem with objects that reference each other here; for now, the solution is “don’t do that.”)
Reference counts are always manipulated explicitly. The normal way is to use the macro
Py_INCREF()
to take a new reference to an object (i.e. increment its reference count by one), and
Py_DECREF()
to release that reference (i.e. decrement the reference count by one). The
Py_DECREF()
macro is considerably more complex than the incref one, since it must check whether the reference count becomes zero and then cause the object’s deallocator to be called. The deallocator is a function pointer contained in the object’s type structure. The type-specific deallocator takes care of releasing references for other objects contained in the object if this is a compound object type, such as a list, as well as performing any additional finalization that’s needed. There’s no chance that the reference count can overflow; at least as many bits are used to hold the reference count as there are distinct memory locations in virtual memory (assuming
sizeof(Py_ssize_t) >= sizeof(void*)
). Thus, the reference count increment is a simple operation.
It is not necessary to hold a
强引用
(i.e. increment the reference count) for every local variable that contains a pointer to an object. In theory, the object’s reference count goes up by one when the variable is made to point to it and it goes down by one when the variable goes out of scope. However, these two cancel each other out, so at the end the reference count hasn’t changed. The only real reason to use the reference count is to prevent the object from being deallocated as long as our variable is pointing to it. If we know that there is at least one other reference to the object that lives at least as long as our variable, there is no need to take a new
强引用
(i.e. increment the reference count) temporarily. An important situation where this arises is in objects that are passed as arguments to C functions in an extension module that are called from Python; the call mechanism guarantees to hold a reference to every argument for the duration of the call.
However, a common pitfall is to extract an object from a list and hold on to it for a while without taking a new reference. Some other operation might conceivably remove the object from the list, releasing that reference, and possibly deallocating it. The real danger is that innocent-looking operations may invoke arbitrary Python code which could do this; there is a code path which allows control to flow back to the user from a
Py_DECREF()
, so almost any operation is potentially dangerous.
A safe approach is to always use the generic operations (functions whose name begins with
PyObject_
,
PyNumber_
,
PySequence_
or
PyMapping_
). These operations always create a new
强引用
(i.e. increment the reference count) of the object they return. This leaves the caller with the responsibility to call
Py_DECREF()
when they are done with the result; this soon becomes second nature.
引用计数细节
¶
The reference count behavior of functions in the Python/C API is best explained in terms of
ownership of references
. Ownership pertains to references, never to objects (objects are not owned: they are always shared). “Owning a reference” means being responsible for calling Py_DECREF on it when the reference is no longer needed. Ownership can also be transferred, meaning that the code that receives ownership of the reference then becomes responsible for eventually releasing it by calling
Py_DECREF()
or
Py_XDECREF()
when it’s no longer needed—or passing on this responsibility (usually to its caller). When a function passes ownership of a reference on to its caller, the caller is said to receive a
new
reference. When no ownership is transferred, the caller is said to
borrow
the reference. Nothing needs to be done for a
借位引用
.
Conversely, when a calling function passes in a reference to an object, there are two possibilities: the function
steals
a reference to the object, or it does not.
Stealing a reference
means that when you pass a reference to a function, that function assumes that it now owns that reference, and you are not responsible for it any longer.
Few functions steal references; the two notable exceptions are
PyList_SetItem()
and
PyTuple_SetItem()
, which steal a reference to the item (but not to the tuple or list into which the item is put!). These functions were designed to steal a reference because of a common idiom for populating a tuple or list with newly created objects; for example, the code to create the tuple
(1, 2, "three")
could look like this (forgetting about error handling for the moment; a better way to code this is shown below):
PyObject *t;
t = PyTuple_New(3);
PyTuple_SetItem(t, 0, PyLong_FromLong(1L));
PyTuple_SetItem(t, 1, PyLong_FromLong(2L));
PyTuple_SetItem(t, 2, PyUnicode_FromString("three"));
这里，
PyLong_FromLong()
returns a new reference which is immediately stolen by
PyTuple_SetItem()
. When you want to keep using an object although the reference to it will be stolen, use
Py_INCREF()
to grab another reference before calling the reference-stealing function.
Incidentally,
PyTuple_SetItem()
是
only
way to set tuple items;
PySequence_SetItem()
and
PyObject_SetItem()
refuse to do this since tuples are an immutable data type. You should only use
PyTuple_SetItem()
for tuples that you are creating yourself.
Equivalent code for populating a list can be written using
PyList_New()
and
PyList_SetItem()
.
However, in practice, you will rarely use these ways of creating and populating a tuple or list. There’s a generic function,
Py_BuildValue()
, that can create most common objects from C values, directed by a
format string
. For example, the above two blocks of code could be replaced by the following (which also takes care of the error checking):
PyObject *tuple, *list;
tuple = Py_BuildValue("(iis)", 1, 2, "three");
list = Py_BuildValue("[iis]", 1, 2, "three");
It is much more common to use
PyObject_SetItem()
and friends with items whose references you are only borrowing, like arguments that were passed in to the function you are writing. In that case, their behaviour regarding references is much saner, since you don’t have to take a new reference just so you can give that reference away (“have it be stolen”). For example, this function sets all items of a list (actually, any mutable sequence) to a given item:
int
set_all(PyObject *target, PyObject *item)
{
Py_ssize_t i, n;
n = PyObject_Length(target);
if (n < 0)
return -1;
for (i = 0; i < n; i++) {
PyObject *index = PyLong_FromSsize_t(i);
if (!index)
return -1;
if (PyObject_SetItem(target, index, item) < 0) {
Py_DECREF(index);
return -1;
}
Py_DECREF(index);
}
return 0;
}
The situation is slightly different for function return values. While passing a reference to most functions does not change your ownership responsibilities for that reference, many functions that return a reference to an object give you ownership of the reference. The reason is simple: in many cases, the returned object is created on the fly, and the reference you get is the only reference to the object. Therefore, the generic functions that return object references, like
PyObject_GetItem()
and
PySequence_GetItem()
, always return a new reference (the caller becomes the owner of the reference).
It is important to realize that whether you own a reference returned by a function depends on which function you call only —
the plumage
(the type of the object passed as an argument to the function)
doesn’t enter into it!
Thus, if you extract an item from a list using
PyList_GetItem()
, you don’t own the reference — but if you obtain the same item from the same list using
PySequence_GetItem()
(which happens to take exactly the same arguments), you do own a reference to the returned object.
Here is an example of how you could write a function that computes the sum of the items in a list of integers; once using
PyList_GetItem()
, and once using
PySequence_GetItem()
.
long
sum_list(PyObject *list)
{
Py_ssize_t i, n;
long total = 0, value;
PyObject *item;
n = PyList_Size(list);
if (n < 0)
return -1; /* Not a list */
for (i = 0; i < n; i++) {
item = PyList_GetItem(list, i); /* Can't fail */
if (!PyLong_Check(item)) continue; /* Skip non-integers */
value = PyLong_AsLong(item);
if (value == -1 && PyErr_Occurred())
/* Integer too big to fit in a C long, bail out */
return -1;
total += value;
}
return total;
}
long
sum_sequence(PyObject *sequence)
{
Py_ssize_t i, n;
long total = 0, value;
PyObject *item;
n = PySequence_Length(sequence);
if (n < 0)
return -1; /* Has no length */
for (i = 0; i < n; i++) {
item = PySequence_GetItem(sequence, i);
if (item == NULL)
return -1; /* Not a sequence, or other failure */
if (PyLong_Check(item)) {
value = PyLong_AsLong(item);
Py_DECREF(item);
if (value == -1 && PyErr_Occurred())
/* Integer too big to fit in a C long, bail out */
return -1;
total += value;
}
else {
Py_DECREF(item); /* Discard reference ownership */
}
}
return total;
}
类型
¶
There are few other data types that play a significant role in the Python/C API; most are simple C types such as
int
,
long
,
double
and
char
*
. A few structure types are used to describe static tables used to list the functions exported by a module or the data attributes of a new object type, and another is used to describe the value of a complex number. These will be discussed together with the functions that use them.
-
type
Py_ssize_t
¶
-
属于
稳定 ABI (应用程序二进制接口)
.
A signed integral type such that
sizeof(Py_ssize_t) == sizeof(size_t)
. C99 doesn’t define such a thing directly (size_t is an unsigned integral type). See
PEP 353
了解细节。
PY_SSIZE_T_MAX
is the largest positive value of type
Py_ssize_t
.
异常
¶
The Python programmer only needs to deal with exceptions if specific error handling is required; unhandled exceptions are automatically propagated to the caller, then to the caller’s caller, and so on, until they reach the top-level interpreter, where they are reported to the user accompanied by a stack traceback.
For C programmers, however, error checking always has to be explicit. All functions in the Python/C API can raise exceptions, unless an explicit claim is made otherwise in a function’s documentation. In general, when a function encounters an error, it sets an exception, discards any object references that it owns, and returns an error indicator. If not documented otherwise, this indicator is either
NULL
or
-1
, depending on the function’s return type. A few functions return a Boolean true/false result, with false indicating an error. Very few functions return no explicit error indicator or have an ambiguous return value, and require explicit testing for errors with
PyErr_Occurred()
. These exceptions are always explicitly documented.
Exception state is maintained in per-thread storage (this is equivalent to using global storage in an unthreaded application). A thread can be in one of two states: an exception has occurred, or not. The function
PyErr_Occurred()
can be used to check for this: it returns a borrowed reference to the exception type object when an exception has occurred, and
NULL
otherwise. There are a number of functions to set the exception state:
PyErr_SetString()
is the most common (though not the most general) function to set the exception state, and
PyErr_Clear()
clears the exception state.
The full exception state consists of three objects (all of which can be
NULL
): the exception type, the corresponding exception value, and the traceback. These have the same meanings as the Python result of
sys.exc_info()
; however, they are not the same: the Python objects represent the last exception being handled by a Python
try
…
except
statement, while the C level exception state only exists while an exception is being passed on between C functions until it reaches the Python bytecode interpreter’s main loop, which takes care of transferring it to
sys.exc_info()
and friends.
Note that starting with Python 1.5, the preferred, thread-safe way to access the exception state from Python code is to call the function
sys.exc_info()
, which returns the per-thread exception state for Python code. Also, the semantics of both ways to access the exception state have changed so that a function which catches an exception will save and restore its thread’s exception state so as to preserve the exception state of its caller. This prevents common bugs in exception handling code caused by an innocent-looking function overwriting the exception being handled; it also reduces the often unwanted lifetime extension for objects that are referenced by the stack frames in the traceback.
As a general principle, a function that calls another function to perform some task should check whether the called function raised an exception, and if so, pass the exception state on to its caller. It should discard any object references that it owns, and return an error indicator, but it should
not
set another exception — that would overwrite the exception that was just raised, and lose important information about the exact cause of the error.
A simple example of detecting exceptions and passing them on is shown in the
sum_sequence()
example above. It so happens that this example doesn’t need to clean up any owned references when it detects an error. The following example function shows some error cleanup. First, to remind you why you like Python, we show the equivalent Python code:
def incr_item(dict, key):
try:
item = dict[key]
except KeyError:
item = 0
dict[key] = item + 1
Here is the corresponding C code, in all its glory:
int
incr_item(PyObject *dict, PyObject *key)
{
/* Objects all initialized to NULL for Py_XDECREF */
PyObject *item = NULL, *const_one = NULL, *incremented_item = NULL;
int rv = -1; /* Return value initialized to -1 (failure) */
item = PyObject_GetItem(dict, key);
if (item == NULL) {
/* Handle KeyError only: */
if (!PyErr_ExceptionMatches(PyExc_KeyError))
goto error;
/* Clear the error and use zero: */
PyErr_Clear();
item = PyLong_FromLong(0L);
if (item == NULL)
goto error;
}
const_one = PyLong_FromLong(1L);
if (const_one == NULL)
goto error;
incremented_item = PyNumber_Add(item, const_one);
if (incremented_item == NULL)
goto error;
if (PyObject_SetItem(dict, key, incremented_item) < 0)
goto error;
rv = 0; /* Success */
/* Continue with cleanup code */
error:
/* Cleanup code, shared by success and failure path */
/* Use Py_XDECREF() to ignore NULL references */
Py_XDECREF(item);
Py_XDECREF(const_one);
Py_XDECREF(incremented_item);
return rv; /* -1 for error, 0 for success */
}
This example represents an endorsed use of the
goto
statement in C! It illustrates the use of
PyErr_ExceptionMatches()
and
PyErr_Clear()
to handle specific exceptions, and the use of
Py_XDECREF()
to dispose of owned references that may be
NULL
(note the
'X'
in the name;
Py_DECREF()
would crash when confronted with a
NULL
reference). It is important that the variables used to hold owned references are initialized to
NULL
for this to work; likewise, the proposed return value is initialized to
-1
(failure) and only set to success after the final call made is successful.
嵌入 Python
¶
The one important task that only embedders (as opposed to extension writers) of the Python interpreter have to worry about is the initialization, and possibly the finalization, of the Python interpreter. Most functionality of the interpreter can only be used after the interpreter has been initialized.
The basic initialization function is
Py_Initialize()
. This initializes the table of loaded modules, and creates the fundamental modules
builtins
,
__main__
，和
sys
。它还初始化模块搜索路径 (
sys.path
).
Py_Initialize()
does not set the “script argument list” (
sys.argv
). If this variable is needed by Python code that will be executed later, setting
PyConfig.argv
and
PyConfig.parse_argv
must be set: see
Python 初始化配置
.
On most systems (in particular, on Unix and Windows, although the details are slightly different),
Py_Initialize()
calculates the module search path based upon its best guess for the location of the standard Python interpreter executable, assuming that the Python library is found in a fixed location relative to the Python interpreter executable. In particular, it looks for a directory named
lib/pythonX.Y
relative to the parent directory where the executable named
python
is found on the shell command search path (the environment variable
PATH
).
For instance, if the Python executable is found in
/usr/local/bin/python
, it will assume that the libraries are in
/usr/local/lib/pythonX.Y
. (In fact, this particular path is also the “fallback” location, used when no executable file named
python
is found along
PATH
.) The user can override this behavior by setting the environment variable
PYTHONHOME
, or insert additional directories in front of the standard path by setting
PYTHONPATH
.
The embedding application can steer the search by setting
PyConfig.program_name
before
调用
Py_InitializeFromConfig()
。注意，
PYTHONHOME
still overrides this and
PYTHONPATH
is still inserted in front of the standard path. An application that requires total control has to provide its own implementation of
Py_GetPath()
,
Py_GetPrefix()
,
Py_GetExecPrefix()
，和
Py_GetProgramFullPath()
(all defined in
Modules/getpath.c
).
Sometimes, it is desirable to “uninitialize” Python. For instance, the application may want to start over (make another call to
Py_Initialize()
) or the application is simply done with its use of Python and wants to free memory allocated by Python. This can be accomplished by calling
Py_FinalizeEx()
. The function
Py_IsInitialized()
returns true if Python is currently in the initialized state. More information about these functions is given in a later chapter. Notice that
Py_FinalizeEx()
does
not
free all memory allocated by the Python interpreter, e.g. memory allocated by extension modules currently cannot be released.
调试构建
¶
Python can be built with several macros to enable extra checks of the interpreter and extension modules. These checks tend to add a large amount of overhead to the runtime so they are not enabled by default.
A full list of the various types of debugging builds is in the file
Misc/SpecialBuilds.txt
in the Python source distribution. Builds are available that support tracing of reference counts, debugging the memory allocator, or low-level profiling of the main interpreter loop. Only the most frequently used builds will be described in the remainder of this section.
-
Py_DEBUG
¶
Compiling the interpreter with the
Py_DEBUG
macro defined produces what is generally meant by
a debug build of Python
.
Py_DEBUG
is enabled in the Unix build by adding
--with-pydebug
到
./configure
command. It is also implied by the presence of the not-Python-specific
_DEBUG
macro. When
Py_DEBUG
is enabled in the Unix build, compiler optimization is disabled.
In addition to the reference count debugging described below, extra checks are performed, see
Python Debug Build
.
定义
Py_TRACE_REFS
enables reference tracing (see the
configure --with-trace-refs option
). When defined, a circular doubly linked list of active objects is maintained by adding two extra fields to every
PyObject
. Total allocations are tracked as well. Upon exit, all existing references are printed. (In interactive mode this happens after every statement run by the interpreter.)
请参考
Misc/SpecialBuilds.txt
在 Python 源代码分发以获取更多详细信息。
