It is quite easy to add new built-in modules to Python, if you know how to program in C. Such extension modules can do two things that can’t be done directly in Python: they can implement new built-in object types, and they can call C library functions and system calls.
To support extensions, the Python API (Application Programmers Interface) defines a set of functions, macros and variables that provide access to most aspects of the Python run-time system. The Python API is incorporated in a C source file by including the header "Python.h".
The compilation of an extension module depends on its intended use as well as on your system setup; details are given in later chapters.
Note
The C extension interface is specific to CPython, and extension modules do not work on other Python implementations. In many cases, it is possible to avoid writing C extensions and preserve portability to other implementations. For example, if your use case is calling C library functions or system calls, you should consider using the
module or the
library rather than writing custom C code. These modules let you write Python code to interface with C code and are more portable between implementations of Python than writing and compiling a C extension module.
1.1. A Simple Example
Let’s create an extension module called spam (the favorite food of Monty Python fans…) and let’s say we want to create a Python interface to the C library function system()
. This function takes a null-terminated character string as argument and returns an integer. We want this function to be callable from Python as follows:
>>> importspam>>> status=spam.system("ls -l")Begin by creating a file spammodule.c. (Historically, if a module is called spam, the C file containing its implementation is called spammodule.c; if the module name is very long, like spammify, the module name can be just spammify.c.)
The first two lines of our file can be:
#define PY_SSIZE_T_CLEAN#include<Python.h>which pulls in the Python API (you can add a comment describing the purpose of the module and a copyright notice if you like).
Note
Since Python may define some pre-processor definitions which affect the standard headers on some systems, you must include Python.h before any standard headers are included.
#definePY_SSIZE_T_CLEAN was used to indicate that Py_ssize_t should be used in some APIs instead of int. It is not necessary since Python 3.13, but we keep it here for backward compatibility. See
for a description of this macro.
All user-visible symbols defined by Python.h have a prefix of Py or PY, except those defined in standard header files.
Tip
For backward compatibility, Python.h includes several standard header files. C extensions should include the standard headers that they use, and should not rely on these implicit includes. If using the limited C API version 3.13 or newer, the implicit includes are:
<assert.h>
<intrin.h> (on Windows)
<inttypes.h>
<limits.h>
<math.h>
<stdarg.h>
<wchar.h>
<sys/types.h> (if present)
If
is not defined, or is set to version 3.12 or older, the headers below are also included:
<ctype.h>
<unistd.h> (on POSIX)
If
is not defined, or is set to version 3.10 or older, the headers below are also included:
<errno.h>
<stdio.h>
<stdlib.h>
<string.h>
The next thing we add to our module file is the C function that will be called when the Python expression spam.system(string) is evaluated (we’ll see shortly how it ends up being called):
staticPyObject*spam_system(PyObject*self,PyObject*args){constchar*command;intsts;if(!PyArg_ParseTuple(args,"s",&command))returnNULL;sts=system(command);returnPyLong_FromLong(sts);}There is a straightforward translation from the argument list in Python (for example, the single expression "ls-l") to the arguments passed to the C function. The C function always has two arguments, conventionally named self and args.
The self argument points to the module object for module-level functions; for a method it would point to the object instance.
The args argument will be a pointer to a Python tuple object containing the arguments. Each item of the tuple corresponds to an argument in the call’s argument list. The arguments are Python objects — in order to do anything with them in our C function we have to convert them to C values. The function
in the Python API checks the argument types and converts them to C values. It uses a template string to determine the required types of the arguments as well as the types of the C variables into which to store the converted values. More about this later.
returns true (nonzero) if all arguments have the right type and its components have been stored in the variables whose addresses are passed. It returns false (zero) if an invalid argument list was passed. In the latter case it also raises an appropriate exception so the calling function can return NULL immediately (as we saw in the example).
1.2. Intermezzo: Errors and Exceptions
An important convention throughout the Python interpreter is the following: when a function fails, it should set an exception condition and return an error value (usually -1 or a NULL pointer). Exception information is stored in three members of the interpreter’s thread state. These are NULL if there is no exception. Otherwise they are the C equivalents of the members of the Python tuple returned by
. These are the exception type, exception instance, and a traceback object. It is important to know about them to understand how errors are passed around.
The Python API defines a number of functions to set various types of exceptions.
The most common one is
. Its arguments are an exception object and a C string. The exception object is usually a predefined object like
. The C string indicates the cause of the error and is converted to a Python string object and stored as the “associated value” of the exception.
Another useful function is
, which only takes an exception argument and constructs the associated value by inspection of the global variable errno. The most general function is
, which takes two object arguments, the exception and its associated value. You don’t need to
the objects passed to any of these functions.
You can test non-destructively whether an exception has been set with
. This returns the current exception object, or NULL if no exception has occurred. You normally don’t need to call PyErr_Occurred() to see whether an error occurred in a function call, since you should be able to tell from the return value.
When a function f that calls another function g detects that the latter fails, f should itself return an error value (usually NULL or -1). It should not call one of the PyErr_* functions — one has already been called by g. f’s caller is then supposed to also return an error indication to its caller, again without calling PyErr_*, and so on — the most detailed cause of the error was already reported by the function that first detected it. Once the error reaches the Python interpreter’s main loop, this aborts the currently executing Python code and tries to find an exception handler specified by the Python programmer.
(There are situations where a module can actually give a more detailed error message by calling another PyErr_* function, and in such cases it is fine to do so. As a general rule, however, this is not necessary, and can cause information about the cause of the error to be lost: most operations can fail for a variety of reasons.)
To ignore an exception set by a function call that failed, the exception condition must be cleared explicitly by calling
. The only time C code should call PyErr_Clear() is if it doesn’t want to pass the error on to the interpreter but wants to handle it completely by itself (possibly by trying something else, or pretending nothing went wrong).
Every failing malloc() call must be turned into an exception — the direct caller of malloc() (or realloc()) must call
and return a failure indicator itself. All the object-creating functions (for example,
) already do this, so this note is only relevant to those who call malloc() directly.
Also note that, with the important exception of
and friends, functions that return an integer status usually return a positive value or zero for success and -1 for failure, like Unix system calls.
Finally, be careful to clean up garbage (by making
or
calls for objects you have already created) when you return an error indicator!
The choice of which exception to raise is entirely yours. There are predeclared C objects corresponding to all built-in Python exceptions, such as
, which you can use directly. Of course, you should choose exceptions wisely — don’t use
to mean that a file couldn’t be opened (that should probably be
). If something’s wrong with the argument list, the
function usually raises PyExc_TypeError. If you have an argument whose value must be in a particular range or must satisfy other conditions,
is appropriate.
You can also define a new exception that is unique to your module. The simplest way to do this is to declare a static global object variable at the beginning of the file:
staticPyObject*SpamError=NULL;and initialize it by calling
in the module’s
function (spam_module_exec()):
SpamError=PyErr_NewException("spam.error",NULL,NULL);Since SpamError is a global variable, it will be overwritten every time the module is reinitialized, when the
function is called.
For now, let’s avoid the issue: we will block repeated initialization by raising an
:
staticPyObject*SpamError=NULL;staticintspam_module_exec(PyObject*m){if(SpamError!=NULL){PyErr_SetString(PyExc_ImportError,"cannot initialize spam module more than once");return-1;}SpamError=PyErr_NewException("spam.error",NULL,NULL);if(PyModule_AddObjectRef(m,"SpamError",SpamError)<0){return-1;}return0;}staticPyModuleDef_Slotspam_module_slots[]={{Py_mod_exec,spam_module_exec},{0,NULL}};staticstructPyModuleDefspam_module={.m_base=PyModuleDef_HEAD_INIT,.m_name="spam",.m_size=0,// non-negative.m_slots=spam_module_slots,};PyMODINIT_FUNCPyInit_spam(void){returnPyModuleDef_Init(&spam_module);}Note that the Python name for the exception object is spam.error. The
function may create a class with the base class being
(unless another class is passed in instead of NULL), described in
.
Note also that the SpamError variable retains a reference to the newly created exception class; this is intentional! Since the exception could be removed from the module by external code, an owned reference to the class is needed to ensure that it will not be discarded, causing SpamError to become a dangling pointer. Should it become a dangling pointer, C code which raises the exception could cause a core dump or other unintended side effects.
For now, the
call to remove this reference is missing. Even when the Python interpreter shuts down, the global SpamError variable will not be garbage-collected. It will “leak”. We did, however, ensure that this will happen at most once per process.
We discuss the use of
as a function return type later in this sample.
The spam.error exception can be raised in your extension module using a call to
as shown below:
staticPyObject*spam_system(PyObject*self,PyObject*args){constchar*command;intsts;if(!PyArg_ParseTuple(args,"s",&command))returnNULL;sts=system(command);if(sts<0){PyErr_SetString(SpamError,"System command failed");returnNULL;}returnPyLong_FromLong(sts);}1.3. Back to the Example
Going back to our example function, you should now be able to understand this statement:
if(!PyArg_ParseTuple(args,"s",&command))returnNULL;It returns NULL (the error indicator for functions returning object pointers) if an error is detected in the argument list, relying on the exception set by
. Otherwise the string value of the argument has been copied to the local variable command. This is a pointer assignment and you are not supposed to modify the string to which it points (so in Standard C, the variable command should properly be declared as constchar*command).
The next statement is a call to the Unix function system(), passing it the string we just got from
:
sts=system(command);Our spam.system() function must return the value of sts as a Python object. This is done using the function
.
returnPyLong_FromLong(sts);In this case, it will return an integer object. (Yes, even integers are objects on the heap in Python!)
If you have a C function that returns no useful argument (a function returning void), the corresponding Python function must return None. You need this idiom to do so (which is implemented by the
macro):
Py_INCREF(Py_None);returnPy_None;
is the C name for the special Python object None. It is a genuine Python object rather than a NULL pointer, which means “error” in most contexts, as we have seen.
1.4. The Module’s Method Table and Initialization Function
I promised to show how spam_system() is called from Python programs. First, we need to list its name and address in a “method table”:
staticPyMethodDefspam_methods[]={...{"system",spam_system,METH_VARARGS,"Execute a shell command."},...{NULL,NULL,0,NULL}/* Sentinel */};Note the third entry (METH_VARARGS). This is a flag telling the interpreter the calling convention to be used for the C function. It should normally always be METH_VARARGS or METH_VARARGS|METH_KEYWORDS; a value of 0 means that an obsolete variant of
is used.
When using only METH_VARARGS, the function should expect the Python-level parameters to be passed in as a tuple acceptable for parsing via
; more information on this function is provided below.
The
bit may be set in the third field if keyword arguments should be passed to the function. In this case, the C function should accept a third PyObject* parameter which will be a dictionary of keywords. Use
to parse the arguments to such a function.
The method table must be referenced in the module definition structure:
staticstructPyModuleDefspam_module={....m_methods=spam_methods,...};This structure, in turn, must be passed to the interpreter in the module’s initialization function. The initialization function must be named PyInit_name(), where name is the name of the module, and should be the only non-static item defined in the module file:
PyMODINIT_FUNCPyInit_spam(void){returnPyModuleDef_Init(&spam_module);}Note that
declares the function as PyObject* return type, declares any special linkage declarations required by the platform, and for C++ declares the function as extern"C".
PyInit_spam() is called when each interpreter imports its module spam for the first time. (See below for comments about embedding Python.) A pointer to the module definition must be returned via
, so that the import machinery can create the module and store it in sys.modules.
When embedding Python, the PyInit_spam() function is not called automatically unless there’s an entry in the
table. To add the module to the initialization table, use
, optionally followed by an import of the module:
#define PY_SSIZE_T_CLEAN#include<Python.h>intmain(intargc,char*argv[]){PyStatusstatus;PyConfigconfig;PyConfig_InitPythonConfig(&config);/* Add a built-in module, before Py_Initialize */if(PyImport_AppendInittab("spam",PyInit_spam)==-1){fprintf(stderr,"Error: could not extend in-built modules table\n");exit(1);}/* Pass argv[0] to the Python interpreter */status=PyConfig_SetBytesString(&config,&config.program_name,argv[0]);if(PyStatus_Exception(status)){gotoexception;}/* Initialize the Python interpreter. Required. If this step fails, it will be a fatal error. */status=Py_InitializeFromConfig(&config);if(PyStatus_Exception(status)){gotoexception;}PyConfig_Clear(&config);/* Optionally import the module; alternatively, import can be deferred until the embedded script imports it. */PyObject*pmodule=PyImport_ImportModule("spam");if(!pmodule){PyErr_Print();fprintf(stderr,"Error: could not import module 'spam'\n");}// ... use Python C API here ...return0;exception:PyConfig_Clear(&config);Py_ExitStatusException(status);}Note
If you declare a global variable or a local static one, the module may experience unintended side-effects on re-initialisation, for example when removing entries from sys.modules or importing compiled modules into multiple interpreters within a process (or following a fork() without an intervening exec()). If module state is not yet fully
, authors should consider marking the module as having no support for subinterpreters (via
Py_MOD_MULTIPLE_INTERPRETERS_NOT_SUPPORTED
).
A more substantial example module is included in the Python source distribution as Modules/xxlimited.c. This file may be used as a template or simply read as an example.
1.5. Compilation and Linkage
There are two more things to do before you can use your new extension: compiling and linking it with the Python system. If you use dynamic loading, the details may depend on the style of dynamic loading your system uses; see the chapters about building extension modules (chapter
) and additional information that pertains only to building on Windows (chapter
Building C and C++ Extensions on Windows
) for more information about this.
If you can’t use dynamic loading, or if you want to make your module a permanent part of the Python interpreter, you will have to change the configuration setup and rebuild the interpreter. Luckily, this is very simple on Unix: just place your file (spammodule.c for example) in the Modules/ directory of an unpacked source distribution, add a line to the file Modules/Setup.local describing your file:
spamspammodule.o and rebuild the interpreter by running make in the toplevel directory. You can also run make in the Modules/ subdirectory, but then you must first rebuild Makefile there by running ‘make Makefile’. (This is necessary each time you change the Setup file.)
If your module requires additional libraries to link with, these can be listed on the line in the configuration file as well, for instance:
spamspammodule.o-lX11 1.6. Calling Python Functions from C
So far we have concentrated on making C functions callable from Python. The reverse is also useful: calling Python functions from C. This is especially the case for libraries that support so-called “callback” functions. If a C interface makes use of callbacks, the equivalent Python often needs to provide a callback mechanism to the Python programmer; the implementation will require calling the Python callback functions from a C callback. Other uses are also imaginable.
Fortunately, the Python interpreter is easily called recursively, and there is a standard interface to call a Python function. (If you’re interested in how to call the Python parser with a particular string as input, see
.)
Calling a Python function is easy. First, the Python program must somehow pass you the Python function object. You should provide a function (or some other interface) to do this. When this function is called, save a pointer to the Python function object (be careful to
it!) in a global variable — or wherever you see fit. For example, the following function might be part of a module definition:
staticPyObject*my_callback=NULL;staticPyObject*my_set_callback(PyObject*dummy,PyObject*args){PyObject*result=NULL;PyObject*temp;if(PyArg_ParseTuple(args,"O:set_callback",&temp)){if(!PyCallable_Check(temp)){PyErr_SetString(PyExc_TypeError,"parameter must be callable");returnNULL;}Py_XINCREF(temp);/* Add a reference to new callback */Py_XDECREF(my_callback);/* Dispose of previous callback */my_callback=temp;/* Remember new callback *//* Boilerplate to return "None" */Py_INCREF(Py_None);result=Py_None;}returnresult;}This function must be registered with the interpreter using the
flag; this is described in section
The Module’s Method Table and Initialization Function
. The
function and its arguments are documented in section
Extracting Parameters in Extension Functions
.
The macros
and
increment/decrement the reference count of an object and are safe in the presence of NULL pointers (but note that temp will not be NULL in this context). More info on them in section
.
Later, when it is time to call the function, you call the C function
. This function has two arguments, both pointers to arbitrary Python objects: the Python function, and the argument list. The argument list must always be a tuple object, whose length is the number of arguments. To call the Python function with no arguments, pass in NULL, or an empty tuple; to call it with one argument, pass a singleton tuple.
returns a tuple when its format string consists of zero or more format codes between parentheses. For example:
intarg;PyObject*arglist;PyObject*result;...arg=123;.../* Time to call the callback */arglist=Py_BuildValue("(i)",arg);result=PyObject_CallObject(my_callback,arglist);Py_DECREF(arglist);
returns a Python object pointer: this is the return value of the Python function. PyObject_CallObject() is “reference-count-neutral” with respect to its arguments. In the example a new tuple was created to serve as the argument list, which is
-ed immediately after the PyObject_CallObject() call.
The return value of
is “new”: either it is a brand new object, or it is an existing object whose reference count has been incremented. So, unless you want to save it in a global variable, you should somehow
the result, even (especially!) if you are not interested in its value.
Before you do this, however, it is important to check that the return value isn’t NULL. If it is, the Python function terminated by raising an exception. If the C code that called
is called from Python, it should now return an error indication to its Python caller, so the interpreter can print a stack trace, or the calling Python code can handle the exception. If this is not possible or desirable, the exception should be cleared by calling
. For example:
if(result==NULL)returnNULL;/* Pass error back */...useresult...Py_DECREF(result);Depending on the desired interface to the Python callback function, you may also have to provide an argument list to
. In some cases the argument list is also provided by the Python program, through the same interface that specified the callback function. It can then be saved and used in the same manner as the function object. In other cases, you may have to construct a new tuple to pass as the argument list. The simplest way to do this is to call
. For example, if you want to pass an integral event code, you might use the following code:
PyObject*arglist;...arglist=Py_BuildValue("(l)",eventcode);result=PyObject_CallObject(my_callback,arglist);Py_DECREF(arglist);if(result==NULL)returnNULL;/* Pass error back *//* Here maybe use the result */Py_DECREF(result);Note the placement of Py_DECREF(arglist) immediately after the call, before the error check! Also note that strictly speaking this code is not complete:
may run out of memory, and this should be checked.
You may also call a function with keyword arguments by using
, which supports arguments and keyword arguments. As in the above example, we use
to construct the dictionary.
PyObject*dict;...dict=Py_BuildValue("{s:i}","name",val);result=PyObject_Call(my_callback,NULL,dict);Py_DECREF(dict);if(result==NULL)returnNULL;/* Pass error back *//* Here maybe use the result */Py_DECREF(result);1.8. Keyword Parameters for Extension Functions
The
function is declared as follows:
intPyArg_ParseTupleAndKeywords(PyObject*arg,PyObject*kwdict,constchar*format,char*const*kwlist,...);The arg and format parameters are identical to those of the
function. The kwdict parameter is the dictionary of keywords received as the third parameter from the Python runtime. The kwlist parameter is a NULL-terminated list of strings which identify the parameters; the names are matched with the type information from format from left to right. On success,
returns true, otherwise it returns false and raises an appropriate exception.
Note
Nested tuples cannot be parsed when using keyword arguments! Keyword parameters passed in which are not present in the kwlist will cause
to be raised.
Here is an example module which uses keywords, based on an example by Geoff Philbrick (
):
#define PY_SSIZE_T_CLEAN#include<Python.h>staticPyObject*keywdarg_parrot(PyObject*self,PyObject*args,PyObject*keywds){intvoltage;constchar*state="a stiff";constchar*action="voom";constchar*type="Norwegian Blue";staticchar*kwlist[]={"voltage","state","action","type",NULL};if(!PyArg_ParseTupleAndKeywords(args,keywds,"i|sss",kwlist,&voltage,&state,&action,&type))returnNULL;printf("-- This parrot wouldn't %s if you put %i Volts through it.\n",action,voltage);printf("-- Lovely plumage, the %s -- It's %s!\n",type,state);Py_RETURN_NONE;}staticPyMethodDefkeywdarg_methods[]={/* The cast of the function is necessary since PyCFunction values * only take two PyObject* parameters, and keywdarg_parrot() takes * three. */{"parrot",(PyCFunction)(void(*)(void))keywdarg_parrot,METH_VARARGS|METH_KEYWORDS,"Print a lovely skit to standard output."},{NULL,NULL,0,NULL}/* sentinel */};staticstructPyModuleDefkeywdarg_module={.m_base=PyModuleDef_HEAD_INIT,.m_name="keywdarg",.m_size=0,.m_methods=keywdarg_methods,};PyMODINIT_FUNCPyInit_keywdarg(void){returnPyModuleDef_Init(&keywdarg_module);}1.9. Building Arbitrary Values
This function is the counterpart to
. It is declared as follows:
PyObject*Py_BuildValue(constchar*format,...);It recognizes a set of format units similar to the ones recognized by
, but the arguments (which are input to the function, not output) must not be pointers, just values. It returns a new Python object, suitable for returning from a C function called from Python.
One difference with
: while the latter requires its first argument to be a tuple (since Python argument lists are always represented as tuples internally),
does not always build a tuple. It builds a tuple only if its format string contains two or more format units. If the format string is empty, it returns None; if it contains exactly one format unit, it returns whatever object is described by that format unit. To force it to return a tuple of size 0 or one, parenthesize the format string.
Examples (to the left the call, to the right the resulting Python value):
Py_BuildValue("") None Py_BuildValue("i", 123) 123 Py_BuildValue("iii", 123, 456, 789) (123, 456, 789) Py_BuildValue("s", "hello") 'hello' Py_BuildValue("y", "hello") b'hello' Py_BuildValue("ss", "hello", "world") ('hello', 'world') Py_BuildValue("s#", "hello", 4) 'hell' Py_BuildValue("y#", "hello", 4) b'hell' Py_BuildValue("()") () Py_BuildValue("(i)", 123) (123,) Py_BuildValue("(ii)", 123, 456) (123, 456) Py_BuildValue("(i,i)", 123, 456) (123, 456) Py_BuildValue("[i,i]", 123, 456) [123, 456] Py_BuildValue("{s:i,s:i}", "abc", 123, "def", 456) {'abc': 123, 'def': 456} Py_BuildValue("((ii)(ii)) (ii)", 1, 2, 3, 4, 5, 6) (((1, 2), (3, 4)), (5, 6)) 1.10. Reference Counts
In languages like C or C++, the programmer is responsible for dynamic allocation and deallocation of memory on the heap. In C, this is done using the functions malloc() and free(). In C++, the operators new and delete are used with essentially the same meaning and we’ll restrict the following discussion to the C case.
Every block of memory allocated with malloc() should eventually be returned to the pool of available memory by exactly one call to free(). It is important to call free() at the right time. If a block’s address is forgotten but free() is not called for it, the memory it occupies cannot be reused until the program terminates. This is called a memory leak. On the other hand, if a program calls free() for a block and then continues to use the block, it creates a conflict with reuse of the block through another malloc() call. This is called using freed memory. It has the same bad consequences as referencing uninitialized data — core dumps, wrong results, mysterious crashes.
Common causes of memory leaks are unusual paths through the code. For instance, a function may allocate a block of memory, do some calculation, and then free the block again. Now a change in the requirements for the function may add a test to the calculation that detects an error condition and can return prematurely from the function. It’s easy to forget to free the allocated memory block when taking this premature exit, especially when it is added later to the code. Such leaks, once introduced, often go undetected for a long time: the error exit is taken only in a small fraction of all calls, and most modern machines have plenty of virtual memory, so the leak only becomes apparent in a long-running process that uses the leaking function frequently. Therefore, it’s important to prevent leaks from happening by having a coding convention or strategy that minimizes this kind of errors.
Since Python makes heavy use of malloc() and free(), it needs a strategy to avoid memory leaks as well as the use of freed memory. The chosen method is called reference counting. The principle is simple: every object contains a counter, which is incremented when a reference to the object is stored somewhere, and which is decremented when a reference to it is deleted. When the counter reaches zero, the last reference to the object has been deleted and the object is freed.
An alternative strategy is called automatic garbage collection. (Sometimes, reference counting is also referred to as a garbage collection strategy, hence the use of “automatic” to distinguish the two.) The big advantage of automatic garbage collection is that the user doesn’t need to call free() explicitly. (Another claimed advantage is an improvement in speed or memory usage — this is no hard fact however.) The disadvantage is that for C, there is no truly portable automatic garbage collector, while reference counting can be implemented portably (as long as the functions malloc() and free() are available — which the C Standard guarantees). Maybe some day a sufficiently portable automatic garbage collector will be available for C. Until then, we’ll have to live with reference counts.
While Python uses the traditional reference counting implementation, it also offers a cycle detector that works to detect reference cycles. This allows applications to not worry about creating direct or indirect circular references; these are the weakness of garbage collection implemented using only reference counting. Reference cycles consist of objects which contain (possibly indirect) references to themselves, so that each object in the cycle has a reference count which is non-zero. Typical reference counting implementations are not able to reclaim the memory belonging to any objects in a reference cycle, or referenced from the objects in the cycle, even though there are no further references to the cycle itself.
The cycle detector is able to detect garbage cycles and can reclaim them. The
module exposes a way to run the detector (the
function), as well as configuration interfaces and the ability to disable the detector at runtime.
1.10.1. Reference Counting in Python
There are two macros, Py_INCREF(x) and Py_DECREF(x), which handle the incrementing and decrementing of the reference count.
also frees the object when the count reaches zero. For flexibility, it doesn’t call free() directly — rather, it makes a call through a function pointer in the object’s type object. For this purpose (and others), every object also contains a pointer to its type object.
The big question now remains: when to use Py_INCREF(x) and Py_DECREF(x)? Let’s first introduce some terms. Nobody “owns” an object; however, you can own a reference to an object. An object’s reference count is now defined as the number of owned references to it. The owner of a reference is responsible for calling
when the reference is no longer needed. Ownership of a reference can be transferred. There are three ways to dispose of an owned reference: pass it on, store it, or call Py_DECREF(). Forgetting to dispose of an owned reference creates a memory leak.
It is also possible to borrow
a reference to an object. The borrower of a reference should not call
. The borrower must not hold on to the object longer than the owner from which it was borrowed. Using a borrowed reference after the owner has disposed of it risks using freed memory and should be avoided completely
.
The advantage of borrowing over owning a reference is that you don’t need to take care of disposing of the reference on all possible paths through the code — in other words, with a borrowed reference you don’t run the risk of leaking when a premature exit is taken. The disadvantage of borrowing over owning is that there are some subtle situations where in seemingly correct code a borrowed reference can be used after the owner from which it was borrowed has in fact disposed of it.
A borrowed reference can be changed into an owned reference by calling
. This does not affect the status of the owner from which the reference was borrowed — it creates a new owned reference, and gives full owner responsibilities (the new owner must dispose of the reference properly, as well as the previous owner).
1.10.2. Ownership Rules
Whenever an object reference is passed into or out of a function, it is part of the function’s interface specification whether ownership is transferred with the reference or not.
Most functions that return a reference to an object pass on ownership with the reference. In particular, all functions whose function it is to create a new object, such as
and
, pass ownership to the receiver. Even if the object is not actually new, you still receive ownership of a new reference to that object. For instance, PyLong_FromLong() maintains a cache of popular values and can return a reference to a cached item.
Many functions that extract objects from other objects also transfer ownership with the reference, for instance
. The picture is less clear, here, however, since a few common routines are exceptions:
,
,
, and
all return references that you borrow from the tuple, list or dictionary.
The function
also returns a borrowed reference, even though it may actually create the object it returns: this is possible because an owned reference to the object is stored in sys.modules.
When you pass an object reference into another function, in general, the function borrows the reference from you — if it needs to store it, it will use
to become an independent owner. There are exactly two important exceptions to this rule:
and
. These functions take over ownership of the item passed to them — even if they fail! (Note that
and friends don’t take over ownership — they are “normal.”)
When a C function is called from Python, it borrows references to its arguments from the caller. The caller owns a reference to the object, so the borrowed reference’s lifetime is guaranteed until the function returns. Only when such a borrowed reference must be stored or passed on, it must be turned into an owned reference by calling
.
The object reference returned from a C function that is called from Python must be an owned reference — ownership is transferred from the function to its caller.
1.10.3. Thin Ice
There are a few situations where seemingly harmless use of a borrowed reference can lead to problems. These all have to do with implicit invocations of the interpreter, which can cause the owner of a reference to dispose of it.
The first and most important case to know about is using
on an unrelated object while borrowing a reference to a list item. For instance:
voidbug(PyObject*list){PyObject*item=PyList_GetItem(list,0);PyList_SetItem(list,1,PyLong_FromLong(0L));PyObject_Print(item,stdout,0);/* BUG! */}This function first borrows a reference to list[0], then replaces list[1] with the value 0, and finally prints the borrowed reference. Looks harmless, right? But it’s not!
Let’s follow the control flow into
. The list owns references to all its items, so when item 1 is replaced, it has to dispose of the original item 1. Now let’s suppose the original item 1 was an instance of a user-defined class, and let’s further suppose that the class defined a __del__() method. If this class instance has a reference count of 1, disposing of it will call its __del__() method. Internally, PyList_SetItem() calls
on the replaced item, which invokes replaced item’s corresponding
function. During deallocation, tp_dealloc calls
, which is mapped to the __del__() method for class instances (see
). This entire sequence happens synchronously within the PyList_SetItem() call.
Since it is written in Python, the __del__() method can execute arbitrary Python code. Could it perhaps do something to invalidate the reference to item in bug()? You bet! Assuming that the list passed into bug() is accessible to the __del__() method, it could execute a statement to the effect of dellist[0], and assuming this was the last reference to that object, it would free the memory associated with it, thereby invalidating item.
The solution, once you know the source of the problem, is easy: temporarily increment the reference count. The correct version of the function reads:
voidno_bug(PyObject*list){PyObject*item=PyList_GetItem(list,0);Py_INCREF(item);PyList_SetItem(list,1,PyLong_FromLong(0L));PyObject_Print(item,stdout,0);Py_DECREF(item);}This is a true story. An older version of Python contained variants of this bug and someone spent a considerable amount of time in a C debugger to figure out why his __del__() methods would fail…
The second case of problems with a borrowed reference is a variant involving threads. Normally, multiple threads in the Python interpreter can’t get in each other’s way, because there is a
protecting Python’s entire object space. However, it is possible to temporarily release this lock using the macro
, and to re-acquire it using
. This is common around blocking I/O calls, to let other threads use the processor while waiting for the I/O to complete. Obviously, the following function has the same problem as the previous one:
voidbug(PyObject*list){PyObject*item=PyList_GetItem(list,0);Py_BEGIN_ALLOW_THREADS...someblockingI/Ocall...Py_END_ALLOW_THREADSPyObject_Print(item,stdout,0);/* BUG! */}1.10.4. NULL Pointers
In general, functions that take object references as arguments do not expect you to pass them NULL pointers, and will dump core (or cause later core dumps) if you do so. Functions that return object references generally return NULL only to indicate that an exception occurred. The reason for not testing for NULL arguments is that functions often pass the objects they receive on to other function — if each function were to test for NULL, there would be a lot of redundant tests and the code would run more slowly.
It is better to test for NULL only at the “source:” when a pointer that may be NULL is received, for example, from malloc() or from a function that may raise an exception.
The macros
and
do not check for NULL pointers — however, their variants
and
do.
The macros for checking for a particular object type (Pytype_Check()) don’t check for NULL pointers — again, there is much code that calls several of these in a row to test an object against various different expected types, and this would generate redundant tests. There are no variants with NULL checking.
The C function calling mechanism guarantees that the argument list passed to C functions (args in the examples) is never NULL — in fact it guarantees that it is always a tuple
.
It is a severe error to ever let a NULL pointer “escape” to the Python user.
1.11. Writing Extensions in C++
It is possible to write extension modules in C++. Some restrictions apply. If the main program (the Python interpreter) is compiled and linked by the C compiler, global or static objects with constructors cannot be used. This is not a problem if the main program is linked by the C++ compiler. Functions that will be called by the Python interpreter (in particular, module initialization functions) have to be declared using extern"C". It is unnecessary to enclose the Python header files in extern"C"{...} — they use this form already if the symbol __cplusplus is defined (all recent C++ compilers define this symbol).
1.12. Providing a C API for an Extension Module
Many extension modules just provide new functions and types to be used from Python, but sometimes the code in an extension module can be useful for other extension modules. For example, an extension module could implement a type “collection” which works like lists without order. Just like the standard Python list type has a C API which permits extension modules to create and manipulate lists, this new collection type should have a set of C functions for direct manipulation from other extension modules.
At first sight this seems easy: just write the functions (without declaring them static, of course), provide an appropriate header file, and document the C API. And in fact this would work if all extension modules were always linked statically with the Python interpreter. When modules are used as shared libraries, however, the symbols defined in one module may not be visible to another module. The details of visibility depend on the operating system; some systems use one global namespace for the Python interpreter and all extension modules (Windows, for example), whereas others require an explicit list of imported symbols at module link time (AIX is one example), or offer a choice of different strategies (most Unices). And even if symbols are globally visible, the module whose functions one wishes to call might not have been loaded yet!
Portability therefore requires not to make any assumptions about symbol visibility. This means that all symbols in extension modules should be declared static, except for the module’s initialization function, in order to avoid name clashes with other extension modules (as discussed in section
The Module’s Method Table and Initialization Function
). And it means that symbols that should be accessible from other extension modules must be exported in a different way.
Python provides a special mechanism to pass C-level information (pointers) from one extension module to another one: Capsules. A Capsule is a Python data type which stores a pointer (void*). Capsules can only be created and accessed via their C API, but they can be passed around like any other Python object. In particular, they can be assigned to a name in an extension module’s namespace. Other extension modules can then import this module, retrieve the value of this name, and then retrieve the pointer from the Capsule.
There are many ways in which Capsules can be used to export the C API of an extension module. Each function could get its own Capsule, or all C API pointers could be stored in an array whose address is published in a Capsule. And the various tasks of storing and retrieving the pointers can be distributed in different ways between the module providing the code and the client modules.
Whichever method you choose, it’s important to name your Capsules properly. The function
takes a name parameter (constchar*); you’re permitted to pass in a NULL name, but we strongly encourage you to specify a name. Properly named Capsules provide a degree of runtime type-safety; there is no feasible way to tell one unnamed Capsule from another.
In particular, Capsules used to expose C APIs should be given a name following this convention:
modulename.attributenameThe convenience function
makes it easy to load a C API provided via a Capsule, but only if the Capsule’s name matches this convention. This behavior gives C API users a high degree of certainty that the Capsule they load contains the correct C API.
The following example demonstrates an approach that puts most of the burden on the writer of the exporting module, which is appropriate for commonly used library modules. It stores all C API pointers (just one in the example!) in an array of void pointers which becomes the value of a Capsule. The header file corresponding to the module provides a macro that takes care of importing the module and retrieving its C API pointers; client modules only have to call this macro before accessing the C API.
The exporting module is a modification of the spam module from section
. The function spam.system() does not call the C library function system() directly, but a function PySpam_System(), which would of course do something more complicated in reality (such as adding “spam” to every command). This function PySpam_System() is also exported to other extension modules.
The function PySpam_System() is a plain C function, declared static like everything else:
staticintPySpam_System(constchar*command){returnsystem(command);}The function spam_system() is modified in a trivial way:
staticPyObject*spam_system(PyObject*self,PyObject*args){constchar*command;intsts;if(!PyArg_ParseTuple(args,"s",&command))returnNULL;sts=PySpam_System(command);returnPyLong_FromLong(sts);}In the beginning of the module, right after the line
#include<Python.h>two more lines must be added:
#define SPAM_MODULE#include"spammodule.h"The #define is used to tell the header file that it is being included in the exporting module, not a client module. Finally, the module’s
function must take care of initializing the C API pointer array:
staticintspam_module_exec(PyObject*m){staticvoid*PySpam_API[PySpam_API_pointers];PyObject*c_api_object;/* Initialize the C API pointer array */PySpam_API[PySpam_System_NUM]=(void*)PySpam_System;/* Create a Capsule containing the API pointer array's address */c_api_object=PyCapsule_New((void*)PySpam_API,"spam._C_API",NULL);if(PyModule_Add(m,"_C_API",c_api_object)<0){return-1;}return0;}Note that PySpam_API is declared static; otherwise the pointer array would disappear when PyInit_spam() terminates!
The bulk of the work is in the header file spammodule.h, which looks like this:
#ifndef Py_SPAMMODULE_H#define Py_SPAMMODULE_H#ifdef __cplusplusextern"C"{#endif/* Header file for spammodule *//* C API functions */#define PySpam_System_NUM 0#define PySpam_System_RETURN int#define PySpam_System_PROTO (const char *command)/* Total number of C API pointers */#define PySpam_API_pointers 1#ifdef SPAM_MODULE/* This section is used when compiling spammodule.c */staticPySpam_System_RETURNPySpam_SystemPySpam_System_PROTO;#else/* This section is used in modules that use spammodule's API */staticvoid**PySpam_API;#define PySpam_System \ (*(PySpam_System_RETURN (*)PySpam_System_PROTO) PySpam_API[PySpam_System_NUM])/* Return -1 on error, 0 on success. * PyCapsule_Import will set an exception if there's an error. */staticintimport_spam(void){PySpam_API=(void**)PyCapsule_Import("spam._C_API",0);return(PySpam_API!=NULL)?0:-1;}#endif#ifdef __cplusplus}#endif#endif /* !defined(Py_SPAMMODULE_H) */All that a client module must do in order to have access to the function PySpam_System() is to call the function (or rather macro) import_spam() in its
function:
staticintclient_module_exec(PyObject*m){if(import_spam()<0){return-1;}/* additional initialization can happen here */return0;}The main disadvantage of this approach is that the file spammodule.h is rather complicated. However, the basic structure is the same for each function that is exported, so it has to be learned only once.
Finally it should be mentioned that Capsules offer additional functionality, which is especially useful for memory allocation and deallocation of the pointer stored in a Capsule. The details are described in the Python/C API Reference Manual in the section
and in the implementation of Capsules (files Include/pycapsule.h and Objects/capsule.c in the Python source code distribution).
Footnotes