sphinx / Doc-26 / c-api / init.rst

Initialization, Finalization, and Threads

Thread State and the Global Interpreter Lock

The Python interpreter is not fully thread safe. In order to support multi- threaded Python programs, there's a global lock that must be held by the current thread before it can safely access Python objects. Without the lock, even the simplest operations could cause problems in a multi-threaded program: for example, when two threads simultaneously increment the reference count of the same object, the reference count could end up being incremented only once instead of twice.

Therefore, the rule exists that only the thread that has acquired the global interpreter lock may operate on Python objects or call Python/C API functions. In order to support multi-threaded Python programs, the interpreter regularly releases and reacquires the lock --- by default, every 100 bytecode instructions (this can be changed with :func:`sys.setcheckinterval`). The lock is also released and reacquired around potentially blocking I/O operations like reading or writing a file, so that other threads can run while the thread that requests the I/O is waiting for the I/O operation to complete.

The Python interpreter needs to keep some bookkeeping information separate per thread --- for this it uses a data structure called :ctype:`PyThreadState`. There's one global variable, however: the pointer to the current :ctype:`PyThreadState` structure. While most thread packages have a way to store "per-thread global data," Python's internal platform independent thread abstraction doesn't support this yet. Therefore, the current thread state must be manipulated explicitly.

This is easy enough in most cases. Most code manipulating the global interpreter lock has the following simple structure:

Save the thread state in a local variable.
Release the interpreter lock.
...Do some blocking I/O operation...
Reacquire the interpreter lock.
Restore the thread state from the local variable.

This is so common that a pair of macros exists to simplify it:

...Do some blocking I/O operation...

The :cmacro:`Py_BEGIN_ALLOW_THREADS` macro opens a new block and declares a hidden local variable; the :cmacro:`Py_END_ALLOW_THREADS` macro closes the block. Another advantage of using these two macros is that when Python is compiled without thread support, they are defined empty, thus saving the thread state and lock manipulations.

When thread support is enabled, the block above expands to the following code:

PyThreadState *_save;

_save = PyEval_SaveThread();
...Do some blocking I/O operation...

Using even lower level primitives, we can get roughly the same effect as follows:

PyThreadState *_save;

_save = PyThreadState_Swap(NULL);
...Do some blocking I/O operation...

There are some subtle differences; in particular, :cfunc:`PyEval_RestoreThread` saves and restores the value of the global variable :cdata:`errno`, since the lock manipulation does not guarantee that :cdata:`errno` is left alone. Also, when thread support is disabled, :cfunc:`PyEval_SaveThread` and :cfunc:`PyEval_RestoreThread` don't manipulate the lock; in this case, :cfunc:`PyEval_ReleaseLock` and :cfunc:`PyEval_AcquireLock` are not available. This is done so that dynamically loaded extensions compiled with thread support enabled can be loaded by an interpreter that was compiled with disabled thread support.

The global interpreter lock is used to protect the pointer to the current thread state. When releasing the lock and saving the thread state, the current thread state pointer must be retrieved before the lock is released (since another thread could immediately acquire the lock and store its own thread state in the global variable). Conversely, when acquiring the lock and restoring the thread state, the lock must be acquired before storing the thread state pointer.

Why am I going on with so much detail about this? Because when threads are created from C, they don't have the global interpreter lock, nor is there a thread state data structure for them. Such threads must bootstrap themselves into existence, by first creating a thread state data structure, then acquiring the lock, and finally storing their thread state pointer, before they can start using the Python/C API. When they are done, they should reset the thread state pointer, release the lock, and finally free their thread state data structure.

Beginning with version 2.3, threads can now take advantage of the :cfunc:`PyGILState_\*` functions to do all of the above automatically. The typical idiom for calling into Python from a C thread is now:

PyGILState_STATE gstate;
gstate = PyGILState_Ensure();

/* Perform Python actions here.  */
result = CallSomeFunction();
/* evaluate result */

/* Release the thread. No Python API allowed beyond this point. */

Note that the :cfunc:`PyGILState_\*` functions assume there is only one global interpreter (created automatically by :cfunc:`Py_Initialize`). Python still supports the creation of additional interpreters (using :cfunc:`Py_NewInterpreter`), but mixing multiple interpreters and the :cfunc:`PyGILState_\*` API is unsupported.

The following macros are normally used without a trailing semicolon; look for example usage in the Python source distribution.

All of the following functions are only available when thread support is enabled at compile time, and must be called only when the interpreter lock has been created.

Profiling and Tracing

The Python interpreter provides some low-level support for attaching profiling and execution tracing facilities. These are used for profiling, debugging, and coverage analysis tools.

Starting with Python 2.2, the implementation of this facility was substantially revised, and an interface from C was added. This C interface allows the profiling or tracing code to avoid the overhead of calling through Python-level callable objects, making a direct C function call instead. The essential attributes of the facility have not changed; the interface allows trace functions to be installed per-thread, and the basic events reported to the trace function are the same as had been reported to the Python-level trace functions in previous versions.

Advanced Debugger Support

These functions are only intended to be used by advanced debugging tools.