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 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 PyThreadState. This is new in Python 1.5; in earlier versions, such state was stored in global variables, and switching threads could cause problems. In particular, exception handling is now thread safe, when the application uses sys.exc_info() to access the exception last raised in the current thread.
There's one global variable left, however: the pointer to the current PyThreadStatestructure. 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:
Py_BEGIN_ALLOW_THREADS ...Do some blocking I/O operation... Py_END_ALLOW_THREADS
The Py_BEGIN_ALLOW_THREADSmacro opens a new block and declares a hidden local variable; the Py_END_ALLOW_THREADSmacro 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... PyEval_RestoreThread(_save);
Using even lower level primitives, we can get roughly the same effect as follows:
PyThreadState *_save; _save = PyThreadState_Swap(NULL); PyEval_ReleaseLock(); ...Do some blocking I/O operation... PyEval_AcquireLock(); PyThreadState_Swap(_save);
There are some subtle differences; in particular, PyEval_RestoreThread()saves and restores the value of the global variable errno, since the lock manipulation does not guarantee that errno is left alone. Also, when thread support is disabled, PyEval_SaveThread()and PyEval_RestoreThread() don't manipulate the lock; in this case, PyEval_ReleaseLock()and 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.
When creating a thread data structure, you need to provide an
interpreter state data structure. The interpreter state data
structure hold global data that is shared by all threads in an
interpreter, for example the module administration
sys.modules). Depending on your needs, you can either create
a new interpreter state data structure, or share the interpreter state
data structure used by the Python main thread (to access the latter,
you must obtain the thread state and access its interp member;
this must be done by a thread that is created by Python or by the main
thread after Python is initialized).
Assuming you have access to an interpreter object, the typical idiom for calling into Python from a C thread is
PyThreadState *tstate; PyObject *result; /* interp is your reference to an interpreter object. */ tstate = PyThreadState_New(interp); PyEval_AcquireThread(tstate); /* Perform Python actions here. */ result = CallSomeFunction(); /* evaluate result */ /* Release the thread. No Python API allowed beyond this point. */ PyEval_ReleaseThread(tstate); /* You can either delete the thread state, or save it until you need it the next time. */ PyThreadState_Delete(tstate);
Threads belonging to different interpreters initially share nothing, except process state like available memory, open file descriptors and such. The global interpreter lock is also shared by all threads, regardless of to which interpreter they belong.
PyEval_ReleaseThread(tstate). It is not needed before calling PyEval_SaveThread()or PyEval_RestoreThread().
This is a no-op when called for a second time. It is safe to call this function before calling Py_Initialize().
When only the main thread exists, no lock operations are needed. This is a common situation (most Python programs do not use threads), and the lock operations slow the interpreter down a bit. Therefore, the lock is not created initially. This situation is equivalent to having acquired the lock: when there is only a single thread, all object accesses are safe. Therefore, when this function initializes the lock, it also acquires it. Before the Python threadmodule creates a new thread, knowing that either it has the lock or the lock hasn't been created yet, it calls PyEval_InitThreads(). When this call returns, it is guaranteed that the lock has been created and that it has acquired it.
It is not safe to call this function when it is unknown which thread (if any) currently has the global interpreter lock.
This function is not available when thread support is disabled at compile time.
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.
|long id, PyObject *exc)|
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