Mailman 3 August 2008 - Python-ideas

"While" suggestion
by Stavros Korokithakis 22 Aug '08

22 Aug '08

Hello all, I have noticed that sometimes "while" loops produce "unpythonic" patterns, such as the following: data = my_file.read(1024) while data: do_something(data) data = my_file.read(1024) The assignment is repeated, which is less than optimal. Since we don't have a while statement that does the check in the end, would it not be better if the syntax of while could be amended to include something like this (in the spirit of the new "with" keyword)?: while my_file.read(1024) as data: do_something(data) This would terminate the loop when myfile.read() evaluated to False, and it is more pythonic than repeating onesself. I contacted GvR about this, and he replied that this syntax would have to be part of the expression than part of the while, which I agree would be less than ideal. However, I don't see why it would have to be part of the expression, since the "while" could easily assign the value of the expression to the variable and break if it evaluates to False. I would appreciate any thoughts on this, Stavros Korokithakis

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New PEP proposal: C Micro-Threading (ala Twisted) (long!)
by Bruce Frederiksen 03 Aug '08

03 Aug '08

Here is a proposal for making the benefits of Twisted available without having to write python code in an event driven style. (I'm not sure whether this list accepts HTML, so am just copying plain text -- you may want to run rst2html on this)... I look forward to your ideas and feedback! -bruce Abstract ======== This PEP adds micro-threading (or `green threads`_) at the C level so that micro-threading is built in and can be used with very little coding effort at the python level. The implementation is quite similar to the Twisted_ [#twisted-fn]_ Deferred_/Reactor_ model, but applied at the C level by extending the `C API`_ [#c_api]_ slightly. Doing this provides the Twisted capabilities to python, without requiring the python programmer to code in the Twisted event driven style. Thus, legacy python code would gain the benefits that Twisted provides with very little modification. Burying the event driven mechanism in the C level should also give the same benefits to python GUI interface tools so that the python programmers don't have to deal with event driven programming there either. This capability may also be used to provide some of the features that `Stackless Python`_ [#stackless]_ provides, such as microthreads and channels (here, called micro_pipes). .. _Twisted: http://twistedmatrix.com/trac/ .. _Deferred: http://twistedmatrix.com/projects/core/documentation/howto/defer.html .. _Reactor: http://twistedmatrix.com/projects/core/documentation/howto/reactor-basics.h… .. _C API: http://docs.python.org/api/api.html .. _green threads: http://en.wikipedia.org/wiki/Green_threads Motivation ========== The popularity of the Twisted project has demonstrated the need for micro-threads as an alternative the standard thread_ [#thread-module]_ and threading_ [#threading-module]_ packages. These allow large numbers (1000's) of simultaneous connections to python servers, as well as fan-outs to large numbers of downstream connections. The advantages to the Twisted approach over standard threads are: #. much less memory is required per thread #. faster thread creation #. faster context switching (I'm guessing on this one, is this really true?) #. synchronization between threads is easier because there is no preemption, making it much easier to write critical sections of code. The disadvantages are: #. the python developer must write his/her program in an event driven style #. the approach can not be used with standard python code that wasn't written in this event driven style #. the approach does not take advantage of multiple processor architectures #. since there is no preemption, a long running micro-thread will starve other micro-threads This PEP attempts to retain all of the advantages that Twisted has demonstrated, but resolve the first two disadvantages to make these advantages accessible to all python programs, including legacy programs not written in the Twisted style. This should make it very easy for legacy programs like WSGI apps, Django and TurboGears to reap the Twisted benefits. Another example of event driven mechanisms are the GUI/windows events. This PEP would also make it easy for python GUI interface toolkits (like wxpython and qtpython) to hide the GUI/windows event driven style of programming from the python programmer. For example, you would no longer need to use modal dialog boxes just to make the programming easier. This PEP does not address the last two disadvantages, and thus also has these disadvantages itself. Specification of C Layer Enhancements ===================================== Deferred -------- ``PyDef_Deferred`` is written as a new exception type for use by the C code to defer execution. This is a subclass of ``NotImplementedError``. Instances are not raised as a normal exception (e.g., with ``PyErr_SetObject``), but by calling ``PyWatch_Defer``. This registers the ``PyDef_Deferred`` associated with the currently running micro_thread as the current error object, but also readies it for its primary job -- deferring execution. As an exception, it creates its own error message, if needed, which is "Deferred execution not yet implemented by %s" % c_function_name. ``PyErr_ExceptionMatches`` may be used with these. This allows them to be treated as exceptions by non micro-threading aware (unmodified) C functions. But these ``PyDef_Deferred`` objects are special indicators that are treated differently than normal exceptions by micro-threading aware (modified) C code. Modified C functions do this by calling ``PyDef_AddCallback``, ``PyDef_Final`` or explicitly checking ``PyErr_ExceptionMatches(PyDef_Deferred)`` after receiving an error return status from a called function. ``PyDef_Deferred`` instances offer the following methods (in addition to the normal exception methods): - ``int PyDef_AddCallbackEx(PyObject *deferred, const char *caller_name, const char *called_name, PyObject *(*callback_fn)(PyObject *returned_object, void *state), void *state)`` - The *caller_name* and *called_name* are case sensitive. The *called_name* must match exactly the *caller_name* used by the called function when it dealt with this ``PyExc_Deferred``. If the names are different, the ``PyExc_Deferred`` knows that an intervening unmodified C function was called. This is what triggers it to act like an exception. The *called_name* must be ``NULL`` when called by the function that executed the ``PyWatch_Defer`` to defer execution. - The *callback_fn* will be called with the ``PyObject`` of the results of the prior registered callback_fn. An exception is passed to *callback_fn* by setting the exception and passing ``NULL`` (just like returning an exception from a C function). In the case that the ``PyExc_Deferred`` initially accepts *callback_fn* and then later has to reject it (because of the exception case, above), it will pass a ``SystemError`` to all registered callback_fns to allow them to clean up. But this ``SystemError`` is only a "behind the scenes" measure that will only be seen by these callback_fns. It will be cleared and the prior error indicator reestablished before ``PyDef_AddCallback`` returns. - The *callback_fn* is always guaranteed to be called exactly once at some point in the future. It will be passed the same *state* value as was passed with it to ``PyDef_AddCallback``. It is up to the *callback_fn* to deal with the memory management of this *state* object. - The *callback_fn* may be ``NULL`` if no callback is required. But in this case ``PyDef_AddCallback`` must still be called to notify the ``PyExc_Deferred`` that the C function is micro-threading aware. - This returns 0 if it fails (is acting like an exception), 1 otherwise. If it fails, the caller should do any needed clean up because the caller won't be resumed by the ``PyExc_Deferred`` (i.e., *callback_fn* will not be called). - ``int PyDef_AddCallback(const char *caller_name, const char *called_name, PyObject *(*callback_fn)(PyObject *returned_object, void *state), void *state)`` - Same as ``PyDef_AddCallbackEx``, except that the deferred object is taken from the *value* object returned by ``PyErr_Fetch``. If the *type* returned by ``PyErr_Fetch`` is not ``PyDef_Deferred``, 0 is returned. Thus, this function can be called after any exception and then other standard exception processing done if 0 is returned (including checking for other kinds of exceptions). - ``int PyDef_FinalEx(PyObject *deferred, const char *called_fn)`` - Only used by the top-level C function (a reactor) to verify that its *called_fn* is micro-threading aware. Returns 1 if everything looks good, 0 otherwise. If 0 is returned, then this instance to be treated as an exception. - ``int PyDef_Final(const char *called_fn)`` - Same as ``PyDef_FinalEx``, except that the deferred object is taken from the *value* object returned by ``PyErr_Fetch``. If the *type* returned by ``PyErr_Fetch`` is not ``PyDef_Deferred``, 0 is returned. Thus, this function can be called after any exception and then other standard exception processing done if 0 is returned (including checking for other kinds of exceptions). - ``int PyDef_IsExceptionEx(PyObject *deferred)`` - Only used by the top-level C function (a reactor) to determine whether to treat the *deferred* as an exception or to do deferred processing. - ``int PyDef_IsException(void)`` - Same as ``PyDef_IsExceptionEx``, except that the deferred object is taken from the *value* object returned by ``PyErr_Fetch``. If the *type* returned by ``PyErr_Fetch`` is not ``PyDef_Deferred``, 1 is returned. Thus, this function can be called after any exception and then other standard exception processing done if 1 is returned (including checking for other kinds of exceptions). - ``PyObject *PyDef_Callback(PyObject *deferred, PyObject *returned_object)`` - This calls the callback_fn sequence passing *returned_object* to the first registered callback_fn, and each callback_fn's returned ``PyObject`` to the next registered callback_fn. The result of the final callback_fn is returned (which may be ``NULL`` if an exception was encountered). - To signal an exception to the callbacks, first set the error indicator (e.g. with ``PyErr_SetString``) and then call ``PyDef_Callback`` passing ``NULL`` as the returned_object (just like returning ``NULL`` from a C function to signal an exception). - If a callback_fn wants to defer execution, this same ``PyDef_Deferred`` object will be used (since the callback_fn is running in the same micro_thread). The ``PyDef_Deferred`` keeps the newly added callback_fns in the proper sequence relative the existing callback_fns that have not yet been executed. When ``PyDef_Deferred`` is returned from the callback_fn, no further callback_fns are called. Note that this check is also done on the starting *returned_object*, so that if this ``PyDef_Deferred`` exception is passed in, then none of its callback_fns are executed and it simply returns. - If this function returns a ``PyDef_Deferred`` exception (itself), no ``PyDef_Final`` needs to be done on it, rather a ``PyDef_IsException`` is done to see whether to treat it as an exception or not. - ``void PyDef_Abort(PyObject *deferred)`` - Acts as if ``SystemError`` has been raised to its callback_fns. Clears the ``SystemError`` and reestablishes any prior error indicator prior to returning. - If *deferred* is currently registered with a Watcher_, deregister it. - ``void PyDef_Terminate(PyObject *deferred)`` - Acts as if ``SystemExit`` has been raised to its callback_fns. Clears the ``SystemExit`` and reestablishes any prior error indicator prior to returning. - If *deferred* is currently registered with a Watcher_, deregister it. Each micro_thread has its own ``PyDef_Deferred`` object associated with it. This is possible because each micro_thread may only be suspended for one thing at a time. This also allows us to re-use ``PyDef_Deferreds`` and, through the following trick, means that we don't need a lot of ``PyDef_Deferred`` instances when a micro_thread is deferred many times at different points in the call stack. One peculiar thing about the stored callbacks, is that they're not really a queue. When the ``PyDef_Deferred`` is first used and has no saved callbacks, the callbacks are saved in straight FIFO manor. Let's say that four callbacks are saved in this order: ``D'``, ``C'``, ``B'``, ``A'`` (meaning that ``A`` called ``B``, called ``C``, called ``D`` which deferred): - after ``D'`` is added, the queue looks like: ``D'`` - after ``C'`` is added, the queue looks like: ``D'``, ``C'`` - after ``B'`` is added, the queue looks like: ``D'``, ``C'``, ``B'`` - after ``A'`` is added, the queue looks like: ``D'``, ``C'``, ``B'``, ``A'`` Upon resumption, ``D'`` is called, then ``C'`` is called. ``C'`` then calls ``E`` which calls ``F`` which now wants to defer execution again. ``B'`` and ``A'`` are still in the deferred's callback queue. When ``F'``, then ``E'`` then ``C''`` are pushed, they go in front of the callbacks still present from the last defer: - after ``F'`` is added, the queue looks like: ``F'``, ``B'``, ``A'`` - after ``E'`` is added, the queue looks like: ``F'``, ``E'``, ``B'``, ``A'`` - after ``C''`` is added, the queue looks like: ``F'``, ``E'``, ``C''``, ``B'``, ``A'`` These callback functions are basically a reflection of the C stack at the point the micro_thread is deferred. Reactor ------- The Reactor logic is divided into two levels: - The top level function. There is only one long running invocation of this function (per standard thread_). - A list of Watchers_. Each of these knows how to watch for a different type of external event, such as a file being ready for IO or a signal having been received. .. _Watchers: Watcher_ Top Level ''''''''' The top level function pops (deferred, returned_object) pairs, doing the ``PyDef_Callback`` on each, until either the ``EventCheckingThreshold`` number of deferreds have been popped, or there are no more deferreds scheduled. It then runs through the ``WatcherList`` (which is maintained in descending ``PyWatch_Priority`` order) to give each watcher_ a chance to poll for its events. If there are then still no deferreds scheduled, it goes to each watcher in turn asking it to do a ``PyWatch_TimedWait`` for ``TimedWaitSeconds`` until one doesn't return -1. Then it polls the remaining watchers again and goes back to running scheduled deferreds. If there is only one watcher, a ``PyWatch_WaitForever`` is used, rather than first polling with ``PyWatch_Poll`` and then ``PyWatch_TimedWait``. The top level also manages a list of timers for the watchers. It calls ``PyWatch_Timeout`` each time a timer pops. - ``int PyTop_Schedule(PyObject *deferred, PyObject *returned_object)`` - Returns 0 on error, 1 otherwise. - ``int PyTop_ScheduleException(PyObject *deferred, PyObject *exc_type, PyObject *exc_value, PyObject *exc_traceback)`` - Returns 0 on error, 1 otherwise. - ``int PyTop_SetTimer(PyObject *watcher, PyObject *deferred, double seconds)`` - Returns 0 on error, 1 otherwise. - ``int PyTop_ClearTimer(PyObject *watcher, PyObject *deferred)`` - Returns 0 on error, 1 otherwise. - ``int PyTop_SetEventCheckingThreshold(long num_continues)`` - Returns 0 on error, 1 otherwise. - ``int PyTop_SetTimedWaitSeconds(double seconds)`` - Returns 0 on error, 1 otherwise. Watcher ''''''' - ``int PyWatch_Priority(PyObject *watcher)`` - Returns the priority of this watcher. (-1 for error). Higher numbers have higher priorities. - ``int PyWatch_RegisterDeferred(PyObject *watcher, PyObject *deferred, PyObject *wait_reason, double max_wait_seconds)`` - *Max_wait_seconds* of 0.0 means no time limit. Otherwise, register *deferred* with ``PyTop_SetTimer`` (above). - Adds *deferred* to the list of waiting objects, for *wait_reason*. - The meaning of *wait_reason* is determined by the watcher. It can be used, for example, to indicate whether to wait for input or output on a file. - Returns 0 on error, 1 otherwise. - ``void PyWatch_Defer(PyObject *watcher, PyObject *wait_reason, double max_wait_seconds)`` - Passes the ``PyDef_Deferred`` of the current micro_thread to ``PyWatch_RegisterDeferred``, and then raises the ``PyDef_Deferred`` as an exception. *Wait_reason* and *max_wait_seconds* are passed on to ``PyWatch_RegisterDeferred``. - This function has no return value. It always generates an exception. - ``int PyWatch_Poll(PyObject *watcher)`` - Poll for events and schedule the appropriate ``PyDef_Deferreds``. Do not cause the process to be put to sleep. Return 0 on error, 1 on success (whether or not any events were discovered). - ``int PyWatch_TimedWait(PyObject *watcher, double seconds)`` - Wait for events and schedule the appropriate ``PyDef_Deferreds``. Do not cause the process to be put to sleep for more than the indicated number of *seconds*. Return -1 if *watcher* is not capable of doing timed sleeps, 0 on error, 1 on success (whether or not any events were discovered). Return a 1 if the wait was terminated due to the process having received a signal. - If *watcher* is not capable of doing timed waits, it does a poll and returns -1. - ``int PyWatch_WaitForever(PyObject *watcher)`` - Suspend the process until an event occurs and schedule the appropriate ``PyDef_Deferreds``. The process may be put to sleep indefinitely. Return 0 on error, 1 on success (whether or not any ``PyDef_Deferreds`` were scheduled). Return a 1 if the wait was terminated due to the process having received a signal. - ``int PyWatch_Timeout(PyObject *watcher, PyObject *deferred)`` - Called by top level when the timer set by ``PyTop_SetTimer`` expires. - Passes a ``TimeoutException`` to the deferred using ``PyDef_Callback``. - Return 0 on error, 1 otherwise. - ``int PyWatch_DeregisterDeferred(PyObject *watcher, PyObject *deferred, PyObject *returned_object)`` - Deregisters *deferred*. - Passes *returned_object* to *deferred* using ``PyDef_Callback``. - *Returned_object* may be ``NULL`` to indicate an exception to the callbacks. - Returns 0 on error, 1 otherwise. Specification of Python Layer Enhancements ========================================== Fortunately, at the python level, the programmer does not see deferred, reactor, or watcher objects. The python programmer will see three things: #. An addition of non_blocking modes of accessing files, sockets, time.sleep and other functions that may block. It is not clear yet exactly what these will look like. The possibilities are: - Add an argument to the object creation functions to specify blocking or non-blocking. - Add an operation to change the blocking mode after the object has been created. - Add new non-blocking versions of the methods on the objects that may block (e.g., read_d/write_d/send_d/recv_d/sleep_d). - Some combination of these. If an object is used in blocking mode, then all microthreads (within its Posix thread_) will block. So the python programmer must set non-blocking mode on these objects as a first step to take advantage of micro-threading. #. Micro_thread objects. Each of these will have a re-usable ``PyDef_Deferred`` object attached to it, since each micro_thread can only be suspended waiting for one thing at a time. The current micro_thread would be stored within a C global variable, much like _PyThreadState_Current. If the python programmer isn't interested in micro_threading, micro_threads can be safely ignored (like posix threads_, you get one for free, but don't have to be aware of it). If the programmer *is* interested in micro-threading, then s/he must create micro_threads. This would be done with:: micro_thread(function, *args, **kwargs) I am thinking that there are three usage scenarios: #. Create a micro-thread to do something, without regard to any final return value from *function*. An example here would be a web server that has a top-level ``socket.accept`` loop that runs a ``handle_client`` function on each new connection. Once launched, the ``socket.accept`` thread is no longer interested in the ``handle_client`` threads. In this case, the normal return value of the ``handle_client`` function can be discarded. But what should be done with exceptions that are not caught in the child threads? Therefore, this style of use would be indicated by providing an top-level exception handler for the new thread as a keyword argument, e.g. ``exception_handler=traceback.print_exception`` in a developer environment and ``exception_handler=my_exception_logger`` in a production environment. If this keyword argument is provided, then the parent thread does not need to do any kind of *wait* after the child thread is complete. It will either complete normally and go away silently, or raise an uncaught exception, which is passed to the indicated exception_handler, and then go away with no more ado. #. Create micro_threads to run multiple long-running *functions* in parallel where the final return value from each *function* is needed in the parent thread. In this case, the ``exception_handler`` argument is not specified and the parent thread needs to *wait* on the child thread (when the parent is ready to do so). Thus, completed micro_threads will form zombie threads until their parents retrieve their final return values (much like unix processes). This ends up being a kind of parallel execution strategy and it might be nice to have a ``threaded_map`` function that will create a micro_thread for each element of its *iterable* argument in order to run the *function* on them in parallel and then return an iterable of the waited for results. On doing the *wait*, an uncaught exception in the child micro_thread is re-raised in the parent micro_thread. #. In the above examples, the child micro_threads are completely independent of each other. This final scenario uses *micro_pipes* to allow threads to cooperatively solve problems (much like unix pipes). #. Micro_pipes. Micro_pipes are one-way pipes that allow synchronized communication between micro_threads. The protocol for the receiving side of the pipe is simply the standard python iterator protocol. The sending side has these methods: - ``put(object)`` to send *object* to the receiving side (retrieved with the ``__next__`` method). - ``take_from(iterable)`` to send a series of objects to the receiving side (retrieved with multiple ``__next__`` calls). - ``close()`` causes ``StopIteration`` on the next ``__next__`` call. A ``put`` done after a ``close`` silently terminates the micro_thread doing the ``put`` (in case the receiving side closes the micro_pipe). Micro_pipes are automatically associated with micro_threads, making it less likely to hang the program: >>> pipe = micro_pipe() >>> next(pipe) # hangs the program! No micro_thread created to feed pipe... So each micro_thread will automatically have a stdout micro_pipe assigned to it and can (optionally) be assigned a stdin micro_pipe (some other micro_thread's stdout micro_pipe). When the micro_thread terminates, it automatically calls ``close`` on its stdout micro_pipe. To access the stdout micro_pipe of the current micro_thread, new ``put`` and ``take_from`` built-in functions are provided. Micro_pipes lets us write generator functions in a new way by having the generator do ``put(object)`` rather than ``yield object``. In this case, the generator function has no ``yield`` statement, so is not treated specially by the compiler. Basically this means that calling a new-style generator does not automatically create a new micro_thread (sort of what calling an old-style generator does). The ``put(object)`` does the same thing as ``yield object``, but allows the generator to share the micro_pipe with other new-style generators functions (by simply calling them) and old-style generators (or any iterable) by calling ``take_from`` on them. This lets the generator delegate to other generators without having to get involved with passing the results back to its caller. For example, a generator to output all the even numbers from 1-n, followed by all of the odd numbers:: def even_odd(n): take_from(range(2, n, 2)) take_from(range(1, n, 2)) These "new-style" generators would have to be run in their own micro_thread: >>> pipe = micro_thread(even_odd, 100).stdout >>> # now pipe is an iterable representing the generator: >>> print tuple(pipe) But the generator is then not restricted to running within its own micro_thread. It could also sometimes be used as a helper by other generators within their micro_thread. This would allow generators to still use each other as helpers. For example:: def even(n): take_from(range(2, n, 2)) def odd(n): take_from(range(1, n, 2)) def even_odd(n): even(n) odd(n) At this point a micro_thread may be created on any of the above generators. Open Questions ============== #. How are exceptions propagated from one ``PyDef_Deferred`` to the next? - This would happen when the final result of a micro_thread is needed by another micro_thread. This happens in two cases: - When a *wait* is done. In this case the exception is propagated to the caller. - When dealing with pipes, it seems that an exception on the ``put`` side should be propagated to the ``__next__`` side. This is another reason to have the stdout pipe associated with the micro_thread. Because when the exception occurs, it will not be in the ``put`` call; so without attaching the pipe to the micro_thread, there would be no way of knowing which pipe that micro_thread was outputting to. Thus exception would propagate from the ``put`` side of a micro_pipe to the ``__next__`` side, but not the other direction. #. How are tracebacks handled? #. Do we: #. Treat each python-to-python call as a separate C call, with it's own callback_fn? #. Only register one callback_fn for each continuous string of python-to-python calls and then process them iteratively rather than recursively in the callback_fn (but not in the original calls)? #. Treat python-to-python calls iteratively both in the original calls and in the callback_fn? #. How is process termination handled? - I guess we can keep a list of micro_threads and terminate each of them. There's a question of whether to allow the micro_threads to complete or to abort them mid-stream. Kind of like a unix shutdown. Maybe two kinds of process termination? #. How does this interact with the existing posix thread_ package? - Each micro_thread would be associated with a posix thread. Or, conversely, each posix thread would have its own list of micro_threads. #. How does this impact the debugger/profiler/sys.settrace? #. Should functions (C and python) that may defer be indicated with some naming convention (e.g., ends in '_d') to make it easier for programmers to avoid them within their critical sections of code (in terms of synchronization)? Rationale ========= The implementation is done by treating the C-level deferreds as a special case of C-level exceptions so that the new ``PyDef_Deferred`` objects will be be treated like any other exception if *any* C function within the call chain hasn't been modified to deal with them. In this case, the normal execution of the program is interrupted, but in a well understood way (by an exception) with the name of the offending C function contained in the exception message so that the python developer knows where to go to fix it. Only when *all* C functions in the call chain properly recognize and deal with the ``PyDef_Deferred`` is the new deferred object applied to implement the new micro-threading behavior. No change is required for C functions that could never get a ``PyDef_Deferred``. This takes advantage of the fact that the current exception mechanism already unwinds the C stack. It also adds deferred processing without adding additional checks after each C function call to see whether to defer execution. The check that is already being done for exceptions doubles as a check for deferred processing. Other Approaches ================ Here's a brief comparison to other approaches to micro-threading in python: - `Stackless Python`_ [#stackless]_ - As near as I can tell, stackless went through two incarnations: #. The first incarnation involved an implementation of Frame continuations which were then used to provide the rest of the stackless functionality. - A new ``Py_UnwindToken`` was created to unwind the C stack. This is similar to the new ``PyDef_Deferred`` proposed in this PEP, except that ``Py_UnwindToken`` is treated as a special case of a normal ``PyObject`` return value, while the ``PyDef_Deferred`` is treated as a special case of a normal exception. Consider the case where unmodified C function ``A`` calls modified C function ``B``, and function ``B`` wants to defer execution. In both cases function ``B`` returns a special value indicating it wants to defer. The difference comes in how function ``A`` (which isn't prepared to cooperate with this new type of request) handles this situation. With the stackless ``Py_UnwindToken`` as a return value, function ``A`` tries to act on ``Py_UnwindToken`` as an ordinary return value, which it is not. This may or may not lead to some other exception being thrown due to a type inconsistency between ``Py_UnwindToken`` and what was expected by function ``A``. It also means that further up the call chain in the code that called function ``A``, the fact that a ``Py_UnwindToken`` was returned isn't known if function ``A`` does not simply return it. In short, who knows what will happen... But this PEP treats the request to defer as a special exception. So function ``A``, receiving a ``PyDef_Deferred`` exception will treat it as an ordinary exception. Function ``A``, not recognizing this exception, will perform any required clean up and simply forward the ``PyDef_Deferred`` on to its caller (re-raise it). The top-level code that receives this ``PyDef_Deferred`` knows that it's a broken ``PyDef_Deferred`` and raises it as a normal exception, which will state "Deferred execution not yet implemented by A". This makes much more sense. - Another difference between the two styles of continuations is that the stackless continuation is designed to be able to be continued multiple times. In other words, you can continue the execution of the program from the point the continuation was made as many times as you wish, passing different seed values each time. The ``PyDef_Deferred`` described in this PEP (like the Twisted Deferred) is designed to be continued oonce. - The stackless approach provides a python-level continuation mechanism (at the Frame level) that only makes python functions continuable. It provides no way for C functions to register continuations so that C functions can be unwound from the stack and later continued (other than those related to the byte code interpreter). In contrast, this PEP proposes a C-level continuation mechanism very similar to the Twisted Deferred. Each C function registers a callback to be run when the Deferred is continued. From this perspective, the byte code interpreter is just another C function. #. The second incarnation involved a way of hacking the underlying C stack to copy it and later restore it as a means of continuing the execution. - This doesn't appear to be portable to different CPU/C Compiler configurations. - This doesn't deal with other global state (global/static variables, file pointers, etc) that may also be used by this saved stack. - In contrast, this PEP uses a single C stack and makes no assumptions about the underlying C stack implementation. It is completely portable to any CPU/C compiler configuration. - `Implementing "weightless threads" with Python generators`_ [#weightless]_ - This requires you code each thread as generators. The generator executes a 'yield' to relinquish control. - It's not clear how this scales. It seems that to pause in a lower python function, it and all intermediate functions must be generators. - python-safethread_ [#safethread]_ - This is an alternate implementation to thread_ that adds monitors for mutable types, deadlock detection, improves exception propagation across threads and program finalization, and removes the GIL lock. As such, it is not a "micro" threading approach, though by removing the GIL lock it may be able to better use multiple processor configurations than the approach proposed in this PEP. - `Sandboxed Threads in Python`_ [#sandboxed-threads]_ - Another alternate implementation to thread_, this one only shares immutable objects between threads, modifying the referencing counting system to avoid synchronization issues with the reference count for shared objects. Again, not a "micro" threading approach, but perhaps also better with multiple processors. .. _Implementing "weightless threads" with Python generators: http://www.ibm.com/developerworks/library/l-pythrd.html .. _python-safethread: https://launchpad.net/python-safethread .. _Sandboxed Threads in Python: http://mail.python.org/pipermail/python-dev/2005-October/057082.html .. _Stackless Python: http://www.stackless.com/ .. _thread: http://docs.python.org/lib/module-thread.html .. _threading: http://docs.python.org/lib/module-threading.html Backwards Compatibility ======================= This PEP doesn't break any existing code. Existing code just won't take advantage of any of the new features. But there are two possible problem areas: #. Code uses micro-threading, but then causes an unmodified C function to call a modified C function which tries to defer execution. In this case an exception will be generated stating that the unmodified C function needs to be converted before this program will work. #. Code originally written in a single threaded environment is now used in a micro-threaded environment. The old code was not written taking synchronization issues into account, which may cause problems if the old code calls a function which causes it to defer in the middle of its critical section. This could cause very strange behavior, but can't result in any C-level errors (e.g., segmentation violation). This old code would have to be fixed to run with the new features. I expect that this will not be a frequent problem. References ========== .. [#twisted-fn] Twisted, Twisted Matrix Labs (http://twistedmatrix.com/trac/) .. [#c_api] Python/C API Reference Manual, Rossum (http://docs.python.org/api/api.html) .. [#stackless] Stackless Python, Tismer (http://www.stackless.com/) .. [#thread-module] thread -- Multiple threads of control (http://docs.python.org/lib/module-thread.html) .. [#threading-module] threading -- Higher-level threading interface (http://docs.python.org/lib/module-threading.html) .. [#weightless] Charming Python: Implementing "weightless threads" with Python generators, Mertz (http://www.ibm.com/developerworks/library/l-pythrd.html) .. [#safethread] Threading extensions to the Python Language, (https://launchpad.net/python-safethread) .. [#sandboxed-threads] Sandboxed Threads in Python, Olsen (http://mail.python.org/pipermail/python-dev/2005-October/057082.html) Copyright ========= This document has been placed in the public domain.

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Re: [Python-ideas] [Python-Dev] Base-96
by Guido van Rossum 02 Aug '08

02 Aug '08

This sounds more like something to bring up in python-ideas(a)python.org. Also, rather than being vague about the motivation ("would be very interesting", you ought to think of a realistic use case. For example, are there existing encodings of binary data using base-96? I'm not aware of any. On Fri, Aug 1, 2008 at 4:06 PM, Kless <jonas.esp(a)googlemail.com> wrote: > I think that would be very interesting thay Python would have a module > for working on base 96 too. [1] > > It could be converted to base 96 the digests from hashlib module, and > random bytes used on crypto (to create the salt, the IV, or a key). > > As you can see here [2], the printable ASCII characters are 94 > (decimal code range of 33-126). So only left to add another 2 > characters more; the space (code 32), and one not-printable char > (which doesn't create any problem) by last. > > > [1] http://svn.python.org/view/python/trunk/Modules/binascii.c > [2] http://en.wikipedia.org/wiki/ISO/IEC_8859-1 -- --Guido van Rossum (home page: http://www.python.org/~guido/)

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