stackless — The built-in extension module

New in version 1.5.2.

The stackless module is the way in which programmers must access the enhanced functionality provided by Stackless Python.

Functions

The main scheduling related functions:

stackless.run(timeout=0, threadblock=False, soft=False, ignore_nesting=False, totaltimeout=False)

When run without arguments, scheduling is cooperative. It us up to you to ensure your tasklets yield, perhaps by calling schedule(), giving other tasklets a turn to run. The scheduler will exit when there are no longer any runnable tasklets left within it. This might be because all the tasklets have exited, whether by completing or erroring, but it also might be because some are blocked on channels. You should not assume that when run() exits, your tasklets have all run to completion, unless you know for sure that is how you structured your application.

The optional argument timeout is primarily used to run the scheduler in a different manner, providing pre-emptive scheduling. A non-zero value indicates that as each tasklet is given a chance to run, it should only be allowed to run as long as the number of Python virtual instructions are below this value. If a tasklet hits this limit, then it is interrupted and the scheduler exits returning the now no longer scheduled tasklet to the caller.

Example - run until 1000 opcodes have been executed:

interrupted_tasklet = stackless.run(1000)
# interrupted_tasklet is no longer scheduled, reschedule it.
interrupted_tasklet.insert()
# Now run your custom logic.
...

The optional argument threadblock affects the way Stackless works when channels are used for communication between threads. Normally,

The optional argument soft affects how pre-emptive scheduling behaves. When a pre-emptive interruption would normally occur, instead of interrupting and returning the running tasklet, the scheduler exits at the next convenient scheduling moment.

The optional argument ignore_nesting affects the behaviour of the attribute tasklet.nesting_level on individual tasklets. If set, interrupts are allowed at any interpreter nesting level, causing the tasklet-level attribute to be ignored.

The optional argument totaltimeout affects how pre-emptive scheduling behaves. Normally the scheduler is interrupted when any given tasklet has been running for timeout instructions. If a value is given for totaltimeout, instead the scheduler is interrupted when it has run for totaltimeout instructions.

Note

The most common use of this function is to call it either without arguments, or with a value for timeout.

stackless.schedule(retval=stackless.current)

Yield execution of the currently running tasklet. When called, the tasklet is blocked and moved to the end of the chain of runnable tasklets. The next tasklet in the chain is executed next.

If your application employs cooperative scheduling and you do not use custom yielding mechanisms built around channels, you will most likely call this in your tasklets.

Example - typical usage of schedule():

stackless.schedule()

As illustrated in the example, the typical use of this function ignores both the optional argument retval and the return value. Note that as the variable name retval hints, the return value is the value of the optional argument.

stackless.schedule_remove(retval=stackless.current)

Yield execution of the currently running tasklet. When called, the tasklet is blocked and removed from the chain of runnable tasklets. The tasklet following calling tasklet in the chain is executed next.

The most likely reason to use this, rather than schedule(), is to build your own yielding primitive without using channels. This is where the otherwise ignored optional argument retval and the return value are useful.

tasklet.tempval is used to store the value to be returned, and as expected, when this function is called it is set to retval. Custom utility functions can take advantage of this and set a new value for tasklet.tempval before reinserting the tasklet back into the scheduler.

Example - a utility function:

def wait_for_result():
    waiting_tasklets.append(stackless.current)
    return stackless.schedule_remove()

def event_callback(result):
    for tasklet in waiting_tasklets:
        tasklet.tempval = result
        tasklet.insert()

    waiting_tasklets = []

def tasklet_function():
    result = wait_for_result()
    print "received result", result

One drawback of this approach over channels, is that it bypasses the useful tasklet.block_trap attribute. The ability to guard against a tasklet being blocked on a channel, is in practice a useful ability to have.

Callback related functions:

stackless.set_channel_callback(callable)

Install a callback for channels. Every send or receive action will result in callable being called. Setting a value of None will result in the callback being disabled.

Example - installing a callback:

def channel_cb(channel, tasklet, sending, willblock):
    pass

stackless.set_channel_callback(channel_cb)

The channel callback argument is the channel on which the action is being performed.

The tasklet callback argument is the tasklet that is performing the action on channel.

The sending callback argument is an integer, a non-zero value of which indicates that the channel action is a send rather than a receive.

The willblock callback argument is an integer, a non-zero value of which indicates that the channel action will result in tasklet being blocked on channel.

stackless.set_schedule_callback(callable)

Install a callback for scheduling. Every scheduling event, whether explicit or implicit, will result in callable being called.

Example - installing a callback:

def schedule_cb(prev, next):
    pass

stackless.set_schedule_callback(callable)

The prev callback argument is the tasklet that was just running.

The next callback argument is the tasklet that is going to run now.

Scheduler state introspection related functions:

stackless.get_thread_info(thread_id)

Return a tuple containing the threads main tasklet, current tasklet and run-count.

Example:

main_tasklet, current_tasklet, runcount = get_thread_info(thread_id)
stackless.getcurrent()
Return the currently executing tasklet of this thread.
stackless.getmain()
Return the main tasklet of this thread.
stackless.getruncount()
Return the number of currently runnable tasklets.

Debugging related functions:

stackless.enable_softswitch(flag)

Control the switching behaviour. Tasklets can be either switched by moving stack slices around or by avoiding stack changes at all. The latter is only possible in the top interpreter level.

Example - safely disabling soft switching:

old_value = stackless.enable_softswitch(False)
# Logic executed without soft switching.
enable_softswitch(old_value)

Note

Disabling soft switching in this manner is exposed for timing and debugging purposes.

Attributes

stackless.current
The currently executing tasklet of this thread.
stackless.main
The main tasklet of this thread.
stackless.runcount

The number of currently runnable tasklets.

Example - usage:

>>> stackless.runcount
1

Note

The minimum value of runcount will be 1, as the calling tasklet will be included.

stackless.threads

A list of all thread ids, starting with the id of the main thread.

Example - usage:

>>> stackless.threads
[5148]

Exceptions

exception stackless.TaskletExit

This exception is used to silently kill a tasklet. It should not be caught by your code, and along with other important exceptions like SystemExit, be propagated up to the scheduler.

The following use of the except clause should be avoided:

try:
    some_function()
except:
    pass

This will catch every exception raised within it, including TaskletExit. Unless you guarantee you actually raise the exceptions that should reach the scheduler, you are better to use except in the following manner:

try:
    some_function()
except Exception:
    pass

Here only the more common exceptions are caught, as the ones that should not be caught and discarded inherit from BaseException, rather than Exception.

This class is derived from EnvironmentError.

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