Tasklets — Lightweight threads

Tasklets wrap functions, allowing them to be launched as microthreads to be run within the scheduler.

Launching a tasklet:

stackless.tasklet(callable)(*args, **kwargs)

That is the most common way of launching a tasklet. This does not just create a tasklet, but it also automatically inserts the created tasklet into the scheduler.

Example - launching a more concrete tasklet:

>>> def func(*args, **kwargs):
...     print("scheduled with", args, "and", kwargs)
...
>>> stackless.tasklet(func)(1, 2, 3, string="test")
<stackless.tasklet object at 0x01C58030>
>>> stackless.run()
scheduled with (1, 2, 3) and {'string': 'test'}

Tasklets, main, current and more

There are two especially notable tasklets, the main tasklet and the current tasklet.

The main tasklet is fixed, and it is the initial thread of execution of your application. Chances are that it is running the scheduler.

The current tasklet however, is the tasklet that is currently running. It might be the main tasklet, if no other tasklets are being run. Otherwise, it is the entry in the scheduler’s chain of runnable tasklets, that is currently executing.

Example - is the main tasklet the current tasklet:

stackless.main == stackless.current

Example - is the current tasklet the main tasklet:

stackless.current.is_main == 1

Example - how many tasklets are scheduled:

stackless.runcount

Note

The main tasklet factors into the stackless.runcount value. If you are checking how many tasklets are in the scheduler from your main loop, you need to keep in mind that there will be another tasklet in there over and above the ones you explicitly created.

The tasklet class

class tasklet(func=None, args=None, kwargs=None)

This class exposes the form of lightweight thread (the tasklet) provided by Stackless-Python. Wrapping a callable object and arguments to pass into it when it is invoked, the callable is run within the tasklet.

Tasklets are usually created in the following manner:

>>> stackless.tasklet(func)(1, 2, 3, name="test")

The above code is equivalent to:

>>> t = stackless.tasklet()
>>> t.bind(func)
>>> t.setup(1, 2, 3, name="test")

and

>>> t = stackless.tasklet()
>>> t.bind(func, (1, 2, 3), {"name":"test"})
>>> t.insert()

In fact, the tasklet.__init__ method just calls tasklet.bind() and the tasklet.__call__ method calls tasklet.setup().

Note that when an implicit tasklet.insert() is invoked, there is no need to hold a reference to the created tasklet.

tasklet.bind(func=None, args=None, kwargs=None)

Bind the tasklet to the given callable object, func:

>>> t = stackless.tasklet()
>>> t.bind(func)

In most every case, programmers will instead pass func into the tasklet constructor:

>>> t = stackless.tasklet(func)

Note that the tasklet cannot be run until it has been provided with arguments to call func. They can be provided as args and/or kwargs to this function, or through a subsequent call to tasklet.setup(). The difference is that when providing them to tasklet.bind(), the tasklet is not made runnable yet.

func can be None when providing arguments, in which case a previous call to tasklet.bind() must have provided the function.

To clear the binding of a tasklet set all arguments to None. This is especially useful, if you run a tasklet only partially:

>>> def func():
...     try:
...        ... # part 1
...        stackless.schedule_remove()
...        ... # part 2
...     finally:
...        ... # cleanup
>>> t = stackless.tasklet(func)()
>>> stackless.enable_softswitch(True)
>>> stackless.run() # execute part 1 of func
>>> t.bind(None)    # unbind func(). Don't execute the finally block

If a tasklet is alive, it can be rebound only if the tasklet is not the current tasklet and if the tasklet is not scheduled and if the tasklet is restorable. bind() raises RuntimeError, if these conditions are not met.

tasklet.setup(*args, **kwargs)

Provide the tasklet with arguments to pass into its bound callable:

>>> t = stackless.tasklet()
>>> t.bind(func)
>>> t.setup(1, 2, name="test")

In most every case, programmers will instead pass the arguments and callable into the tasklet constructor instead:

>>> t = stackless.tasklet(func)(1, 2, name="test")

Note that when tasklets have been bound to a callable object and provided with arguments to pass to it, they are implicitly scheduled and will be run in turn when the scheduler is next run.

The method setup() is equivalent to:

>>> def setup(self, *args, **kwargs):
>>>     assert isinstance(self, stackless.tasklet)
>>>     with stackless.atomic():
>>>         if self.alive:
>>>             raise(RuntimeError("tasklet is alive")
>>>         self.bind(None, args, kwargs)
>>>         self.insert()
>>>         return self
tasklet.insert()

Insert a tasklet at the end of the scheduler runnables queue, given that it isn’t blocked. Blocked tasklets need to be reactivated by channels.

tasklet.remove()

Remove a tasklet from the runnables queue.

Note

If this tasklet has a non-trivial C-state attached, Stackless will kill the tasklet when the containing thread terminates. Since this will happen in some unpredictable order, it may cause unwanted side-effects. Therefore it is recommended to either run tasklets to the end or to explicitly kill() them.

tasklet.run()

If the tasklet is alive and not blocked on a channel, then it will be run immediately. However, this behaves differently depending on whether the tasklet is in the scheduler’s chain of runnable tasklets.

Example - running a tasklet that is scheduled:

>>> def f(name):
...     while True:
...         c=stackless.current
...         m=stackless.main
...         assert c.scheduled
...         print("%s id=%s, next.id=%s, main.id=%s, main.scheduled=%r" % (name,id(c), id(c.next), id(m), m.scheduled))
...         stackless.schedule()
...
>>> t1 = stackless.tasklet(f)("t1")
>>> t2 = stackless.tasklet(f)("t2")
>>> t3 = stackless.tasklet(f)("t3")
>>>
>>> t1.run()
t1 id=36355632, next.id=36355504, main.id=30571120, main.scheduled=True
t2 id=36355504, next.id=36355888, main.id=30571120, main.scheduled=True
t3 id=36355888, next.id=30571120, main.id=30571120, main.scheduled=True

What you see here is that t1 is not the only tasklet that ran. When t1 yields, the next tasklet in the chain is scheduled and so forth until the tasklet that actually ran t1 - that is the main tasklet - is scheduled and resumes execution.

If you were to run t2 instead of t1, then we would have only seen the output of t2 and t3, because the tasklet calling run is before t1 in the chain.

Removing the tasklet to be run from the scheduler before it is actually run, gives more predictable results as shown in the following example. But keep in mind that the scheduler is still being run and the chain is still involved, the only reason it looks correct is tht the act of removing the tasklet effectively moves it before the tasklet that calls remove().

Example - running a tasklet that is not scheduled:

>>> t2.remove()
<stackless.tasklet object at 0x022ABDB0>
>>> t2.run()
t2 id=36355504, next.id=36356016, main.id=36356016, main.scheduled=True
>>> t2.scheduled
True

While the ability to run a tasklet directly is useful on occasion, that the scheduler is still involved and that this is merely directing its operation in limited ways, is something you need to be aware of.

tasklet.switch()

Similar to tasklet.run() except that the calling tasklet is paused. This function can be used to implement raw scheduling without involving the scheduling queue.

The target tasklet must belong to the same thread as the caller.

Example - switch to a tasklet that is scheduled. Function f is defined as in the previous example:

>>> t1 = stackless.tasklet(f)("t1")
>>> t2 = stackless.tasklet(f)("t2")
>>> t3 = stackless.tasklet(f)("t3")
>>> t1.switch()
t1 id=36413744, next.id=36413808, main.id=36413680, main.scheduled=False
t2 id=36413808, next.id=36413872, main.id=36413680, main.scheduled=False
t3 id=36413872, next.id=36413744, main.id=36413680, main.scheduled=False
t1 id=36413744, next.id=36413808, main.id=36413680, main.scheduled=False
t2 id=36413808, next.id=36413872, main.id=36413680, main.scheduled=False
t3 id=36413872, next.id=36413744, main.id=36413680, main.scheduled=False
t1 id=36413744, next.id=36413808, main.id=36413680, main.scheduled=False
...
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "<stdin>", line 6, in f
KeyboardInterrupt
>>>

What you see here is that the main tasklet was removed from the scheduler. Therefore the scheduler runs until it got interrupted by a keyboard interrupt.

tasklet.raise_exception(exc_class, *args)

Raise an exception on the given tasklet. exc_class is required to be a sub-class of Exception. It is instantiated with the given arguments args and raised within the given tasklet.

In order to make best use of this function, you should be familiar with how tasklets and the scheduler deal with exceptions, and the purpose of the TaskletExit exception.

If you try to raise an exception on a tasklet, that is not alive, the method fails, except if exc_class is TaskletExit and the tasklet already ended.

Changed in version 3.3.7: In case of an error Stackless versions before 3.3.7 raise exc_class(*args). Later versions raises RuntimeError.

tasklet.throw(exc=None, val=None, tb=None, pending=False)

Raise an exception on the given tasklet. The semantics are similar to the raise keywords, and so, this can be used to send an existing exception to the tasklet.

if pending evaluates to True, then the target tasklet will be made runnable and the caller continues. Otherwise, the target will be inserted before the current tasklet in the queue and switched to immediately.

If you try to raise an exception on a tasklet, that is not alive, the method raises RuntimeError on the caller. There is one exception: you can safely raise TaskletExit, if the tasklet already ended.

tasklet.kill(pending=False)

Terminates the tasklet and unblocks it, if the tasklet was blocked on a channel. If the tasklet already ran to its end, the method does nothing. If the tasklet has no thread, the method simply ends the tasklet. Otherwise it raises the TaskletExit exception on the tasklet. pending has the same meaning as for tasklet.throw().

This can be considered to be shorthand for:

>>> if t.alive:
>>>     t.throw(TaskletExit, pending=pending)
tasklet.set_atomic(flag)

This method is used to construct a block of code within which the tasklet will not be auto-scheduled when preemptive scheduling. It is useful for wrapping critical sections that should not be interrupted:

old_value = t.set_atomic(1)
# Implement unsafe logic here.
t.set_atomic(old_value)

Note that this will also prevent involuntary thread switching, i.e. the thread will hang on to the GIL for the duration.

tasklet.bind_thread([thread_id])

Rebind the tasklet to the current thread, or a Python® thread with the given thread_id.

This is only safe to do with just-created tasklets, or soft-switchable tasklets. This is the case when a tasklet has just been unpickled. Then it can be useful in order to hand it off to a different thread for execution.

The relationship between tasklets and threads is covered elsewhere.

tasklet.set_ignore_nesting(flag)

It is probably best not to use this until you understand nesting levels:

old_value = t.set_ignore_nesting(1)
# Implement unsafe logic here.
t.set_ignore_nesting(old_value)
tasklet.__del__()

New in version 3.7.

Finalize the tasklet. This is a PEP 442 finalizer. If this tasklet is alive and has a non-trivial C-state attached, the finalizer repeatedly kills the tasklet for upmost 10 times until it is dead. Then, if this tasklet still has non-trivial C-state attached, the finalizer appends the tasklet to gc.garbage. This is done, because releasing the C-state could cause undefined behavior.

You should not call this method from Python®-code.

The following (read-only) attributes allow tasklet state to be checked:

tasklet.alive

This attribute is True while a tasklet is still running. Tasklets that are not running will most likely have either run to completion and exited, or will have unexpectedly exited through an exception of some kind.

tasklet.paused

This attribute is True when a tasklet is alive, but not scheduled or blocked on a channel. This state is entered after a tasklet.bind() with 2 or 3 arguments, a tasklet.remove() or by the main tasklet, when it is acting as a watchdog.

tasklet.blocked

This attribute is True when a tasklet is blocked on a channel.

tasklet.scheduled

This attribute is True when the tasklet is either in the runnables list or blocked on a channel.

tasklet.restorable

This attribute is True, if the tasklet can be completely restored by pickling/unpickling. If a tasklet is restorable, it is possible to continue running the unpickled tasklet from whatever point in execution it may be.

All tasklets can be pickled for debugging/inspection purposes, but an unpickled tasklet might have lost runtime information (C stack). For the tasklet to be runnable, it must not have lost runtime information (C stack usage for instance).

The following attributes allow checking of user set situations:

tasklet.atomic

This attribute is True while this tasklet is within a tasklet.set_atomic() block

tasklet.block_trap

Setting this attribute to True prevents the tasklet from being blocked on a channel.

tasklet.ignore_nesting

This attribute is True while this tasklet is within a tasklet.set_ignore_nesting() block

The following attributes allow identification of tasklet place:

tasklet.is_current

This attribute is True if the tasklet is the current tasklet of the thread it belongs to. To see if a tasklet is the currently executing tasklet in the current thread use the following Python® code:

import stackless
def is_current(tasklet):
    return tasklet is stackless.current
tasklet.is_main

This attribute is True if the tasklet is the main tasklet of the thread it belongs to. To check if a tasklet is the main tasklet of the current thread use the following Python® code:

import stackless
def is_current_main(tasklet):
    return tasklet is stackless.main
tasklet.thread_id

This attribute is the id of the thread the tasklet belongs to. If its thread has terminated, the attribute value is -1.

The relationship between tasklets and threads is covered elsewhere.

In almost every case, tasklets will be linked into a chain of tasklets. This might be the scheduler itself, otherwise it will be a channel the tasklet is blocked on.

The following attributes allow a tasklets place in a chain to be identified:

tasklet.prev

The previous tasklet in the chain that this tasklet is linked into.

tasklet.next

The next tasklet in the chain that this tasklet is linked into.

The following attributes are intended only for implementing debuggers, profilers, coverage tools and the like. Their behavior is part of the implementation platform, rather than part of the language definition, and thus may not be available in all Stackless-Python implementations.

tasklet.trace_function
tasklet.profile_function

The trace / profile function of the tasklet. These attributes are the tasklet counterparts of the functions sys.settrace(), sys.gettrace(), sys.setprofile() and sys.getprofile().

Tasklet Life Cycle

Here is a somewhat simplified state chart that shows the life cycle of a tasklet instance. The chart does not show the nesting-level, the thread-id and the flags atomic, ignore-nesting, block-trap and restorable.

../../_images/tasklet_state_chart.png

Furthermore the diagram does not show the scheduler functions stackless.run(), stackless.schedule() and stackless.schedule_remove(). For the purpose of understanding the state transitions these functions are roughly equivalent to the following Python® definitions:

def run():
    main = stackless.current
    def watchdog():
        while stackless.runcount > 1:
            stackless.current.next.run()
        main.switch()
    stackless.tasklet(watchdog)().switch()

def schedule():
    stackless.current.next.run()

def schedule_remove():
    stackless.current.next.switch()