consider using a priority queue with one or more worker threads to service the tasks. The main thread can add work to the queue, with a timestamp of the soonest it should be serviced. Worker threads pop work off the queue, sleep until the time of priority value is reached, do the work, and then pop another item off the queue.

How about a more fleshed out answer. mklauber makes a good point. If there's a chance all of your workers might be sleeping when you have new, more urgent work, then a queue.PriorityQueue isn't really the solution, although a "priority queue" is still the technique to use, which is available from the heapq module. Instead, we'll make use of a different synchronization primitive; a condition variable, which in python is spelled threading.Condition.

The approach is fairly simple, peek on the heap, and if the work is current, pop it off and do that work. If there was work, but it's scheduled into the future, just wait on the condition until then, or if there's no work at all, sleep forever.

The producer does it's fair share of the work; every time it adds new work, it notifies the condition, so if there are sleeping workers, they'll wake up and recheck the queue for newer work.

import heapq, time, threadingSTART_TIME = time.time()SERIALIZE_STDOUT = threading.Lock()def consumer(message):    """the actual work function.  nevermind the locks here, this just keeps       the output nicely formatted.  a real work function probably won't need       it, or might need quite different synchronization"""    SERIALIZE_STDOUT.acquire()    print time.time() - START_TIME, message    SERIALIZE_STDOUT.release()def produce(work_queue, condition, timeout, message):    """called to put a single item onto the work queue."""    prio = time.time() + float(timeout)    condition.acquire()    heapq.heappush(work_queue, (prio, message))    condition.notify()    condition.release()def worker(work_queue, condition):    condition.acquire()    stopped = False    while not stopped:        now = time.time()        if work_queue:            prio, data = work_queue[0]            if data == 'stop':                stopped = True                continue            if prio < now:                heapq.heappop(work_queue)                condition.release()                # do some work!                consumer(data)                condition.acquire()            else:                condition.wait(prio - now)        else:            # the queue is empty, wait until notified            condition.wait()    condition.release()if __name__ == '__main__':    # first set up the work queue and worker pool    work_queue = []    cond = threading.Condition()    pool = [threading.Thread(target=worker, args=(work_queue, cond))            for _ignored in range(4)]    map(threading.Thread.start, pool)    # now add some work    produce(work_queue, cond, 10, 'Grumpy')    produce(work_queue, cond, 10, 'Sneezy')    produce(work_queue, cond, 5, 'Happy')    produce(work_queue, cond, 10, 'Dopey')    produce(work_queue, cond, 15, 'Bashful')    time.sleep(5)    produce(work_queue, cond, 5, 'Sleepy')    produce(work_queue, cond, 10, 'Doc')    # and just to make the example a bit more friendly, tell the threads to stop after all    # the work is done    produce(work_queue, cond, float('inf'), 'stop')    map(threading.Thread.join, pool)

This answer has actually two suggestions - my first one and another I have discovered after the first one.

sched

I suspect you are looking for the sched module.

EDIT: my bare suggestion seemed little helpful after I have read it. So I decided to test the sched module to see if it can work as I suggested. Here comes my test: I would use it with a sole thread, more or less this way:

class SchedulingThread(threading.Thread):    def __init__(self):        threading.Thread.__init__(self)        self.scheduler = sched.scheduler(time.time, time.sleep)        self.queue = []        self.queue_lock = threading.Lock()        self.scheduler.enter(1, 1, self._schedule_in_scheduler, ())    def run(self):        self.scheduler.run()    def schedule(self, function, delay):        with self.queue_lock:            self.queue.append((delay, 1, function, ()))    def _schedule_in_scheduler(self):        with self.queue_lock:            for event in self.queue:                self.scheduler.enter(*event)                print "Registerd event", event            self.queue = []        self.scheduler.enter(1, 1, self._schedule_in_scheduler, ())

First, I'd create a thread class which would have its own scheduler and a queue. At least one event would be registered in the scheduler: one for invoking a method for scheduling events from the queue.

class SchedulingThread(threading.Thread):    def __init__(self):        threading.Thread.__init__(self)        self.scheduler = sched.scheduler(time.time, time.sleep)        self.queue = []        self.queue_lock = threading.Lock()        self.scheduler.enter(1, 1, self._schedule_in_scheduler, ())

The method for scheduling events from the queue would lock the queue, schedule each event, empty the queue and schedule itself again, for looking for new events some time in the future. Note that the period for looking for new events is short (one second), you may change it:

    def _schedule_in_scheduler(self):        with self.queue_lock:            for event in self.queue:                self.scheduler.enter(*event)                print "Registerd event", event            self.queue = []        self.scheduler.enter(1, 1, self._schedule_in_scheduler, ())

The class should also have a method for scheduling user events. Naturally, this method should lock the queue while updating it:

    def schedule(self, function, delay):        with self.queue_lock:            self.queue.append((delay, 1, function, ()))

Finally, the class should invoke the scheduler main method:

    def run(self):        self.scheduler.run()

Here comes an example of using:

def print_time():    print "scheduled:", time.time()if __name__ == "__main__":    st = SchedulingThread()    st.start()              st.schedule(print_time, 10)    while True:        print "main thread:", time.time()        time.sleep(5)    st.join()

Its output in my machine is:

$ python schedthread.pymain thread: 1311089765.77Registerd event (10, 1, <function print_time at 0x2f4bb0>, ())main thread: 1311089770.77main thread: 1311089775.77scheduled: 1311089776.77main thread: 1311089780.77main thread: 1311089785.77

This code is just a quick'n'dirty example, it may need some work. However, I have to confess that I am a bit fascinated by the sched module, so did I suggest it. You may want to look for other suggestions as well :)

APScheduler

Looking in Google for solutions like the one I've post, I found this amazing APScheduler module. It is so practical and useful that I bet it is your solution. My previous example would be way simpler with this module:

from apscheduler.scheduler import Schedulerimport timesch = Scheduler()sch.start()@sch.interval_schedule(seconds=10)def print_time():    print "scheduled:", time.time()    sch.unschedule_func(print_time)while True:    print "main thread:", time.time()    time.sleep(5)

(Unfortunately I did not find how to schedule an event to execute only once, so the function event should unschedule itself. I bet it can be solved with some decorator.)

If you have a bunch of tasks that need to get performed later, and you want them to persist even if you shut down the calling program or your workers, you should really look into Celery, which makes it super easy to create new tasks, have them executed on any machine you'd like, and wait for the results.

From the Celery page, "This is a simple task adding two numbers:"

from celery.task import task@taskdef add(x, y):    return x + y

You can execute the task in the background, or wait for it to finish:

>>> result = add.delay(8, 8)>>> result.wait() # wait for and return the result16