How to efficiently do many tasks a “little later” in Python?

Question

I have a process, that needs to perform a bunch of actions \"later\" (after 10-60 seconds usually). The problem is that those \"later\" actions can be a lot (1000s), so using a

Answer 1

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.py
main thread: 1311089765.77
Registerd event (10, 1, <function print_time at 0x2f4bb0>, ())
main thread: 1311089770.77
main thread: 1311089775.77
scheduled: 1311089776.77
main thread: 1311089780.77
main 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 Scheduler
import time

sch = 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.)

Answer 2

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, threading

START_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)

Answer 3

You wrote:

one of the problem is that the process uses zeromq for communication so I would need some integration (eventlet already has it)

Seems like your choice will be heavily influenced by these details, which are a bit unclear—how is zeromq being used for communication, how much resources will the integration will require, and what are your requirements and available resources.

---

There's a project called django-ztask which uses zeromq and provides a task decorator similar to celery's one. However, it is (obviously) Django-specific and so may not be suitable in your case. I haven't used it, prefer celery myself.

Been using celery for a couple of projects (these are hosted at ep.io PaaS hosting, which provides an easy way to use it).

Celery looks like quite flexible solution, allowing delaying tasks, callbacks, task expiration & retrying, limiting task execution rate, etc. It may be used with Redis, Beanstalk, CouchDB, MongoDB or an SQL database.

Example code (definition of task and asynchronous execution after a delay):

from celery.decorators import task

@task
def my_task(arg1, arg2):
    pass # Do something

result = my_task.apply_async(
    args=[sth1, sth2], # Arguments that will be passed to `my_task()` function.
    countdown=3, # Time in seconds to wait before queueing the task.
)

See also a section in celery docs.

Answer 4

Well in my opinion you could use something called "cooperative multitasking". It's twisted-based thing and its really cool. Just look at PyCon presentation from 2010: http://blip.tv/pycon-us-videos-2009-2010-2011/pycon-2010-cooperative-multitasking-with-twisted-getting-things-done-concurrently-11-3352182

Well you will need transport queue to do this too...