287 lines
11 KiB
Python
287 lines
11 KiB
Python
# replacement of CoroutinePool implemented with proc module
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from eventlet import coros, proc, api
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class Pool(object):
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def __init__(self, min_size=0, max_size=4, track_events=False):
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if min_size > max_size:
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raise ValueError('min_size cannot be bigger than max_size')
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self.max_size = max_size
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self.sem = coros.Semaphore(max_size)
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self.procs = proc.RunningProcSet()
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if track_events:
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self.results = coros.queue()
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else:
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self.results = None
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@property
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def current_size(self):
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return len(self.procs)
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def free(self):
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return self.sem.counter
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def execute(self, func, *args, **kwargs):
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"""Execute func in one of the coroutines maintained
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by the pool, when one is free.
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Immediately returns a Proc object which can be queried
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for the func's result.
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>>> pool = Pool()
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>>> task = pool.execute(lambda a: ('foo', a), 1)
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>>> task.wait()
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('foo', 1)
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"""
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# if reentering an empty pool, don't try to wait on a coroutine freeing
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# itself -- instead, just execute in the current coroutine
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if self.sem.locked() and api.getcurrent() in self.procs:
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p = proc.spawn(func, *args, **kwargs)
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try:
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p.wait()
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except:
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pass
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else:
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self.sem.acquire()
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p = self.procs.spawn(func, *args, **kwargs)
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# assuming the above line cannot raise
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p.link(lambda p: self.sem.release())
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if self.results is not None:
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p.link(self.results)
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return p
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execute_async = execute
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def _execute(self, evt, func, args, kw):
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p = self.execute(func, *args, **kw)
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p.link(evt)
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return p
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def waitall(self):
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return self.procs.waitall()
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wait_all = waitall
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def wait(self):
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"""Wait for the next execute in the pool to complete,
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and return the result."""
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return self.results.wait()
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def killall(self):
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return self.procs.killall()
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def launch_all(self, function, iterable):
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"""For each tuple (sequence) in iterable, launch function(*tuple) in
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its own coroutine -- like itertools.starmap(), but in parallel.
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Discard values returned by function(). You should call wait_all() to
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wait for all coroutines, newly-launched plus any previously-submitted
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execute() or execute_async() calls, to complete.
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>>> pool = Pool()
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>>> def saw(x):
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... print "I saw %s!" % x
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...
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>>> pool.launch_all(saw, "ABC")
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>>> pool.wait_all()
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I saw A!
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I saw B!
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I saw C!
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"""
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for tup in iterable:
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self.execute(function, *tup)
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def process_all(self, function, iterable):
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"""For each tuple (sequence) in iterable, launch function(*tuple) in
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its own coroutine -- like itertools.starmap(), but in parallel.
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Discard values returned by function(). Don't return until all
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coroutines, newly-launched plus any previously-submitted execute() or
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execute_async() calls, have completed.
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>>> from eventlet import coros
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>>> pool = coros.CoroutinePool()
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>>> def saw(x): print "I saw %s!" % x
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...
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>>> pool.process_all(saw, "DEF")
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I saw D!
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I saw E!
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I saw F!
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"""
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self.launch_all(function, iterable)
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self.wait_all()
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def generate_results(self, function, iterable, qsize=None):
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"""For each tuple (sequence) in iterable, launch function(*tuple) in
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its own coroutine -- like itertools.starmap(), but in parallel.
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Yield each of the values returned by function(), in the order they're
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completed rather than the order the coroutines were launched.
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Iteration stops when we've yielded results for each arguments tuple in
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iterable. Unlike wait_all() and process_all(), this function does not
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wait for any previously-submitted execute() or execute_async() calls.
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Results are temporarily buffered in a queue. If you pass qsize=, this
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value is used to limit the max size of the queue: an attempt to buffer
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too many results will suspend the completed CoroutinePool coroutine
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until the requesting coroutine (the caller of generate_results()) has
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retrieved one or more results by calling this generator-iterator's
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next().
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If any coroutine raises an uncaught exception, that exception will
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propagate to the requesting coroutine via the corresponding next() call.
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What I particularly want these tests to illustrate is that using this
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generator function:
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for result in generate_results(function, iterable):
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# ... do something with result ...
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executes coroutines at least as aggressively as the classic eventlet
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idiom:
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events = [pool.execute(function, *args) for args in iterable]
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for event in events:
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result = event.wait()
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# ... do something with result ...
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even without a distinct event object for every arg tuple in iterable,
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and despite the funny flow control from interleaving launches of new
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coroutines with yields of completed coroutines' results.
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(The use case that makes this function preferable to the classic idiom
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above is when the iterable, which may itself be a generator, produces
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millions of items.)
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>>> from eventlet import coros
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>>> import string
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>>> pool = coros.CoroutinePool(max_size=5)
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>>> pausers = [coros.event() for x in xrange(2)]
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>>> def longtask(evt, desc):
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... print "%s woke up with %s" % (desc, evt.wait())
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...
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>>> pool.launch_all(longtask, zip(pausers, "AB"))
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>>> def quicktask(desc):
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... print "returning %s" % desc
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... return desc
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...
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(Instead of using a for loop, step through generate_results()
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items individually to illustrate timing)
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>>> step = iter(pool.generate_results(quicktask, string.ascii_lowercase))
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>>> print step.next()
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returning a
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returning b
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returning c
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a
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>>> print step.next()
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b
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>>> print step.next()
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c
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>>> print step.next()
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returning d
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returning e
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returning f
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d
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>>> pausers[0].send("A")
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>>> print step.next()
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e
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>>> print step.next()
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f
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>>> print step.next()
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A woke up with A
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returning g
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returning h
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returning i
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g
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>>> print "".join([step.next() for x in xrange(3)])
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returning j
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returning k
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returning l
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returning m
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hij
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>>> pausers[1].send("B")
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>>> print "".join([step.next() for x in xrange(4)])
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B woke up with B
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returning n
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returning o
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returning p
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returning q
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klmn
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"""
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# Get an iterator because of our funny nested loop below. Wrap the
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# iterable in enumerate() so we count items that come through.
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tuples = iter(enumerate(iterable))
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# If the iterable is empty, this whole function is a no-op, and we can
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# save ourselves some grief by just quitting out. In particular, once
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# we enter the outer loop below, we're going to wait on the queue --
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# but if we launched no coroutines with that queue as the destination,
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# we could end up waiting a very long time.
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try:
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index, args = tuples.next()
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except StopIteration:
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return
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# From this point forward, 'args' is the current arguments tuple and
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# 'index+1' counts how many such tuples we've seen.
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# This implementation relies on the fact that _execute() accepts an
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# event-like object, and -- unless it's None -- the completed
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# coroutine calls send(result). We slyly pass a queue rather than an
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# event -- the same queue instance for all coroutines. This is why our
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# queue interface intentionally resembles the event interface.
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q = coros.queue(max_size=qsize)
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# How many results have we yielded so far?
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finished = 0
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# This first loop is only until we've launched all the coroutines. Its
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# complexity is because if iterable contains more args tuples than the
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# size of our pool, attempting to _execute() the (poolsize+1)th
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# coroutine would suspend until something completes and send()s its
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# result to our queue. But to keep down queue overhead and to maximize
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# responsiveness to our caller, we'd rather suspend on reading the
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# queue. So we stuff the pool as full as we can, then wait for
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# something to finish, then stuff more coroutines into the pool.
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try:
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while True:
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# Before each yield, start as many new coroutines as we can fit.
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# (The self.free() test isn't 100% accurate: if we happen to be
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# executing in one of the pool's coroutines, we could _execute()
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# without waiting even if self.free() reports 0. See _execute().)
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# The point is that we don't want to wait in the _execute() call,
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# we want to wait in the q.wait() call.
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# IMPORTANT: at start, and whenever we've caught up with all
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# coroutines we've launched so far, we MUST iterate this inner
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# loop at least once, regardless of self.free() -- otherwise the
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# q.wait() call below will deadlock!
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# Recall that index is the index of the NEXT args tuple that we
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# haven't yet launched. Therefore it counts how many args tuples
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# we've launched so far.
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while self.free() > 0 or finished == index:
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# Just like the implementation of execute_async(), save that
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# we're passing our queue instead of None as the "event" to
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# which to send() the result.
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self._execute(q, function, args, {})
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# We've consumed that args tuple, advance to next.
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index, args = tuples.next()
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# Okay, we've filled up the pool again, yield a result -- which
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# will probably wait for a coroutine to complete. Although we do
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# have q.ready(), so we could iterate without waiting, we avoid
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# that because every yield could involve considerable real time.
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# We don't know how long it takes to return from yield, so every
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# time we do, take the opportunity to stuff more requests into the
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# pool before yielding again.
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yield q.wait()
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# Be sure to count results so we know when to stop!
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finished += 1
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except StopIteration:
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pass
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# Here we've exhausted the input iterable. index+1 is the total number
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# of coroutines we've launched. We probably haven't yielded that many
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# results yet. Wait for the rest of the results, yielding them as they
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# arrive.
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while finished < index + 1:
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yield q.wait()
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finished += 1
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if __name__=='__main__':
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import doctest
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doctest.testmod()
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