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@@ -215,6 +215,141 @@ class event(object):
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hub.schedule_call(0, greenlib.switch, waiter, self._result)
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class semaphore(object):
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"""Classic semaphore implemented with a counter and an event.
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Optionally initialize with a resource count, then acquire() and release()
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resources as needed. Attempting to acquire() when count is zero suspends
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the calling coroutine until count becomes nonzero again.
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>>> from eventlet import coros, api
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>>> sem = coros.semaphore(2, limit=3)
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>>> sem.acquire()
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>>> sem.acquire()
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>>> def releaser(sem):
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... print "releasing one"
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... sem.release()
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...
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>>> _ = api.spawn(releaser, sem)
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>>> sem.acquire()
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releasing one
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>>> sem.counter
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0
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>>> for x in xrange(3):
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... sem.release()
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...
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>>> def acquirer(sem):
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... print "acquiring one"
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... sem.acquire()
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...
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>>> _ = api.spawn(acquirer, sem)
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>>> sem.release()
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acquiring one
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>>> sem.counter
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3
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"""
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def __init__(self, count=0, limit=None):
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if limit is not None and count > limit:
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# Prevent initializing with inconsistent values
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count = limit
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self.counter = count
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self.limit = limit
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self.acqevent = event()
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self.relevent = event()
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if self.counter > 0:
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# If we initially have items, then don't block acquire()s.
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self.acqevent.send()
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if self.limit is None or self.counter < self.limit:
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# If either there's no limit or we're below it, don't block on
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# release()s.
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self.relevent.send()
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def acquire(self):
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# This logic handles the self.limit is None case because None != any integer.
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while self.counter == 0:
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# Loop until there are resources to acquire. We loop because we
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# could be one of several coroutines waiting for a single item. If
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# we all get notified, only one is going to claim it, and the rest
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# of us must continue waiting.
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self.acqevent.wait()
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# claim the resource
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self.counter -= 1
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if self.counter == 0:
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# If we just transitioned from having a resource to having none,
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# make anyone else's wait() actually wait.
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self.acqevent.reset()
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if self.counter + 1 == self.limit:
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# If we just transitioned from being full to having room for one
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# more resource, notify whoever was waiting to release one.
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self.relevent.send()
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def release(self):
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# This logic handles the self.limit is None case because None != any integer.
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while self.counter == self.limit:
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self.relevent.wait()
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self.counter += 1
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if self.counter == self.limit:
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self.relevent.reset()
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if self.counter == 1:
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# If self.counter was 0 before we incremented it, then wake up
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# anybody who was waiting
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self.acqevent.send()
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class metaphore(object):
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"""This is sort of an inverse semaphore: a counter that starts at 0 and
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waits only if nonzero. It's used to implement a "wait for all" scenario.
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>>> from eventlet import api, coros
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>>> count = coros.metaphore()
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>>> count.wait()
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>>> def decrementer(count, id):
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... print "%s decrementing" % id
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... count.dec()
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...
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>>> _ = api.spawn(decrementer, count, 'A')
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>>> _ = api.spawn(decrementer, count, 'B')
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>>> count.inc(2)
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>>> count.wait()
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A decrementing
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B decrementing
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"""
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def __init__(self):
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self.counter = 0
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self.event = event()
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# send() right away, else we'd wait on the default 0 count!
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self.event.send()
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def inc(self, by=1):
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"""Increment our counter. If this transitions the counter from zero to
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nonzero, make any subsequent wait() call wait.
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"""
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assert by > 0
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self.counter += by
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if self.counter == by:
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# If we just incremented self.counter by 'by', and the new count
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# equals 'by', then the old value of self.counter was 0.
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# Transitioning from 0 to a nonzero value means wait() must
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# actually wait.
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self.event.reset()
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def dec(self, by=1):
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"""Decrement our counter. If this transitions the counter from nonzero
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to zero, a current or subsequent wait() call need no longer wait.
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"""
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assert by > 0
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self.counter -= by
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if self.counter <= 0:
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# Don't leave self.counter < 0, that will screw things up in
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# future calls.
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self.counter = 0
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# Transitioning from nonzero to 0 means wait() need no longer wait.
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self.event.send()
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def wait(self):
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"""Suspend the caller only if our count is nonzero. In that case,
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resume the caller once the count decrements to zero again.
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"""
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self.event.wait()
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def execute(func, *args, **kw):
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""" Executes an operation asynchronously in a new coroutine, returning
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an event to retrieve the return value.
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@@ -272,21 +407,56 @@ class CoroutinePool(pools.Pool):
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self._next_event = None
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else:
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self._tracked_events = None
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self.requested = metaphore()
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super(CoroutinePool, self).__init__(min_size, max_size)
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## This doesn't yet pass its own doctest -- but I'm not even sure it's a
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## wonderful idea.
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## def __del__(self):
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## """Experimental: try to prevent the calling script from exiting until
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## all coroutines in this pool have run to completion.
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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!"
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## ...
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## >>> pool.launch_all(saw, "GHI")
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## >>> del pool
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## I saw G!
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## I saw H!
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## I saw I!
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## """
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## self.wait_all()
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def _main_loop(self, sender):
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""" Private, infinite loop run by a pooled coroutine. """
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try:
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while True:
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recvd = sender.wait()
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# Delete the sender's result here because the very
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# first event through the loop is referenced by
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# spawn_startup, and therefore is not itself deleted.
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# This means that we have to free up its argument
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# because otherwise said argument persists in memory
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# forever. This is generally only a problem in unit
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# tests.
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sender._result = NOT_USED
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sender = event()
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(evt, func, args, kw) = recvd
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self._safe_apply(evt, func, args, kw)
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api.get_hub().cancel_timers(api.getcurrent())
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# Likewise, delete these variables or else they will
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# be referenced by this frame until replaced by the
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# next recvd, which may or may not be a long time from
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# now.
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del evt, func, args, kw, recvd
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self.put(sender)
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finally:
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# if we get here, something broke badly, and all we can really
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# do is try to keep the pool from leaking items
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# do is try to keep the pool from leaking items.
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# Shouldn't even try to print the exception.
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self.put(self.create())
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def _safe_apply(self, evt, func, args, kw):
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@@ -345,6 +515,20 @@ class CoroutinePool(pools.Pool):
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sender = event()
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self._greenlets.add(api.spawn(self._main_loop, sender))
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return sender
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def get(self):
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"""Override of eventlet.pools.Pool interface"""
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# Track the number of requested CoroutinePool coroutines
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self.requested.inc()
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# forward call to base class
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return super(CoroutinePool, self).get()
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def put(self, item):
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"""Override of eventlet.pools.Pool interface"""
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# forward call to base class
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super(CoroutinePool, self).put(item)
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# Track the number of outstanding CoroutinePool coroutines
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self.requested.dec()
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def execute(self, func, *args, **kw):
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"""Execute func in one of the coroutines maintained
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@@ -407,6 +591,241 @@ class CoroutinePool(pools.Pool):
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for g in self._greenlets:
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api.kill(g)
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def wait_all(self):
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"""Wait until all coroutines started either by execute() or
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execute_async() have completed. If you kept the event objects returned
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by execute(), you can then call their individual wait() methods to
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retrieve results with no further actual waiting.
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>>> from eventlet import coros
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>>> pool = coros.CoroutinePool()
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>>> pool.wait_all()
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>>> def hi(name):
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... print "Hello, %s!" % name
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... return name
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...
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>>> evt = pool.execute(hi, "world")
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>>> pool.execute_async(hi, "darkness, my old friend")
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>>> pool.wait_all()
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Hello, world!
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Hello, darkness, my old friend!
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>>> evt.wait()
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'world'
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>>> pool.wait_all()
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"""
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self.requested.wait()
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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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>>> from eventlet import coros
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>>> pool = coros.CoroutinePool()
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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_async(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 = 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.
|
|
|
|
|
try:
|
|
|
|
|
while True:
|
|
|
|
|
# Before each yield, start as many new coroutines as we can fit.
|
|
|
|
|
# (The self.free() test isn't 100% accurate: if we happen to be
|
|
|
|
|
# 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().)
|
|
|
|
|
# The point is that we don't want to wait in the _execute() call,
|
|
|
|
|
# we want to wait in the q.wait() call.
|
|
|
|
|
# IMPORTANT: at start, and whenever we've caught up with all
|
|
|
|
|
# coroutines we've launched so far, we MUST iterate this inner
|
|
|
|
|
# loop at least once, regardless of self.free() -- otherwise the
|
|
|
|
|
# q.wait() call below will deadlock!
|
|
|
|
|
# Recall that index is the index of the NEXT args tuple that we
|
|
|
|
|
# haven't yet launched. Therefore it counts how many args tuples
|
|
|
|
|
# we've launched so far.
|
|
|
|
|
while self.free() > 0 or finished == index:
|
|
|
|
|
# Just like the implementation of execute_async(), save that
|
|
|
|
|
# we're passing our queue instead of None as the "event" to
|
|
|
|
|
# which to send() the result.
|
|
|
|
|
self._execute(q, function, args, {})
|
|
|
|
|
# We've consumed that args tuple, advance to next.
|
|
|
|
|
index, args = tuples.next()
|
|
|
|
|
# Okay, we've filled up the pool again, yield a result -- which
|
|
|
|
|
# will probably wait for a coroutine to complete. Although we do
|
|
|
|
|
# have q.ready(), so we could iterate without waiting, we avoid
|
|
|
|
|
# that because every yield could involve considerable real time.
|
|
|
|
|
# We don't know how long it takes to return from yield, so every
|
|
|
|
|
# time we do, take the opportunity to stuff more requests into the
|
|
|
|
|
# pool before yielding again.
|
|
|
|
|
yield q.wait()
|
|
|
|
|
# Be sure to count results so we know when to stop!
|
|
|
|
|
finished += 1
|
|
|
|
|
except StopIteration:
|
|
|
|
|
pass
|
|
|
|
|
# Here we've exhausted the input iterable. index+1 is the total number
|
|
|
|
|
# of coroutines we've launched. We probably haven't yielded that many
|
|
|
|
|
# results yet. Wait for the rest of the results, yielding them as they
|
|
|
|
|
# arrive.
|
|
|
|
|
while finished < index + 1:
|
|
|
|
|
yield q.wait()
|
|
|
|
|
finished += 1
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
class pipe(object):
|
|
|
|
|
""" Implementation of pipe using events. Not tested! Not used, either."""
|
|
|
|
@@ -429,6 +848,70 @@ class pipe(object):
|
|
|
|
|
return buf
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
class queue(object):
|
|
|
|
|
"""Cross-coroutine queue, using semaphore to synchronize.
|
|
|
|
|
The API is like a generalization of event to be able to hold more than one
|
|
|
|
|
item at a time (without reset() or cancel()).
|
|
|
|
|
|
|
|
|
|
>>> from eventlet import coros
|
|
|
|
|
>>> q = coros.queue(max_size=2)
|
|
|
|
|
>>> def putter(q):
|
|
|
|
|
... q.send("first")
|
|
|
|
|
...
|
|
|
|
|
>>> _ = api.spawn(putter, q)
|
|
|
|
|
>>> q.ready()
|
|
|
|
|
False
|
|
|
|
|
>>> q.wait()
|
|
|
|
|
'first'
|
|
|
|
|
>>> q.ready()
|
|
|
|
|
False
|
|
|
|
|
>>> q.send("second")
|
|
|
|
|
>>> q.ready()
|
|
|
|
|
True
|
|
|
|
|
>>> q.send("third")
|
|
|
|
|
>>> def getter(q):
|
|
|
|
|
... print q.wait()
|
|
|
|
|
...
|
|
|
|
|
>>> _ = api.spawn(getter, q)
|
|
|
|
|
>>> q.send("fourth")
|
|
|
|
|
second
|
|
|
|
|
"""
|
|
|
|
|
def __init__(self, max_size=None):
|
|
|
|
|
"""If you omit max_size, the queue will attempt to store an unlimited
|
|
|
|
|
number of items.
|
|
|
|
|
Specifying max_size means that when the queue already contains
|
|
|
|
|
max_size items, an attempt to send() one more item will suspend the
|
|
|
|
|
calling coroutine until someone else retrieves one.
|
|
|
|
|
"""
|
|
|
|
|
self.items = collections.deque()
|
|
|
|
|
self.sem = semaphore(count=0, limit=max_size)
|
|
|
|
|
|
|
|
|
|
def send(self, result=None, exc=None):
|
|
|
|
|
"""If you send(exc=SomeExceptionClass), the corresponding wait() call
|
|
|
|
|
will raise that exception.
|
|
|
|
|
Otherwise, the corresponding wait() will return result (default None).
|
|
|
|
|
"""
|
|
|
|
|
self.items.append((result, exc))
|
|
|
|
|
self.sem.release()
|
|
|
|
|
|
|
|
|
|
def wait(self):
|
|
|
|
|
"""Wait for an item sent by a send() call, in FIFO order.
|
|
|
|
|
If the corresponding send() specifies exc=SomeExceptionClass, this
|
|
|
|
|
wait() will raise that exception.
|
|
|
|
|
Otherwise, this wait() will return the corresponding send() call's
|
|
|
|
|
result= parameter.
|
|
|
|
|
"""
|
|
|
|
|
self.sem.acquire()
|
|
|
|
|
result, exc = self.items.popleft()
|
|
|
|
|
if exc is not None:
|
|
|
|
|
raise exc
|
|
|
|
|
return result
|
|
|
|
|
|
|
|
|
|
def ready(self):
|
|
|
|
|
# could also base this on self.sem.counter...
|
|
|
|
|
return len(self.items) > 0
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
class Actor(object):
|
|
|
|
|
""" A free-running coroutine that accepts and processes messages.
|
|
|
|
|
|
|
|
|
|