675 lines
25 KiB
Python
675 lines
25 KiB
Python
"""\
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@file pools.py
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@author Donovan Preston, Aaron Brashears
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Copyright (c) 2007, Linden Research, Inc.
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in
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all copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
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THE SOFTWARE.
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"""
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import collections
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import traceback
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from eventlet import api
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from eventlet import channel
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from eventlet import coros
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class FanFailed(RuntimeError):
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pass
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class SomeFailed(FanFailed):
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pass
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class AllFailed(FanFailed):
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pass
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class Pool(object):
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"""
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When using the pool, if you do a get, you should ALWAYS do a put.
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The pattern is:
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thing = self.pool.get()
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try:
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# do stuff
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finally:
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self.pool.put(thing)
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The maximum size of the pool can be modified at runtime via the max_size attribute.
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Adjusting this number does not affect existing items checked out of the pool, nor
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on any waiters who are waiting for an item to free up. Some indeterminate number
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of get/put cycles will be necessary before the new maximum size truly matches the
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actual operation of the pool.
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"""
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def __init__(self, min_size=0, max_size=4, order_as_stack=False):
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""" Pre-populates the pool with *min_size* items. Sets a hard limit to
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the size of the pool -- it cannot contain any more items than
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*max_size*, and if there are already *max_size* items 'checked out' of
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the pool, the pool will cause any getter to cooperatively yield until an
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item is put in.
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*order_as_stack* governs the ordering of the items in the free pool. If
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False (the default), the free items collection (of items that were
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created and were put back in the pool) acts as a round-robin, giving
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each item approximately equal utilization. If True, the free pool acts
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as a FILO stack, which preferentially re-uses items that have most
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recently been used.
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"""
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self.min_size = min_size
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self.max_size = max_size
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self.order_as_stack = order_as_stack
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self.current_size = 0
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self.channel = channel.channel()
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self.free_items = collections.deque()
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for x in xrange(min_size):
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self.current_size += 1
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self.free_items.append(self.create())
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def get(self):
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"""Return an item from the pool, when one is available
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"""
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if self.free_items:
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return self.free_items.popleft()
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if self.current_size < self.max_size:
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created = self.create()
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self.current_size += 1
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return created
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return self.channel.receive()
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def put(self, item):
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"""Put an item back into the pool, when done
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"""
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if self.current_size > self.max_size:
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self.current_size -= 1
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return
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if self.channel.balance < 0:
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self.channel.send(item)
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else:
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if self.order_as_stack:
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self.free_items.appendleft(item)
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else:
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self.free_items.append(item)
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def resize(self, new_size):
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"""Resize the pool
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"""
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self.max_size = new_size
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def free(self):
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"""Return the number of free items in the pool.
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"""
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return len(self.free_items) + self.max_size - self.current_size
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def waiting(self):
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"""Return the number of routines waiting for a pool item.
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"""
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if self.channel.balance < 0:
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return -self.channel.balance
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return 0
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def create(self):
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"""Generate a new pool item
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"""
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raise NotImplementedError("Implement in subclass")
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def fan(self, block, input_list):
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queue = coros.queue(0)
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results = []
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exceptional_results = 0
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for index, input_item in enumerate(input_list):
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pool_item = self.get()
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## Fan out
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api.spawn(
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self._invoke, block, pool_item, input_item, index, queue)
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## Fan back in
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for i in range(len(input_list)):
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## Wait for all guys to send to the queue
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index, value = queue.wait()
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if isinstance(value, Exception):
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exceptional_results += 1
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results.append((index, value))
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results.sort()
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results = [value for index, value in results]
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if exceptional_results:
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if exceptional_results == len(results):
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raise AllFailed(results)
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raise SomeFailed(results)
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return results
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def _invoke(self, block, pool_item, input_item, index, queue):
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try:
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result = block(pool_item, input_item)
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except Exception, e:
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self.put(pool_item)
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queue.send((index, e))
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return
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self.put(pool_item)
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queue.send((index, result))
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class Token(object):
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pass
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class TokenPool(Pool):
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"""A pool which gives out tokens, an object indicating that
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the person who holds the token has a right to consume some
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limited resource.
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"""
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def create(self):
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return Token()
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class ConnectionPool(Pool):
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"""A Pool which can limit the number of concurrent http operations
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being made to a given server.
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*NOTE: *TODO:
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This does NOT currently keep sockets open. It discards the created
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http object when it is put back in the pool. This is because we do
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not yet have a combination of http clients and servers which can work
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together to do HTTP keepalive sockets without errors.
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"""
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def __init__(self, proto, netloc, use_proxy, min_size=0, max_size=4):
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self.proto = proto
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self.netloc = netloc
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self.use_proxy = use_proxy
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Pool.__init__(self, min_size, max_size)
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def create(self):
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import httpc
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return httpc.make_connection(self.proto, self.netloc, self.use_proxy)
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def put(self, item):
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## Discard item, create a new connection for the pool
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Pool.put(self, self.create())
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class ExceptionWrapper(object):
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def __init__(self, e):
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self.e = e
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class CoroutinePool(Pool):
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""" Like a thread pool, but with coroutines.
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Coroutine pools are useful for splitting up tasks or globally controlling
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concurrency. You don't retrieve the coroutines directly with get() --
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instead use the execute() and execute_async() methods to run code.
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>>> from eventlet import coros, api
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>>> p = coros.CoroutinePool(max_size=2)
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>>> def foo(a):
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... print "foo", a
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...
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>>> evt = p.execute(foo, 1)
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>>> evt.wait()
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foo 1
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Once the pool is exhausted, calling an execute forces a yield.
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>>> p.execute_async(foo, 2)
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>>> p.execute_async(foo, 3)
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>>> p.free()
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0
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>>> p.execute_async(foo, 4)
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foo 2
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foo 3
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>>> api.sleep(0)
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foo 4
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"""
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def __init__(self, min_size=0, max_size=4, track_events=False):
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self._greenlets = set()
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if track_events:
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self._tracked_events = []
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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 = coros.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 = coros.NOT_USED
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sender = coros.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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# 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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""" Private method that runs the function, catches exceptions, and
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passes back the return value in the event."""
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try:
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result = func(*args, **kw)
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if evt is not None:
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evt.send(result)
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if self._tracked_events is not None:
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if self._next_event is None:
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self._tracked_events.append(result)
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else:
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ne = self._next_event
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self._next_event = None
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ne.send(result)
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except api.GreenletExit, e:
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# we're printing this out to see if it ever happens
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# in practice
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print "GreenletExit raised in coroutine pool", e
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if evt is not None:
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evt.send(e) # sent as a return value, not an exception
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except KeyboardInterrupt:
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raise # allow program to exit
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except Exception, e:
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traceback.print_exc()
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if evt is not None:
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evt.send(exc=e)
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if self._tracked_events is not None:
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if self._next_event is None:
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self._tracked_events.append(ExceptionWrapper(e))
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else:
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ne = self._next_event
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self._next_event = None
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ne.send(exc=e)
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def _execute(self, evt, func, args, kw):
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""" Private implementation of the execute methods.
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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.free() == 0 and api.getcurrent() in self._greenlets:
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self._safe_apply(evt, func, args, kw)
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else:
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sender = self.get()
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sender.send((evt, func, args, kw))
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def create(self):
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"""Private implementation of eventlet.pools.Pool
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interface. Creates an event and spawns the
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_main_loop coroutine, passing the event.
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The event is used to send a callable into the
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new coroutine, to be executed.
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"""
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sender = coros.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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by the pool, when one is free.
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Immediately returns an eventlet.coros.event object which
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func's result will be sent to when it is available.
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>>> from eventlet import coros
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>>> p = coros.CoroutinePool()
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>>> evt = p.execute(lambda a: ('foo', a), 1)
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>>> evt.wait()
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('foo', 1)
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"""
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receiver = coros.event()
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self._execute(receiver, func, args, kw)
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return receiver
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def execute_async(self, func, *args, **kw):
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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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No return value is provided.
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>>> from eventlet import coros, api
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>>> p = coros.CoroutinePool()
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>>> def foo(a):
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... print "foo", a
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...
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>>> p.execute_async(foo, 1)
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>>> api.sleep(0)
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foo 1
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"""
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self._execute(None, func, args, kw)
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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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You must pass track_events=True to the CoroutinePool constructor
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in order to use this method.
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"""
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assert self._tracked_events is not None, (
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"Must pass track_events=True to the constructor to use CoroutinePool.wait()")
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if self._next_event is not None:
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return self._next_event.wait()
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if not self._tracked_events:
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self._next_event = coros.event()
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return self._next_event.wait()
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result = self._tracked_events.pop(0)
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if isinstance(result, ExceptionWrapper):
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raise result.e
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if not self._tracked_events:
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self._next_event = coros.event()
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return result
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def killall(self):
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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.
|
|
|
|
(The use case that makes this function preferable to the classic idiom
|
|
above is when the iterable, which may itself be a generator, produces
|
|
millions of items.)
|
|
|
|
>>> from eventlet import coros
|
|
>>> import string
|
|
>>> pool = coros.CoroutinePool(max_size=5)
|
|
>>> pausers = [coros.event() for x in xrange(2)]
|
|
>>> def longtask(evt, desc):
|
|
... print "%s woke up with %s" % (desc, evt.wait())
|
|
...
|
|
>>> pool.launch_all(longtask, zip(pausers, "AB"))
|
|
>>> def quicktask(desc):
|
|
... print "returning %s" % desc
|
|
... return desc
|
|
...
|
|
|
|
(Instead of using a for loop, step through generate_results()
|
|
items individually to illustrate timing)
|
|
|
|
>>> step = iter(pool.generate_results(quicktask, string.ascii_lowercase))
|
|
>>> print step.next()
|
|
returning a
|
|
returning b
|
|
returning c
|
|
a
|
|
>>> print step.next()
|
|
b
|
|
>>> print step.next()
|
|
c
|
|
>>> print step.next()
|
|
returning d
|
|
returning e
|
|
returning f
|
|
d
|
|
>>> pausers[0].send("A")
|
|
>>> print step.next()
|
|
e
|
|
>>> print step.next()
|
|
f
|
|
>>> print step.next()
|
|
A woke up with A
|
|
returning g
|
|
returning h
|
|
returning i
|
|
g
|
|
>>> print "".join([step.next() for x in xrange(3)])
|
|
returning j
|
|
returning k
|
|
returning l
|
|
returning m
|
|
hij
|
|
>>> pausers[1].send("B")
|
|
>>> print "".join([step.next() for x in xrange(4)])
|
|
B woke up with B
|
|
returning n
|
|
returning o
|
|
returning p
|
|
returning q
|
|
klmn
|
|
"""
|
|
# Get an iterator because of our funny nested loop below. Wrap the
|
|
# iterable in enumerate() so we count items that come through.
|
|
tuples = iter(enumerate(iterable))
|
|
# If the iterable is empty, this whole function is a no-op, and we can
|
|
# save ourselves some grief by just quitting out. In particular, once
|
|
# we enter the outer loop below, we're going to wait on the queue --
|
|
# but if we launched no coroutines with that queue as the destination,
|
|
# we could end up waiting a very long time.
|
|
try:
|
|
index, args = tuples.next()
|
|
except StopIteration:
|
|
return
|
|
# From this point forward, 'args' is the current arguments tuple and
|
|
# 'index+1' counts how many such tuples we've seen.
|
|
# This implementation relies on the fact that _execute() accepts an
|
|
# event-like object, and -- unless it's None -- the completed
|
|
# coroutine calls send(result). We slyly pass a queue rather than an
|
|
# event -- the same queue instance for all coroutines. This is why our
|
|
# queue interface intentionally resembles the event interface.
|
|
q = coros.queue(max_size=qsize)
|
|
# How many results have we yielded so far?
|
|
finished = 0
|
|
# This first loop is only until we've launched all the coroutines. Its
|
|
# complexity is because if iterable contains more args tuples than the
|
|
# size of our pool, attempting to _execute() the (poolsize+1)th
|
|
# coroutine would suspend until something completes and send()s its
|
|
# result to our queue. But to keep down queue overhead and to maximize
|
|
# responsiveness to our caller, we'd rather suspend on reading the
|
|
# queue. So we stuff the pool as full as we can, then wait for
|
|
# 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()
|
|
# 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
|
|
|
|
if __name__=='__main__':
|
|
import doctest
|
|
doctest.testmod()
|