我最近制作了一个python程序，它将从消费者/生产者并行计算策略中受益匪浅。我试图开发一个模块（Class）来简化这种处理策略的实现，但我很快遇到了问题。
我的ProducerConsumer类：
class ProducerConsumer(object):
def __init__(self, workers_qt, producer, consumer, min_producer_qt=1):
self.producer_functor = producer # Pointer to the producer function
self.consumer_functor = consumer # Pointer to the consumer function
self.buffer = deque([]) # Thread-safe double-ended queue item for intermediate result buffer
self.workers_qt = workers_qt
self.min_producer_qt = min_producer_qt # Minimum quantity of active producers (if enough remaining input data)
self.producers = [] # List of producers async results
self.consumers = [] # List of consumers async results
def produce(self, params, callback=None):
result = self.producer_functor(*params) # Execute the producer function
if callback is not None:
callback() # Call the callback (if there is one)
return result
def consume(self, params, callback=None):
result = self.consumer_functor(params) # Execute the producer function
if callback is not None:
callback() # Call the callback (if there is one)
return result
# Map a list of producer's input data to a list of consumer's output data
def map_result(self, producers_param):
result = [] # Result container
producers_param = deque(producers_param) # Convert input to double-ended queue (for popleft() member)
with Pool(self.workers_qt) as p: # Create a worker pool
while self.buffer or producers_param or self.consumers or self.producers: # Work remaining
# Create consumers
if self.buffer and (len(self.producers) >= self.min_producer_qt or not producers_param):
consumer_param = self.buffer.popleft() # Pop one set from the consumer param queue
if not isinstance(consumer_param, tuple):
consumer_param = (consumer_param,) # Force tuple type
self.consumers.append(p.apply_async(func=self.consume, args=consumer_param)) # Start new consumer
# Create producers
elif producers_param:
producer_param = producers_param.popleft() # Pop one set from the consumer param queue
if not isinstance(producer_param, tuple):
producer_param = (producer_param,) # Force tuple type
self.producers.append(p.apply_async(func=self.produce, args=producer_param)) # Start new producer
# Filter finished async_tasks
finished_producers = [r for r in self.producers if r.ready()] if self.producers else []
finished_consumers = [r for r in self.consumers if r.ready()] if self.consumers else []
# Remove finished async_tasks from the running tasks list
self.producers = [r for r in self.producers if r not in finished_producers]
self.consumers = [r for r in self.consumers if r not in finished_consumers]
# Extract result from finished async_tasks
for r in finished_producers:
assert r.ready()
self.buffer.append(r.get()) # Get the producer result and put it in the buffer
for r in finished_consumers:
assert r.ready()
result.append(r.get()) # Get the consumer tesult and put in in the function local result var
return result
在成员map_result（）中，当我尝试“get（）”apply_async（...）函数的结果时，我得到以下错误（请注意我正在运行python3）：
Traceback (most recent call last):
File "ProducerConsumer.py", line 91, in <module>
test()
File "ProducerConsumer.py", line 85, in test
result = pc.map_result(input)
File "ProducerConsumer.py", line 64, in map_result
self.buffer.append(r.get()) # Get the producer result and put it in the buffer
File "/usr/lib/python3.5/multiprocessing/pool.py", line 608, in get
raise self._value
File "/usr/lib/python3.5/multiprocessing/pool.py", line 385, in _handle_tasks
put(task)
File "/usr/lib/python3.5/multiprocessing/connection.py", line 206, in send
self._send_bytes(ForkingPickler.dumps(obj))
File "/usr/lib/python3.5/multiprocessing/reduction.py", line 50, in dumps
cls(buf, protocol).dump(obj)
TypeError: can't pickle _thread.lock objects
这里有一些代码来重现我的错误（显然取决于类）：
def test_producer(val):
return val*12
def test_consumer(val):
return val/4
def test():
pc = ProducerConsumer(4, test_producer, test_consumer)
input = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] # Input for the test of the ProducerConsumer class
expected = [0, 3, 6, 9, 15, 18, 21, 23, 27] # Expected output for the test of the ProducerConsumer class
result = pc.map_result(input)
print('got : {}'.format(result))
print('expected : {}'.format(expected))
if __name__ == '__main__':
test()
请注意，在我的类的map_result（）成员中，我只有“get（）”结果为“ready（）”。
根据我对酸洗的了解（我承认并不是那么多），我会说我在成员函数上使用Pool.apply_async（...）的事实可以发挥作用，但我真的想保留如果可以的话，班级结构。
感谢您的帮助！
所以，当我也纠正了一些概念错误时，问题已得到纠正：
我的3个缓冲区变量（缓冲区，生成器，消费者）与该类的成员无关，因为它们在语义上绑定到“map_result（）”成员本身。
因此补丁正在删除这些成员并将其创建为成员“map_result（）”的局部变量。
问题是，即使概念有缺陷，我仍然很难理解为什么工人不能腌制锁（我现在假设的那个）所以......如果有人对发生的事情有明确的解释（或者某些链接）真的很感激。