Python multiprocessing apply_async + Value

Question

我尝试通过apply_async将共享计数器传递给多处理任务，但它失败了这样的错误"RuntimeError:同步对象应仅通过继承在进程之间共享"。发生什么事了

def processLine(lines, counter, mutex):
    pass
counter = multiprocessing.Value('i', 0)
mutex = multiprocessing.Lock()
pool = Pool(processes = 8)
lines = []
for line in inputStream:
    lines.append(line)
    if len(lines) >= 5000:
         #don't queue more than 1'000'000 lines
         while counter.value > 1000000:
                 time.sleep(0.05)
         mutex.acquire()
         counter.value += len(lines)
         mutex.release()
         pool.apply_async(processLine, args=(lines, counter, ), callback = collectResults)
         lines = []

Answer 1

让池处理调度:

for result in pool.imap(process_single_line, input_stream):
    pass

如果顺序无关:

for result in pool.imap_unordered(process_single_line, input_stream):
    pass

pool.*map*()函数有chunksize参数，您可以更改该参数以查看它是否会影响您的情况下的性能。

如果您的代码希望在一次调用中传递多行:

from itertools import izip_longest
chunks = izip_longest(*[iter(inputStream)]*5000, fillvalue='') # grouper recipe
for result in pool.imap(process_lines, chunks):
    pass

限制排队项数的一些替代方法有:

• multiprocessing.Queue设置最大大小(你不需要一个池在这种情况下)。queue.put()将在达到最大大小时阻塞，直到其他进程调用queue.get()
• 使用多处理原语(如Condition或BoundedSemaphor)手动实现生产者/消费者模式。

注意:每个Value都有关联的锁，不需要单独的锁

Answer 2

我用一种不太优雅的方式解决了这个问题

def processLine(lines):
    pass
def collectResults(result):
    global counter
    counter -= len(result)
counter = 0
pool = Pool(processes = 8)
lines = []
for line in inputStream:
    lines.append(line)
    if len(lines) >= 5000:
         #don't queue more than 1'000'000 lines
         while counter.value > 1000000:
             time.sleep(0.05)
         counter.value += len(lines)
         pool.apply_async(processLine, args=(lines), callback = collectResults)
         lines = []