Python 多处理池突然停止

我正在尝试根据我的要求执行并行处理，并且代码似乎可以按预期并行处理 4k-5k 元素。但是，一旦要处理的元素开始增加，代码就会处理一些列表，然后在没有抛出任何错误的情况下，程序突然停止运行。

我检查过，程序没有挂起，RAM 可用（我有 16 Gb RAM），CPU 利用率甚至不到 30%。似乎无法弄清楚发生了什么。我有 100 万个元素需要处理。

def get_items_to_download():
    #iterator to fetch all items that are to be downloaded
    yield download_item

def start_download_process():
    multiproc_pool = multiprocessing.Pool(processes=10)
    for download_item in get_items_to_download():
        multiproc_pool.apply_async(start_processing, args = (download_item, ), callback = results_callback)
    
    multiproc_pool.close()
    multiproc_pool.join()

def start_processing(download_item):
    try:
        # Code to download item from web API
        # Code to perform some processing on the data
        # Code to update data into database
        return True
    except Exception as e:
        return False

def results_callback(result):
    print(result)

if __name__ == "__main__":
    start_download_process()

UPDATE -

发现错误 - BrokenPipeError: [Errno 32] Broken pipeline

Trace -

Traceback (most recent call last):
File "/usr/lib/python3.6/multiprocessing/pool.py", line 125, in worker
put((job, i, result))
File "/usr/lib/python3.6/multiprocessing/queues.py", line 347, in put
self._writer.send_bytes(obj)
File "/usr/lib/python3.6/multiprocessing/connection.py", line 200, in send_bytes
self._send_bytes(m[offset:offset + size])
File "/usr/lib/python3.6/multiprocessing/connection.py", line 404, in _send_bytes
self._send(header + buf)
File "/usr/lib/python3.6/multiprocessing/connection.py", line 368, in _send
n = write(self._handle, buf)
BrokenPipeError: [Errno 32] Broken pipe

代码看起来是正确的。我唯一能想到的是你的所有进程都挂起等待完成。这里有一个建议：而不是使用提供的回调机制apply_async， 使用AsyncResult返回的对象以从进程获取返回值。您可以致电get在此对象上指定一个超时值（下面任意指定 30 秒，可能不够长）。如果任务在这段时间内没有完成，则会抛出超时异常（如果您愿意，您可以捕获它）。但这将检验进程挂起的假设。只需确保指定足够大的超时值，以便任务应在该时间段内完成。我还将任务提交分成了1000个批次，并不是因为我认为1,000,000个的大小是一个问题per se，但只是为了让您没有 1,000,000 个结果对象的列表。但是，如果您发现不再因此而挂起，请尝试增加批处理大小，看看是否确实会产生影响。

import multiprocessing

def get_items_to_download():
    #iterator to fetch all items that are to be downloaded
    yield download_item

BATCH_SIZE = 1000

def start_download_process():
    with multiprocessing.Pool(processes=10) as multiproc_pool:
        results = []
        for download_item in get_items_to_download():
            results.append(multiproc_pool.apply_async(start_processing, args = (download_item, )))
            if len(results) == BATCH_SIZE:
                process_results(results)
                results = []
        if len(results):
            process_results(results)
    

def start_processing(download_item):
    try:
        # Code to download item from web API
        # Code to perform some processing on the data
        # Code to update data into database
        return True
    except Exception as e:
        return False

TIMEOUT_VALUE = 30 # or some suitable value

def process_results(results):
    for result in results:
        return_value = result.get(TIMEOUT_VALUE) # will cause an exception if process is hanging
        print(return_value)

if __name__ == "__main__":
    start_download_process()

Update

根据谷歌搜索多个页面来查找损坏的管道错误，您的错误似乎可能是内存耗尽的结果。看Python 多处理：增加池大小后出现损坏的管道异常 https://stackoverflow.com/questions/45230593/python-multiprocessing-broken-pipe-exception-after-increasing-pool-size， 例如。以下返工attempts使用更少的内存。如果有效，您可以尝试增加批量大小：

import multiprocessing


BATCH_SIZE = 1000
POOL_SIZE = 10


def get_items_to_download():
    #iterator to fetch all items that are to be downloaded
    yield download_item


def start_download_process():
    with multiprocessing.Pool(processes=POOL_SIZE) as multiproc_pool:
        items = []
        for download_item in get_items_to_download():
            items.append(download_item)
            if len(items) == BATCH_SIZE:
                process_items(multiproc_pool, items)
                items = []
        if len(items):
            process_items(multiproc_pool, items)


def start_processing(download_item):
    try:
        # Code to download item from web API
        # Code to perform some processing on the data
        # Code to update data into database
        return True
    except Exception as e:
        return False


def compute_chunksize(iterable_size):
    if iterable_size == 0:
        return 0
    chunksize, extra = divmod(iterable_size, POOL_SIZE * 4)
    if extra:
        chunksize += 1
    return chunksize


def process_items(multiproc_pool, items):
    chunksize = compute_chunksize(len(items))
    # you must iterate the iterable returned:
    for return_value in multiproc_pool.imap(start_processing, items, chunksize):
        print(return_value)


if __name__ == "__main__":
    start_download_process()