Question

请问以下问题？

data是由save函数修改的共享资源。

determine_ixmac是每个将来执行的功能。

data = {
            "ip_spotlight": {
                "ixmac": {_: {} for _ in INFO["dev"].keys()},
                "ipacc": {_: {} for _ in INFO["dev"].keys()},
                "bgpic": {_: {} for _ in INFO["dev"].keys()},
            }
        }

        def save(app, namespace, netelement, job):
            data[app][namespace][netelement] = job.result()

        with concurrent.futures.ProcessPoolExecutor(
            max_workers=multiprocessing.cpu_count()
        ) as executor:
            for k, v in INFO["dev"].items():
                job_ixmac = executor.submit(
                    determine_ixmac, k, v["dev_ip"], v["snmp_comm"]
                )
                job_ixmac.add_done_callback(
                    functools.partial(save, "ip_spotlight", "ixmac", k)

但是，当所有期货都完成后，就会出现死锁：

Traceback (most recent call last):
  File "/usr/lib64/python3.6/multiprocessing/process.py", line 258, in _bootstrap
    self.run()
  File "/usr/lib64/python3.6/multiprocessing/process.py", line 93, in run
    self._target(*self._args, **self._kwargs)
  File "/usr/lib64/python3.6/concurrent/futures/process.py", line 169, in _process_worker
    call_item = call_queue.get(block=True)
  File "/usr/lib64/python3.6/multiprocessing/queues.py", line 93, in get
    with self._rlock:
  File "/usr/lib64/python3.6/multiprocessing/synchronize.py", line 96, in __enter__
    return self._semlock.__enter__()
KeyboardInterrupt

我已经尝试过，但是没有运气：

mlock = multiprocessing.Lock

def save(app, namespace, netelement, job):
    with mlock:
         data[app][namespace][netelement] = job.result()

您能告诉我们缺少什么吗？

共享资源的未来会导致僵局

0 个答案: