python - 何时在进程上调用 .join()？

Question

我正在阅读有关 Python 中的多处理模块的各种教程，但无法理解为什么/何时调用 process.join()。例如，我偶然发现了这个例子:

nums = range(100000)
nprocs = 4

def worker(nums, out_q):
    """ The worker function, invoked in a process. 'nums' is a
        list of numbers to factor. The results are placed in
        a dictionary that's pushed to a queue.
    """
    outdict = {}
    for n in nums:
        outdict[n] = factorize_naive(n)
    out_q.put(outdict)

# Each process will get 'chunksize' nums and a queue to put his out
# dict into
out_q = Queue()
chunksize = int(math.ceil(len(nums) / float(nprocs)))
procs = []

for i in range(nprocs):
    p = multiprocessing.Process(
            target=worker,
            args=(nums[chunksize * i:chunksize * (i + 1)],
                  out_q))
    procs.append(p)
    p.start()

# Collect all results into a single result dict. We know how many dicts
# with results to expect.
resultdict = {}
for i in range(nprocs):
    resultdict.update(out_q.get())

# Wait for all worker processes to finish
for p in procs:
    p.join()

print resultdict

据我了解，process.join() 会阻塞调用进程，直到调用join方法的进程完成执行。我也相信上面代码示例中启动的子进程在完成目标函数后，即在他们将结果推送到out_q之后完成执行。最后，我相信 out_q.get() 会阻塞调用过程，直到有结果被提取。因此，如果您考虑代码:

resultdict = {}
for i in range(nprocs):
    resultdict.update(out_q.get())

# Wait for all worker processes to finish
for p in procs:
    p.join()

主进程被 out_q.get() 调用阻塞，直到每个工作进程 完成将其结果推送到队列。因此，当主进程退出 for 循环时，每个子进程都应该已完成执行，对吗？

如果是这样的话，此时是否有任何理由调用 p.join() 方法？不是所有的工作进程都已经完成，那么这如何导致主进程“等待所有工作进程完成”？我之所以问，主要是因为我在多个不同的示例中看到了这一点，并且我很好奇我是否未能理解某些内容。

Answer 1

尝试运行这个:

import math
import time
from multiprocessing import Queue
import multiprocessing

def factorize_naive(n):
    factors = []
    for div in range(2, int(n**.5)+1):
        while not n % div:
            factors.append(div)
            n //= div
    if n != 1:
        factors.append(n)
    return factors

nums = range(100000)
nprocs = 4

def worker(nums, out_q):
    """ The worker function, invoked in a process. 'nums' is a
        list of numbers to factor. The results are placed in
        a dictionary that's pushed to a queue.
    """
    outdict = {}
    for n in nums:
        outdict[n] = factorize_naive(n)
    out_q.put(outdict)

# Each process will get 'chunksize' nums and a queue to put his out
# dict into
out_q = Queue()
chunksize = int(math.ceil(len(nums) / float(nprocs)))
procs = []

for i in range(nprocs):
    p = multiprocessing.Process(
            target=worker,
            args=(nums[chunksize * i:chunksize * (i + 1)],
                  out_q))
    procs.append(p)
    p.start()

# Collect all results into a single result dict. We know how many dicts
# with results to expect.
resultdict = {}
for i in range(nprocs):
    resultdict.update(out_q.get())

time.sleep(5)

# Wait for all worker processes to finish
for p in procs:
    p.join()

print resultdict

time.sleep(15)

然后打开任务管理器。您应该能够看到 4 个子进程在被操作系统终止之前进入僵尸状态几秒钟(由于加入调用):

在更复杂的情况下，子进程可能永远处于僵尸状态(就像您在另一个 question 中询问的情况)，如果您创建足够多的子进程，您可能会填充进程表，从而给进程带来麻烦操作系统(可能会杀死您的主进程以避免失败)。