背景

日常开发中，难免遇到并发场景，而并发场景难免需要做流量控制，即需要对并发的进程或者线程的总量进行控制。 今天简单总结两种常用的控制线程个数的方法。

方法一：进程池/线程池

如下例demo所示， 创建了一个大小是4的进程池，然后创建5个进程，并启动

from multiprocessing import Pool
import os, time, random


def long_time_task(name):
    print('Run task %s (%s)...' % (name, os.getpid()))
    start = time.time()
    time.sleep(random.random() * 3)
    end = time.time()
    print('Task %s runs %0.2f seconds.' % (name, (end - start)))


if __name__ == '__main__':
    print('Parent process %s.' % os.getpid())
    p = Pool(4)
    for i in range(5):
        p.apply_async(long_time_task, args=(i,))
    print('Waiting for all subprocesses done...')
    p.close()
    p.join()
    print('All subprocesses done.')

运行结果如下，可以看到第5个进程会等池子里的进程完成一个后才会被启动

Run task 0 (32952)...
Run task 1 (32951)...
Run task 2 (32953)...
Run task 3 (32954)...
Task 2 runs 0.68 seconds.
Run task 4 (32953)...
Task 1 runs 1.41 seconds.
Task 0 runs 1.44 seconds.
Task 4 runs 2.15 seconds.
Task 3 runs 2.98 seconds.
All subprocesses done.

方法二：queue

queue 模块即队列，特别适合处理信息在多个线程间安全交换的多线程程序中。 下面的demo展示了如何通过queue来限制线程的并发个数

import threading
import queue
import time
import random
import os

maxThreads = 4


class Store(threading.Thread):
    def __init__(self, q):
        threading.Thread.__init__(self)
        self.queue = q
        # self.store = store

    def run(self):
        try:
            print('Run task  (%s)...' % (os.getpid()))
            start = time.time()
            time.sleep(random.random() * 3)
            end = time.time()
            t = threading.currentThread()
            # 线程ID
            print('Thread id : %d' % t.ident)
            print('Thread name : %s' % t.getName())
            print('Task runs %0.2f seconds.' % (end - start))
        except Exception as e:
            print(e)
        finally:
            self.queue.get()
            self.queue.task_done()


def main():
    q = queue.Queue(maxThreads)
    for s in range(6):
        q.put(s)
        t = Store(q)
        t.start()
    q.join()
    print('over')


if __name__ == '__main__':
    main()

运行结果如下：

Run task  (33259)...
Run task  (33259)...
Run task  (33259)...
Run task  (33259)...
Thread id : 123145444999168
Thread name : Thread-13
Task runs 0.04 seconds.
Run task  (33259)...
Thread id : 123145394630656
Thread name : Thread-10
Task runs 1.02 seconds.
Run task  (33259)...
Thread id : 123145428209664
Thread name : Thread-12
Task runs 1.20 seconds.
Thread id : 123145394630656
Thread name : Thread-17
Task runs 0.68 seconds.
Thread id : 123145444999168
Thread name : Thread-14
Task runs 1.79 seconds.
Thread id : 123145411420160
Thread name : Thread-11
Task runs 2.96 seconds.
over