背景

日常開發(fā)中，難免遇到并發(fā)場景，而并發(fā)場景難免需要做流量控制，即需要對并發(fā)的進程或者線程的總量進行控制。 今天簡單總結兩種常用的控制線程個數(shù)的方法。

方法一：進程池/線程池

如下例demo所示， 創(chuàng)建了一個大小是4的進程池，然后創(chuàng)建5個進程，并啟動

from multiprocessing import Poolimport os, time, randomdef 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.’)

運行結果如下，可以看到第5個進程會等池子里的進程完成一個后才會被啟動

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 模塊即隊列，特別適合處理信息在多個線程間安全交換的多線程程序中。 下面的demo展示了如何通過queue來限制線程的并發(fā)個數(shù)

import threadingimport queueimport timeimport randomimport osmaxThreads = 4class 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() # 線程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()

運行結果如下：

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

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