python 控制进程或线程 python怎样控制进程或者线程的个数
项昂之的 人气:1想了解python怎样控制进程或者线程的个数的相关内容吗,项昂之的在本文为您仔细讲解python 控制进程或线程的相关知识和一些Code实例,欢迎阅读和指正,我们先划重点:python,控制进程个数,python,控制线程个数,下面大家一起来学习吧。
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
加载全部内容