add python_thread_multiprocess.py
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### python_coroutine.py: Python进阶: 理解Python中的异步IO和协程(Coroutine), 并应用在爬虫中
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### python_aiohttp.py: Python中最好用的异步爬虫库Aiohttp代码实例
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### python_thread_multiprocess.py: Python进阶: 聊聊IO密集型任务、计算密集型任务,以及多线程、多进程
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===================================================================================================
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### 您可以fork该项目,并在修改后提交Pull request
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@ -0,0 +1,115 @@
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# _*_ coding: utf-8 _*_
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"""
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python_thread_multiprocee.py by xianhu
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"""
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import time
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import threading
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import multiprocessing
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# 定义全局变量Queue
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g_queue = multiprocessing.Queue()
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g_search_list = list(range(10000))
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# 定义一个IO密集型任务:利用time.sleep()
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def task_io(task_id):
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print("IOTask[%s] start" % task_id)
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while not g_queue.empty():
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time.sleep(1)
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try:
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data = g_queue.get(block=True, timeout=1)
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print("IOTask[%s] get data: %s" % (task_id, data))
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except Exception as excep:
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print("IOTask[%s] error: %s" % (task_id, str(excep)))
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print("IOTask[%s] end" % task_id)
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return
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# 定义一个计算密集型任务:利用一些复杂加减乘除、列表查找等
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def task_cpu(task_id):
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print("CPUTask[%s] start" % task_id)
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while not g_queue.empty():
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count = 0
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for i in range(10000):
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count += pow(3*2, 3*2) if i in g_search_list else 0
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try:
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data = g_queue.get(block=True, timeout=1)
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print("CPUTask[%s] get data: %s" % (task_id, data))
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except Exception as excep:
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print("CPUTask[%s] error: %s" % (task_id, str(excep)))
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print("CPUTask[%s] end" % task_id)
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return task_id
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def init_queue():
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print("init g_queue start")
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while not g_queue.empty():
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g_queue.get()
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for _index in range(10):
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g_queue.put(_index)
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print("init g_queue end")
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return
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if __name__ == '__main__':
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print("cpu count:", multiprocessing.cpu_count(), "\n")
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print("========== 直接执行IO密集型任务 ==========")
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init_queue()
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time_0 = time.time()
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task_io(0)
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print("结束:", time.time() - time_0, "\n")
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print("========== 多线程执行IO密集型任务 ==========")
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init_queue()
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time_0 = time.time()
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thread_list = [threading.Thread(target=task_io, args=(i,)) for i in range(5)]
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for t in thread_list:
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t.start()
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for t in thread_list:
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if t.is_alive():
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t.join()
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print("结束:", time.time() - time_0, "\n")
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print("========== 多进程执行IO密集型任务 ==========")
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init_queue()
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time_0 = time.time()
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process_list = [multiprocessing.Process(target=task_io, args=(i,)) for i in range(multiprocessing.cpu_count())]
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for p in process_list:
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p.start()
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for p in process_list:
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if p.is_alive():
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p.join()
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print("结束:", time.time() - time_0, "\n")
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print("========== 直接执行CPU密集型任务 ==========")
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init_queue()
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time_0 = time.time()
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task_cpu(0)
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print("结束:", time.time() - time_0, "\n")
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print("========== 多线程执行CPU密集型任务 ==========")
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init_queue()
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time_0 = time.time()
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thread_list = [threading.Thread(target=task_cpu, args=(i,)) for i in range(5)]
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for t in thread_list:
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t.start()
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for t in thread_list:
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if t.is_alive():
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t.join()
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print("结束:", time.time() - time_0, "\n")
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print("========== 多进程执行cpu密集型任务 ==========")
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init_queue()
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time_0 = time.time()
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process_list = [multiprocessing.Process(target=task_cpu, args=(i,)) for i in range(multiprocessing.cpu_count())]
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for p in process_list:
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p.start()
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for p in process_list:
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if p.is_alive():
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p.join()
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print("结束:", time.time() - time_0, "\n")
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exit()
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