args=(i,output_queue))threads.append(thread)thread.start()# 等待所有线程完成forthreadinthreads:thread.join()# 获取所有线程的返回值results=[]whilenotoutput_queue.empty():results.append(output_queue.get())print("线程返回的结果:",results
t = threading.Thread(target=process_data, args=(data,)) threads.append(t) t.start() # 等待所有线程执行完毕 fortinthreads: t.join() print("All threads have finished processing") 在这个示例中,我们首先定义了一个处理数据的函数process_data,然后创建了一个包含需要处理的数据的列表data_list.接着,...
importthreadingimportrequestsdefdownload(url):response=requests.get(url)print(f"下载{url}的内容长度:{len(response.content)}")urls=["http://example.com","http://example.org","http://example.net"]threads=[]forurlinurls:thread=threading.Thread(target=download,args=(url,))threads.append(thread)...
worker_threads = build_worker_pool(queue, 4) start_time =time.time()#Add the urls to processfor urlinurls: queue.put(url)#Add the 'quit' messagefor workerinworker_threads: queue.put('quit')for workerinworker_threads: worker.join()print'Done! Time taken: {}'.format(time.time() -st...
The object itself takes care of separating accesses from different threads to its attributes. When get_session_for_thread() is called, the session it looks up is specific to the particular thread on which it’s running. So each thread will create a single session the first time it calls ...
() threads.append(thread) threadID+=1 #填充队列 queueLock.acquire() for word in nameList: workQueue.put(word) queueLock.release() #等待队列清空 while not workQueue.empty(): pass #通知线程是时候退出 exitFlag=1 #等待所有线程完成 for t in threads: t.join() print("退出主线程") 以上...
threads.append(thread) thread.start() # 等待所有线程完成 for thread in threads: thread.join() print("所有线程都完成了工作") 在这个例子中,我们创建了三个线程,每个线程执行相同的worker函数,并交替输出工作信息。 1.3 多进程编程 multiprocessing模块提供了更高的并行度,适用于CPU密集型任务。例如,计算密集...
()threads=[]data=[[1,2,3],[3,4,5],[4,4,4],[5,5,5]]foriinrange(4):t=threading.Thread(target=job,args=(data[i],q))t.start()threads.append(t)forthreadinthreads:thread.join()results=[]for_inrange(4):results.append(q.get())print(results)if__name__=='__main__':...
Contains a local invocation_id for logging from created threads. trace_context The context for distributed tracing. For more information, see Trace Context. retry_context The context for retries to the function. For more information, see retry-policies. Global variables It isn't guaranteed that ...
withcoffee_lock:print(f"开始为订单{order_id}磨咖啡豆...")# 磨豆子(同步操作)time.sleep(1)print(f"完成订单{order_id}的磨豆工作!")# 分别在两个线程中执行threads=[threading.Thread(target=prepare_coffee,args=(i,))foriinrange(1,3)]forthreadinthreads:thread.start()forthreadinthreads:thread...