", future.done())print("Result:", future.result())#新建ThreadPoolExecutor对象并指定最大的线程数量with ThreadPoolExecutor(max_workers=3) as executor:#提交多个任务到线程池中,并添加“完成时”回调函数future_1 = executor.submit(pow, 2, 4) f
import concurrent.futures # 创建一个线程池 with concurrent.futures.ThreadPoolExecutor()asexecutor: # 提交任务给线程池 task1=executor.submit(func1, arg1) task2=executor.submit(func2, arg2) task3=executor.submit(func3, arg3) # 使用 wait 方法等待所有任务完成 concurrent.futures.wait([task1, tas...
当with块执行完毕时,线程池会自动关闭,释放资源。 with ThreadPoolExecutor(max_workers=3) as executor: future = executor.submit(func, 1) result = future.result() print(f"Task result is {result}") ThreadPoolExecutor 的优点 ThreadPoolExecutor可以充分利用多核CPU的优势,实现并行执行任务的目的。现代CPU...
from concurrent.futures import ThreadPoolExecutor,ProcessPoolExecutor import os,time def task(n): print('%s is runing进程号' %os.getpid()) time.sleep(2) return n**2 def main(): start_time = time.time() with ThreadPoolExecutor(max_workers=3) as executor: futures = executor.map(task, ...
ThreadPoolExecutor用于创建线程池,而ProcessPoolExecutor用于创建进程池。要使用线程池,首先需要导入concurrent.futures模块,然后创建一个Executor对象。接下来,可以使用Executor对象的submit()方法提交任务到线程池。submit()方法接受一个可调用对象和一个可选参数元组,以及任意数量的关键字参数。提交的任务可以是任何可调用...
with ThreadPoolExecutor() as executor: future1 = executor.submit(task, 5) future2 = executor.submit(task, 2) future3 = executor.submit(task, 4) # 所有函数执行完毕(with语句结束)后才会往下执行 第四种: executor = ThreadPoolExecutor() ...
with ThreadPoolExecutor(max_workers=4) as executor: futures = [executor.submit(compute_square, i) for i in range(10)] for future in as_completed(futures): print(f"结果:{future.result()}") 线程池可以有效地管理并调度并发任务,特别适合CPU密集型的计算任务。
with concurrent.futures.ThreadPoolExecutor(15) as exector: #先开15个线程试试,哈哈 future_to_url = {} #这个字典是用来存储对应的url和task的关联关系的,一一映射 channel = 1 #线程编号,就是asyncWorker中的threadId for item in urlIter: if item['parse'] == 'proxylists': for param in map(...
deftest(num):print("Tasks"num)# 新建ThreadPoolExecutor对象并指定最大的线程数量withThreadPoolExecutor(max_workers=3)asexecutor:# 提交多个任务到线程池中,并使用result方法等待任务完成 future_1=executor.submit(test,1)future_2=executor.submit(test,2)future_3=executor.submit(test,3)print(future_1.res...
lock=Lock()withlock:pass 池化技术 代码语言:javascript 代码运行次数:0 运行 AI代码解释 from concurrent.futuresimportThreadPoolExecutorwithThreadPoolExecutor()asexecutor:# 方法1results=executor.map(func,[1,2,3])# 方法2future=executor.submit(func,1)result=future.result()...