from concurrent.futures import ProcessPoolExecutor import time import requests def task(url): time.sleep(1) response = requests.get(url) print(response.url,response.status_code) pool = ProcessPoolExecutor(2) #
from concurrent.futures import ProcessPoolExecutor, as_completed import time def task(n): """模拟一个耗时任务""" time.sleep(n) return f"Task {n} completed" def main(): with ProcessPoolExecutor(max_workers=3) as executor: # 提交多个任务 futures = [executor.submit(task, i) for i in ...
from concurrent.futures import ThreadPoolExecutor, as_completed def task_distribution(data): """ 数据分组 按照公司 分组后的数据按照tb_date 从小到大排序 多少个组开多少个线程 向保会通发起请求 """ thread_count = len(data) pool = ThreadPoolExecutor(thread_count) # 使用 submit 函数来提交线程需要...
from concurrent.futures import ProcessPoolExecutor, as_completed NUMBERS = range(25,38) def fib(n):ifn<=2:return1returnfib(n-1) + fib(n-2) start = time.time() with ProcessPoolExecutor(max_workers=3) as executor:fornum, result in zip(NUMBERS, executor.map(fib, NUMBERS)): print'fib(...
import threading from concurrent.futures import ThreadPoolExecutor,as_completed,wait import time # # def task(name): # print('task: %s'%name)
python线程池 as_completed python线程池和进程池 1、线程池 从Python3.2开始,标准库为我们提供了concurrent.futures模块,它提供了ThreadPoolExecutor和ProcessPoolExecutor两个类,实现了对threading和multiprocessing的更高级的抽象,是使用异步实现,充分利用CPU提高程序执行效率,对编写线程池/进程池提供了直接的支持,而传统的...
concurrent.futures.ProcessPoolExecutor，进程池执行类，继承自Executor，使用进程池异步执行提交的任务。conc...
as_completed方法是一个生成器,在没有任务完成的时候,会阻塞,在有某个任务完成的时候,会yield这个任务,就能执行for循环下面的语句,然后继续阻塞住,循环到所有的任务结束。从结果也可以看出,先完成的任务会先通知主线程。 map 除了上面的as_completed方法,还可以使用executor.map方法,但是有一点不同。
futures.ProcessPoolExecutor() as executor: for i in range(N): executor.submit(label_data, i) for future in as_completed(futures): iteration = futures[future] t1 = time.clock() - t0 print("Time elapsed: ", t1) # CPU seconds elapsed (floating point) time_concurrent = t1 """ ...
fromconcurrent.futuresimportProcessPoolExecutor, as_completed ex= ProcessPoolExecutor(max_workers=50) tasks= [ex.submit(process_function, file)forfileinfile_list] results=[] finish_num=0 start_time=time.time()forfutureinas_completed(tasks): ...