多線程適合于多io操作

多進程適合于耗cpu(計算)的操作

# 多進程編程# 耗cpu的操作,用多進程編程, 對于io操作來說,使用多線程編程import timefrom concurrent.futures import ThreadPoolExecutor, as_completedfrom concurrent.futures import ProcessPoolExecutordef fib(n): if n <= 2: return 1 return fib(n - 2) + fib(n - 1)if __name__ == ’__main__’: # 1. 對于耗cpu操作,多進程優(yōu)于多線程 # with ThreadPoolExecutor(3) as executor: # all_task = [executor.submit(fib, num) for num in range(25, 35)] # start_time = time.time() # for future in as_completed(all_task): # data = future.result() # print(data) # print('last time :{}'.format(time.time() - start_time)) # 3.905290126800537 # 多進程 ,在window環(huán)境 下必須放在main方法中執(zhí)行,否則拋異常 with ProcessPoolExecutor(3) as executor: all_task = [executor.submit(fib, num) for num in range(25, 35)] start_time = time.time() for future in as_completed(all_task): data = future.result() print(data) print('last time :{}'.format(time.time() - start_time)) # 2.6130592823028564

可以看到在耗cpu的應用中,多進程明顯優(yōu)于多線程 2.6130592823028564 < 3.905290126800537

下面模擬一個io操作

# 多進程編程# 耗cpu的操作,用多進程編程, 對于io操作來說,使用多線程編程import timefrom concurrent.futures import ThreadPoolExecutor, as_completedfrom concurrent.futures import ProcessPoolExecutordef io_operation(n): time.sleep(2) return nif __name__ == ’__main__’: # 1. 對于耗cpu操作,多進程優(yōu)于多線程 # with ThreadPoolExecutor(3) as executor: # all_task = [executor.submit(io_operation, num) for num in range(25, 35)] # start_time = time.time() # for future in as_completed(all_task): # data = future.result() # print(data) # print('last time :{}'.format(time.time() - start_time)) # 8.00358772277832 # 多進程 ,在window環(huán)境 下必須放在main方法中執(zhí)行,否則拋異常 with ProcessPoolExecutor(3) as executor: all_task = [executor.submit(io_operation, num) for num in range(25, 35)] start_time = time.time() for future in as_completed(all_task): data = future.result() print(data) print('last time :{}'.format(time.time() - start_time)) # 8.12435245513916

可以看到 8.00358772277832 < 8.12435245513916, 即是多線程比多進程更牛逼!

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