关于第四章第2节书中程序的疑问

[Microndgt]

在第二小节如何做中，书中给了一段程序：

import concurrent.futures
import time
number_list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

def evaluate_item(x):
    # 计算总和，这里只是为了消耗时间
    result_item = count(x)
    # 打印输入和输出结果
    print ("item " + str(x) + " result " + str(result_item))

def  count(number) :
    for i in range(0, 10000000):
        i=i+1
    return i * number

if __name__ == "__main__":
    # 顺序执行
    start_time = time.clock()
    for item in number_list:
        evaluate_item(item)
    print("Sequential execution in " + str(time.clock() - start_time), "seconds")
    # 线程池执行
    start_time_1 = time.clock()
    with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:
        for item in number_list:
            executor.submit(evaluate_item,  item)
    print ("Thread pool execution in " + str(time.clock() - start_time_1), "seconds")
    # 进程池
    start_time_2 = time.clock()
    with concurrent.futures.ProcessPoolExecutor(max_workers=5) as executor:
        for item in number_list:
            executor.submit(evaluate_item,  item)
    print ("Process pool execution in " + str(time.clock() - start_time_2), "seconds")

首先time.clock() 在UNIX系统上，它返回的应该是"进程时间"，它是用秒表示的浮点数。对于第一个顺序执行和第二个多线程执行，应该是准确的，因为都在当前进程执行，统计时间也是当前进程执行的时间。但是对于第三个多进程执行，当前进程只起到调度作用，执行时间分布到了其他进程里，因此我认为统计的时间是有问题的。按照常理也不可能顺序执行时间是6秒，多进程就0.03秒，这个提升了近200倍。

其次，executor.submit应该是排定任务，但是没有具体执行，会返回一个Future，但是并不是立即执行(需要看pool中是否有可用线程或者进程)，所以我认为书中给出的测试程序是存在问题的。按照模块文档中给出的例子，应该是这样：

import concurrent.futures
import time
number_list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

def evaluate_item(x):
    # 计算总和，这里只是为了消耗时间
    result_item = count(x)
    # 打印输入和输出结果
    return result_item

def  count(number) :
    for i in range(0, 10000000):
        i=i+1
    return i * number

if __name__ == "__main__":
    # 顺序执行
    start_time = time.time()
    for item in number_list:
        print(evaluate_item(item))
    print("Sequential execution in " + str(time.time() - start_time), "seconds")
    # 线程池执行
    start_time_1 = time.time()
    with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:
        futures = [executor.submit(evaluate_item, item) for item in number_list]
        for future in concurrent.futures.as_completed(futures):
            print(future.result())
    print ("Thread pool execution in " + str(time.time() - start_time_1), "seconds")
    # 进程池
    start_time_2 = time.time()
    with concurrent.futures.ProcessPoolExecutor(max_workers=5) as executor:
        futures = [executor.submit(evaluate_item, item) for item in number_list]
        for future in concurrent.futures.as_completed(futures):
            print(future.result())
    print ("Process pool execution in " + str(time.time() - start_time_2), "seconds")

使用as_completed函数，可以保证等待所有Future对象运行完成，这时候统计的时间应该才是准确的。我的电脑CPU: Intel 酷睿i5 5257U，顺序执行和多线程在6.3秒，多进程在3.7秒。

P.S.

1. concurrent.futures模块文档
2. concurrent.futures翻译

[laixintao]

@Microndgt 确实是这样，确认了一下，即使单独跑一次也是超过0.2s的

In [1]: import time
   ...: def  count(number) :
   ...:     time1 = time.time()
   ...:     for i in range(0, 10000000):
   ...:         i=i+1
   ...:     time2 = time.time()
   ...:     print time2 - time1
   ...:     return i * number
   ...:

In [3]: count(1)
0.74836397171
Out[3]: 10000000

ps: 翻译中运算结果有些是我贴的我自己跑的结果。

我在原文中改成你的代码。