fix code in ch4.2 concurrent.futures.ProcessPoolExecutor

close #16

chapter4/02_Using_the_concurrent.futures_Python_modules.rst

@@ -41,82 +41,81 @@ Executor是抽象类，可以通过子类访问，即线程或进程的 ``Execut
 - 通过有5个worker的线程池执行
 - 通过有5个worker的进程池执行
 
+（译者注：原文的代码是错误的，这里贴出的代码以及运行结果是修改后的，详见： `关于第四章第2节书中程序的疑问 #16 <https://github.com/laixintao/python-parallel-programming-cookbook-cn/issues/16>`_ ，感谢 `@Microndgt <https://github.com/Microndgt>`_ 提出）
 代码如下：::
 
-        # -*- coding: utf-8 -*-
-
-        """ Concurrent.Futures Pooling - Chapter 4 Asynchronous Programming """
-
-        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")
+		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")
 
 运行这个代码，我们可以看到运行时间的输出：::
 
 		$ python3 pool.py
-		item 1 result 10000000
-		item 2 result 20000000
-		item 3 result 30000000
-		item 4 result 40000000
-		item 5 result 50000000
-		item 6 result 60000000
-		item 7 result 70000000
-		item 8 result 80000000
-		item 9 result 90000000
-		item 10 result 100000000
-		Sequential execution in 7.495329 seconds
-		item 1 result 10000000
-		item 2 result 20000000
-		item 4 result 40000000
-		item 3 result 30000000
-		item 5 result 50000000
-		item 8 result 80000000
-		item 7 result 70000000
-		item 9 result 90000000
-		item 6 result 60000000
-		item 10 result 100000000
-		Thread pool execution in 8.349609000000001 seconds
-		item 1 result 10000000
-		item 2 result 20000000
-		item 3 result 30000000
-		item 4 result 40000000
-		item 5 result 50000000
-		item 7 result 70000000
-		item 8 result 80000000
-		item 6 result 60000000
-		item 9 result 90000000
-		item 10 result 100000000
-		Process pool execution in 0.02012900000000073 seconds
+		10000000
+		20000000
+		30000000
+		40000000
+		50000000
+		60000000
+		70000000
+		80000000
+		90000000
+		100000000
+		Sequential execution in 7.936585903167725 seconds
+		10000000
+		30000000
+		40000000
+		20000000
+		50000000
+		70000000
+		90000000
+		100000000
+		80000000
+		60000000
+		Thread pool execution in 7.633088827133179 seconds
+		40000000
+		50000000
+		10000000
+		30000000
+		20000000
+		70000000
+		90000000
+		60000000
+		80000000
+		100000000
+		Process pool execution in 4.787093639373779 seconds
 
 |work|
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