1/ concurrent.futures模块
线程池：concurrent.futures.ThreadPoolExecutor(max_workers)
进程池：concurrent.futures.ProcessPoolExecutor(max_workers)
2、使用对比：进程的性能更好
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")
3、
