PythonStudy——单线程并发的实现

# 使用yield实现单线程并发多个任务
# 引子 : 生成器回顾
# def func1():
#     print('1')
#     yield
#     print('2')
#     yield
#     print('3')
#     yield


# res = func1()
# print(res) # <generator object func1 at 0x109032e60>
# next(res) # 1
# next(res) # 2
# next(res) # 3

# ------------------------------------------------------
# 正式使用yield实现单线程并发多个任务
# import time
#
# # 加法函数 被并发的那个函数
# def func1():
#     a = 1
#     for i in range(1000):
#         a += 1
#         print("a run")
#         yield
#
# def func2():
#     res = func1()
#     b = 1
#     for i in range(1000):
#         b += 1
#         print("b run")
#         next(res)
#
# start_time = time.time()
# func2()
# end_time = time.time()
# print("耗时>>>%s"%(end_time-start_time))
# >>>0.007849931716918945
# -------------------------------------------------------

# 不采取yield并发程序(默认运行)
# import time
#
#
# def func1():
#     a = 1
#     for i in range(1000):
#         a += 1
#         print("a run")
#
#
# def func2():
#     b = 1
#     for i in  range(1000):
#         b += 1
#         print("b run")
#
#
# start_time = time.time()
# func1()
# func2()
# end_time = time.time()
# print("耗时>>>%s"%(end_time-start_time))
# # 耗时>>>0.006770133972167969

# 我们通过数值比较发现
# 使用yield生成器强行并发单线程中的函数 反而会导致运行速度下降 用时增加