上传文件至 'Other'
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**Alan Cooper ——VB之父**
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**Bjarne Stroustrup——C++之父**
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**James Gosling——Java之父**
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**Rob Pike——GoLang之父**
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**Brendan Eich——JavaScript之父**
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**Rasmus Lerdorf——PHP之父**
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**头顶茂密程度：★★★**
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**Guido van Rossum——Python之父**
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# 多任务介绍
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什么是多任务？
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操作系统可以同时执行多个任务
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现在的操作系统:windows mac linux unix
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这些操作系统 都支持多任务
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单核CPU如何实现多任务？
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表面看，每个任务都是同时执行，实际上是每个任务在轮询着执行,只是因为CPU的调度太快，导致我们感觉像是所有任务都在同时执行
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多核CPU如何实现多任务?
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是真正实现了多个任务同时执行
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并发：看上去一起执行，任务数大于CPU核心数
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并行：一起执行，任务数必须小于等于CPU核心数
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实现多任务的方式:
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1、多进程方式
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2、多线程方式
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3、协程方式
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4、多进程+多线程
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### 没有多进程
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```python
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import time
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def func():
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while True:
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print("this is a process2")
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time.sleep(1.5)
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if __name__ == '__main__':
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while True:
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print("this is a process1")
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time.sleep(1)
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func()
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```
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### multiprocessing 多进程
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```python
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from multiprocessing import Process
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import time,os
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def func(str):
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#os.getpid 获取当前进程的进程号
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#os.getppid 获取当前进程的父进程
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while True:
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print("this is process 2--%s--%s--%s"%(str,os.getpid(),os.getppid()))
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time.sleep(1.5)
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if __name__ == '__main__':
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print("父进程启动...--%s--%s"%(os.getpid(),os.getppid()))
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# 创建子进程
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# target 说明进程的任务
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p = Process(target=func,args=("python",))
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# 启动进程
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p.start()
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# 主进程中的
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while True:
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print("this is a process 1--%s--%s"%(os.getpid(),os.getppid()))
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time.sleep(1)
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```
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### 让父进程等待子进程结束之后父进程再结束
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```python
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from multiprocessing import Process
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from time import sleep
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def func():
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print("子进程启动...")
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sleep(3)
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print("子进程结束...")
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if __name__ == '__main__':
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print("父进程启动...")
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p = Process(target = func)
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p.start()
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# sleep(1)
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# 让父进程等待子进程结束之后父进程再结束
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# timeout 超时时间 父进程的等待时间
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p.join()
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#执行后一直等待
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print("父进程结束...")
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```
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### 在子进程中修改全局变量 对父进程中的全局变量没有影响
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```python
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from multiprocessing import Process
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num = 100
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def run1():
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print("孙子进程开始...")
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print("孙子进程结束...%s"%(num))
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def run():
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print("子进程开始...")
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global num
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num += 1
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print(num)
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p = Process(target=run1)
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p.start()
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p.join()
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print("子进程结束...")
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if __name__ == '__main__':
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print("父进程开始...")
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p = Process(target=run)
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p.start()
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p.join()
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num += 2
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# 在子进程中修改全局变量 对父进程中的全局变量没有影响
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# 我们在创建子进程的时候 对全局变量做了一个备份
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# 子进程和父进程的Num是两个完全不同的变量
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# 所有进程对全局变量的修改 都不会影响其它进程
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print("父进程结束...%d"%(num))
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```
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### 进程池
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```python
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from multiprocessing import Pool,Process
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import time,random
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def foo():
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print("孙子进程开始...")
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print("孙子进程结束...")
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def func(__name__):
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print("子进程%s启动..."%(__name__))
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start = time.time()
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time.sleep(random.choice([1,2,3]))
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end = time.time()
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print("子进程%s结束...耗时%.2f"%(__name__,end - start))
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if __name__ == '__main__':
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print("父进程开始...")
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# 创建进程池
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# 如果没有参数 默认大小为自己电脑的CPU核心数
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# 表示可以同时执行的进程数量
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pp = Pool(2)
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for i in range(4):
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# 创建进程,放入进程池统一管理
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pp.apply_async(func,args=(i,))
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# 在调用join之前必须先关掉进程池
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# 进程池一旦关闭 就不能再添加新的进程了
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pp.close()
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# 进程池对象调用join,会等待进程池中所有的子进程结束之后再结束父进程
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pp.join()
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print("父进程结束...")
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```
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# 多线程:
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在一个进程内部，要同时干很多事，就需要同时执行多个子任务
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那么我们把进程内的这些子任务叫做线程
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线程的内存空间是共享的 每个线程都共享同一个进程的资源
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模块:
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1、_thread模块 低级模块
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2、threading模块 高级模块 对_thread模块进行了封装
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"""
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### 创建线程
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```python
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import threading,time
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def run(num):
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print("子线程%s开始..."%(threading.current_thread().__name__))
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time.sleep(2)
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print(num)
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time.sleep(2)
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# current_thread 返回一个当前线程的实例
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print("子线程%s结束..."%(threading.current_thread().__name__))
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if __name__ == '__main__':
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print("主线程%s启动..."%(threading.current_thread().__name__))
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# 创建子线程
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t = threading.Thread(target = run,args = (1,))
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t.start()
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t.join()
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print("主线程%s结束..."%(threading.current_thread().__name__))
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```
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### 多线程共享资源
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```python
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import threading
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num = 0
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var = 0
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def run(n):
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global num
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for i in range(1000000):
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num += n
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num -= n
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def run1(n):
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global var
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for i in range(100):
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var += n
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var -= n
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if __name__ == '__main__':
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t1 = threading.Thread(target=run,args=(6,))
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t2 = threading.Thread(target=run,args=(9,))
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t3 = threading.Thread(target=run,args=(5,))
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t3.start()
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t1.start()
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t2.start()
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t1.join()
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t2.join()
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t3.join()
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print("num = %s"%(num))
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```
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### 互斥锁
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```python
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import threading
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创建锁对象
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lock = threading.Lock()
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num = 0
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def run(n):
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global num
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for i in range(1000000):
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# 加锁 为了确保下面代码只能由一个线程从头到尾的执行
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# 会阻止多线程的并发执行，所以效率会大大降低
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"""
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lock.acquire()
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try:
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num = num - n
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num = num + n
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finally:
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# 解锁
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lock.release()
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"""
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with lock:
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num = num + n
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num = num - n
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if __name__ == '__main__':
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t1 = threading.Thread(target=run,args=(6,))
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t2 = threading.Thread(target=run,args=(9,))
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t1.start()
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t2.start()
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t1.join()
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t2.join()
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print("num = %s"%(num))
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```
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# 生产者消费者模型
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## 概念
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```
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生产者消费者模式就是通过一个容器来解决生产者和消费者的强耦合问题。生产者和消费者彼此之间不直接通讯，而通过阻塞队列来进行通讯，所以生产者生产完数据之后不用等待消费者处理，直接扔给阻塞队列，消费者不找生产者要数据，而是直接从阻塞队列里取，阻塞队列就相当于一个缓冲区，平衡了生产者和消费者的处理能力。这个阻塞队列就是用来给生产者和消费者解耦的。
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```
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```
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生产者直接将商品交给消费者，如果消费者承受能力不佳，就会发生崩溃，这个时候需要另一个角色--仓库。
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生产者 --- 仓库 --- 消费者
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优点
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解耦–生产者-消费者之间不直接通信，降低了耦合度。
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并发
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```
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测试代码
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```python
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import time
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import random
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from multiprocessing import Queue,Process
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def consumer(name,q):
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while True:
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res=q.get()
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if res is None:break
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time.sleep(random.randint(1,3))
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print('\033[40m 食客%s 开始吃%s'%(name,res))
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def producer(name,q):
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for i in range(5):
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time.sleep(random.randint(1,2))
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res='炒粉%s'%i
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q.put(res)
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print('\033[40m 厨师%s 生产了%s'%(name,res))
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if __name__ == '__main__':
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q=Queue()#一个队列
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p1=Process(target=producer,args=('老刘',q))
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c1=Process(target=consumer,args=('小明',q))
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# c2=Process(target=consumer,args=('小红',q))
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p1.start()
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c1.start()
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# c2.start()
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p1.join()
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q.put(None)
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```
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# 发布者订阅者模型
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测试代码
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```python
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发布者
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#!/usr/bin/env python
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#coding:utf-8
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import redis
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pool=redis.ConnectionPool(host='127.0.0.1',
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port=6379,db=0,
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password='123456')
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r=redis.StrictRedis(connection_pool=pool)
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while True:
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msg=input("publish: >>")
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if msg=="over":
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print("停止发布")
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break
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r.publish('spub',msg)
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订阅者
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#!/usr/bin/env python
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import redis
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pool=redis.ConnectionPool(host='127.0.0.1',
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port=6379,db=0,
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password='123456')
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r=redis.StrictRedis(connection_pool=pool)
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p=r.pubsub()
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p.subscribe("spub","cctv1")
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for item in p.listen():
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print("Listen on channel : %s "%item['channel'].decode())
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if item['type']=='message':
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data=item['data'].decode()
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print("From %s get message : %s"%(item['channel'].decode(),item['data'].decode()))
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if item['data']=='over':
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print(item['channel'].decode(),'停止发布')
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break
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p.unsubscribe('spub')
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print("取消订阅")
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```
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