一、进程

1、进程间数据不共享，如下示例：

import multiprocessing    data_list = []    def task(arg):        data_list.append(arg)        print(data_list)  # 每个进程都有自己的一个列表    def run():        for i in range(10):            p = multiprocessing.Process(target=task,args=(i,))            p.start()    if __name__ == '__main__':        run()

2、进程的常用功能

import multiprocessing    import time    def task(arg):        time.sleep(2)        print(arg)    def run():        print(11111111)        p1 = multiprocessing.Process(target=task,args=(1,))        p1.start()        p1.join(6)  # 等待进程完成，最多等6秒        print(22222222)        p2 = multiprocessing.Process(target=task,args=(2,))        p2.start()        p2.join()        print(33333333)    if __name__ == '__main__':        run()

import multiprocessing    import time    def task(arg):        time.sleep(2)        print(arg)    def run():        print(11111111)        p1 = multiprocessing.Process(target=task,args=(1,))        p1.daemon = False  # 等待进程完成，默认        p1.start()        print(22222222)        p2 = multiprocessing.Process(target=task,args=(2,))        p2.daemon = True  # 不等进程完成        p2.start()        print(33333333)    if __name__ == '__main__':        run()

import multiprocessing    import time    def task(arg):        time.sleep(2)        p = multiprocessing.current_process()  # 获取当前进程        name = p.name        id1 = p.ident  # 获取进程id        id2 = p.pid  # 获取进程id        print(arg,name,id1,id2)    def run():        print(11111111)        p1 = multiprocessing.Process(target=task,args=(1,))        p1.name = 'pp1'  # 为进程设置名字pp1        p1.start()        print(22222222)        p2 = multiprocessing.Process(target=task,args=(2,))        p2.name = 'pp2'  # 为进程设置名字pp2        p2.start()        print(33333333)    if __name__ == '__main__':        run()

二、数据共享（内存级别）

1、Queue

import multiprocessing    q = multiprocessing.Queue()    def task(arg,q):        q.put(arg)    def run():        for i in range(10):            p = multiprocessing.Process(target=task, args=(i, q,))            p.start()        while True:            v = q.get()            print(v)    run()

import multiprocessing    def task(arg,q):        q.put(arg)    if __name__ == '__main__':        q = multiprocessing.Queue()        for i in range(10):            p = multiprocessing.Process(target=task,args=(i,q,))            p.start()        while True:            v = q.get()            print(v)

2、Manager

import multiprocessing    m = multiprocessing.Manager()    dic = m.dict()    def task(arg):        dic[arg] = 100    def run():        for i in range(10):            p = multiprocessing.Process(target=task, args=(i,))            p.start()        input('>>>')        print(dic.values())    if __name__ == '__main__':        run()

import multiprocessing    import time    def task(arg,dic):        time.sleep(2)        dic[arg] = 100    if __name__ == '__main__':        m = multiprocessing.Manager()        dic = m.dict()        process_list = []        for i in range(10):            p = multiprocessing.Process(target=task, args=(i,dic,))            p.start()            process_list.append(p)        while True:            count = 0            for p in process_list:                if not p.is_alive():  # 如果某进程已经执行完毕，则count加1                    count += 1            if count == len(process_list):                break        print(dic)

三、进程锁

       进程锁同线程锁的种类和用法一样，参见线程锁。如下是进程锁RLock示例：

import time    import multiprocessing    lock = multiprocessing.RLock()    def task(arg):        print('鬼子来了')        lock.acquire()        time.sleep(2)        print(arg)        lock.release()    if __name__ == '__main__':        p1 = multiprocessing.Process(target=task,args=(1,))        p1.start()        p2 = multiprocessing.Process(target=task, args=(2,))        p2.start()

问题1：为什么要加进程锁？

       线程锁是为了在线程不安全的时候，为一段代码加上锁来控制实现线程安全，即线程间数据隔离；

       进程间的数据本来就是隔离的，所以一般不用加锁，当进程间共用某个数据的时候需要加锁；

四、进程池

import time    from concurrent.futures import ProcessPoolExecutor    def task(arg):        time.sleep(2)        print(arg)    if __name__ == '__main__':        pool = ProcessPoolExecutor(5)  # 创建一个进程池        for i in range(10):            pool.submit(task,i)

五、requests模块和bs4（beautifulsoup）模块 -- （初识爬虫）

1、安装：

       pip3 install requests

       pip3 install beautifulsoup4

2、示例代码（爬取抽屉网的标题和链接）：

import requests    from bs4 import BeautifulSoup    from concurrent.futures import     ThreadPoolExecutor,ProcessPoolExecutor    # 模拟浏览器发送请求    # 内部创建 sk = socket.socket()    # 和抽屉进行socket连接 sk.connect(...)    # sk.sendall('...')    # sk.recv(...)    def task(url):        print(url)        r1 = requests.get(            url=url,            headers={            'User-Agent':'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3497.92 Safari/537.36'            }        )        # 查看下载下来的文本信息        soup = BeautifulSoup(r1.text,'html.parser')        # print(soup.text)        content_list = soup.find('div',attrs={
   'id':'content-list'})        for item in content_list.find_all('div',attrs={
   'class':'item'}):            title = item.find('a').text.strip()            target_url = item.find('a').get('href')            print(title,target_url)    def run():        pool = ThreadPoolExecutor(5)        for i in range(1,50):            pool.submit(task,'https://dig.chouti.com/all/hot/recent/%s' %i)    if __name__ == '__main__':        run()

总结：

       1）以上示例进程和线程哪个好？

              线程好，因为socket属于IO请求，不占用CPU，所以用多线程即节省资源又提高效率；

       2）requests模块模拟浏览器发送请求：

              requests.get( . . . ) 本质：

                     创建socket客户端

                     连接【阻塞】

                     发送请求

                     接收请求【阻塞】

                     断开连接