进程和线程的区别, 面相对象补充, 进程, 数据共享, 锁, 进程池, 爬虫模块(requests, bs4(beautifulsoup))

2021-07-01 14:05

阅读:554

标签:run   key   模拟   进程间   manage   常用   mozilla   多进程   win10   

一. 进程和线程的区别?
    第一:
        进程是cpu资源分配的最小单元。
        线程是cpu计算的最小单元。
    第二:
        一个进程中可以有多个线程。
    第三:
        对于Python来说他的进程和线程和其他语言有差异,是有GIL锁。
        GIL锁保证一个进程中同一时刻只有一个线程被cpu调度。

    IO密集型操作可以使用多线程;计算密集型可以使用多进程;
    
二. 面向对象补充:

class Foo(object):

    def __init__(self):
        object.__setattr__(self, ‘info‘, {}) # 在继承的对象中设置值的本质

    def __setattr__(self, key, value):          # 会拦截所有属性的的赋值语句
        self.info[key] = value

    def __getattr__(self, item):         #  拦截点号运算。当对未定义的属性名称和实例进行点号
        # 运算时,就会用属性名作为字符串调用这个方法。如果继承树可以找到该属性,则不调用此方法
        print(item)             # name
        return self.info[item]

obj = Foo()
obj.name = ‘nacho‘
print(obj.name)     # nacho
print(obj.info)     # {‘name‘: ‘nacho‘}

 

三. 进程
    - 进程间数据不共享

data_list = []
def task(arg):
    data_list.append(arg)
    print(data_list)

def run():
    for i in range(10):
        p = multiprocessing.Process(target=task,args=(i,))
        # p = threading.Thread(target=task,args=(i,))
        p.start()

if __name__ == ‘__main__‘:      # win10需要用这个, linux不需要
    run()

    - 常用功能:
        - join
        - deamon
        - name
        - multiprocessing.current_process()
        - multiprocessing.current_process().ident/pid

    - 类继承方式创建进程

class MyProcess(multiprocessing.Process):

    def run(self):
        print(‘当前进程‘,multiprocessing.current_process())


    def run():
        p1 = MyProcess()
        p1.start()

        p2 = MyProcess()
        p2.start()

if __name__ == ‘__main__‘:
    run()

 

四. 进程间数据共享

Queue:
    linux:
        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()
    windows:
        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)

Manager:(*)
    Linux:
        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()

    windows:
        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():
                        count += 1
                if count == len(process_list):
                    break
            print(dic)

 

五. 进程锁

import time
import threading
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()

 

六. 进程池

import time
from concurrent.futures import ThreadPoolExecutor,ProcessPoolExecutor

def task(arg):
    time.sleep(2)
    print(arg)

if __name__ == ‘__main__‘:

    pool = ProcessPoolExecutor(6)      # 取决于CPU的核心数
    for i in range(10):
        pool.submit(task,i)

 
七. 爬虫:
    示例:

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()

 

进程和线程的区别, 面相对象补充, 进程, 数据共享, 锁, 进程池, 爬虫模块(requests, bs4(beautifulsoup))

标签:run   key   模拟   进程间   manage   常用   mozilla   多进程   win10   

原文地址:https://www.cnblogs.com/NachoLau/p/9637177.html


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