python 线程及线程池

 

一、多线程

import threading
from time import ctime,sleep


def music(func):
    for i in range(2):
        print("I was listening to %s. %s" %(func,ctime()))
        sleep(1)

def move(func):
    for i in range(2):
        print("I was at the %s! %s" %(func,ctime()))
        sleep(5)

threads = []
t1 = threading.Thread(target=music,args=(u'爱情买卖',))
threads.append(t1)
t2 = threading.Thread(target=move,args=(u'阿凡达',))
threads.append(t2)

if __name__ == '__main__':
    for t in threads:
        t.setDaemon(True)
        t.start()
    
    t.join()

    print("all over %s" %ctime())

 

 

 

二、线程池（自实现）

'''
线程池的概念就是我们将1000件活，原本由1000个人来做，
现在只分配5个人来做，这5个人就是线程池数，
并且他们处与一直运行状态，除非主程序结束，否则，将不会结束。
'''

from queue import Queue
from threading import Thread
import random
import time

def person(i,q):
    while True:  #这个人一直处与可以接活干的状态
        q.get()
        print("Thread",i,"is doing the job")
        time.sleep(random.randint(1,5))#每个人干活的时间不一样，自然就会导致每个人分配的件数不同(这里是干活的地方)
        q.task_done()   #接到的活做完了，向上汇报

q = Queue()

#分配1000件活
for x in range(100):
    q.put(x)

#叫了5个人去干活    
for i in range(5):
    worker=Thread(target=person, args=(i,q))
    worker.setDaemon(True)
    worker.start()

q.join()  #这5个人把1000件活都做完后，结束.

 

 

三、线程池（库实现）

看吧！只用4行代码就搞定了！其中三行还是固定写法。

import requests 
from multiprocessing.dummy import Pool as ThreadPool 

urls = [
    'http://www.baidu.com',
    'http://www.163.com',
    'http://www.sina.cn',
    'http://www.live.com',
    'http://www.mozila.org',
    'http://www.sohu.com',
    'http://www.tudou.com',
    'http://www.qq.com',
    'http://www.taobao.com',
    'http://www.alibaba.com',
        ]

# Make the Pool of workers
pool = ThreadPool(4) 

# 注意此处的 map 函数！！！！
# Open the urls in their own threads
# and return the results
results = pool.map(requests.get, urls)

#close the pool and wait for the work to finish 
pool.close() 
pool.join()

 

from multiprocessing import Pool

def f(x):
    return x*x


with Pool(5) as p:
    print(p.map(f, [1, 2, 3]))

 

 

 

 

四、如何更加高效（生产、消费者模式）

比起经典的方式来说简单很多，效率高，易懂，而且没什么死锁的陷阱。

from multiprocessing import Pool, Queue
import redis
import requests

queue = Queue(20)

def consumer():
    r = redis.Redis(host='127.0.0.1',port=6379,db=1)
    while True:
        k, url = r.blpop(['pool',])
        queue.put(url)

def worker():
    while True:
        url = queue.get()
        print(requests.get(url).text)

def process(ptype):
    try:
        if ptype:
            consumer()
        else:
            worker()
    except:
        pass

pool = Pool(5)
print pool.map(process, [1,0,0,0,0])
pool.close()
pool.join()