11.多线程、多进程和线程池编程

1.1.线程同步lock和rlock

(1)lock

• 用锁会影响性能
• 用锁会产生死锁

import threading
from threading import lock

total = 0
lock = lock()

def add():
    global total
    global local
    for i in range(100000):
        lock.acquire()
        # lock.acquire()   #如果再加把锁会产生死锁
        total += 1
        lock.release()

def desc():
    global total
    global local
    for i in range(100000):
        lock.acquire()     #获取锁
        total -= 1
        lock.release()     #释放锁

thread1 = threading.thread(target=add)
thread2 = threading.thread(target=desc)
thread1.start()
thread2.start()
thread1.join()
thread2.join()
print(total)   #0

（2）rlock

rlock:在同一个线程里面，可以连续多次调用acquire,一定要注意acquire和release的次数相等

import threading
from threading import lock,rlock

total = 0
lock = rlock()

def add():
    global total
    global local
    for i in range(100000):
        #用rlock在同一线程里面，可以多次调用acquire，不会产生死锁
        lock.acquire()
        lock.acquire()
        total += 1
        #release的次数和acquire的次数相等
        lock.release()
        lock.release()

def desc():
    global total
    global local
    for i in range(100000):
        lock.acquire()     #获取锁
        total -= 1
        lock.release()     #释放锁

thread1 = threading.thread(target=add)
thread2 = threading.thread(target=desc)
thread1.start()
thread2.start()
thread1.join()
thread2.join()
print(total)   #0

1.2.线程同步 - condition 

使用condition模拟对话

import threading
from threading import condition

 #通过condition，完成协同读诗
class xiaoai(threading.thread):
    def __init__(self,cond):
        super().__init__(name='小爱')
        self.cond = cond

    def run(self):
        with self.cond:
            #等待
            self.cond.wait()
            print("{} : 在".format(self.name))
            #通知
            self.cond.notify()

            self.cond.wait()
            print("{} : 好啊".format(self.name))
            self.cond.notify()

            self.cond.wait()
            print("{} : 君住长江尾".format(self.name))
            self.cond.notify()

class tianmao(threading.thread):
    def __init__(self,cond):
        super().__init__(name="天猫精灵")
        self.cond = cond

    def run(self):
        with self.cond:
            print("{} : 小爱同学".format(self.name))
            self.cond.notify()
            self.cond.wait()

            print("{} : 我们来对古诗吧".format(self.name))
            self.cond.notify()
            self.cond.wait()

            print("{} : 我在长江头".format(self.name))
            self.cond.notify()
            self.cond.wait()

if __name__ == '__main__':
    cond = threading.condition()
    xiaoai = xiaoai(cond)
    tianmao = tianmao(cond)

    xiaoai.start()
    tianmao.start()

结果：

 

1.3.线程同步 - semaphore 

控制线程并发数量

#samaphore是用于控制进入数量的锁

import threading
import time

class htmlspider(threading.thread):
    def __init__(self,url,sem):
        super().__init__()
        self.url = url
        self.sem = sem

    def run(self):
        time.sleep(2)
        print("got html text success!")
        self.sem.release()   #释放锁

class urlproducer(threading.thread):
    def __init__(self, sem):
        super().__init__()
        self.sem = sem
    def run(self):
        for i in range(20):
            self.sem.acquire()    #加锁
            html_htread = htmlspider("baidu.com/{}".format(i), self.sem)
            html_htread.start()

if __name__ == '__main__':
    #控制线程并发数量为3
    sem = threading.semaphore(3)
    url_producer = urlproducer(sem)
    url_producer.start()

11.4.threadpoolexecutor线程池

线程池

from concurrent.futures import threadpoolexecutor, as_completed
import time

#为什么要线程池
#主线程中可以获取某一个线程的状态或者某一个任务的状态，以及返回值
#当一个线程完成的时候，主线程立马知道
#futures可以让多线程和多进程编码接口一致

def get_html(times):
    time.sleep(times)
    print("get page {} success".format(times))
    return times

executor = threadpoolexecutor(max_workers=2)

#通过submit提交执行的函数到线程池中，sumbit是立即返回
task1 = executor.submit(get_html, (3))    #函数和参数

#done方法用于判定某个任务是否完成
print(task1.done())      #false
time.sleep(4)
print(task1.done())      #true
#result方法查看task函数执行的结构
print(task1.result())    #3

用as_completed获取任务结束的返回

from concurrent.futures import threadpoolexecutor, as_completed
import time

#为什么要线程池
#主线程中可以获取某一个线程的状态或者某一个任务的状态，以及返回值
#当一个线程完成的时候，主线程立马知道
#futures可以让多线程和多进程编码接口一致

# def get_html(times):
#     time.sleep(times)
#     print("get page {} success".format(times))
#     return times
#
# executor = threadpoolexecutor(max_workers=2)
#
# #通过submit提交执行的函数到线程池中，sumbit是立即返回
# task1 = executor.submit(get_html, (3))    #函数和参数
#
# #done方法用于判定某个任务是否完成
# print(task1.done())      #false
# time.sleep(4)
# print(task1.done())      #true
# #result方法查看task函数执行的结构
# print(task1.result())    #3

def get_html(times):
    time.sleep(times)
    print("get page {} success".format(times))
    return times

executor = threadpoolexecutor(max_workers=2)

#获取已经成功的task的返回
urls = [3,2,4]
all_task = [executor.submit(get_html, (url)) for url in urls]

for future in as_completed(all_task):
    data = future.result()
    print(data)   #已经成功的task函数的return

11.5.进程间通信 - queue

queue

import time

from multiprocessing import process, queue

def producer(queue):
    queue.put("a")
    time.sleep(2)

def consumer(queue):
    time.sleep(2)
    data = queue.get()
    print(data)

if __name__ == '__main__':
    queue = queue(10)
    my_producer = process(target=producer, args=(queue,))
    my_consumer = process(target=consumer, args=(queue,))

    my_producer.start()
    my_consumer.start()
    my_producer.join()
    my_consumer.join()

11.6.进程间通信 - manager

manger

import time

from multiprocessing import process, queue, manager,pool

def producer(queue):
    queue.put("a")
    time.sleep(2)

def consumer(queue):
    time.sleep(2)
    data = queue.get()
    print(data)

if __name__ == '__main__':
    #pool中的进程间通信需要使用manger中的queue
    queue = manager().queue(10)
    pool = pool(2)   #创建进程池

    pool.apply_async(producer, args=(queue, ))
    pool.apply_async(consumer, args=(queue, ))

    pool.close()
    pool.join()

11.7.进程间通信 - pipe

pipe实现进程间通信（只能两个进程之间）

#pipe进程间通信
from multiprocessing import process, pipe

def producer(pipe):
    pipe.send("derek")

def consumer(pipe):
    print(pipe.recv())

if __name__ == '__main__':
    receive_pipe, send_pipe = pipe()
    my_producer = process(target=producer, args=(send_pipe, ))
    my_consumer = process(target=consumer, args=(receive_pipe, ))

    my_producer.start()
    my_consumer.start()
    my_producer.join()
    my_producer.join()