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python 多线程
Lock对比Rlock
#coding:utf-8 import threading lock = threading.Lock() #Lock对象 lock.acquire() lock.acquire() #产生了死锁。 lock.release() lock.release() print lock.acquire() import threading rLock = threading.RLock() #RLock对象 rLock.acquire() rLock.acquire() #在同一线程内,程序不会堵塞。 rLock.release() rLock.release()
多线程与队列
# Python queue队列,实现并发,在网站多线程推荐最后也一个例子,比这货简单,但是不够规范 # encoding: utf-8 __author__ = ‘yeayee.com‘ # 由本站增加注释,可随意Fork、Copy from queue import Queue # Queue在3.x中改成了queue import random import threading import time class Producer(threading.Thread): """ Producer thread 制作线程 """ def __init__(self, t_name, queue): # 传入线程名、实例化队列 threading.Thread.__init__(self, name=t_name) # t_name即是threadName self.data =http://www.mamicode.com/ queue """ run方法 和start方法: 它们都是从Thread继承而来的,run()方法将在线程开启后执行, 可以把相关的逻辑写到run方法中(通常把run方法称为活动[Activity]); start()方法用于启动线程。 """ def run(self): for i in range(5): # 生成0-4五条队列 print("%s: %s is producing %d to the queue!" % (time.ctime(), self.getName(), i)) # 当前时间t生成编号d并加入队列 self.data.put(i) # 写入队列编号 time.sleep(random.randrange(10) / 5) # 随机休息一会 print("%s: %s producing finished!" % (time.ctime(), self.getName)) # 编号d队列完成制作 class Consumer(threading.Thread): """ Consumer thread 消费线程,感觉来源于COOKBOOK """ def __init__(self, t_name, queue): threading.Thread.__init__(self, name=t_name) self.data = queue def run(self): for i in range(5): val = self.data.get() print("%s: %s is consuming. %d in the queue is consumed!" % (time.ctime(), self.getName(), val)) # 编号d队列已经被消费 time.sleep(random.randrange(10)) print("%s: %s consuming finished!" % (time.ctime(), self.getName())) # 编号d队列完成消费 def main(): """ Main thread 主线程 """ queue = Queue() # 队列实例化 producer = Producer(‘Pro.‘, queue) # 调用对象,并传如参数线程名、实例化队列 consumer = Consumer(‘Con.‘, queue) # 同上,在制造的同时进行消费 producer.start() # 开始制造 consumer.start() # 开始消费 """ join()的作用是,在子线程完成运行之前,这个子线程的父线程将一直被阻塞。 join()方法的位置是在for循环外的,也就是说必须等待for循环里的两个进程都结束后,才去执行主进程。 """ producer.join() consumer.join() print(‘All threads terminate!‘) if __name__ == ‘__main__‘: main() """运行结果: Thu Feb 4 11:05:48 2016: Pro. is producing 0 to the queue! Thu Feb 4 11:05:48 2016: Pro. is producing 1 to the queue! Thu Feb 4 11:05:48 2016: Con. is consuming. 0 in the queue is consumed! Thu Feb 4 11:05:49 2016: Pro. is producing 2 to the queue! Thu Feb 4 11:05:50 2016: Pro. is producing 3 to the queue! Thu Feb 4 11:05:51 2016: Pro. is producing 4 to the queue! Thu Feb 4 11:05:52 2016: Con. is consuming. 1 in the queue is consumed! Thu Feb 4 11:05:53 2016: <bound method Producer.getName of <Producer(Pro., started 6864)>> producing finished! Thu Feb 4 11:06:00 2016: Con. is consuming. 2 in the queue is consumed! Thu Feb 4 11:06:06 2016: Con. is consuming. 3 in the queue is consumed! Thu Feb 4 11:06:06 2016: Con. is consuming. 4 in the queue is consumed! Thu Feb 4 11:06:12 2016: Con. consuming finished! All threads terminate! """
python 队列
1 FIFO队列先进先出:class Queue.Queue(maxsize)
2 LIFO类似于堆,即先进后出:class Queue.LifoQueue(maxsize)
3 优先级队列级别越低越先出来:class Queue.PriorityQueue(maxsize)
队列实例分别有以下操作方法:
Queue.qsize() 返回队列的大小
Queue.empty() 如果队列为空,返回True,反之False
Queue.full() 如果队列满了,返回True,反之False
Queue.full 与 maxsize 大小对应
Queue.get([block[, timeout]]) 获取队列,timeout等待时间
Queue.get_nowait() 相当Queue.get(False)
Queue.put(item) 写入队列,timeout等待时间
Queue.put_nowait(item) 相当Queue.put(item, False)
Queue.task_done() 在完成一项工作之后,Queue.task_done() 函数向任务已经完成的队列发送一个信号
Queue.join() 实际上意味着等到队列为空,再执行别的操作
python 多线程
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