Python多线程编程中常用方法：
1、join()方法：如果一个线程或者在函数执行的过程中调用另一个线程，并且希望待其完成操作后才能执行，那么在调用线程的时就可以使用被调线程的join方法join([timeout]) timeout：可选参数，线程运行的最长时间
2、isAlive()方法：查看线程是否还在运行
3、getName()方法：获得线程名
4、setDaemon()方法：主线程退出时，需要子线程随主线程退出，则设置子线程的setDaemon()
Python线程同步：
（1）Thread的Lock和RLock实现简单的线程同步：
import threading import time class mythread(threading.Thread): def __init__(self,threadname): threading.Thread.__init__(self,name=threadname) def run(self): global x lock.acquire() for i in range(3): x = x+1 time.sleep(1) print x lock.release() if __name__ == '__main__': lock = threading.RLock() t1 = [] for i in range(10): t = mythread(str(i)) t1.append(t) x = 0 for i in t1: i.start()
（2）使用条件变量保持线程同步：
# coding=utf-8 import threading class Producer(threading.Thread): def __init__(self,threadname): threading.Thread.__init__(self,name=threadname) def run(self): global x con.acquire() if x == 10000: con.wait() pass else: for i in range(10000): x = x+1 con.notify() print x con.release() class Consumer(threading.Thread): def __init__(self,threadname): threading.Thread.__init__(self,name=threadname) def run(self): global x con.acquire() if x == 0: con.wait() pass else: for i in range(10000): x = x-1 con.notify() print x con.release() if __name__ == '__main__': con = threading.Condition() x = 0 p = Producer('Producer') c = Consumer('Consumer') p.start() c.start() p.join() c.join() print x
（3）使用队列保持线程同步：
# coding=utf-8 import threading import Queue import time import random class Producer(threading.Thread): def __init__(self,threadname): threading.Thread.__init__(self,name=threadname) def run(self): global queue i = random.randint(1,5) queue.put(i) print self.getName(),' put %d to queue' %(i) time.sleep(1) class Consumer(threading.Thread): def __init__(self,threadname): threading.Thread.__init__(self,name=threadname) def run(self): global queue item = queue.get() print self.getName(),' get %d from queue' %(item) time.sleep(1) if __name__ == '__main__': queue = Queue.Queue() plist = [] clist = [] for i in range(3): p = Producer('Producer'+str(i)) plist.append(p) for j in range(3): c = Consumer('Consumer'+str(j)) clist.append(c) for pt in plist: pt.start() pt.join() for ct in clist: ct.start() ct.join()
生产者消费者模式的另一种实现：
# coding=utf-8 import time import threading import Queue class Consumer(threading.Thread): def __init__(self, queue): threading.Thread.__init__(self) self._queue = queue def run(self): while True: # queue.get() blocks the current thread until an item is retrieved. msg = self._queue.get() # Checks if the current message is the "quit" if isinstance(msg, str) and msg == 'quit': # if so, exists the loop break # "Processes" (or in our case, prints) the queue item print "I'm a thread, and I received %s!!" % msg # Always be friendly! print 'Bye byes!' class Producer(threading.Thread): def __init__(self, queue): threading.Thread.__init__(self) self._queue = queue def run(self): # variable to keep track of when we started start_time = time.time() # While under 5 seconds.. while time.time() - start_time < 5: # "Produce" a piece of work and stick it in the queue for the Consumer to process self._queue.put('something at %s' % time.time()) # Sleep a bit just to avoid an absurd number of messages time.sleep(1) # This the "quit" message of killing a thread. self._queue.put('quit') if __name__ == '__main__': queue = Queue.Queue() consumer = Consumer(queue) consumer.start() producer1 = Producer(queue) producer1.start()
使用线程池（Thread pool）+同步队列（Queue）的实现方式：
# A more realistic thread pool example # coding=utf-8 import time import threading import Queue import urllib2 class Consumer(threading.Thread): def __init__(self, queue): threading.Thread.__init__(self) self._queue = queue def run(self): while True: content = self._queue.get() if isinstance(content, str) and content == 'quit': break response = urllib2.urlopen(content) print 'Bye byes!' def Producer(): urls = [ 'http://www.python.org', 'http://www.yahoo.com' 'http://www.scala.org', 'http://cn.bing.com' # etc.. ] queue = Queue.Queue() worker_threads = build_worker_pool(queue, 4) start_time = time.time() # Add the urls to process for url in urls: queue.put(url) # Add the 'quit' message for worker in worker_threads: queue.put('quit') for worker in worker_threads: worker.join() print 'Done! Time taken: {}'.format(time.time() - start_time) def build_worker_pool(queue, size): workers = [] for _ in range(size): worker = Consumer(queue) worker.start() workers.append(worker) return workers if __name__ == '__main__': Producer()
另一个使用线程池+Map的实现：
import urllib2 from multiprocessing.dummy import Pool as ThreadPool urls = [ 'http://www.python.org', 'http://www.python.org/about/', 'http://www.python.org/doc/', 'http://www.python.org/download/', 'http://www.python.org/community/' ] # Make the Pool of workers pool = ThreadPool(4) # Open the urls in their own threads # and return the results results = pool.map(urllib2.urlopen, urls) #close the pool and wait for the work to finish pool.close() pool.join()
参考：
http://blog.jobbole.com/58700/
本文转自阿凡卢博客园博客，原文链接：http://www.cnblogs.com/luxiaoxun/p/3827022.html，如需转载请自行联系原作者
