标签：src oop map url col mes add copy sum
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
标签：src oop map url col mes add copy sum
原文地址：http://www.cnblogs.com/lmpsoftware/p/7787710.html
