一、初识Queue模块
Queue模块实现了多生产者、多消费者队列。它特别适用于信息必须在多个线程间安全地交换的多线程程序中。这个模块中的Queue类实现了所有必须的锁语义。它依赖于Python中线程支持的可用性；参见threading模块。
模块实现了三类队列：FIFO（First In First Out，先进先出，默认为该队列）、LIFO（Last In First Out，后进先出）、基于优先级的队列。以下为其常用方法：
先进先出 q = Queue.Queue(maxsize)
后进先出 a = Queue.LifoQueue(maxsize)
优先级 Queue.PriorityQueue(maxsize)
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.put(item) 写入队列，timeout等待时间 非阻塞
Queue.get([block[, timeout]]) 获取队列，timeout等待时间
Queue.get_nowait() 相当Queue.get(False)
Queue.put_nowait(item) 相当Queue.put(item, False)
Queue.task_done() 在完成一项工作之后，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
更详细部分可以参看python标准库之Queue模块介绍。
二、队列示列
1、FIFO(先进先出)
import Queue
q = Queue.Queue()
for i in range(5):
q.put(i)
while not q.empty():
print q.get()
其输出结果如下：
[root@361way queue]# python fifo.py
1
2
3
4
其输出顺序与进入顺序相同。
2、LIFO（后进先出）
import Queue
q = Queue.LifoQueue()
for i in range(5):
q.put(i)
while not q.empty():
print q.get()
执行结果如下：
import Queue
q = Queue.LifoQueue()
for i in range(5):
q.put(i)
while not q.empty():
print q.get()
3、带优先级的队列
import Queue
class Job(object):
def __init__(self, priority, description):
self.priority = priority
self.description = description
print 'New job:', description
return
def __cmp__(self, other):
return cmp(self.priority, other.priority)
q = Queue.PriorityQueue()
q.put( Job(3, 'Mid-level job') )
q.put( Job(10, 'Low-level job') )
q.put( Job(1, 'Important job') )
while not q.empty():
next_job = q.get()
print 'Processing job:', next_job.description
执行结果如下：
[root@361way queue]# python Queue_priority.py
New job: Mid-level job
New job: Low-level job
New job: Important job
Processing job: Important job
Processing job: Mid-level job
Processing job: Low-level job
从上面的执行结果可以看出，优先级值设置越小，越先执行。另外这里是以单线程为例的，在多thread的示例中，多个线程同时get() item 时，这时就可以根据优先级决定哪一个任务先执行。
三、队列与线程
在实际使用队列是与线程结合在一起的。这里列几个队列与线程的代码示例：
from Queue import *
from threading import Thread
import sys
'''this function will process the items in the queue, in serial'''
def processor():
while True:
if queue.empty() == True:
print "the Queue is empty!"
sys.exit(1)
try:
job = queue.get()
print "I'm operating on job item: %s"%(job)
queue.task_done()
except:
print "Failed to operate on job"
'''set variables'''
queue = Queue()
threads = 4
'''a list of job items. you would want this to be more advanced,
like reading from a file or database'''
jobs = [ "job1", "job2", "job3" ]
'''iterate over jobs and put each into the queue in sequence'''
#for job in jobs:
for job in range(100):
print "inserting job into the queue: %s"%(job)
queue.put(job)
'''start some threads, each one will process one job from the queue'''
#for i in range(100):
for i in range(threads):
th = Thread(target=processor)
th.setDaemon(True)
th.start()
'''wait until all jobs are processed before quitting'''
queue.join()
需要注意的是processer函数里的“ while True：”行 ，如果没了这行，当线程（thread）数小于队列数时，第一轮循环完后就会卡住，不执行后面的循环了。所以加上该行，就相当于开始了一个死循环，直到所有的队列结束时，队列为空，循环结束。
示例2：
[root@361way tmp]# python queue-example-1.py
task 0 finished
task 1 finished
task 3 finished
task 2 finished
task 5 finished
task 4 finished
task 6 finished
task 7 finished
task 9 finished
task 8 finished
[root@361way tmp]# more queue-example-1.py
# File: queue-example-1.py
import threading
import Queue
import time, random
WORKERS = 2
class Worker(threading.Thread):
def __init__(self, queue):
self.__queue = queue
threading.Thread.__init__(self)
def run(self):
while 1:
item = self.__queue.get()
if item is None:
break # reached end of queue
# pretend we're doing something that takes 10-100 ms
time.sleep(random.randint(10, 100) / 1000.0)
print "task", item, "finished"
#
# try it
queue = Queue.Queue(0)
for i in range(WORKERS):
Worker(queue).start() # start a worker
for i in range(10):
queue.put(i)
for i in range(WORKERS):
queue.put(None) # add end-of-queue markers
参考页面：