python 多线程编程
使用回调方式
import time
def countdown(n):
while n > 0:
print('T-minus', n)
n -= 1
time.sleep(5)
# Create and launch a thread
from threading import Thread
t = Thread(target=countdown, args=(10,))
t.start()把线程放入一个类
from threading import Thread
class CountdownTask:
def __init__(self):
self._running = True
def terminate(self):
self._running = False
def run(self, n):
while self._running and n > 0:
print('T-minus', n)
n -= 1
time.sleep(5)
c = CountdownTask()
t = Thread(target=c.run, args=(10,))
t.start()
c.terminate() # Signal termination
t.join() # Wait for actual termination (if needed)注意使用变量 self._running 退出线程的方式
使用继承方式
from threading import Thread
class CountdownThread(Thread):
def __init__(self, n):
super().__init__()
self.n = n
def run(self):
while self.n > 0:
print('T-minus', self.n)
self.n -= 1
time.sleep(5)
c = CountdownThread(5)
c.start()
使用 Queue 进行线程间通信
import Queue
import threading
import time
task_queue = Queue.Queue()
class ThreadTest(threading.Thread):
def __init__(self, queue):
threading.Thread.__init__(self)
self.queue = queue
def run(self):
while True:
msg = self.queue.get()
print(msg)
time.sleep(0.1)
self.queue.task_done()
def main():
start = time.time()
# populate queue with data
for i in range(100):
task_queue.put("message")
# spawn a pool of threads, and pass them queue instance
for i in range(5):
t = ThreadTest(task_queue)
t.setDaemon(True)
t.start()
# wait on the queue until everything has been processed
task_queue.join()
print "Elapsed Time: {}".format(time.time() - start)
if __name__ == "__main__":
main()setDaemon 设置为 True, run 函数中不需要退出，主线程结束后所有子线程退出
如果 setDaemon 设置为 False,则改为
def run(self):
while not self.queue.empty():
msg = self.queue.get()
print(msg)
time.sleep(0.1)
self.queue.task_done()并且在主函数结束前 join 所有线程
注意
-
向队列中添加数据项时并不会复制此数据项，线程间通信实际上是在线程间传递对象引用。如果你担心对象的共享状态，那你最好只传递不可修改的数据结构（如：整型、字符串或者元组）或者一个对象的深拷贝。
from queue import Queue from threading import Thread import copy # A thread that produces data def producer(out_q): while True: # Produce some data ... out_q.put(copy.deepcopy(data)) # A thread that consumes data def consumer(in_q): while True: # Get some data data = in_q.get() # Process the data ... - q.qsize() ， q.full() ， q.empty() 等实用方法可以获取一个队列的当前大小和状态。但要注意，这些方法都不是线程安全的。可能你对一个队列使用 empty() 判断出这个队列为空，但同时另外一个线程可能已经向这个队列中插入一个数据项。
