python多线程几种方法实现

python多线程编程

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()