【转】Python中的GIL、多进程和多线程

import threading
import datetime

class ThreadClass(threading.Thread):
     def run(self):
         now = datetime.datetime.now()
         print "%s says Hello World at time: %s" % (self.getName(), now)

for i in range(2):
    t = ThreadClass()
    t.start()

import Queue
import threading
import urllib2
import time
from BeautifulSoup import BeautifulSoup

hosts = ["http://yahoo.com", "http://google.com", "http://amazon.com",
        "http://ibm.com", "http://apple.com"]

queue = Queue.Queue()
out_queue = Queue.Queue()

class ThreadUrl(threading.Thread):
    """Threaded Url Grab"""
    def __init__(self, queue, out_queue):
        threading.Thread.__init__(self)
        self.queue = queue
        self.out_queue = out_queue

    def run(self):
        while True:
            #grabs host from queue
            host = self.queue.get()

            #grabs urls of hosts and then grabs chunk of webpage
            url = urllib2.urlopen(host)
            chunk = url.read()

            #place chunk into out queue
            self.out_queue.put(chunk)

            #signals to queue job is done
            self.queue.task_done()

class DatamineThread(threading.Thread):
    """Threaded Url Grab"""
    def __init__(self, out_queue):
        threading.Thread.__init__(self)
        self.out_queue = out_queue

    def run(self):
        while True:
            #grabs host from queue
            chunk = self.out_queue.get()

            #parse the chunk
            soup = BeautifulSoup(chunk)
            print soup.findAll(['title'])

            #signals to queue job is done
            self.out_queue.task_done()

start = time.time()
def main():

    #spawn a pool of threads, and pass them queue instance
    for i in range(5):
        t = ThreadUrl(queue, out_queue)
        t.setDaemon(True)
        t.start()

    #populate queue with data
    for host in hosts:
        queue.put(host)

    for i in range(5):
        dt = DatamineThread(out_queue)
        dt.setDaemon(True)
        dt.start()


    #wait on the queue until everything has been processed
    queue.join()
    out_queue.join()

main()
print "Elapsed Time: %s" % (time.time() - start)

创建进程是使用Process类:

from multiprocessing import Process

def f(name):
    print 'hello', name

if __name__ == '__main__':
    p = Process(target=f, args=('bob',))
    p.start()
    p.join()

Queue 方式:

from multiprocessing import Process, Queue

def f(q):
    q.put([42, None, 'hello'])

if __name__ == '__main__':
    q = Queue()
    p = Process(target=f, args=(q,))
    p.start()
    print q.get()    # prints "[42, None, 'hello']"
    p.join()

Pipe 方式:

from multiprocessing import Process, Pipe

def f(conn):
    conn.send([42, None, 'hello'])
    conn.close()

if __name__ == '__main__':
    parent_conn, child_conn = Pipe()
    p = Process(target=f, args=(child_conn,))
    p.start()
    print parent_conn.recv()   # prints "[42, None, 'hello']"

添加锁:

from multiprocessing import Process, Lock

def f(l, i):
    l.acquire()
    print 'hello world', i
    l.release()

if __name__ == '__main__':
    lock = Lock()

    for num in range(10):
        Process(target=f, args=(lock, num)).start()

应该尽量避免共享状态.

共享内存方式:

from multiprocessing import Process, Value, Array

def f(n, a):
    n.value = 3.1415927
    for i in range(len(a)):
        a[i] = -a[i]

if __name__ == '__main__':
    num = Value('d', 0.0)
    arr = Array('i', range(10))

    p = Process(target=f, args=(num, arr))
    p.start()
    p.join()

    print num.value
    print arr[:]

Server进程方式:

from multiprocessing import Process, Manager

def f(d, l):
    d[1] = '1'
    d['2'] = 2
    d[0.25] = None
    l.reverse()

if __name__ == '__main__':
    manager = Manager()

    d = manager.dict()
    l = manager.list(range(10))

    p = Process(target=f, args=(d, l))
    p.start()
    p.join()

    print d
    print l

第二种方式支持更多的数据类型，如list, dict, Namespace, Lock, RLock, Semaphore, BoundedSemaphore, Condition, Event, Queue, Value ,Array.

通过Pool类可以建立进程池:

from multiprocessing import Pool

def f(x):
    return x*x

if __name__ == '__main__':
    pool = Pool(processes=4)              # start 4 worker processes
    result = pool.apply_async(f, [10])    # evaluate "f(10)" asynchronously
    print result.get(timeout=1)           # prints "100" unless your computer is *very* slow
    print pool.map(f, range(10))          # prints "[0, 1, 4,..., 81]"