我正在努力理解Python中的线程。我看过文档和示例,但坦率地说,许多示例过于复杂,我很难理解它们。
如何清楚地显示为多线程划分的任务?
我正在努力理解Python中的线程。我看过文档和示例,但坦率地说,许多示例过于复杂,我很难理解它们。
如何清楚地显示为多线程划分的任务?
当前回答
大多数文档和教程都使用Python的“线程和队列”模块,对于初学者来说,它们可能会让人不知所措。
也许可以考虑Python 3的concurrent.futures.ThreadPoolExecutor模块。
结合子句和列表理解,这可能是一个真正的魅力。
from concurrent.futures import ThreadPoolExecutor, as_completed
def get_url(url):
# Your actual program here. Using threading.Lock() if necessary
return ""
# List of URLs to fetch
urls = ["url1", "url2"]
with ThreadPoolExecutor(max_workers = 5) as executor:
# Create threads
futures = {executor.submit(get_url, url) for url in urls}
# as_completed() gives you the threads once finished
for f in as_completed(futures):
# Get the results
rs = f.result()
其他回答
与其他提到的一样,由于GIL,CPython只能在I/O等待时使用线程。
如果您想从多个内核中获得CPU绑定任务的好处,请使用多处理:
from multiprocessing import Process
def f(name):
print 'hello', name
if __name__ == '__main__':
p = Process(target=f, args=('bob',))
p.start()
p.join()
这里是多线程,有一个简单的例子会很有帮助。您可以运行它并轻松了解多线程在Python中的工作方式。我使用了一个锁来防止访问其他线程,直到前面的线程完成它们的工作。通过使用这行代码,
t锁定=线程。有界信号量(值=4)
您可以一次允许多个进程,并保留将在稍后或完成之前的进程后运行的其余线程。
import threading
import time
#tLock = threading.Lock()
tLock = threading.BoundedSemaphore(value=4)
def timer(name, delay, repeat):
print "\r\nTimer: ", name, " Started"
tLock.acquire()
print "\r\n", name, " has the acquired the lock"
while repeat > 0:
time.sleep(delay)
print "\r\n", name, ": ", str(time.ctime(time.time()))
repeat -= 1
print "\r\n", name, " is releaseing the lock"
tLock.release()
print "\r\nTimer: ", name, " Completed"
def Main():
t1 = threading.Thread(target=timer, args=("Timer1", 2, 5))
t2 = threading.Thread(target=timer, args=("Timer2", 3, 5))
t3 = threading.Thread(target=timer, args=("Timer3", 4, 5))
t4 = threading.Thread(target=timer, args=("Timer4", 5, 5))
t5 = threading.Thread(target=timer, args=("Timer5", 0.1, 5))
t1.start()
t2.start()
t3.start()
t4.start()
t5.start()
print "\r\nMain Complete"
if __name__ == "__main__":
Main()
对我来说,线程的最佳示例是监视异步事件。看看这个代码。
# thread_test.py
import threading
import time
class Monitor(threading.Thread):
def __init__(self, mon):
threading.Thread.__init__(self)
self.mon = mon
def run(self):
while True:
if self.mon[0] == 2:
print "Mon = 2"
self.mon[0] = 3;
您可以通过打开IPython会话并执行以下操作来使用此代码:
>>> from thread_test import Monitor
>>> a = [0]
>>> mon = Monitor(a)
>>> mon.start()
>>> a[0] = 2
Mon = 2
>>>a[0] = 2
Mon = 2
等几分钟
>>> a[0] = 2
Mon = 2
给定函数f,如下所示:
import threading
threading.Thread(target=f).start()
向f传递参数
threading.Thread(target=f, args=(a,b,c)).start()
借用本文,我们了解了如何在多线程、多处理和异步/异步之间进行选择及其用法。
Python 3有一个新的内置库,以实现并发和并行-concurrent.futures
因此,我将通过一个实验演示如何通过线程池运行四个任务(即.sleep()方法):
from concurrent.futures import ThreadPoolExecutor, as_completed
from time import sleep, time
def concurrent(max_worker):
futures = []
tic = time()
with ThreadPoolExecutor(max_workers=max_worker) as executor:
futures.append(executor.submit(sleep, 2)) # Two seconds sleep
futures.append(executor.submit(sleep, 1))
futures.append(executor.submit(sleep, 7))
futures.append(executor.submit(sleep, 3))
for future in as_completed(futures):
if future.result() is not None:
print(future.result())
print(f'Total elapsed time by {max_worker} workers:', time()-tic)
concurrent(5)
concurrent(4)
concurrent(3)
concurrent(2)
concurrent(1)
输出:
Total elapsed time by 5 workers: 7.007831811904907
Total elapsed time by 4 workers: 7.007944107055664
Total elapsed time by 3 workers: 7.003149509429932
Total elapsed time by 2 workers: 8.004627466201782
Total elapsed time by 1 workers: 13.013478994369507
[注]:
正如您在上面的结果中看到的,最好的情况是这四项任务有3名员工。如果有进程任务而不是I/O绑定或阻塞(多处理而不是线程),则可以将ThreadPoolExecutor更改为ProcessPoolExecutoor。