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
只需注意:线程不需要队列。
这是我可以想象的最简单的例子,它显示了10个并发运行的进程。
import threading
from random import randint
from time import sleep
def print_number(number):
# Sleeps a random 1 to 10 seconds
rand_int_var = randint(1, 10)
sleep(rand_int_var)
print "Thread " + str(number) + " slept for " + str(rand_int_var) + " seconds"
thread_list = []
for i in range(1, 10):
# Instantiates the thread
# (i) does not make a sequence, so (i,)
t = threading.Thread(target=print_number, args=(i,))
# Sticks the thread in a list so that it remains accessible
thread_list.append(t)
# Starts threads
for thread in thread_list:
thread.start()
# This blocks the calling thread until the thread whose join() method is called is terminated.
# From http://docs.python.org/2/library/threading.html#thread-objects
for thread in thread_list:
thread.join()
# Demonstrates that the main process waited for threads to complete
print "Done"
其他回答
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()
Python 3具有启动并行任务的功能。这使我们的工作更容易。
它有线程池和进程池。
以下内容提供了一个见解:
ThreadPoolExecutor示例(源代码)
import concurrent.futures
import urllib.request
URLS = ['http://www.foxnews.com/',
'http://www.cnn.com/',
'http://europe.wsj.com/',
'http://www.bbc.co.uk/',
'http://some-made-up-domain.com/']
# Retrieve a single page and report the URL and contents
def load_url(url, timeout):
with urllib.request.urlopen(url, timeout=timeout) as conn:
return conn.read()
# We can use a with statement to ensure threads are cleaned up promptly
with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:
# Start the load operations and mark each future with its URL
future_to_url = {executor.submit(load_url, url, 60): url for url in URLS}
for future in concurrent.futures.as_completed(future_to_url):
url = future_to_url[future]
try:
data = future.result()
except Exception as exc:
print('%r generated an exception: %s' % (url, exc))
else:
print('%r page is %d bytes' % (url, len(data)))
ProcessPoolExecutor(源)
import concurrent.futures
import math
PRIMES = [
112272535095293,
112582705942171,
112272535095293,
115280095190773,
115797848077099,
1099726899285419]
def is_prime(n):
if n % 2 == 0:
return False
sqrt_n = int(math.floor(math.sqrt(n)))
for i in range(3, sqrt_n + 1, 2):
if n % i == 0:
return False
return True
def main():
with concurrent.futures.ProcessPoolExecutor() as executor:
for number, prime in zip(PRIMES, executor.map(is_prime, PRIMES)):
print('%d is prime: %s' % (number, prime))
if __name__ == '__main__':
main()
我发现这非常有用:创建与内核一样多的线程,并让它们执行(大量)任务(在本例中,调用shell程序):
import Queue
import threading
import multiprocessing
import subprocess
q = Queue.Queue()
for i in range(30): # Put 30 tasks in the queue
q.put(i)
def worker():
while True:
item = q.get()
# Execute a task: call a shell program and wait until it completes
subprocess.call("echo " + str(item), shell=True)
q.task_done()
cpus = multiprocessing.cpu_count() # Detect number of cores
print("Creating %d threads" % cpus)
for i in range(cpus):
t = threading.Thread(target=worker)
t.daemon = True
t.start()
q.join() # Block until all tasks are done
注意:对于Python中的实际并行化,您应该使用多处理模块来分叉并行执行的多个进程(由于全局解释器锁,Python线程提供了交织,但实际上它们是串行执行的,而不是并行执行的,并且仅在交织I/O操作时有用)。
然而,如果您只是在寻找交错(或者正在执行可以并行化的I/O操作,尽管存在全局解释器锁),那么线程模块就是开始的地方。作为一个非常简单的例子,让我们考虑通过并行对子范围求和来对大范围求和的问题:
import threading
class SummingThread(threading.Thread):
def __init__(self,low,high):
super(SummingThread, self).__init__()
self.low=low
self.high=high
self.total=0
def run(self):
for i in range(self.low,self.high):
self.total+=i
thread1 = SummingThread(0,500000)
thread2 = SummingThread(500000,1000000)
thread1.start() # This actually causes the thread to run
thread2.start()
thread1.join() # This waits until the thread has completed
thread2.join()
# At this point, both threads have completed
result = thread1.total + thread2.total
print result
请注意,以上是一个非常愚蠢的示例,因为它绝对没有I/O,并且由于全局解释器锁,虽然在CPython中交错执行(增加了上下文切换的开销),但仍将串行执行。
只需注意:线程不需要队列。
这是我可以想象的最简单的例子,它显示了10个并发运行的进程。
import threading
from random import randint
from time import sleep
def print_number(number):
# Sleeps a random 1 to 10 seconds
rand_int_var = randint(1, 10)
sleep(rand_int_var)
print "Thread " + str(number) + " slept for " + str(rand_int_var) + " seconds"
thread_list = []
for i in range(1, 10):
# Instantiates the thread
# (i) does not make a sequence, so (i,)
t = threading.Thread(target=print_number, args=(i,))
# Sticks the thread in a list so that it remains accessible
thread_list.append(t)
# Starts threads
for thread in thread_list:
thread.start()
# This blocks the calling thread until the thread whose join() method is called is terminated.
# From http://docs.python.org/2/library/threading.html#thread-objects
for thread in thread_list:
thread.join()
# Demonstrates that the main process waited for threads to complete
print "Done"