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
作为第二个anwser的python3版本:
import queue as Queue
import threading
import urllib.request
# Called by each thread
def get_url(q, url):
q.put(urllib.request.urlopen(url).read())
theurls = ["http://google.com", "http://yahoo.com", "http://www.python.org","https://wiki.python.org/moin/"]
q = Queue.Queue()
def thread_func():
for u in theurls:
t = threading.Thread(target=get_url, args = (q,u))
t.daemon = True
t.start()
s = q.get()
def non_thread_func():
for u in theurls:
get_url(q,u)
s = q.get()
您可以测试它:
start = time.time()
thread_func()
end = time.time()
print(end - start)
start = time.time()
non_thread_func()
end = time.time()
print(end - start)
non_thread_func()花费的时间应该是thread_func()的4倍
其他回答
这很容易理解。这里有两种简单的线程处理方法。
import time
from concurrent.futures import ThreadPoolExecutor, as_completed
import threading
def a(a=1, b=2):
print(a)
time.sleep(5)
print(b)
return a+b
def b(**kwargs):
if "a" in kwargs:
print("am b")
else:
print("nothing")
to_do=[]
executor = ThreadPoolExecutor(max_workers=4)
ex1=executor.submit(a)
to_do.append(ex1)
ex2=executor.submit(b, **{"a":1})
to_do.append(ex2)
for future in as_completed(to_do):
print("Future {} and Future Return is {}\n".format(future, future.result()))
print("threading")
to_do=[]
to_do.append(threading.Thread(target=a))
to_do.append(threading.Thread(target=b, kwargs={"a":1}))
for threads in to_do:
threads.start()
for threads in to_do:
threads.join()
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()
使用线程/多处理的最简单方法是使用更多高级库,如autothread。
import autothread
from time import sleep as heavyworkload
@autothread.multithreaded() # <-- This is all you need to add
def example(x: int, y: int):
heavyworkload(1)
return x*y
现在,您可以为函数提供int列表。Autothread将为您处理所有事务,并只提供并行计算的结果。
result = example([1, 2, 3, 4, 5], 10)
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()
import threading
import requests
def send():
r = requests.get('https://www.stackoverlow.com')
thread = []
t = threading.Thread(target=send())
thread.append(t)
t.start()