典型的Python线程池结构如下:
def run(self):
while True:
z=self.some_task_queue.get()
do_work(z)
看起来任务队列正在进行持续监控。
这种持续监控任务队列会占用多少CPU资源?
是否应该引入一些sleep(几毫秒)时间来降低CPU负载?通过这种方式,当所有线程都繁忙时,可以停止对任务队列的监控,并降低CPU负载。
典型的Python线程池结构如下:
def run(self):
while True:
z=self.some_task_queue.get()
do_work(z)
看起来任务队列正在进行持续监控。
这种持续监控任务队列会占用多少CPU资源?
是否应该引入一些sleep(几毫秒)时间来降低CPU负载?通过这种方式,当所有线程都繁忙时,可以停止对任务队列的监控,并降低CPU负载。
我的机器上有1000个线程在.get()
上阻塞,但CPU负载只有0.0%
:
#!/usr/bin/env python
from __future__ import print_function
import os
import time
from threading import Thread
from Queue import Queue
try: import psutil # pip install psutil
except ImportError:
psutil = None
def f(queue):
while True:
item = queue.get() # block until an item is available
print("got %s" % (item,))
break # end thread
# create threads
q = Queue()
threads = [Thread(target=f, args=(q,)) for _ in xrange(1000)]
# starts them
for t in threads:
t.daemon = True # die with the program
t.start()
# show cpu load while the threads are blocked on `queue.get()`
if psutil is None:
print('Observe cpu load yourself (or install psutil and rerun the script)')
time.sleep(10) # observe cpu load
else:
p = psutil.Process(os.getpid())
for _ in xrange(10):
print("cpu %s%%" % (p.get_cpu_percent(interval=0),))
time.sleep(1)
# finish threads
for i in range(len(threads)):
q.put_nowait(i) #note: queue is unlimited so there is no reason to wait
for t in threads: t.join() # wait for completion
print('done')
Queue.get()
将阻塞直到有可用的项目,因此它取决于阻塞的实现方式。