#!/usr/bin/env python
#coding=utf-8
"""
Author: Squall
Last modified: 2011-10-18 16:50
Filename: pool.py
Description: a simple sample for pool class
"""

from multiprocessing import Pool
from time import sleep

def f(x):
  for i in range(10):
    print '%s --- %s ' % (i, x)
    sleep(1)


def main():
  pool = Pool(processes=3)  # set the processes max number 3
  for i in range(11,20):
    result = pool.apply_async(f, (i,))
  pool.close()
  pool.join()
  if result.successful():
    print 'successful'


if __name__ == "__main__":
  main()

import multiprocessing
import time

def func(msg):
for i in xrange(3):
print msg
time.sleep(1)

if __name__ == "__main__":
p = multiprocessing.Process(target=func, args=("hello", ))</ 
p.start()
p.join()
print "Sub-process done."

import
multiprocessing
import
time
 
def
func(msg):
  for
i
in
xrange(3):
    print
msg
    time.sleep(1)
 
if
__name__
==
"__main__":
  pool
=
multiprocessing.Pool(processes=4)
  for
i
in
xrange(10):
    msg
=
"hello
 %d"
%(i)
    pool.apply_async(func,
(msg,
))
  pool.close()
  pool.join()
  print
"Sub-process(es)
 done."
 

import multiprocessing

import time



def func(msg):

for i in xrange(3):

print msg

time.sleep(1)

return "done " + msg



if __name__ == "__main__":

pool = multiprocessing.Pool(processes=4)

result = []

for i in xrange(10):

msg = "hello %d" %(i)

result.append(pool.apply_async(func, (msg, )))

pool.close()

pool.join()

for res in result:

print res.get()

print "Sub-process(es) done."

multiprocessing.freeze_support()

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]"

from multiprocessing import Pool

def analyse_url(url):
  #do something with this url
  return analysis_result

if __name__ == '__main__':
  pool = Pool(processes=10)
  result = pool.map(analyse_url, url_list)

#result = pool.map(analyse_url, url_list)
result = pool.map_async(analyse_url, url_list).get(120)