1. 体验多进程的运行速度
#!/usr/bin/env python
# _*_ coding:UTF-8 _*_
from multiprocessing import Pool
import time
def foo(n):
time.sleep(1)
return n * n
if __name__ == "__main__":
pool = Pool(10)
data_list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
# 这里只需要等待1S就能得到结果， 因为使用了多进程
print pool.map(foo, data_list)
# 这里需要等待10S 才能得到结果， 因为没有使用多进程
print map(foo, data_list)
结果：
/Users/liudaoqiang/PycharmProjects/numpy/venv/bin/python /Users/liudaoqiang/Project/python_project/day24/processing_test.py [1, 4, 9, 16, 25, 36, 49, 64, 81, 100] [1, 4, 9, 16, 25, 36, 49, 64, 81, 100] Process finished with exit code 0
注意：
（1）多进程的优势在于充分利用多核优势，因为多线程都是利用单核，只有多进程才能利用多核
2.子进程与父进程的关系
#!/usr/bin/env python
# _*_ coding:UTF-8 _*_
from multiprocessing import Process
import os
def info(title):
print "+++++++++++++" + title + "++++++++++++++"
if hasattr(os, 'getppid'):
print "ppid: %d" % os.getppid()
print "pid: %d" % os.getpid()
if __name__ == "__main__":
info("main process")
process = Process(target=info, args=("other process", ))
process.start()
process.join()
结果：
/Users/liudaoqiang/PycharmProjects/numpy/venv/bin/python /Users/liudaoqiang/Project/python_project/day24/processing_test_2.py +++++++++++++main process++++++++++++++ ppid: 490 pid: 593 +++++++++++++other process++++++++++++++ ppid: 593 pid: 594 Process finished with exit code 0
3.进程与线程的区别
#!/usr/bin/env python
# _*_ coding:UTF-8 _*_
from multiprocessing import Process
from threading import Thread
def foo(li, item):
li.append(item)
print li
if __name__ == "__main__":
li = []
#注意：进程是不共享内存的，每个进程都有独立的内存空间；可以利用多核优势；是真正意义上的并发
print "+++++++++process+++++++++"
for item in range(5):
process = Process(target=foo, args=(li, item))
process.start()
process.join()
#注意：线程是共享同一份内存的，每个线程都在抢占内存空间；一个时间片只有一个线程占用内存；不是真正的并发
print "+++++++++thread++++++++++"
for item in range(5):
thread = Thread(target=foo, args=(li, item))
thread.start()
thread.join()
结果：
+++++++++process+++++++++ [0] [1] [2] [3] [4] +++++++++thread++++++++++ [0] [0, 1] [0, 1, 2] [0, 1, 2, 3] [0, 1, 2, 3, 4]
4. 实现进程间的内存共享（使用进程的队列）
#!/usr/bin/env python
# _*_ coding:UTF-8 _*_
from multiprocessing import Process, Queue
def foo(que, item):
que.put(item)
if __name__ == "__main__":
que = Queue()
for item in range(5):
process = Process(target=foo, args=(que, item))
process.start()
while True:
print que.get()
结果：
1 2 0 3 4
5. 实现进程间的内存共享（使用Value和Array）
#!/usr/bin/env python
# _*_ coding:UTF-8 _*_
from multiprocessing import Process, Value, Array
def foo(n, a):
n.value = 3.1415926
for i in range(len(a)):
a[i] = -a[i]
if __name__ == "__main__":
# 这里采用多进程提供的Value对象和Array对象
num = Value('d', 0)
arr = Array('i', range(10))
# 调用foo方法，修改子进程的内存数据
process = Process(target=foo, args=(num, arr))
process.start()
process.join()
# 打印主进程定义的使用Value和Array定义的数据，发现也被修改；从而断定子进程与主进程共享了内存空间
print num.value
print arr[:]
结果：
/Users/liudaoqiang/PycharmProjects/numpy/venv/bin/python /Users/liudaoqiang/Project/python_project/day24/processing_test_5.py 3.1415926 [0, -1, -2, -3, -4, -5, -6, -7, -8, -9] Process finished with exit code 0
6. 使用进程池开启进程
#!/usr/bin/env python
#! _*_ coding:UTF-8 _*_
from multiprocessing import Pool
import time
def foo(x):
print x * x
time.sleep(1)
return x * x
if __name__ == "__main__":
# 这里定义进程池，每次最多只有4个进程并行运行
pool = Pool(processes=4)
res_list = []
for i in range(10):
# 开启新的进程并启动，相当于Process(target=foo, args=(i))
res = pool.apply_async(foo, (i, ))
# 要将进程的运行结果放入列表中，这时其实进程还还有执行函数foo
res_list.append(res)
for item in res_list:
# 只有在区结果的时候，进程才真正执行函数
print item.get()
结果：看见每个时间段只有4个进程运行
/Users/liudaoqiang/PycharmProjects/numpy/venv/bin/python /Users/liudaoqiang/Project/python_project/day24/processing_test_6.py 0 1 4 9 16 25 36 0 1 49 4 9 64 81 16 25 36 49 64 81 Process finished with exit code 0