15.1 multiprocessing
multiprocessing是多进程模块，多进程提供了任务并发性，能充分利用多核处理器。避免了GIL（全局解释锁）对资源的影响。
有以下常用类：
| 类 |
描述 |
| Process(group=None, target=None, name=None, args=(), kwargs={}) | 派生一个进程对象，然后调用start()方法启动 |
| Pool(processes=None, initializer=None, initargs=()) |
返回一个进程池对象，processes进程池进程数量 |
| Pipe(duplex=True) | 返回两个连接对象由管道连接 |
| Queue(maxsize=0) | 返回队列对象，操作方法跟Queue.Queue一样 |
| multiprocessing.dummy | 这个库是用于实现多线程 |
Process()类有以下些方法：
| run() | |
| start() | 启动进程对象 |
| join([timeout]) | 等待子进程终止，才返回结果。可选超时。 |
| name | 进程名字 |
| is_alive() | 返回进程是否存活 |
| daemon | 进程的守护标记，一个布尔值 |
| pid | 返回进程ID |
| exitcode | 子进程退出状态码 |
| terminate() | 终止进程。在unix上使用SIGTERM信号，在windows上使用TerminateProcess()。 |
Pool()类有以下些方法：
| apply(func, args=(), kwds={}) | 等效内建函数apply() |
| apply_async(func, args=(), kwds={}, callback=None) | 异步，等效内建函数apply() |
| map(func, iterable, chunksize=None) | 等效内建函数map() |
| map_async(func, iterable, chunksize=None, callback=None) | 异步，等效内建函数map() |
| imap(func, iterable, chunksize=1) | 等效内建函数itertools.imap() |
| imap_unordered(func, iterable, chunksize=1) | 像imap()方法，但结果顺序是任意的 |
| close() | 关闭进程池 |
| terminate() | 终止工作进程，垃圾收集连接池对象 |
| join() | 等待工作进程退出。必须先调用close()或terminate() |
Pool.apply_async()和Pool.map_aysnc()又提供了以下几个方法：
| get([timeout]) | 获取结果对象里的结果。如果超时没有，则抛出TimeoutError异常 |
| wait([timeout]) | 等待可用的结果或超时 |
| ready() | 返回调用是否已经完成 |
| successful() |
举例：
1）简单的例子，用子进程处理函数
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from
multiprocessing
import
Process
import
os
def
worker(name):
print
name
print
'parent process id:'
, os.getppid()
print
'process id:'
, os.getpid()
if
__name__
=
=
'__main__'
:
p
=
Process(target
=
worker, args
=
(
'function worker.'
,))
p.start()
p.join()
print
p.name
# python test.py
function worker.
parent process
id
:
9079
process
id
:
9080
Process
-
1
|
Process实例传入worker函数作为派生进程执行的任务，用start()方法启动这个实例。
2）加以说明join()方法
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from
multiprocessing
import
Process
import
os
def
worker(n):
print
'hello world'
, n
if
__name__
=
=
'__main__'
:
print
'parent process id:'
, os.getppid()
for
n
in
range
(
5
):
p
=
Process(target
=
worker, args
=
(n,))
p.start()
p.join()
print
'child process id:'
, p.pid
print
'child process name:'
, p.name
# python test.py
parent process
id
:
9041
hello world
0
child process
id
:
9132
child process name: Process
-
1
hello world
1
child process
id
:
9133
child process name: Process
-
2
hello world
2
child process
id
:
9134
child process name: Process
-
3
hello world
3
child process
id
:
9135
child process name: Process
-
4
hello world
4
child process
id
:
9136
child process name: Process
-
5
# 把p.join()注释掉再执行
# python test.py
parent process
id
:
9041
child process
id
:
9125
child process name: Process
-
1
child process
id
:
9126
child process name: Process
-
2
child process
id
:
9127
child process name: Process
-
3
child process
id
:
9128
child process name: Process
-
4
hello world
0
hello world
1
hello world
3
hello world
2
child process
id
:
9129
child process name: Process
-
5
hello world
4
|
可以看出，在使用join()方法时，输出的结果都是顺序排列的。相反是乱序的。因此join()方法是堵塞父进程，要等待当前子进程执行完后才会继续执行下一个子进程。否则会一直生成子进程去执行任务。
在要求输出的情况下使用join()可保证每个结果是完整的。
3）给子进程命名，方便管理
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from
multiprocessing
import
Process
import
os, time
def
worker1(n):
print
'hello world'
, n
def
worker2():
print
'worker2...'
if
__name__
=
=
'__main__'
:
print
'parent process id:'
, os.getppid()
for
n
in
range
(
3
):
p1
=
Process(name
=
'worker1'
, target
=
worker1, args
=
(n,))
p1.start()
p1.join()
print
'child process id:'
, p1.pid
print
'child process name:'
, p1.name
p2
=
Process(name
=
'worker2'
, target
=
worker2)
p2.start()
p2.join()
print
'child process id:'
, p2.pid
print
'child process name:'
, p2.name
# python test.py
parent process
id
:
9041
hello world
0
child process
id
:
9248
child process name: worker1
hello world
1
child process
id
:
9249
child process name: worker1
hello world
2
child process
id
:
9250
child process name: worker1
worker2...
child process
id
:
9251
child process name: worker2
|
4）设置守护进程，父进程退出也不影响子进程运行
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from
multiprocessing
import
Process
def
worker1(n):
print
'hello world'
, n
def
worker2():
print
'worker2...'
if
__name__
=
=
'__main__'
:
for
n
in
range
(
3
):
p1
=
Process(name
=
'worker1'
, target
=
worker1, args
=
(n,))
p1.daemon
=
True
p1.start()
p1.join()
p2
=
Process(target
=
worker2)
p2.daemon
=
False
p2.start()
p2.join()
|
5）使用进程池
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#!/usr/bin/python
# -*- coding: utf-8 -*-
from
multiprocessing
import
Pool, current_process
import
os, time, sys
def
worker(n):
print
'hello world'
, n
print
'process name:'
, current_process().name
# 获取当前进程名字
time.sleep(
1
)
# 休眠用于执行时有时间查看当前执行的进程
if
__name__
=
=
'__main__'
:
p
=
Pool(processes
=
3
)
for
i
in
range
(
8
):
r
=
p.apply_async(worker, args
=
(i,))
r.get(timeout
=
5
)
# 获取结果中的数据
p.close()
# python test.py
hello world
0
process name: PoolWorker
-
1
hello world
1
process name: PoolWorker
-
2
hello world
2
process name: PoolWorker
-
3
hello world
3
process name: PoolWorker
-
1
hello world
4
process name: PoolWorker
-
2
hello world
5
process name: PoolWorker
-
3
hello world
6
process name: PoolWorker
-
1
hello world
7
process name: PoolWorker
-
2
|
进程池生成了3个子进程，通过循环执行8次worker函数，进程池会从子进程1开始去处理任务，当到达最大进程时，会继续从子进程1开始。
在运行此程序同时，再打开一个终端窗口会看到生成的子进程：
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# ps -ef |grep python
root
40244
9041
4
16
:
43
pts
/
3
00
:
00
:
00
python test.py
root
40245
40244
0
16
:
43
pts
/
3
00
:
00
:
00
python test.py
root
40246
40244
0
16
:
43
pts
/
3
00
:
00
:
00
python test.py
root
40247
40244
0
16
:
43
pts
/
3
00
:
00
:
00
python test.py
|
6）进程池map()方法
map()方法是将序列中的元素通过函数处理返回新列表。
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from
multiprocessing
import
Pool
def
worker(url):
return
'http://%s'
%
url
urls
=
[
'www.baidu.com'
,
'www.jd.com'
]
p
=
Pool(processes
=
2
)
r
=
p.
map
(worker, urls)
p.close()
print
r
# python test.py
[
'http://www.baidu.com'
,
'http://www.jd.com'
]
|
7）Queue进程间通信
multiprocessing支持两种类型进程间通信：Queue和Pipe。
Queue库已经封装到multiprocessing库中，在第十章 Python常用标准库已经讲解到Queue库使用，有需要请查看以前博文。
例如：一个子进程向队列写数据，一个子进程读取队列数据
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#!/usr/bin/python
# -*- coding: utf-8 -*-
from
multiprocessing
import
Process, Queue
# 写数据到队列
def
write(q):
for
n
in
range
(
5
):
q.put(n)
print
'Put %s to queue.'
%
n
# 从队列读数据
def
read(q):
while
True
:
if
not
q.empty():
value
=
q.get()
print
'Get %s from queue.'
%
value
else
:
break
if
__name__
=
=
'__main__'
:
q
=
Queue()
pw
=
Process(target
=
write, args
=
(q,))
pr
=
Process(target
=
read, args
=
(q,))
pw.start()
pw.join()
pr.start()
pr.join()
# python test.py
Put
0
to queue.
Put
1
to queue.
Put
2
to queue.
Put
3
to queue.
Put
4
to queue.
Get
0
from
queue.
Get
1
from
queue.
Get
2
from
queue.
Get
3
from
queue.
Get
4
from
queue.
|
8）Pipe进程间通信
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from
multiprocessing
import
Process, Pipe
def
f(conn):
conn.send([
42
,
None
,
'hello'
])
conn.close()
if
__name__
=
=
'__main__'
:
parent_conn, child_conn
=
Pipe()
p
=
Process(target
=
f, args
=
(child_conn,))
p.start()
print
parent_conn.recv()
p.join()
# python test.py
[
42
,
None
,
'hello'
]
|
Pipe()创建两个连接对象，每个链接对象都有send()和recv()方法，
9）进程间对象共享
Manager类返回一个管理对象，它控制服务端进程。提供一些共享方式：Value()、Array()、list()、dict()、Event()等
创建Manger对象存放资源，其他进程通过访问Manager获取。
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from
multiprocessing
import
Process, Manager
def
f(v, a, l, d):
v.value
=
100
a[
0
]
=
123
l.append(
'Hello'
)
d[
'a'
]
=
1
mgr
=
Manager()
v
=
mgr.Value(
'v'
,
0
)
a
=
mgr.Array(
'd'
,
range
(
5
))
l
=
mgr.
list
()
d
=
mgr.
dict
()
p
=
Process(target
=
f, args
=
(v, a, l, d))
p.start()
p.join()
print
(v)
print
(a)
print
(l)
print
(d)
# python test.py
Value(
'v'
,
100
)
array(
'd'
, [
123.0
,
1.0
,
2.0
,
3.0
,
4.0
])
[
'Hello'
]
{
'a'
:
1
}
|
10）写一个多进程的例子
比如：多进程监控URL是否正常
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from
multiprocessing
import
Pool, current_process
import
urllib2
urls
=
[
'http://www.baidu.com'
,
'http://www.jd.com'
,
'http://www.sina.com'
,
'http://www.163.com'
,
]
def
status_code(url):
print
'process name:'
, current_process().name
try
:
req
=
urllib2.urlopen(url, timeout
=
5
)
return
req.getcode()
except
urllib2.URLError:
return
p
=
Pool(processes
=
4
)
for
url
in
urls:
r
=
p.apply_async(status_code, args
=
(url,))
if
r.get(timeout
=
5
)
=
=
200
:
print
"%s OK"
%
url
else
:
print
"%s NO"
%
url
# python test.py
process name: PoolWorker
-
1
http:
/
/
www.baidu.com OK
process name: PoolWorker
-
2
http:
/
/
www.jd.com OK
process name: PoolWorker
-
3
http:
/
/
www.sina.com OK
process name: PoolWorker
-
4
http:
/
/
www.
163.com
OK
|
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QQ群：323779636（Shell/Python运维开发群）
15.2 threading
threading模块类似于multiprocessing多进程模块，使用方法也基本一样。threading库是对thread库进行二次封装，我们主要用到Thread类，用Thread类派生线程对象。
1）使用Thread类实现多线程
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from
threading
import
Thread, current_thread
def
worker(n):
print
'thread name:'
, current_thread().name
print
'hello world'
, n
for
n
in
range
(
5
):
t
=
Thread(target
=
worker, args
=
(n, ))
t.start()
t.join()
# 等待主进程结束
# python test.py
thread name: Thread
-
1
hello world
0
thread name: Thread
-
2
hello world
1
thread name: Thread
-
3
hello world
2
thread name: Thread
-
4
hello world
3
thread name: Thread
-
5
hello world
4
|
2）还有一种方式继承Thread类实现多线程，子类可以重写__init__和run()方法实现功能逻辑。
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#!/usr/bin/python
# -*- coding: utf-8 -*-
from
threading
import
Thread, current_thread
class
Test(Thread):
# 重写父类构造函数，那么父类构造函数将不会执行
def
__init__(
self
, n):
Thread.__init__(
self
)
self
.n
=
n
def
run(
self
):
print
'thread name:'
, current_thread().name
print
'hello world'
,
self
.n
if
__name__
=
=
'__main__'
:
for
n
in
range
(
5
):
t
=
Test(n)
t.start()
t.join()
# python test.py
thread name: Thread
-
1
hello world
0
thread name: Thread
-
2
hello world
1
thread name: Thread
-
3
hello world
2
thread name: Thread
-
4
hello world
3
thread name: Thread
-
5
hello world
4
|
3）Lock
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from
threading
import
Thread, Lock, current_thread
lock
=
Lock()
class
Test(Thread):
# 重写父类构造函数，那么父类构造函数将不会执行
def
__init__(
self
, n):
Thread.__init__(
self
)
self
.n
=
n
def
run(
self
):
lock.acquire()
# 获取锁
print
'thread name:'
, current_thread().name
print
'hello world'
,
self
.n
lock.release()
# 释放锁
if
__name__
=
=
'__main__'
:
for
n
in
range
(
5
):
t
=
Test(n)
t.start()
t.join()
|
众所周知，Python多线程有GIL全局锁，意思是把每个线程执行代码时都上了锁，执行完成后会自动释放GIL锁，意味着同一时间只有一个线程在运行代码。由于所有线程共享父进程内存、变量、资源，很容易多个线程对其操作，导致内容混乱。
当你在写多线程程序的时候如果输出结果是混乱的，这时你应该考虑到在不使用锁的情况下，多个线程运行时可能会修改原有的变量，导致输出不一样。
由此看来Python多线程是不能利用多核CPU提高处理性能，但在IO密集情况下，还是能提高一定的并发性能。也不必担心，多核CPU情况可以使用多进程实现多核任务。Python多进程是复制父进程资源，互不影响，有各自独立的GIL锁，保证数据不会混乱。能用多进程就用吧！
本文转自 李振良OK 51CTO博客，原文链接：http://blog.51cto.com/lizhenliang/1875753，如需转载请自行联系原作者
