语法
1. 进程间通讯
2. 进程池　　　　

操作系统发展史

手工操作（无操作系统）

1946年第一台计算机诞生--20世纪50年代中期，还未出现操作系统，计算机工作采用手工操作方式。

手工操作
程序员将对应于程序和数据的已穿孔的纸带（或卡片）装入输入机，然后启动输入机把程序和数据输入计算机内存，接着通过控制台开关启动程序针对数据运行；计算完毕，打印机输出计算结果；用户取走结果并卸下纸带（或卡片）后，才让下一个用户上机。

1 #_*_coding:utf-8_*_
2 __author__ = 'Alex Li'
3 import threading
4 import time
5 import random
6
7 def door():
8     door_open_time_counter = 0
9     while True:
10         if door_swiping_event.is_set():
11             print("\033[32;1mdoor opening....\033[0m")
12             door_open_time_counter +=1
13
14         else:
15             print("\033[31;1mdoor closed...., swipe to open.\033[0m")
16             door_open_time_counter = 0 #清空计时器
17             door_swiping_event.wait()
18
19
20         if door_open_time_counter > 3:#门开了已经3s了,该关了
21             door_swiping_event.clear()
22
23         time.sleep(0.5)
24
25
26 def staff(n):
27
28     print("staff [%s] is comming..." % n )
29     while True:
30         if door_swiping_event.is_set():
31             print("\033[34;1mdoor is opened, passing.....\033[0m")
32             break
33         else:
34             print("staff [%s] sees door got closed, swipping the card....." % n)
35             print(door_swiping_event.set())
36             door_swiping_event.set()
37             print("after set ",door_swiping_event.set())
38         time.sleep(0.5)
39 door_swiping_event  = threading.Event() #设置事件
40
41
42 door_thread = threading.Thread(target=door)
43 door_thread.start()
44
45
46
47 for i in range(5):
48     p = threading.Thread(target=staff,args=(i,))
49     time.sleep(random.randrange(3))
50     p.start()

queue队列 

queue is especially useful in threaded programming when information must be exchanged safely between multiple threads.

class 

queue.

Queue

(maxsize=0) #先入先出

class 

queue.

LifoQueue

(maxsize=0) #last in fisrt out 

class 

queue.

PriorityQueue

(maxsize=0) #存储数据时可设置优先级的队列

Constructor for a priority queue. maxsize is an integer that sets the upperbound limit on the number of items that can be placed in the queue. Insertion will block once this size has been reached, until queue items are consumed. If maxsize is less than or equal to zero, the queue size is infinite.

The lowest valued entries are retrieved first (the lowest valued entry is the one returned by 

sorted(list(entries))[0]

). A typical pattern for entries is a tuple in the form: 

(priority_number, data)

.

exception 

queue.

Empty

Exception raised when non-blocking 

get()

 (or 

get_nowait()

) is called on a 

Queue

 object which is empty.

exception 

queue.

Full

Exception raised when non-blocking 

put()

 (or 

put_nowait()

) is called on a 

Queue

 object which is full.

Queue.

qsize

()

Queue.

empty

() #return True if empty  

Queue.

full

() # return True if full 

Queue.

put

(item, block=True, timeout=None)

Put item into the queue. If optional args block is true and timeout is None (the default), block if necessary until a free slot is available. If timeout is a positive number, it blocks at most timeout seconds and raises the 

Full

 exception if no free slot was available within that time. Otherwise (block is false), put an item on the queue if a free slot is immediately available, else raise the 

Full

 exception (timeout is ignored in that case).

Queue.

put_nowait

(item)

Equivalent to 

put(item, False)

.

Queue.

get

(block=True, timeout=None)

Remove and return an item from the queue. If optional args block is true and timeout is None (the default), block if necessary until an item is available. If timeout is a positive number, it blocks at most timeout seconds and raises the 

Empty

 exception if no item was available within that time. Otherwise (block is false), return an item if one is immediately available, else raise the 

Empty

 exception (timeout is ignored in that case).

Queue.

get_nowait

()

Equivalent to 

get(False)

.

Two methods are offered to support tracking whether enqueued tasks have been fully processed by daemon consumer threads.

Queue.

task_done

()

Indicate that a formerly enqueued task is complete. Used by queue consumer threads. For each 

get()

 used to fetch a task, a subsequent call to 

task_done()

 tells the queue that the processing on the task is complete.

If a 

join()

 is currently blocking, it will resume when all items have been processed (meaning that a 

task_done()

 call was received for every item that had been 

put()

 into the queue).

Raises a 

ValueError

 if called more times than there were items placed in the queue.

Queue.

join

() block直到queue被消费完毕

生产者消费者模型

在并发编程中使用生产者和消费者模式能够解决绝大多数并发问题。该模式通过平衡生产线程和消费线程的工作能力来提高程序的整体处理数据的速度。

为什么要使用生产者和消费者模式

在线程世界里，生产者就是生产数据的线程，消费者就是消费数据的线程。在多线程开发当中，如果生产者处理速度很快，而消费者处理速度很慢，那么生产者就必须等待消费者处理完，才能继续生产数据。同样的道理，如果消费者的处理能力大于生产者，那么消费者就必须等待生产者。为了解决这个问题于是引入了生产者和消费者模式。

什么是生产者消费者模式

生产者消费者模式是通过一个容器来解决生产者和消费者的强耦合问题。生产者和消费者彼此之间不直接通讯，而通过阻塞队列来进行通讯，所以生产者生产完数据之后不用等待消费者处理，直接扔给阻塞队列，消费者不找生产者要数据，而是直接从阻塞队列里取，阻塞队列就相当于一个缓冲区，平衡了生产者和消费者的处理能力。

下面来学习一个最基本的生产者消费者模型的例子

多进程multiprocessing

multiprocessing

threading

multiprocessing

multiprocessing

To show the individual process IDs involved, here is an expanded example:

进程间通讯　　

不同进程间内存是不共享的，要想实现两个进程间的数据交换，可以用以下方法：

Queues

使用方法跟threading里的queue差不多

Pipes

The 

Pipe()

Pipe()

send()

recv()

A manager object returned by 

Manager()

A manager returned by 

Manager()

list

dict

Namespace

Lock

RLock

Semaphore

BoundedSemaphore

Condition

Event

Barrier

Queue

Value

Array

进程同步

Without using the lock output from the different processes is liable to get all mixed up.

进程池　　

进程池内部维护一个进程序列，当使用时，则去进程池中获取一个进程，如果进程池序列中没有可供使用的进进程，那么程序就会等待，直到进程池中有可用进程为止。

进程池中有两个方法：

• apply
• apply_async

作业需求：

题目:简单主机批量管理工具

需求:

1. 主机分组
2. 主机信息配置文件用configparser解析
3. 可批量执行命令、发送文件，结果实时返回，执行格式如下  batch_run  -h h1,h2,h3   -g web_clusters,db_servers    -cmd  "df -h"　
4. batch_scp   -h h1,h2,h3   -g web_clusters,db_servers  -action put  -local test.py  -remote /tmp/