Python之路,Day9 - 异步IO\数据库\队列\缓存 -清零世界

概述

本节内容

到目前为止，我们已经学了网络并发编程的2个套路，多进程，多线程，这哥俩的优势和劣势都非常的明显，我们一起来回顾下

协程

协程，又称微线程，纤程。英文名Coroutine。一句话说明什么是线程：协程是一种用户态的轻量级线程。

协程拥有自己的寄存器上下文和栈。协程调度切换时，将寄存器上下文和栈保存到其他地方，在切回来的时候，恢复先前保存的寄存器上下文和栈。因此：

协程能保留上一次调用时的状态（即所有局部状态的一个特定组合），每次过程重入时，就相当于进入上一次调用的状态，换种说法：进入上一次离开时所处逻辑流的位置。

协程的好处：

缺点：

使用yield实现协程操作例子　　　　

starting eating baozi...")
    while True:
        new_baozi = yield
        print("[%s] is eating baozi %s" % (name,new_baozi))
        #time.sleep(1)
def producer():
r = con.__next__()
r = con2.__next__()
n = 0
while n < 5:
    n +=1
    con.send(n)
    con2.send(n)
    print("\033[32;1m[producer]\033[0m is making baozi <a href="https://www.jb51.cc/tag/s/" target="_blank" class="keywords">%s</a>" %n )
if name == 'main':

con = consumer("c1")

con2 = consumer("c2")

p = producer()

r = con.__next__()
r = con2.__next__()
n = 0
while n < 5:
    n +=1
    con.send(n)
    con2.send(n)
    print("\033[32;1m[producer]\033[0m is making baozi <a href="https://www.jb51.cc/tag/s/" target="_blank" class="keywords">%s</a>" %n )

def producer():

if name == 'main':
con = consumer("c1")
con2 = consumer("c2")
p = producer()

def producer():

r = con.__next__()
r = con2.__next__()
n = 0
while n < 5:
    n +=1
    con.send(n)
    con2.send(n)
    print("\033[32;1m[producer]\033[0m is making baozi <a href="https://www.jb51.cc/tag/s/" target="_blank" class="keywords">%s</a>" %n )

if name == 'main':
con = consumer("c1")
con2 = consumer("c2")
p = producer()

看楼上的例子，我问你这算不算做是协程呢？你说，我他妈哪知道呀，你前面说了一堆废话，但是并没告诉我协程的标准形态呀，我腚眼一想，觉得你说也对，那好，我们先给协程一个标准定义，即符合什么条件就能称之为协程：

基于上面这4点定义，我们刚才用yield实现的程并不能算是合格的线程，因为它有一点功能没实现，哪一点呢？

greenlet是一个用C实现的协程模块，相比与python自带的yield，它可以使你在任意函数之间随意切换，而不需把这个函数先声明为generator

from greenlet import greenlet
def test1():

print(12)

gr2.switch()

print(34)

gr2.switch()
def test2():

print(56)

gr1.switch()

print(78)
gr1 = greenlet(test1)

gr2 = greenlet(test2)

gr1.switch()

def test1():
print(12)
gr2.switch()
print(34)
gr2.switch()

def test2():
print(56)
gr1.switch()
print(78)

gr1 = greenlet(test1)
gr2 = greenlet(test2)
gr1.switch()

def test1():
print(12)
gr2.switch()
print(34)
gr2.switch()

def test2():
print(56)
gr1.switch()
print(78)

gr1 = greenlet(test1)
gr2 = greenlet(test2)
gr1.switch()

感觉确实用着比generator还简单了呢，但好像还没有解决一个问题，就是遇到IO操作，自动切换，对不对？

Gevent 是一个第三方库，可以轻松通过gevent实现并发同步或异步编程，在gevent中用到的主要模式是Greenlet,它是以C扩展模块形式接入Python的轻量级协程。 Greenlet全部运行在主程序操作系统进程的内部，但它们被协作式地调度。

def func1():

print('\033[31;1m李闯在跟海涛搞...\033[0m')

gevent.sleep(2)

print('\033[31;1m李闯又回去跟继续跟海涛搞...\033[0m')
def func2():

print('\033[32;1m李闯切换到了跟海龙搞...\033[0m')

gevent.sleep(1)

print('\033[32;1m李闯搞完了海涛，回来继续跟海龙搞...\033[0m')
gevent.joinall([

gevent.spawn(func1),gevent.spawn(func2),#gevent.spawn(func3),])

def func2():
print('\033[32;1m李闯切换到了跟海龙搞...\033[0m')
gevent.sleep(1)
print('\033[32;1m李闯搞完了海涛，回来继续跟海龙搞...\033[0m')

gevent.joinall([
gevent.spawn(func1),gevent.spawn(func2),#gevent.spawn(func3),])

def func2():
print('\033[32;1m李闯切换到了跟海龙搞...\033[0m')
gevent.sleep(1)
print('\033[32;1m李闯搞完了海涛，回来继续跟海龙搞...\033[0m')

gevent.joinall([
gevent.spawn(func1),gevent.spawn(func2),#gevent.spawn(func3),])

输出：

同步与异步的性能区别

def task(pid):

"""

Some non-deterministic task

"""

gevent.sleep(0.5)

print('Task %s done' % pid)
def synchronous():

for i in range(1,10):

task(i)
def asynchronous():

threads = [gevent.spawn(task,i) for i in range(10)]

gevent.joinall(threads)
print('Synchronous:')

synchronous()
print('Asynchronous:')

asynchronous()

def synchronous():
for i in range(1,10):
task(i)

def asynchronous():
threads = [gevent.spawn(task,i) for i in range(10)]
gevent.joinall(threads)

print('Synchronous:')
synchronous()

print('Asynchronous:')
asynchronous()

def synchronous():
for i in range(1,10):
task(i)

def asynchronous():
threads = [gevent.spawn(task,i) for i in range(10)]
gevent.joinall(threads)

print('Synchronous:')
synchronous()

print('Asynchronous:')
asynchronous()

上面程序的重要部分是将task函数封装到Greenlet内部线程的gevent.spawn。初始化的greenlet列表存放在数组threads中，此数组被传给gevent.joinall 函数，后者阻塞当前流程，并执行所有给定的greenlet。执行流程只会在所有greenlet执行完后才会继续向下走。　　

遇到IO阻塞时会自动切换任务

def f(url):

print('GET: %s' % url)

resp = urlopen(url)

data = resp.read()

print('%d bytes received from %s.' % (len(data),url))
gevent.joinall([

gevent.spawn(f,'https://www.python.org/'),gevent.spawn(f,'https://www.yahoo.com/'),'https://github.com/'),])

gevent.joinall([
gevent.spawn(f,'https://www.python.org/'),gevent.spawn(f,'https://www.yahoo.com/'),'https://github.com/'),])

通过gevent实现单线程下的多socket并发

server side

from gevent import socket,monkey

monkey.patch_all()
def server(port):

s = socket.socket()

s.bind(('0.0.0.0',port))

s.listen(500)

while True:

cli,addr = s.accept()

gevent.spawn(handle_request,cli)
def handle_request(conn):

try:

while True:

data = conn.recv(1024)

print("recv:",data)

conn.send(data)

if not data:

conn.shutdown(socket.SHUT_WR)
except Exception as  ex:
    print(ex)
finally:
    conn.close()
if name == 'main':

server(8001)

except Exception as  ex:
    print(ex)
finally:
    conn.close()

def server(port):
s = socket.socket()
s.bind(('0.0.0.0',port))
s.listen(500)
while True:
cli,addr = s.accept()
gevent.spawn(handle_request,cli)

def handle_request(conn):
try:
while True:
data = conn.recv(1024)
print("recv:",data)
conn.send(data)
if not data:
conn.shutdown(socket.SHUT_WR)

if name == 'main':
server(8001)

def server(port):
s = socket.socket()
s.bind(('0.0.0.0',port))
s.listen(500)
while True:
cli,addr = s.accept()
gevent.spawn(handle_request,cli)

def handle_request(conn):
try:
while True:
data = conn.recv(1024)
print("recv:",data)
conn.send(data)
if not data:
conn.shutdown(socket.SHUT_WR)

except Exception as  ex:
    print(ex)
finally:
    conn.close()

if name == 'main':
server(8001)

client side 　　

HOST = 'localhost'    # The remote host

PORT = 8001           # The same port as used by the server

s = socket.socket(socket.AF_INET,socket.SOCK_STREAM)

s.connect((HOST,PORT))

while True:

msg = bytes(input(">>:"),encoding="utf8")

s.sendall(msg)

data = s.recv(1024)
print(data)
print('Received',repr(data))
s.close()

print('Received',repr(data))

s.close()

print('Received',repr(data))

s.close()

client </span>=<span style="color: #000000;"&gt; socket<a href="https://www.jb51.cc/tag/so/" target="_blank" class="keywords">.so</a>cket()

client.connect((</span><span style="color: #800000;"&gt;"</span><span style="color: #800000;"&gt;localhost</span><span style="color: #800000;"&gt;"</span>,8001<span style="color: #000000;"&gt;))
count </span>=<span style="color: #000000;"&gt; 0
</span><span style="color: #0000ff;"&gt;while</span><span style="color: #000000;"&gt; True:
    </span><span style="color: #008000;"&gt;#</span><span style="color: #008000;"&gt;msg = input("&gt;>:").strip()</span>
    <span style="color: #008000;"&gt;#</span><span style="color: #008000;"&gt;if len(msg) == 0:continue</span>
    client.send( (<span style="color: #800000;"&gt;"</span><span style="color: #800000;"&gt;hello <a href="https://www.jb51.cc/tag/s/" target="_blank" class="keywords">%s</a></span><span style="color: #800000;"&gt;"</span> %count).encode(<span style="color: #800000;"&gt;"</span><span style="color: #800000;"&gt;utf-8</span><span style="color: #800000;"&gt;"</span><span style="color: #000000;"&gt;))

    data </span>= client.recv(1024<span style="color: #000000;"&gt;)

    </span><span style="color: #0000ff;"&gt;print</span>(<span style="color: #800000;"&gt;"</span><span style="color: #800000;"&gt;[<a href="https://www.jb51.cc/tag/s/" target="_blank" class="keywords">%s</a>]recv from server:</span><span style="color: #800000;"&gt;"</span> % threading.get_ident(),data.decode()) <span style="color: #008000;"&gt;#</span><span style="color: #008000;"&gt;结果</span>
    count +=1<span style="color: #000000;"&gt;
client.close()

for i in range(100):
t = threading.Thread(target=sock_conn)
t.start()

在UI编程中，常常要对鼠标点击进行相应，首先如何获得鼠标点击呢？方式一：创建一个线程，该线程一直循环检测是否有鼠标点击，那么这个方式有以下几个缺点：1. cpu资源浪费，可能鼠标点击的频率非常小，但是扫描线程还是会一直循环检测，这会造成很多的cpu资源浪费；如果扫描鼠标点击的接口是阻塞的呢？2. 如果是堵塞的，又会出现下面这样的问题，如果我们不但要扫描鼠标点击，还要扫描键盘是否按下，由于扫描鼠标时被堵塞了，那么可能永远不会去扫描键盘；3. 如果一个循环需要扫描的设备非常多，这又会引来响应时间的问题；所以，该方式是非常不好的。方式二：就是事件驱动模型目前大部分的UI编程都是事件驱动模型，如很多UI平台都会提供onClick()事件，这个事件就代表鼠标按下事件。事件驱动模型大体思路如下：1. 有一个事件（消息）队列；2. 鼠标按下时，往这个队列中增加一个点击事件（消息）；3. 有个循环，不断从队列取出事件，根据不同的事件，调用不同的函数，如onClick()、onKeyDown()等；4. 事件（消息）一般都各自保存各自的处理函数指针，这样，每个消息都有独立的处理函数；

事件驱动编程是一种编程范式，这里程序的执行流由外部事件来决定。它的特点是包含一个事件循环，当外部事件发生时使用回调机制来触发相应的处理。另外两种常见的编程范式是（单线程）同步以及多线程编程。

让我们用例子来比较和对比一下单线程、多线程以及事件驱动编程模型。下图展示了随着时间的推移，这三种模式下程序所做的工作。这个程序有3个任务需要完成，每个任务都在等待I/O操作时阻塞自身。阻塞在I/O操作上所花费的时间已经用灰色框标示出来了。

在单线程同步模型中，任务按照顺序执行。如果某个任务因为I/O而阻塞，其他所有的任务都必须等待，直到它完成之后它们才能依次执行。这种明确的执行顺序和串行化处理的行为是很容易推断得出的。如果任务之间并没有互相依赖的关系，但仍然需要互相等待的话这就使得程序不必要的降低了运行速度。

在多线程版本中，这3个任务分别在独立的线程中执行。这些线程由操作系统来管理，在多处理器系统上可以并行处理，或者在单处理器系统上交错执行。这使得当某个线程阻塞在某个资源的同时其他线程得以继续执行。与完成类似功能的同步程序相比，这种方式更有效率，但程序员必须写代码来保护共享资源，防止其被多个线程同时访问。多线程程序更加难以推断，因为这类程序不得不通过线程同步机制如锁、可重入函数、线程局部存储或者其他机制来处理线程安全问题，如果实现不当就会导致出现微妙且令人痛不欲生的bug。

在事件驱动版本的程序中，3个任务交错执行，但仍然在一个单独的线程控制中。当处理I/O或者其他昂贵的操作时，注册一个回调到事件循环中，然后当I/O操作完成时继续执行。回调描述了该如何处理某个事件。事件循环轮询所有的事件，当事件到来时将它们分配给等待处理事件的回调函数。这种方式让程序尽可能的得以执行而不需要用到额外的线程。事件驱动型程序比多线程程序更容易推断出行为，因为程序员不需要关心线程安全问题。

当我们面对如下的环境时，事件驱动模型通常是一个好的选择：

当应用程序需要在任务间共享可变的数据时，这也是一个不错的选择，因为这里不需要采用同步处理。

网络应用程序通常都有上述这些特点，这使得它们能够很好的契合事件驱动编程模型。

此处要提出一个问题，就是，上面的事件驱动模型中，只要一遇到IO就注册一个事件，然后主程序就可以继续干其它的事情了，只到io处理完毕后，继续恢复之前中断的任务，这本质上是怎么实现的呢？哈哈，下面我们就来一起揭开这神秘的面纱。。。。

Select\Poll\Epoll异步IO　

http://www.cnblogs.com/alex3714/p/4372426.html　

番外篇 http://www.cnblogs.com/alex3714/articles/5876749.html

import select
import socket
import sys
import queue

server = socket.socket()
server.setblocking(0)

server_addr = ('localhost',10000)

print('starting up on %s port %s' % server_addr)
server.bind(server_addr)

server.listen(5)

inputs = [server,] #自己也要监测呀,因为server本身也是个fd
outputs = []

message_queues = {}

while True:
print("waiting for next event...")

readable,writeable,exeptional </span>= select.select(inputs,outputs,inputs) <span style="color: #008000;"&gt;#</span><span style="color: #008000;"&gt;如果没有任何fd就绪,那程序就会一直阻塞<a href="https://www.jb51.cc/tag/zaizheli/" target="_blank" class="keywords">在这里</a></span>

<span style="color: #0000ff;"&gt;for</span> s <span style="color: #0000ff;"&gt;in</span> readable: <span style="color: #008000;"&gt;#</span><span style="color: #008000;"&gt;每个s就是<a href="https://www.jb51.cc/tag/yige/" target="_blank" class="keywords">一个</a>socket</span>

    <span style="color: #0000ff;"&gt;if</span> s <span style="color: #0000ff;"&gt;is</span> server: <span style="color: #008000;"&gt;#</span><span style="color: #008000;"&gt;别忘记,上面我们server自己也当做<a href="https://www.jb51.cc/tag/yige/" target="_blank" class="keywords">一个</a>fd放在了inputs列表里,传给了select,如果这个s是server,代表server这个fd就绪了,</span>
        <span style="color: #008000;"&gt;#</span><span style="color: #008000;"&gt;就是有活动了,什么情况下它才有活动? 当然 是有新连接进来的时候 呀</span>
        <span style="color: #008000;"&gt;#</span><span style="color: #008000;"&gt;新连接进来了,接受这个连接</span>
        conn,client_addr =<span style="color: #000000;"&gt; s.accept()
        </span><span style="color: #0000ff;"&gt;print</span>(<span style="color: #800000;"&gt;"</span><span style="color: #800000;"&gt;new connection from</span><span style="color: #800000;"&gt;"</span><span style="color: #000000;"&gt;,client_addr)
        conn.setblocking(0)
        inputs.append(conn) </span><span style="color: #008000;"&gt;#</span><span style="color: #008000;"&gt;为了不阻塞整个程序,我们不会立刻<a href="https://www.jb51.cc/tag/zaizheli/" target="_blank" class="keywords">在这里</a>开始接收客户端发来的数据,把它放到inputs里,下一次loop时,这个新连接</span>
        <span style="color: #008000;"&gt;#</span><span style="color: #008000;"&gt;就会被交给select去监听,如果这个连接的客户端发来了数据,那这个连接的fd在server端就会变成就续的,select就会把这个连接返回,返回到</span>
        <span style="color: #008000;"&gt;#</span><span style="color: #008000;"&gt;readable 列表里,然后你就可以loop readable列表,取出这个连接,开始接收数据了,下面就是这么干 的</span>

message_queues[conn] = queue.Queue() #接收到客户端的数据后,不立刻返回,暂存在队列里,以后发送

    <span style="color: #0000ff;"&gt;else</span>: <span style="color: #008000;"&gt;#</span><span style="color: #008000;"&gt;s不是server的话,那就只能是<a href="https://www.jb51.cc/tag/yige/" target="_blank" class="keywords">一个</a> 与客户端建立的连接的fd了</span>
        <span style="color: #008000;"&gt;#</span><span style="color: #008000;"&gt;客户端的数据过来了,<a href="https://www.jb51.cc/tag/zaizhe/" target="_blank" class="keywords">在这</a>接收</span>
        data = s.recv(1024<span style="color: #000000;"&gt;)
        </span><span style="color: #0000ff;"&gt;if</span><span style="color: #000000;"&gt; data:
            </span><span style="color: #0000ff;"&gt;print</span>(<span style="color: #800000;"&gt;"</span><span style="color: #800000;"&gt;收到来自[<a href="https://www.jb51.cc/tag/s/" target="_blank" class="keywords">%s</a>]的数据:</span><span style="color: #800000;"&gt;"</span> %<span style="color: #000000;"&gt; s.getpeername()[0],data)
            message_queues[s].put(data) </span><span style="color: #008000;"&gt;#</span><span style="color: #008000;"&gt;收到的数据先放到queue里,一会返回给客户端</span>
            <span style="color: #0000ff;"&gt;if</span> s <span style="color: #0000ff;"&gt;not</span>  <span style="color: #0000ff;"&gt;in</span><span style="color: #000000;"&gt; outputs:
                outputs.append(s) </span><span style="color: #008000;"&gt;#</span><span style="color: #008000;"&gt;为了不影响处理与其它客户端的连接,这里不立刻返回数据给客户端</span>


        <span style="color: #0000ff;"&gt;else</span>:<span style="color: #008000;"&gt;#</span><span style="color: #008000;"&gt;如果收不到data代表什么呢? 代表客户端断开了呀</span>
            <span style="color: #0000ff;"&gt;print</span>(<span style="color: #800000;"&gt;"</span><span style="color: #800000;"&gt;客户端断开了</span><span style="color: #800000;"&gt;"</span><span style="color: #000000;"&gt;,s)

            </span><span style="color: #0000ff;"&gt;if</span> s <span style="color: #0000ff;"&gt;in</span><span style="color: #000000;"&gt; outputs:
                outputs.remove(s) </span><span style="color: #008000;"&gt;#</span><span style="color: #008000;"&gt;清理已断开的连接</span>

inputs.remove(s) #清理已断开的连接

            <span style="color: #0000ff;"&gt;del</span> message_queues[s] <span style="color: #008000;"&gt;#</span><span style="color: #008000;"&gt;#清理已断开的连接</span>


<span style="color: #0000ff;"&gt;for</span> s <span style="color: #0000ff;"&gt;in</span><span style="color: #000000;"&gt; writeable:
    </span><span style="color: #0000ff;"&gt;try</span><span style="color: #000000;"&gt; :
        next_msg </span>=<span style="color: #000000;"&gt; message_queues[s].get_<a href="https://www.jb51.cc/tag/Now/" target="_blank" class="keywords">Now</a>ait()

    </span><span style="color: #0000ff;"&gt;except</span><span style="color: #000000;"&gt; queue.Empty:
        </span><span style="color: #0000ff;"&gt;print</span>(<span style="color: #800000;"&gt;"</span><span style="color: #800000;"&gt;client [<a href="https://www.jb51.cc/tag/s/" target="_blank" class="keywords">%s</a>]</span><span style="color: #800000;"&gt;"</span> <a href="https://www.jb51.cc/tag/s/" target="_blank" class="keywords">%s</a>.getpeername()[0],<span style="color: #800000;"&gt;"</span><span style="color: #800000;"&gt;queue is empty..</span><span style="color: #800000;"&gt;"</span><span style="color: #000000;"&gt;)
        outputs.remove(s)

    </span><span style="color: #0000ff;"&gt;else</span><span style="color: #000000;"&gt;:
        </span><span style="color: #0000ff;"&gt;print</span>(<span style="color: #800000;"&gt;"</span><span style="color: #800000;"&gt;sending msg to [<a href="https://www.jb51.cc/tag/s/" target="_blank" class="keywords">%s</a>]</span><span style="color: #800000;"&gt;"</span>%<span style="color: #000000;"&gt;s.getpeername()[0],next_msg)
        s.send(next_msg.upper())


</span><span style="color: #0000ff;"&gt;for</span> s <span style="color: #0000ff;"&gt;in</span><span style="color: #000000;"&gt; exeptional:
    </span><span style="color: #0000ff;"&gt;print</span>(<span style="color: #800000;"&gt;"</span><span style="color: #800000;"&gt;handling exception for </span><span style="color: #800000;"&gt;"</span><span style="color: #000000;"&gt;,s.getpeername())
    inputs.remove(s)
    </span><span style="color: #0000ff;"&gt;if</span> s <span style="color: #0000ff;"&gt;in</span><span style="color: #000000;"&gt; outputs:
        outputs.remove(s)
    s.close()

    </span><span style="color: #0000ff;"&gt;del</span> message_queues[s]</pre>

import socket
import sys

messages = [ b'This is the message. ',b'It will be sent ',b'in parts.',]
server_address = ('localhost',10000)

# Create a TCP/IP socket
socks = [ socket.socket(socket.AF_INET,socket.soCK_STREAM),socket.socket(socket.AF_INET,]

# Connect the socket to the port where the server is listening
print('connecting to %s port %s' % server_address)
for s in socks:
s.connect(server_address)

for message in messages:

</span><span style="color: #008000;"&gt;#</span><span style="color: #008000;"&gt; Send messages on both sockets</span>
<span style="color: #0000ff;"&gt;for</span> s <span style="color: #0000ff;"&gt;in</span><span style="color: #000000;"&gt; socks:
    </span><span style="color: #0000ff;"&gt;print</span>(<span style="color: #800000;"&gt;'</span><span style="color: #800000;"&gt;<a href="https://www.jb51.cc/tag/s/" target="_blank" class="keywords">%s</a>: sending "<a href="https://www.jb51.cc/tag/s/" target="_blank" class="keywords">%s</a>"</span><span style="color: #800000;"&gt;'</span> %<span style="color: #000000;"&gt; (s.getsockname(),message) )
    s.send(message)

</span><span style="color: #008000;"&gt;#</span><span style="color: #008000;"&gt; Read responses on both sockets</span>
<span style="color: #0000ff;"&gt;for</span> s <span style="color: #0000ff;"&gt;in</span><span style="color: #000000;"&gt; socks:
    data </span>= s.recv(1024<span style="color: #000000;"&gt;)
    </span><span style="color: #0000ff;"&gt;print</span>( <span style="color: #800000;"&gt;'</span><span style="color: #800000;"&gt;<a href="https://www.jb51.cc/tag/s/" target="_blank" class="keywords">%s</a>: received "<a href="https://www.jb51.cc/tag/s/" target="_blank" class="keywords">%s</a>"</span><span style="color: #800000;"&gt;'</span> %<span style="color: #000000;"&gt; (s.getsockname(),data) )
    </span><span style="color: #0000ff;"&gt;if</span> <span style="color: #0000ff;"&gt;not</span><span style="color: #000000;"&gt; data:
        </span><span style="color: #0000ff;"&gt;print</span>(sys.stderr,<span style="color: #800000;"&gt;'</span><span style="color: #800000;"&gt;closing socket</span><span style="color: #800000;"&gt;'</span>,s.getsockname() )</pre>

selectors模块

This module allows high-level and efficient I/O multiplexing,built upon the module primitives. Users are encouraged to use this module instead,unless they want precise control over the OS-level primitives used.

sel = selectors.DefaultSelector()
def accept(sock,mask):

conn,addr = sock.accept()  # Should be ready

print('accepted',conn,'from',addr)

conn.setblocking(False)

sel.register(conn,selectors.EVENT_READ,read)
def read(conn,mask):

data = conn.recv(1000)  # Should be ready

if data:

print('echoing',repr(data),'to',conn)

conn.send(data)  # Hope it won't block

else:

print('closing',conn)

sel.unregister(conn)

conn.close()
sock = socket.socket()

sock.bind(('localhost',10000))

sock.listen(100)

sock.setblocking(False)

sel.register(sock,accept)
while True:

events = sel.select()

for key,mask in events:

callback = key.data

callback(key.fileobj,mask)

def accept(sock,mask):
conn,addr = sock.accept() # Should be ready
print('accepted',conn,'from',addr)
conn.setblocking(False)
sel.register(conn,selectors.EVENT_READ,read)

def read(conn,mask):
data = conn.recv(1000) # Should be ready
if data:
print('echoing',repr(data),'to',conn)
conn.send(data) # Hope it won't block
else:
print('closing',conn)
sel.unregister(conn)
conn.close()

sock = socket.socket()
sock.bind(('localhost',10000))
sock.listen(100)
sock.setblocking(False)
sel.register(sock,accept)

while True:
events = sel.select()
for key,mask in events:
callback = key.data
callback(key.fileobj,mask)

def accept(sock,mask):
conn,addr = sock.accept() # Should be ready
print('accepted',conn,'from',addr)
conn.setblocking(False)
sel.register(conn,selectors.EVENT_READ,read)

sock = socket.socket()
sock.bind(('localhost',10000))
sock.listen(100)
sock.setblocking(False)
sel.register(sock,accept)

while True:
events = sel.select()
for key,mask in events:
callback = key.data
callback(key.fileobj,mask)

数据库操作与Paramiko模块

http://www.cnblogs.com/wupeiqi/articles/5095821.html

RabbitMQ队列　　

安装 http://www.rabbitmq.com/install-standalone-mac.html

安装python rabbitMQ module

pika or easy_install pika or 源码

https://pypi.python.org/pypi/pika

实现最简单的队列通信

send端

pika
connection = pika.BlockingConnection(pika.ConnectionParameters(

'localhost'))

channel = connection.channel()
声明queue
channel.queue_declare(queue='hello')
n RabbitMQ a message can never be sent directly to the queue,it always needs to go through an exchange.
channel.basic_publish(exchange='',routing_key='hello',body='Hello World!')

print(" [x] Sent 'Hello World!'")

connection.close()

connection = pika.BlockingConnection(pika.ConnectionParameters(
'localhost'))
channel = connection.channel()

channel.queue_declare(queue='hello')

channel.basic_publish(exchange='',routing_key='hello',body='Hello World!')
print(" [x] Sent 'Hello World!'")
connection.close()

connection = pika.BlockingConnection(pika.ConnectionParameters(
'localhost'))
channel = connection.channel()

channel.queue_declare(queue='hello')

channel.basic_publish(exchange='',routing_key='hello',body='Hello World!')
print(" [x] Sent 'Hello World!'")
connection.close()

receive端

pika
connection = pika.BlockingConnection(pika.ConnectionParameters(

'localhost'))

channel = connection.channel()
You may ask why we declare the queue again ? we have already declared it in our prevIoUs code.
We Could avoid that if we were sure that the queue already exists. For example if send.py program
was run before. But we're not yet sure which program to run first. In such cases it's a good
practice to repeat declaring the queue in both programs.
channel.queue_declare(queue='hello')
def callback(ch,method,properties,body):

print(" [x] Received %r" % body)
channel.basic_consume(callback,queue='hello',no_ack=True)
print(' [*] Waiting for messages. To exit press CTRL+C')

channel.start_consuming()

connection = pika.BlockingConnection(pika.ConnectionParameters(
'localhost'))
channel = connection.channel()

channel.queue_declare(queue='hello')

def callback(ch,method,properties,body):
print(" [x] Received %r" % body)

channel.basic_consume(callback,queue='hello',no_ack=True)

print(' [*] Waiting for messages. To exit press CTRL+C')
channel.start_consuming()

connection = pika.BlockingConnection(pika.ConnectionParameters(
'localhost'))
channel = connection.channel()

channel.queue_declare(queue='hello')

def callback(ch,method,properties,body):
print(" [x] Received %r" % body)

channel.basic_consume(callback,queue='hello',no_ack=True)

print(' [*] Waiting for messages. To exit press CTRL+C')
channel.start_consuming()

远程连接rabbitmq server的话，需要配置权限噢

首先在rabbitmq server上创建一个用户

同时还要配置权限，允许从外面访问

The name of the virtual host to which to grant the user access,defaulting to

The name of the user to grant access to the specified virtual host.

A regular expression matching resource names for which the user is granted configure permissions.

A regular expression matching resource names for which the user is granted write permissions.

A regular expression matching resource names for which the user is granted read permissions.

客户端连接的时候需要配置认证参数

pika.PlainCredentials('alex','alex3714')
connection = pika.BlockingConnection(pika.ConnectionParameters(

'10.211.55.5',5672,'/',credentials))

channel = connection.channel()

connection = pika.BlockingConnection(pika.ConnectionParameters(
'10.211.55.5',5672,'/',credentials))
channel = connection.channel()

在这种模式下，RabbitMQ会默认把p发的消息依次分发给各个消费者(c),跟负载均衡差不多

消息提供者代码

pika
import time
connection = pika.BlockingConnection(pika.ConnectionParameters(
    'localhost'))
channel = connection.channel()
声明queue
channel.queue_declare(queue='task_queue')
n RabbitMQ a message can never be sent directly to the queue,it always needs to go through an exchange.
import sys
message = ' '.join(sys.argv[1:]) or "Hello World! %s" % time.time()

channel.basic_publish(exchange='',routing_key='task_queue',body=message,properties=pika.BasicProperties(

delivery_mode=2,# make message persistent

)

)

print(" [x] Sent %r" % message)

connection.close()

channel.queue_declare(queue='task_queue')

import sys

message = ' '.join(sys.argv[1:]) or "Hello World! %s" % time.time()
channel.basic_publish(exchange='',routing_key='task_queue',body=message,properties=pika.BasicProperties(
delivery_mode=2,# make message persistent
)
)
print(" [x] Sent %r" % message)
connection.close()

channel.queue_declare(queue='task_queue')

import sys

消费者代码

import pika,time
connection = pika.BlockingConnection(pika.ConnectionParameters(

'localhost'))

channel = connection.channel()
def callback(ch,body):

print(" [x] Received %r" % body)

time.sleep(20)

print(" [x] Done")

print("method.delivery_tag",method.delivery_tag)

ch.basic_ack(delivery_tag=method.delivery_tag)
channel.basic_consume(callback,queue='task_queue',no_ack=True

)
print(' [*] Waiting for messages. To exit press CTRL+C')

channel.start_consuming()

connection = pika.BlockingConnection(pika.ConnectionParameters(
'localhost'))
channel = connection.channel()

def callback(ch,body):
print(" [x] Received %r" % body)
time.sleep(20)
print(" [x] Done")
print("method.delivery_tag",method.delivery_tag)
ch.basic_ack(delivery_tag=method.delivery_tag)

channel.basic_consume(callback,queue='task_queue',no_ack=True
)

print(' [*] Waiting for messages. To exit press CTRL+C')
channel.start_consuming()

connection = pika.BlockingConnection(pika.ConnectionParameters(
'localhost'))
channel = connection.channel()

def callback(ch,body):
print(" [x] Received %r" % body)
time.sleep(20)
print(" [x] Done")
print("method.delivery_tag",method.delivery_tag)
ch.basic_ack(delivery_tag=method.delivery_tag)

channel.basic_consume(callback,queue='task_queue',no_ack=True
)

print(' [*] Waiting for messages. To exit press CTRL+C')
channel.start_consuming()

此时，先启动消息生产者，然后再分别启动3个消费者，通过生产者多发送几条消息，你会发现，这几条消息会被依次分配到各个消费者身上　　

Doing a task can take a few seconds. You may wonder what happens if one of the consumers starts a long task and dies with it only partly done. With our current code once RabbitMQ delivers message to the customer it immediately removes it from memory. In this case,if you kill a worker we will lose the message it was just processing. We'll also lose all the messages that were dispatched to this particular worker but were not yet handled.

But we don't want to lose any tasks. If a worker dies,we'd like the task to be delivered to another worker.

In order to make sure a message is never lost,RabbitMQ supports message ackNowledgments. An ack(Nowledgement) is sent back from the consumer to tell RabbitMQ that a particular message had been received,processed and that RabbitMQ is free to delete it.

If a consumer dies (its channel is closed,connection is closed,or TCP connection is lost) without sending an ack,RabbitMQ will understand that a message wasn't processed fully and will re-queue it. If there are other consumers online at the same time,it will then quickly redeliver it to another consumer. That way you can be sure that no message is lost,even if the workers occasionally die.

There aren't any message timeouts; RabbitMQ will redeliver the message when the consumer dies. It's fine even if processing a message takes a very,very long time.

Message ackNowledgments are turned on by default. In prevIoUs examples we explicitly turned them off via the Nowledgment from the worker,once we're done with a task.

%r" % (body,)
    time.sleep( body.count('.') )
    print " [x] Done"
    ch.basic_ack(delivery_tag = method.delivery_tag)
channel.basic_consume(callback,queue='hello')

channel.basic_consume(callback,queue='hello')

　　Using this code we can be sure that even if you kill a worker using CTRL+C while it was processing a message,nothing will be lost. Soon after the worker dies all unackNowledged messages will be redelivered

We have learned how to make sure that even if the consumer dies,the task isn't lost(by default,if wanna disable use no_ack=True). But our tasks will still be lost if RabbitMQ server stops.

When RabbitMQ quits or crashes it will forget the queues and messages unless you tell it not to. Two things are required to make sure that messages aren't lost: we need to mark both the queue and messages as durable.

First,we need to make sure that RabbitMQ will never lose our queue. In order to do so,we need to declare it as durable:

Although this command is correct by itself,it won't work in our setup. That's because we've already defined a queue called redefine an existing queue with different parameters and will return an error to any program that tries to do that. But there is a quick workaround - let's declare a queue with different name,for example

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Python之路,Day9 - 异步IO\数据库\队列\缓存

概述

协程

print(data)

Select\Poll\Epoll异步IO

数据库操作与Paramiko模块

RabbitMQ队列

声明queue

n RabbitMQ a message can never be sent directly to the queue,it always needs to go through an exchange.

You may ask why we declare the queue again ? we have already declared it in our prevIoUs code.

We Could avoid that if we were sure that the queue already exists. For example if send.py program

was run before. But we're not yet sure which program to run first. In such cases it's a good

practice to repeat declaring the queue in both programs.

声明queue

n RabbitMQ a message can never be sent directly to the queue,it always needs to go through an exchange.

分类汇总

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