本节内容
- Gevent协程
- Select\Poll\Epoll异步IO与事件驱动
- Python连接Mysql数据库操作
- RabbitMQ队列
- Redis\Memcached缓存
- Paramiko SSH
- Twsited网络框架
引子
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：
- 无需线程上下文切换的开销
- 无需原子操作锁定及同步的开销
- "原子操作(atomic operation)是不需要synchronized"，所谓原子操作是指不会被线程调度机制打断的操作；这种操作一旦开始，就一直运行到结束，中间不会有任何 context switch （切换到另一个线程）。原子操作可以是一个步骤，也可以是多个操作步骤，但是其顺序是不可以被打乱，或者切割掉只执行部分。视作整体是原子性的核心。
- 方便切换控制流，简化编程模型
- 高并发+高扩展性+低成本：一个CPU支持上万的协程都不是问题。所以很适合用于高并发处理。
缺点：
- 无法利用多核资源：协程的本质是个单线程,它不能同时将 单个CPU 的多个核用上,协程需要和进程配合才能运行在多CPU上.当然我们日常所编写的绝大部分应用都没有这个必要，除非是cpu密集型应用。
- 进行阻塞（Blocking）操作（如IO时）会阻塞掉整个程序
使用yield实现协程操作例子
| 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | import time import queue def consumer(name): print ( "--->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 %s" % 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：
- 必须在只有一个单线程里实现并发
- 修改共享数据不需加锁
- 用户程序里自己保存多个控制流的上下文栈
- 一个协程遇到IO操作自动切换到其它协程
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？
Gevent
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。
| 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | import gevent def func1(): print ( '\033[31;1m小闯在跟小涛搞...\033[0m' ) gevent.sleep( 2 ) print ( '\033[31;1m小闯又回去跟继续跟小涛搞...\033[0m' ) def func2(): print ( '\033[32;1m小闯切换到了跟小龙搞...\033[0m' ) gevent.sleep( 1 ) print ( '\033[32;1m小闯搞完了小涛，回来继续跟小龙搞...\033[0m' ) gevent.joinall([ gevent.spawn(func1), gevent.spawn(func2), #gevent.spawn(func3), ]) |
输出：
小闯在跟小涛搞...
小闯切换到了跟小龙搞...小闯搞完了小涛，回来继续跟小龙搞...小闯又回去跟继续跟小涛搞...
同步与异步的性能区别
| 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | import gevent 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() |
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
| 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | from gevent import monkey; monkey.patch_all() import gevent from urllib.request import urlopen 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/' ), gevent.spawn(f, 'https://github.com/' ), ]) |
通过gevent实现单线程下的多socket并发
server side
| 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | import sys import socket import time import gevent 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 ) |
client side
| 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | import socket 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() |
import socketimport threadingdef sock_conn(): client = socket.socket() client.connect(("localhost",8001)) count = 0 while True: #msg = input(">>:").strip() #if len(msg) == 0:continue client.send( ("hello %s" %count).encode("utf-8")) data = client.recv(1024) print("[%s]recv from server:" % threading.get_ident(),data.decode()) #结果 count +=1 client.close()for i in range(100): t = threading.Thread(target=sock_conn) t.start()
论事件驱动与异步IO
看图说话讲事件驱动模型
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：
- 程序中有许多任务，而且…
- 任务之间高度独立（因此它们不需要互相通信，或者等待彼此）而且…
- 在等待事件到来时，某些任务会阻塞。
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？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
select 多并发socket 例子
#_*_coding:utf-8_*___author__ = 'Alex Li'import selectimport socketimport sysimport queueserver = 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fdoutputs = []message_queues = {}while True: print("waiting for next event...") readable, writeable, exeptional = select.select(inputs,outputs,inputs) #如果没有任何fd就绪,那程序就会一直阻塞在这里 for s in readable: #每个s就是一个socket if s is server: #别忘记,上面我们server自己也当做一个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, #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 #新连接进来了,接受这个连接 conn, client_addr = s.accept() print("new connection from",client_addr) conn.setblocking(0) inputs.append(conn) #为了不阻塞整个程序,我们不会立刻在这里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 #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 #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 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 else: #s不是server的话,那就只能是一个 与客户端建立的连接的fd了 #客户端的数据过来了,在这接收 data = s.recv(1024) if data: print("收到来自[%s]的数据:" % s.getpeername()[0], data) message_queues[s].put(data) #收到的数据先放到queue里,一会返回给客户端 if s not in outputs: outputs.append(s) #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 else:#如果收不到data代表什么呢? 代表客户端断开了呀 print("客户端断开了",s) if s in outputs: outputs.remove(s) #清理已断开的连接 inputs.remove(s) #清理已断开的连接 del message_queues[s] ##清理已断开的连接 for s in writeable: try : next_msg = message_queues[s].get_nowait() except queue.Empty: print("client [%s]" %s.getpeername()[0], "queue is empty..") outputs.remove(s) else: print("sending msg to [%s]"%s.getpeername()[0], next_msg) s.send(next_msg.upper()) for s in exeptional: print("handling exception for ",s.getpeername()) inputs.remove(s) if s in outputs: outputs.remove(s) s.close() del message_queues[s] #_*_coding:utf-8_*___author__ = 'Alex Li'import socketimport sysmessages = [ b'This is the message. ', b'It will be sent ', b'in parts.', ]server_address = ('localhost', 10000)# Create a TCP/IP socketsocks = [ socket.socket(socket.AF_INET, socket.SOCK_STREAM), socket.socket(socket.AF_INET, socket.SOCK_STREAM), ]# Connect the socket to the port where the server is listeningprint('connecting to %s port %s' % server_address)for s in socks: s.connect(server_address)for message in messages: # Send messages on both sockets for s in socks: print('%s: sending "%s"' % (s.getsockname(), message) ) s.send(message) # Read responses on both sockets for s in socks: data = s.recv(1024) print( '%s: received "%s"' % (s.getsockname(), data) ) if not data: print(sys.stderr, 'closing socket', s.getsockname() )
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.
| 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | import selectors import socket 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, selectors.EVENT_READ, 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
| 1 2 3 4 5 6 7 | pip install 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
| 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | #!/usr/bin/env python import 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() |
receive端
| 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | #_*_coding:utf-8_*_ __author__ = 'Alex Li' import 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() |
Work Queues
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
| 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | import 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() |
消费者代码
| 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | #_*_coding:utf-8_*_ import pika, time connection = pika.BlockingConnection(pika.ConnectionParameters( 'localhost' )) channel = connection.channel() def callback(ch, method, properties, 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 no_ack=True flag. It's time to remove this flag and send a proper acknowledgment from the worker, once we're done with a task.
| 1 2 3 4 5 6 7 8 | def callback(ch, method, properties, body): print " [x] Received %r" % (body,) time.sleep( body.count( '.' ) ) print " [x] Done" ch.basic_ack(delivery_tag = method.delivery_tag) 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
消息持久化
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:
| 1 | channel.queue_declare(queue = 'hello' , durable = True ) |
Although this command is correct by itself, it won't work in our setup. That's because we've already defined a queue called hello which is not durable. RabbitMQ doesn't allow you to 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 exampletask_queue:
| 1 | channel.queue_declare(queue = 'task_queue' , durable = True ) |
This queue_declare change needs to be applied to both the producer and consumer code.
At that point we're sure that the task_queue queue won't be lost even if RabbitMQ restarts. Now we need to mark our messages as persistent - by supplying a delivery_mode property with a value 2.
| 1 2 3 4 5 6 | channel.basic_publish(exchange = '', routing_key = "task_queue" , body = message, properties = pika.BasicProperties( delivery_mode = 2 , # make message persistent )) |
消息公平分发
如果Rabbit只管按顺序把消息发到各个消费者身上，不考虑消费者负载的话，很可能出现，一个机器配置不高的消费者那里堆积了很多消息处理不完，同时配置高的消费者却一直很轻松。为解决此问题，可以在各个消费者端，配置perfetch=1,意思就是告诉RabbitMQ在我这个消费者当前消息还没处理完的时候就不要再给我发新消息了。
| 1 | channel.basic_qos(prefetch_count = 1 ) |
带消息持久化+公平分发的完整代码