Python-day-9- RabbitMQ队列

RabbitMQ队列　　

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

安装python rabbitMQ module

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

#!/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

#_*_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

import pika

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!"
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

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(body.count(b'.'))
    print(" [x] Done")
    ch.basic_ack(delivery_tag = method.delivery_tag)

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

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.

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:

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:

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.

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。

channel.basic_qos(prefetch_count=1)

带消息持久化+公平分发的完整代码

生产者端

#!/usr/bin/env python
import pika
import sys

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

channel.queue_declare(queue='task_queue', durable=True)

message = ' '.join(sys.argv[1:]) or "Hello World!"
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()

消费者端

#!/usr/bin/env python
import pika
import time

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

channel.queue_declare(queue='task_queue', durable=True)
print(' [*] Waiting for messages. To exit press CTRL+C')

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

channel.basic_qos(prefetch_count=1)
channel.basic_consume(callback,
                      queue='task_queue')

channel.start_consuming()

Publish\Subscribe(消息发布\订阅)　

之前的例子都基本都是1对1的消息发送和接收，即消息只能发送到指定的queue里，但有些时候你想让你的消息被所有的Queue收到，类似广播的效果，这时候就要用到exchange了，

An exchange is a very simple thing. On one side it receives messages from producers and the other side it pushes them to queues. The exchange must know exactly what to do with a message it receives. Should it be appended to a particular queue? Should it be appended to many queues? Or should it get discarded. The rules for that are defined by the exchange type.

Exchange在定义的时候是有类型的，以决定到底是哪些Queue符合条件，可以接收消息

fanout: 所有bind到此exchange的queue都可以接收消息
direct: 通过routingKey和exchange决定的那个唯一的queue可以接收消息
topic:所有符合routingKey(此时可以是一个表达式)的routingKey所bind的queue可以接收消息

　　 表达式符号说明：#代表一个或多个字符，*代表任何字符
      例：#.a会匹配a.a，aa.a，aaa.a等
          *.a会匹配a.a，b.a，c.a等
     注：使用RoutingKey为#，Exchange Type为topic的时候相当于使用fanout　

headers: 通过headers 来决定把消息发给哪些queue

消息publisher

import pika
import sys

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

channel.exchange_declare(exchange='logs',
                         type='fanout')

message = ' '.join(sys.argv[1:]) or "info: Hello World!"
channel.basic_publish(exchange='logs',
                      routing_key='',
                      body=message)
print(" [x] Sent %r" % message)
connection.close()

消息subscriber

#_*_coding:utf-8_*_
__author__ = 'liudong'
import pika

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

channel.exchange_declare(exchange='logs',
                         type='fanout')

result = channel.queue_declare(exclusive=True) #不指定queue名字,rabbit会随机分配一个名字,exclusive=True会在使用此queue的消费者断开后,自动将queue删除
queue_name = result.method.queue

channel.queue_bind(exchange='logs',
                   queue=queue_name)

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

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

channel.basic_consume(callback,
                      queue=queue_name,
                      no_ack=True)

channel.start_consuming()

有选择的接收消息(exchange type=direct)　

RabbitMQ还支持根据关键字发送，即：队列绑定关键字，发送者将数据根据关键字发送到消息exchange，exchange根据 关键字 判定应该将数据发送至指定队列。

publisher

import pika
import sys

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

channel.exchange_declare(exchange='direct_logs',
                         type='direct')

severity = sys.argv[1] if len(sys.argv) > 1 else 'info'
message = ' '.join(sys.argv[2:]) or 'Hello World!'
channel.basic_publish(exchange='direct_logs',
                      routing_key=severity,
                      body=message)
print(" [x] Sent %r:%r" % (severity, message))
connection.close()

subscriber　

import pika
import sys

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

channel.exchange_declare(exchange='direct_logs',
                         type='direct')

result = channel.queue_declare(exclusive=True)
queue_name = result.method.queue

severities = sys.argv[1:]
if not severities:
    sys.stderr.write("Usage: %s [info] [warning] [error]\n" % sys.argv[0])
    sys.exit(1)

for severity in severities:
    channel.queue_bind(exchange='direct_logs',
                       queue=queue_name,
                       routing_key=severity)

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

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

channel.basic_consume(callback,
                      queue=queue_name,
                      no_ack=True)

channel.start_consuming()

更细致的消息过滤

Although using the direct exchange improved our system, it still has limitations - it can't do routing based on multiple criteria.

In our logging system we might want to subscribe to not only logs based on severity, but also based on the source which emitted the log. You might know this concept from the syslog unix tool, which routes logs based on both severity (info/warn/crit...) and facility (auth/cron/kern...).

That would give us a lot of flexibility - we may want to listen to just critical errors coming from 'cron' but also all logs from 'kern'.

publisher

import pika
import sys

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

channel.exchange_declare(exchange='topic_logs',
                         type='topic')

routing_key = sys.argv[1] if len(sys.argv) > 1 else 'anonymous.info'
message = ' '.join(sys.argv[2:]) or 'Hello World!'
channel.basic_publish(exchange='topic_logs',
                      routing_key=routing_key,
                      body=message)
print(" [x] Sent %r:%r" % (routing_key, message))
connection.close()

To receive all the logs run:

python receive_logs_topic.py "#"

To receive all logs from the facility "kern":

python receive_logs_topic.py "kern.*"

Or if you want to hear only about "critical" logs:

python receive_logs_topic.py "*.critical"

You can create multiple bindings:

python receive_logs_topic.py "kern.*" "*.critical"

And to emit a log with a routing key "kern.critical" type:

python emit_log_topic.py "kern.critical" "A critical kernel error"

Remote procedure call (RPC)

To illustrate how an RPC service could be used we're going to create a simple client class. It's going to expose a method named call which sends an RPC request and blocks until the answer is received:

fibonacci_rpc = FibonacciRpcClient()
result = fibonacci_rpc.call(4)
print("fib(4) is %r" % result)

RPC server

#_*_coding:utf-8_*_
__author__ = 'liudong'
import pika
import time
connection = pika.BlockingConnection(pika.ConnectionParameters(
        host='localhost'))

channel = connection.channel()

channel.queue_declare(queue='rpc_queue')

def fib(n):
    if n == 0:
        return 0
    elif n == 1:
        return 1
    else:
        return fib(n-1) + fib(n-2)

def on_request(ch, method, props, body):
    n = int(body)

    print(" [.] fib(%s)" % n)
    response = fib(n)

    ch.basic_publish(exchange='',
                     routing_key=props.reply_to,
                     properties=pika.BasicProperties(correlation_id = \
                                                         props.correlation_id),
                     body=str(response))
    ch.basic_ack(delivery_tag = method.delivery_tag)

channel.basic_qos(prefetch_count=1)
channel.basic_consume(on_request, queue='rpc_queue')

print(" [x] Awaiting RPC requests")
channel.start_consuming()

RPC client

import pika
import uuid

class FibonacciRpcClient(object):
    def __init__(self):
        self.connection = pika.BlockingConnection(pika.ConnectionParameters(
                host='localhost'))

        self.channel = self.connection.channel()

        result = self.channel.queue_declare(exclusive=True)
        self.callback_queue = result.method.queue

        self.channel.basic_consume(self.on_response, no_ack=True,
                                   queue=self.callback_queue)

    def on_response(self, ch, method, props, body):
        if self.corr_id == props.correlation_id:
            self.response = body

    def call(self, n):
        self.response = None
        self.corr_id = str(uuid.uuid4())
        self.channel.basic_publish(exchange='',
                                   routing_key='rpc_queue',
                                   properties=pika.BasicProperties(
                                         reply_to = self.callback_queue,
                                         correlation_id = self.corr_id,
                                         ),
                                   body=str(n))
        while self.response is None:
            self.connection.process_data_events()
        return int(self.response)

fibonacci_rpc = FibonacciRpcClient()

print(" [x] Requesting fib(30)")
response = fibonacci_rpc.call(30)
print(" [.] Got %r" % response)

Memcached & Redis使用

http://www.cnblogs.com/wupeiqi/articles/5132791.html

Twsited异步网络框架

Twisted是一个事件驱动的网络框架，其中包含了诸多功能，例如：网络协议、线程、数据库管理、网络操作、电子邮件等。

事件驱动

简而言之，事件驱动分为二个部分：第一，注册事件；第二，触发事件。

自定义事件驱动框架，命名为：“弑君者”：

#!/usr/bin/env python
# -*- coding:utf-8 -*-

# event_drive.py

event_list = []

def run():
    for event in event_list:
        obj = event()
        obj.execute()

class BaseHandler(object):
    """
    用户必须继承该类，从而规范所有类的方法（类似于接口的功能）
    """
    def execute(self):
        raise Exception('you must overwrite execute')

最牛逼的事件驱动框架

程序员使用“弑君者框架”：

#!/usr/bin/env python
# -*- coding:utf-8 -*-

from source import event_drive

class MyHandler(event_drive.BaseHandler):

    def execute(self):
        print 'event-drive execute MyHandler'

event_drive.event_list.append(MyHandler)
event_drive.run()

Protocols

Protocols描述了如何以异步的方式处理网络中的事件。HTTP、DNS以及IMAP是应用层协议中的例子。Protocols实现了IProtocol接口，它包含如下的方法：

makeConnection               在transport对象和服务器之间建立一条连接
connectionMade               连接建立起来后调用
dataReceived                 接收数据时调用
connectionLost               关闭连接时调用

Transports

Transports代表网络中两个通信结点之间的连接。Transports负责描述连接的细节，比如连接是面向流式的还是面向数据报的，流控以及可靠性。TCP、UDP和Unix套接字可作为transports的例子。它们被设计为“满足最小功能单元，同时具有最大程度的可复用性”，而且从协议实现中分离出来，这让许多协议可以采用相同类型的传输。Transports实现了ITransports接口，它包含如下的方法：

write                   以非阻塞的方式按顺序依次将数据写到物理连接上
writeSequence           将一个字符串列表写到物理连接上
loseConnection          将所有挂起的数据写入，然后关闭连接
getPeer                 取得连接中对端的地址信息
getHost                 取得连接中本端的地址信息

将transports从协议中分离出来也使得对这两个层次的测试变得更加简单。可以通过简单地写入一个字符串来模拟传输，用这种方式来检查。

EchoServer

from twisted.internet import protocol
from twisted.internet import reactor

class Echo(protocol.Protocol):
    def dataReceived(self, data):
        self.transport.write(data)

def main():
    factory = protocol.ServerFactory()
    factory.protocol = Echo

    reactor.listenTCP(1234,factory)
    reactor.run()

if __name__ == '__main__':
    main()

EchoClient

from twisted.internet import reactor, protocol

# a client protocol

class EchoClient(protocol.Protocol):
    """Once connected, send a message, then print the result."""

    def connectionMade(self):
        self.transport.write("hello alex!")

    def dataReceived(self, data):
        "As soon as any data is received, write it back."
        print "Server said:", data
        self.transport.loseConnection()

    def connectionLost(self, reason):
        print "connection lost"

class EchoFactory(protocol.ClientFactory):
    protocol = EchoClient

    def clientConnectionFailed(self, connector, reason):
        print "Connection failed - goodbye!"
        reactor.stop()

    def clientConnectionLost(self, connector, reason):
        print "Connection lost - goodbye!"
        reactor.stop()

# this connects the protocol to a server running on port 8000
def main():
    f = EchoFactory()
    reactor.connectTCP("localhost", 1234, f)
    reactor.run()

# this only runs if the module was *not* imported
if __name__ == '__main__':
    main()

运行服务器端脚本将启动一个TCP服务器，监听端口1234上的连接。服务器采用的是Echo协议，数据经TCP transport对象写出。运行客户端脚本将对服务器发起一个TCP连接，回显服务器端的回应然后终止连接并停止reactor事件循环。这里的Factory用来对连接的双方生成protocol对象实例。两端的通信是异步的，connectTCP负责注册回调函数到reactor事件循环中，当socket上有数据可读时通知回调处理。

一个传送文件的例子 

server side 

#_*_coding:utf-8_*_
# This is the Twisted Fast Poetry Server, version 1.0

import optparse, os

from twisted.internet.protocol import ServerFactory, Protocol

def parse_args():
    usage = """usage: %prog [options] poetry-file

This is the Fast Poetry Server, Twisted edition.
Run it like this:

  python fastpoetry.py <path-to-poetry-file>

If you are in the base directory of the twisted-intro package,
you could run it like this:

  python twisted-server-1/fastpoetry.py poetry/ecstasy.txt

to serve up John Donne's Ecstasy, which I know you want to do.
"""

    parser = optparse.OptionParser(usage)

    help = "The port to listen on. Default to a random available port."
    parser.add_option('--port', type='int', help=help)

    help = "The interface to listen on. Default is localhost."
    parser.add_option('--iface', help=help, default='localhost')

    options, args = parser.parse_args()
    print("--arg:",options,args)

    if len(args) != 1:
        parser.error('Provide exactly one poetry file.')

    poetry_file = args[0]

    if not os.path.exists(args[0]):
        parser.error('No such file: %s' % poetry_file)

    return options, poetry_file

class PoetryProtocol(Protocol):

    def connectionMade(self):
        self.transport.write(self.factory.poem)
        self.transport.loseConnection()

class PoetryFactory(ServerFactory):

    protocol = PoetryProtocol

    def __init__(self, poem):
        self.poem = poem

def main():
    options, poetry_file = parse_args()

    poem = open(poetry_file).read()

    factory = PoetryFactory(poem)

    from twisted.internet import reactor

    port = reactor.listenTCP(options.port or 9000, factory,
                             interface=options.iface)

    print 'Serving %s on %s.' % (poetry_file, port.getHost())

    reactor.run()

if __name__ == '__main__':
    main()

client side

# This is the Twisted Get Poetry Now! client, version 3.0.

# NOTE: This should not be used as the basis for production code.

import optparse

from twisted.internet.protocol import Protocol, ClientFactory

def parse_args():
    usage = """usage: %prog [options] [hostname]:port ...

This is the Get Poetry Now! client, Twisted version 3.0
Run it like this:

  python get-poetry-1.py port1 port2 port3 ...
"""

    parser = optparse.OptionParser(usage)

    _, addresses = parser.parse_args()

    if not addresses:
        print parser.format_help()
        parser.exit()

    def parse_address(addr):
        if ':' not in addr:
            host = '127.0.0.1'
            port = addr
        else:
            host, port = addr.split(':', 1)

        if not port.isdigit():
            parser.error('Ports must be integers.')

        return host, int(port)

    return map(parse_address, addresses)

class PoetryProtocol(Protocol):

    poem = ''

    def dataReceived(self, data):
        self.poem += data

    def connectionLost(self, reason):
        self.poemReceived(self.poem)

    def poemReceived(self, poem):
        self.factory.poem_finished(poem)

class PoetryClientFactory(ClientFactory):

    protocol = PoetryProtocol

    def __init__(self, callback):
        self.callback = callback

    def poem_finished(self, poem):
        self.callback(poem)

def get_poetry(host, port, callback):
    """
    Download a poem from the given host and port and invoke

      callback(poem)

    when the poem is complete.
    """
    from twisted.internet import reactor
    factory = PoetryClientFactory(callback)
    reactor.connectTCP(host, port, factory)

def poetry_main():
    addresses = parse_args()

    from twisted.internet import reactor

    poems = []

    def got_poem(poem):
        poems.append(poem)
        if len(poems) == len(addresses):
            reactor.stop()

    for address in addresses:
        host, port = address
        get_poetry(host, port, got_poem)

    reactor.run()

    for poem in poems:
        print poem

if __name__ == '__main__':
    poetry_main()

Twisted深入

http://krondo.com/an-introduction-to-asynchronous-programming-and-twisted/

http://blog.csdn.net/hanhuili/article/details/9389433