Reference: https://www.rabbitmq.com/tutorials/tutorial-three-python.html

"Hello World!"

The simplest thing that doessomething

Work queues

Distributing tasks among workers (the competing consumers pattern)

Publish/Subscribe

Sending messages to many consumers at once

Routing

Receiving messages selectively

Topics

Receiving messages based on a pattern (topics)

RPC

Request/reply ("RPC") patternexample

Publish/Subscribe

(using the pika 0.10.0 Python client)

Prerequisites

This tutorial assumes RabbitMQ is installedand running on localhost on standard port (5672). In case you use a different host, port or credentials, connections settings would require adjusting.

Where to get help

If you're having trouble going through this tutorial you can contact us through the mailing list.

In the previous tutorial we created a work queue. The assumption behind a work queue is that each task is delivered to exactly one worker. In this part we'll do something completely different -- we'll deliver a message to multiple consumers. This pattern is known as "publish/subscribe".

To illustrate the pattern, we're going to build a simple logging system. It will consist of two programs -- the first will emit log messages and the second will receive and print them.

In our logging system every running copy of the receiver program will get the messages. That way we'll be able to run one receiver and direct the logs to disk; and at the same time we'll be able to run another receiver and see the logs on the screen.

Essentially, published log messages are going to be broadcast to all the receivers.

Exchanges

In previous parts of the tutorial we sent and received messages to and from a queue. Now it's time to introduce the full messaging model in Rabbit.

Let's quickly go over what we covered in the previous tutorials:

  • producer is a user application that sends messages.
  • queue is a buffer that stores messages.
  • consumer is a user application that receives messages.

The core idea in the messaging model in RabbitMQ is that the producer never sends any messages directly to a queue. Actually, quite often the producer doesn't even know if a message will be delivered to any queue at all.

Instead, the producer can only send messages to an exchange. 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.

There are a few exchange types available: direct, topic, headers and fanout. We'll focus on the last one -- the fanout. Let's create an exchange of that type, and call it logs:

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

The fanout exchange is very simple. As you can probably guess from the name, it just broadcasts all the messages it receives to all the queues it knows. And that's exactly what we need for our logger.

Listing exchanges

To list the exchanges on the server you can run the ever useful rabbitmqctl:

$ sudo rabbitmqctl list_exchanges
Listing exchanges ...
logs fanout
amq.direct direct
amq.topic topic
amq.fanout fanout
amq.headers headers
...done.

In this list there are some amq.* exchanges and the default (unnamed) exchange. These are created by default, but it is unlikely you'll need to use them at the moment.

Nameless exchange

In previous parts of the tutorial we knew nothing about exchanges, but still were able to send messages to queues. That was possible because we were using a default exchange, which we identify by the empty string ("").

Recall how we published a message before:

channel.basic_publish(exchange='',
routing_key='hello',
body=message)

The exchange parameter is the name of the exchange. The empty string denotes the default or namelessexchange: messages are routed to the queue with the name specified by routing_key, if it exists.

Now, we can publish to our named exchange instead:

channel.basic_publish(exchange='logs',
routing_key='',
body=message)

Temporary queues

As you may remember previously we were using queues which had a specified name (remember hello andtask_queue?). Being able to name a queue was crucial for us -- we needed to point the workers to the same queue. Giving a queue a name is important when you want to share the queue between producers and consumers.

But that's not the case for our logger. We want to hear about all log messages, not just a subset of them. We're also interested only in currently flowing messages not in the old ones. To solve that we need two things.

Firstly, whenever we connect to Rabbit we need a fresh, empty queue. To do it we could create a queue with a random name, or, even better - let the server choose a random queue name for us. We can do this by not supplying the queue parameter to queue_declare:

result = channel.queue_declare()

At this point result.method.queue contains a random queue name. For example it may look likeamq.gen-JzTY20BRgKO-HjmUJj0wLg.

Secondly, once we disconnect the consumer the queue should be deleted. There's an exclusive flag for that:

result = channel.queue_declare(exclusive=True)

Bindings

We've already created a fanout exchange and a queue. Now we need to tell the exchange to send messages to our queue. That relationship between exchange and a queue is called a binding.

channel.queue_bind(exchange='logs',
queue=result.method.queue)

From now on the logs exchange will append messages to our queue.

Listing bindings

You can list existing bindings using, you guessed it, rabbitmqctl list_bindings.

Putting it all together

The producer program, which emits log messages, doesn't look much different from the previous tutorial. The most important change is that we now want to publish messages to our logs exchange instead of the nameless one. We need to supply a routing_key when sending, but its value is ignored for fanout exchanges. Here goes the code for emit_log.py script:

 1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
#!/usr/bin/env python
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()

(emit_log.py source)

As you see, after establishing the connection we declared the exchange. This step is necessary as publishing to a non-existing exchange is forbidden.

The messages will be lost if no queue is bound to the exchange yet, but that's okay for us; if no consumer is listening yet we can safely discard the message.

The code for receive_logs.py:

 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
#!/usr/bin/env python
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)
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()

(receive_logs.py source)

We're done. If you want to save logs to a file, just open a console and type:

$ python receive_logs.py > logs_from_rabbit.log

If you wish to see the logs on your screen, spawn a new terminal and run:

$ python receive_logs.py

And of course, to emit logs type:

$ python emit_log.py

Using rabbitmqctl list_bindings you can verify that the code actually creates bindings and queues as we want. With two receive_logs.py programs running you should see something like:

$ sudo rabbitmqctl list_bindings
Listing bindings ...
logs exchange amq.gen-JzTY20BRgKO-HjmUJj0wLg queue []
logs exchange amq.gen-vso0PVvyiRIL2WoV3i48Yg queue []
...done.

The interpretation of the result is straightforward: data from exchange logs goes to two queues with server-assigned names. And that's exactly what we intended.

To find out how to listen for a subset of messages, let's move on to tutorial 4

python rabittmq 使用的更多相关文章

  1. Python中的多进程与多线程(一)

    一.背景 最近在Azkaban的测试工作中,需要在测试环境下模拟线上的调度场景进行稳定性测试.故而重操python旧业,通过python编写脚本来构造类似线上的调度场景.在脚本编写过程中,碰到这样一个 ...

  2. Python高手之路【六】python基础之字符串格式化

    Python的字符串格式化有两种方式: 百分号方式.format方式 百分号的方式相对来说比较老,而format方式则是比较先进的方式,企图替换古老的方式,目前两者并存.[PEP-3101] This ...

  3. Python 小而美的函数

    python提供了一些有趣且实用的函数,如any all zip,这些函数能够大幅简化我们得代码,可以更优雅的处理可迭代的对象,同时使用的时候也得注意一些情况   any any(iterable) ...

  4. JavaScript之父Brendan Eich,Clojure 创建者Rich Hickey,Python创建者Van Rossum等编程大牛对程序员的职业建议

    软件开发是现时很火的职业.据美国劳动局发布的一项统计数据显示,从2014年至2024年,美国就业市场对开发人员的需求量将增长17%,而这个增长率比起所有职业的平均需求量高出了7%.很多人年轻人会选择编 ...

  5. 可爱的豆子——使用Beans思想让Python代码更易维护

    title: 可爱的豆子--使用Beans思想让Python代码更易维护 toc: false comments: true date: 2016-06-19 21:43:33 tags: [Pyth ...

  6. 使用Python保存屏幕截图(不使用PIL)

    起因 在极客学院讲授<使用Python编写远程控制程序>的课程中,涉及到查看被控制电脑屏幕截图的功能. 如果使用PIL,这个需求只需要三行代码: from PIL import Image ...

  7. Python编码记录

    字节流和字符串 当使用Python定义一个字符串时,实际会存储一个字节串: "abc"--[97][98][99] python2.x默认会把所有的字符串当做ASCII码来对待,但 ...

  8. Apache执行Python脚本

    由于经常需要到服务器上执行些命令,有些命令懒得敲,就准备写点脚本直接浏览器调用就好了,比如这样: 因为线上有现成的Apache,就直接放它里面了,当然访问安全要设置,我似乎别的随笔里写了安全问题,这里 ...

  9. python开发编译器

    引言 最近刚刚用python写完了一个解析protobuf文件的简单编译器,深感ply实现词法分析和语法分析的简洁方便.乘着余热未过,头脑清醒,记下一点总结和心得,方便各位pythoner参考使用. ...

随机推荐

  1. strace命令解析

    strace常用于跟踪和分析进程执行时中系统调用和耗时以及占用cpu的比例,常用的格式如下: 1.sudo /usr/bin/strace -Ttt -p pid 2>pid.log 跟进pid ...

  2. C语言学习笔记 (006) - 二维数组传参的三种表现形式

    # include <stdio.h> # include <stdlib.h> # define M # define N int getdate(int (*sp)[M]) ...

  3. Android之listview运用(美团美食列表)

    首先我们将listview简单实现,有图形,有文字:效果如图 之前我们完成了一个较为简单的listview视图列表,但是生活中我们往往碰到的 是更为复杂列表,有图像有评分标准,不如我们来试一试手,做一 ...

  4. 代码管理(四)SVN和Git对比

    在日常运维工作中,经常会用到版本控制系统,目前用到最广泛的版本控制器就是SVN和Git,那么这两者之间有什么不同之处呢?SVN(Subversion)是集中式管理的版本控制器,而Git是分布式管理的版 ...

  5. mac下 cordova 搭建

    最近遇到一个cordova搭建的项目,于是看了看如何搭建这个.  这个其实 和 phoneGap 差不多,都是为了方便html跨平台才产生的产物.  cordova  也可以生成  iOS  和 安卓 ...

  6. OpenCV 学习笔记03 直线和圆检测

    检测边缘和轮廓不仅重要,还经常用到,它们也是构成其他复杂操作的基础. 直线和形状检测与边缘和轮廓检测有密切的关系. 霍夫hough 变换是直线和形状检测背后的理论基础.霍夫变化是基于极坐标和向量开展的 ...

  7. js html 页面倒计时 精确到秒

    <!doctype html> <html> <head> <meta charset="utf-8"> </head> ...

  8. [转]What are mode and status columns under gp_segment_configuration table

    February 16, 2017 10:39 Goal In this article we will try to understand and answer to the below two q ...

  9. Java设计模式之工厂模式的两种实现方式

    工厂模式(Factory Pattern)是 Java 中最常用的设计模式之一.这种类型的设计模式属于创建型模式,它提供了一种创建对象的最佳方式. 1. 为什么要有工厂模式? "Talk i ...

  10. ios block一定会犯的几个错误

    贴几段斯坦福大学关于gcd的代码,这段代码逐步演示了如何修正错误,其中用到的既是串行队列   1.这个是原始代码 - (void)viewWillAppear:(BOOL)animated { NSD ...