RabbitMQ 适用于云计算集群的远程调用(RPC)

时间:2021-10-13 08:12:07

在云计算环境中,很多时候需要用它其他机器的计算资源,我们有可能会在接收到Message进行处理时,会把一部分计算任务分配到其他节点来完成。那么,RabbitMQ如何使用RPC呢?在本篇文章中,我们将会通过其它节点求来斐波纳契完成示例。

1. 客户端接口 Client interface

为了展示一个RPC服务是如何使用的,我们将创建一段很简单的客户端class。 它将会向外提供名字为call的函数,这个call会发送RPC请求并且阻塞知道收到RPC运算的结果。代码如下:

  1. fibonacci_rpc = FibonacciRpcClient()
  2. result = fibonacci_rpc.call(4)
  3. print "fib(4) is %r" % (result,)
fibonacci_rpc = FibonacciRpcClient()
result = fibonacci_rpc.call(4)
print "fib(4) is %r" % (result,)

2. 回调函数队列 Callback queue

总体来说,在RabbitMQ进行RPC远程调用是比较容易的。client发送请求的Message然后server返回响应结果。为了收到响应client在publish message时需要提供一个”callback“(回调)的queue地址。code如下:

  1. result = channel.queue_declare(exclusive=True)
  2. callback_queue = result.method.queue
  3. channel.basic_publish(exchange='',
  4. routing_key='rpc_queue',
  5. properties=pika.BasicProperties(
  6. reply_to = callback_queue,
  7. ),
  8. body=request)
  9. # ... and some code to read a response message from the callback_queue ...
result = channel.queue_declare(exclusive=True)
callback_queue = result.method.queue channel.basic_publish(exchange='',
routing_key='rpc_queue',
properties=pika.BasicProperties(
reply_to = callback_queue,
),
body=request) # ... and some code to read a response message from the callback_queue ...

2.1 Message properties

AMQP 预定义了14个属性。它们中的绝大多很少会用到。以下几个是平时用的比较多的:

  • delivery_mode: 持久化一个Message(通过设定值为2)。其他任意值都是非持久化。请移步RabbitMQ消息队列(三):任务分发机制
  • content_type: 描述mime-type 的encoding。比如设置为JSON编码:设置该property为application/json。
  • reply_to: 一般用来指明用于回调的queue(Commonly used to name a callback queue)。
  • correlation_id: 在请求中关联处理RPC响应(correlate RPC responses with requests)。

3. 相关id Correlation id

在上个小节里,实现方法是对每个RPC请求都会创建一个callback queue。这是不高效的。幸运的是,在这里有一个解决方法:为每个client创建唯一的callback queue。

这又有其他问题了:收到响应后它无法确定是否是它的,因为所有的响应都写到同一个queue了。上一小节的correlation_id在这种情况下就派上用场了:对于每个request,都设置唯一的一个值,在收到响应后,通过这个值就可以判断是否是自己的响应。如果不是自己的响应,就不去处理。

4. 总结

RabbitMQ 适用于云计算集群的远程调用(RPC)

工作流程:

  • 当客户端启动时,它创建了匿名的exclusive callback queue.
  • 客户端的RPC请求时将同时设置两个properties: reply_to设置为callback queue;correlation_id设置为每个request一个独一无二的值.
  • 请求将被发送到an rpc_queue queue.
  • RPC端或者说server一直在等待那个queue的请求。当请求到达时,它将通过在reply_to指定的queue回复一个message给client。
  • client一直等待callback queue的数据。当message到达时,它将检查correlation_id的值,如果值和它request发送时的一致那么就将返回响应。

5. 最终实现

The code for rpc_server.py:

  1. #!/usr/bin/env python
  2. import pika
  3. connection = pika.BlockingConnection(pika.ConnectionParameters(
  4. host='localhost'))
  5. channel = connection.channel()
  6. channel.queue_declare(queue='rpc_queue')
  7. def fib(n):
  8. if n == 0:
  9. return 0
  10. elif n == 1:
  11. return 1
  12. else:
  13. return fib(n-1) + fib(n-2)
  14. def on_request(ch, method, props, body):
  15. n = int(body)
  16. print " [.] fib(%s)"  % (n,)
  17. response = fib(n)
  18. ch.basic_publish(exchange='',
  19. routing_key=props.reply_to,
  20. properties=pika.BasicProperties(correlation_id = \
  21. props.correlation_id),
  22. body=str(response))
  23. ch.basic_ack(delivery_tag = method.delivery_tag)
  24. channel.basic_qos(prefetch_count=1)
  25. channel.basic_consume(on_request, queue='rpc_queue')
  26. print " [x] Awaiting RPC requests"
  27. channel.start_consuming()
#!/usr/bin/env python
import pika 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()

The server code is rather straightforward:

  • (4) As usual we start by establishing the connection and declaring the queue.
  • (11) We declare our fibonacci function. It assumes only valid positive integer input. (Don't expect this one to work for big numbers, it's probably the slowest recursive implementation possible).
  • (19) We declare a callback for basic_consume, the core of the RPC server. It's executed when the request is received. It does the work and sends the response back.
  • (32) We might want to run more than one server process. In order to spread the load equally over multiple servers we need to set theprefetch_count setting.

The code for rpc_client.py:

  1. #!/usr/bin/env python
  2. import pika
  3. import uuid
  4. class FibonacciRpcClient(object):
  5. def __init__(self):
  6. self.connection = pika.BlockingConnection(pika.ConnectionParameters(
  7. host='localhost'))
  8. self.channel = self.connection.channel()
  9. result = self.channel.queue_declare(exclusive=True)
  10. self.callback_queue = result.method.queue
  11. self.channel.basic_consume(self.on_response, no_ack=True,
  12. queue=self.callback_queue)
  13. def on_response(self, ch, method, props, body):
  14. if self.corr_id == props.correlation_id:
  15. self.response = body
  16. def call(self, n):
  17. self.response = None
  18. self.corr_id = str(uuid.uuid4())
  19. self.channel.basic_publish(exchange='',
  20. routing_key='rpc_queue',
  21. properties=pika.BasicProperties(
  22. reply_to = self.callback_queue,
  23. correlation_id = self.corr_id,
  24. ),
  25. body=str(n))
  26. while self.response is None:
  27. self.connection.process_data_events()
  28. return int(self.response)
  29. fibonacci_rpc = FibonacciRpcClient()
  30. print " [x] Requesting fib(30)"
  31. response = fibonacci_rpc.call(30)
  32. print " [.] Got %r" % (response,)
#!/usr/bin/env python
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,)

The client code is slightly more involved:

  • (7) We establish a connection, channel and declare an exclusive 'callback' queue for replies.
  • (16) We subscribe to the 'callback' queue, so that we can receive RPC responses.
  • (18) The 'on_response' callback executed on every response is doing a very simple job, for every response message it checks if thecorrelation_id is the one we're looking for. If so, it saves the response inself.response and breaks the consuming loop.
  • (23) Next, we define our main call method - it does the actual RPC request.
  • (24) In this method, first we generate a unique correlation_id number and save it - the 'on_response' callback function will use this value to catch the appropriate response.
  • (25) Next, we publish the request message, with two properties: reply_to and correlation_id.
  • (32) At this point we can sit back and wait until the proper response arrives.
  • (33) And finally we return the response back to the user.

开始rpc_server.py:

  1. $ python rpc_server.py
  2. [x] Awaiting RPC requests
$ python rpc_server.py
[x] Awaiting RPC requests

通过client来请求fibonacci数:

  1. $ python rpc_client.py
  2. [x] Requesting fib(30)
$ python rpc_client.py
[x] Requesting fib(30)

现在这个设计并不是唯一的,但是这个实现有以下优势:

  • 如何RPC server太慢,你可以扩展它:启动另外一个RPC server。
  • 在client端, 无所进行加锁能同步操作,他所作的就是发送请求等待响应。

我们的code还是挺简单的,并没有尝试去解决更复杂和重要的问题,比如:

  • 如果没有server在运行,client需要怎么做?
  • RPC应该设置超时机制吗?
  • 如果server运行出错并且抛出了异常,需要将这个问题转发到client吗?
  • 需要边界检查吗?

转载自: anzhsoft:http://blog.csdn.net/anzhsoft/article/details/19633107

参考资料:

1. http://www.rabbitmq.com/tutorials/tutorial-six-python.html