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。
4. 总结
5. 最终实现
The server code is rather straightforward:
The client code is slightly more involved:
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：
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:
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
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:
#!/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:
#!/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：
$ python rpc_server.py [x] Awaiting RPC requests通过client来请求fibonacci数:
$ 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？
尊重原创，转载请注明出处 anzhsoft： http://blog.csdn.net/anzhsoft/article/details/19633107
参考资料：
1. http://www.rabbitmq.com/tutorials/tutorial-six-python.html