最近在学习项目中的通用技术，其中一个是在项目中会经常使用的基于RabbitMQ实现的RPC。这里一共有三个点要学习，分别是：RPC是什么？RabbitMQ是什么？如何使用RabbitMQ实现RPC。奔着这三个目标，查阅了资料。做笔记记录。

RPC

rpc的全称叫：远程过程调用，可以通俗的理解为通过网络调用另一台电脑上的函数的业务处理思想。首先，我们先看看本地的函数调用流程是怎样。

本地调用：

def fun(a,b):

    sum = a + b

    return sum

if __name__ = __main__

print "i use a function to sum "

sum_main = fun(2,3)

print sum_main

本地调用当执行到sum=fun(2,3)时，程序会在内存中查找函数指针fun，然后带着参数进入fun()函数中运算，最后返回给sum_main。如果是远程调用，则是从一个电脑A上调用另一个电脑B上的函数。

RPC思想的好处是：

1、更符合编程思想。想要实现什么功能直接调用相应的函数，这是编程最直接的思想。

2、减少代码重复率。A想实现的功能如果B中已经实现了，那么A就直接调用B的函数，避免自己再重复实现。

RPC调用：

rpc多使用http传输请求，格式有xml，json等,这里是xml。如下是使用python中自带的RPC调用框架来实现的一个最简单的RPC调用。

client.py

from xmlrpclib import ServerProxy            #导入xmlrpclib的包

s = ServerProxy("http://172.171.5.205:8080") #定义xmlrpc客户端

print s.fun_add(2,3)                            #调用服务器端的函数   

server.py

from SimpleXMLRPCServer import SimpleXMLRPCServer   

def fun_add(a,b):

    totle = a + b

    return totle

if __name__ == '__main__':

    s = SimpleXMLRPCServer(('0.0.0.0', 8080))   #开启xmlrpcserver

    s.register_function(fun_add)                #注册函数fun_add

    print "server is online..."

    s.serve_forever()                           #开启循环等待

先启动服务器端

后启动客户端

这样就完成了一次RPC调用。RPC的调用流程如下图所示。调用流程是：

1. client调用以本地调用方式调用服务；
2. client stub接收到调用后负责将方法、参数等组装成能够进行网络传输的消息体；
3. client stub找到服务地址，并将消息发送到服务端；
4. server stub收到消息后进行解码；
5. server stub根据解码结果调用本地的服务；
6. 本地服务执行并将结果返回给server stub；
7. server stub将返回结果打包成消息并发送至消费方；
8. client stub接收到消息，并进行解码；
9. 服务消费方得到最终结果。

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*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" alt="" />

RabbitMQ

RabbitMQ是实现了AMQP(Advanced Message Queuing Protocol，高级消息队列协议)的软件。主要功能是

1. 解耦服务。使用rabbitmq可以将自个服务解耦，实现模块化
2. 扩展性高。系统中增加一项功能不需要 从头开始，自需要增加模块即可
3. 解决高并发瓶颈。消息队列具有缓存消息功能，能够有效解决高并发请求。

以下摘录自知乎：

对于初学者，举一个饭店的例子来解释这三个分别是什么吧。不是百分百恰当，但是应该足以解释这三者的区别。

RPC：假设你是一个饭店里的服务员，顾客向你点菜，但是你不会做菜，所以你采集了顾客要点什么之后告诉后厨去做顾客点的菜，

这叫RPC(remote procedure call)，因为厨房的厨师相对于服务员而言是另外一个人(在计算机的世界里就是remote的机器上的一个进程)。

厨师做好了的菜就是RPC的返回值。

任务队列和消息队列：本质都是队列，所以就只举一个任务队列的例子。假设这个饭店在高峰期顾客很多，而厨师只有很少的几个，

所以服务员们不得不把单子按下单顺序放在厨房的桌子上，供厨师们一个一个做，这一堆单子就是任务队列（当然，取决于问题的语境，

可能要把放订单的桌子也算在里面一起构成所谓的任务队列平台），厨师们每做完一个菜，就从桌子上的订单里再取出一个单子继续做菜。

简单消息队列：

aaarticlea/png;base64,iVBORw0KGgoAAAANSUhEUgAAAYgAAAA7CAYAAABhah1JAAAV2UlEQVR4Ae2dCXgV1dmA31nvJZAQICQIAQGBsIaCBQUrIItalxZlkSLudWurdWX5+7dVxAoi0AWsf/uLgoooqDUoaEERUUQRZJcdZAfZEiC5d+bOzP+cm5tfoFluQpab3HOeZ565mTnb987J+WbO8n0ggyQgCUgCkoAkIAlIApKAJCAJSAKSgCQgCUgCkoAkIAlIApKAJCAJSALlT0Ap/yzPK0cNSIsc9QAVSALE9TwgEDmOA/sBcZahbASGlC2ZTBXHBL4Edsex/GUVXfRljYH6gA/wA7UAB8gB3EhfdggQh7geE6GqFISA9GMgE+iEaf4IRWmNZdXH84RSiC5omoVhHCQUWk8otBZYB6wENkeXQVzH8uJaeil8WQjcDMwqS8I4SdMWuBjoqGh6JpreiZCd5rmOGbX8iuIqunkMvC2eba2J9Gni/DVgRZ1POUWsLAUhyhEK4Rp8vv6EQt1xHJOkJJvOnSEz06B1a2jcGC64ANLSoF490DSoUwcMA06fBsuCvDw4fhwOHID9+2HvXti40WP1aostW3RsW8M0j+I4H4cPyIp8bZQTshqTjfcaMLwSxPkMuBwYDTwjyztvApXNU1Q40lFIBXH202sCXI+m9VVUrZ9nW/UV3XT9zdvYCW1+ZPqbt1OMtHSMho0xUxqjJSaj+mqhGCZqrdp4IRs39xSe6+DkHMc+dgj7+/3YRw4Q2LOVvC1r7Lyta3FO5xiKqllo+peeHfwImB9RGBX+kqefLW+5/3VpuA/y+YYSDKbRokWQq67y0bs3XH45NGliRF1i7dogDqE4hCLp0OHMpKL9+nAcWL8elixpwOLFN7Jo0UBOnXoen+8bgsHXI28/YmhKBklAEpAEykJADBXdopi+oZ4V7KLVTgwldu+vJl7cR0u8uA/+izqoiqqJEZISg6IbaEli9An05BR8zVqfmybcP9qH93Fy1RLz1DdLL8/+4oPu1v5dTyim76BnBd+M9Gli6K9CQkUoiDoCIKb5GyyrPR07WgwfbjJwILRrFxW4MksqvjjEF4k4HnxQC39NLFkCc+d24c03O5GdPR5VnUcoNA1YVOZyZEJJQBKIJwLiBbS/oum/9lz3Oi2xrlv/yl8YyX1vIPHiPoaiVUQ3+gNeI7UJ9a8eHj7Ei3Bg57ccX/x2o2Mfvn5fYPuGBxXD3ODZ1lTgFeD0DynP/1f04/0ll5UIjMEw9uL3/40RI9qxYgWsW2cyZoxQDiXnUN4xxNBU//7wwgsKBw4YvPaaxqWXXouiLMQ0VwPX/fD1XN6Fy/wkAUmgmhMQiuFniiH6CuXftTN7XNPy6Vla5w8PGs1GTyOpe38qWjkUxs/foh0X3Pk7Oryx3mw78yvq//Tm9orpm6roxl5gFCBe0ssllIeCEHnch67vISFhLCNH1mXfPo0XX1T4sZh2iJHg88GwYbB0qcHXX8NVV3VEUeZhmmLyp1uM1FJWQxKQBGKDQHfFML8B5d2knld3aDdrFRn//NSod+VN4TmE2Kgi1G7fjeZ/mK5kLtinpd36eLLqqzVO0fQ9wL2RVaDnVdXzVRAXY5or0bRpPPxwXfbs0Rk3DuqL1VwxHLp2hawsjZUroXv3TBTlS1T1f4DkGK61rJokIAlUPIHkcF+gKMtrd+rRof2sb2g16V0toc2PKr7k8yhBr9uAJr96mk7z9+ipwx5MVlTt74phrAC6nEe24X0GZUkvPr0eR1W/pFu3jqxZo/Lss7GvGM6VtEsX+PRTnZkzFerVuwPTXA/0ODea/FsSkATigkBPRTc36In17mgx9hUl4x+f6LXadK5WggtFkf7wJNq9vlpJaN8tE0UVSuLRsg6ll+ULIhnD+DeaNp4JEzSWLtXPWVFUrYCiKDBiBGzaZNC/fyNUVawifKR6CSFrKwlIAudJ4HEUdWlSjyvTOry92aj/U7Git/qGWhd1pO3/fqanPzBeUzTtWUXXP4hsOi6VUKVVEE0wzWWkpvZm+XKVxx4j3MGWqsgYjZySAu+9pzFpkoqqPoeq/q08xvBiVFpZLUlAEsgnoKGq01DV8U0f+7PaanKWJt7Ca0RQFNJufZyM6ctUPbnhFYphfgFcUBrZSqMg0jHNr2jduhUrVhgxNQFdGomLiyu+Jh56CN59V8Ew7scwxLKx0jAqLnd5TxKQBGKLgKbo+mxVN+9tNWWemnrTAzXnhfcMzrU7dKfdzBWGr2mr1ooeXpSTfsbtYn9G2/k1xDSX0q1bQ5YtM8K7nYvNtprfvO46sdlOwzCGhN8uqrk4svqSgCRQCAFNm6bovoFt/rlEq3vZNYVEqDmXxF6KttOXGbXaZDZUTJ/Yjd0wGumiURAGfv98LrqoCe+/b5AkbOfFQbjkEpg3z0BV70FVH44DiaWIkkD8EFDVxxRF/WWrKVm6eMOOh6DVqUub5xcZvsYtmiuG//2IEdRiRS9ZQajqk9Spk8nChQZ16xabWY272bcvzJihoigTga41Tj4pkCQQnwS6KSjjW/5ptpbYrW9cERBKovXzC02tVkJnVPWJkoQvSUH0BEYye7ZJE2GXKg7D8OFw++3g978VMdUbhxCkyJJAjSHgU3z+OSk3/JLkvjfWGKFKI4iZmk6LZ2YLC7P/BQh7eUWG4hSEQkLCCzzwgEK/fkVmEBc3/vpXjZSUC1DV38SFvFJISaCmElDV3xr1GqalPzxZ+JiJ25B0yQAaDr7fU30Jfy9uj0RxCmIYmpbBE08UF6diAIvVRGceup5vwXXIEMK7nyum1KJzTUiASZN86Lr4JMs3v1h0bHlHEpAEYpNAA0XV/pD+yBS/6k+o/Bp6Hsc/fovtj/yctVddwKpLTb65vA4bb+rE7mfu5/S65ZVap8b3jdVQlfbA4KIKLrrz9/v/xGOPmSTHgPUJYcZb+H+YOxd69BC7n4uSp+KuC+WUni7M7z5QcYXInCUBSaACCTzoa9JSr3dF5Q8tObkn2frba9gxcjAnPs3CPnow3x9E3mnytq/n+7deYNMdlWvEQU+qT9rNj5qKz/90UcyLUhCXEQxeGB57LyplZVz3PBBHKCSswsKVV4Jtw+9/Xxmln12G+KK5/34ffv99MbY3QuzqEe4LZZAE4oFAWdu7qhj+e1MG3euris29u/57BDnLPgj7f0h/6Dk6vrOVrl8E6fLZaTrM2ciFv/sHdTLFlG/lhpSf3YFnBVsVNRdRuIJQ1Vv4yU9CNGtWubUtqjTh56FjR5g5Mz+GsMZaFeGWWyAYFDsRe1VF8UWUeRWQCwg44ndcj60WwUherjkEytree3uhYGqDqyvfhIZQDOKrQawgajt9GWkjHsXXtFW+Zzl/AsJ8d8oNd5Mx/fNKf0pm4+bU6dzTBm4trPDCFYRpDqB//+i9vRWWc0VcE2/xVRmEK1ThFS+2FEQBkWGAsLfyPSCch1xW3ORTQSJ5lgSqKYHStvc+vqatLL1+aqWLe/S9GeEyG902Cn9z4bY6tkJit35imGlAYbUqTEHUJxBoER7rLyxFVVwTcxAbN+YvNxXlV6WfiT59TAyjd1VgKKHMAoUuJtHvBoTRQeFARLiBziwhrbwtCVQ3AqVq74rh65XYtXfFerQsguCpdcIEEiRfcUMRMar2cp3Ol4lhposKc3dQmIJoEX7zbNmyamstSi9YySRWMQkf1AsWgPAS99RTVVe3li0VNE2M2cVyEGucRRD+c4Wp3zXAFuB3QAw82Ejt5EkSKB8CJbd3RWnla1I1TT909FBYSjGcE4vB17iFmOsVwzP/AagwZ6r53n4axJBFQzEH0bAhXHYZjB5dtV8Qqali0lzYMRkSIw/7khLqUfCmJTyi/xEYB6wTaU6UkFDelgTOISDamhivrspQpvbu2VaqohfokaqsfuyVrSenFFTqPzr9whRE7XDsOuXm1rSg8NKfxQqmWAtiT0Qo5AfejLWqRVGfAmXRScR9HrgCqAJv4VFUVUaJQQIPAuKoLuGH9u65HJ33EnV/ck14UrgyBdAbpGEd+A5r/66YnINQaycW4PiPzSGFDTHlv1geO1aQSJ7PJHD0KJimYCQ+yWLhiHZZRsGb3+rIsFN4n71UDmc+XPm7BAKirVV1my9Te1d0PTtl0L2VrhwEzzqd8vc3nPjkXyXgrZrboeyjBQVnF/woOBemIMQqGPg+/1QQUZ4jBI4cAVU9Uk14WJF67gTEZpiMiI/aydWk/rKakkBpCBTd3jX9+zM6wtLked5xG1x3WziPgzMmENgtpgJjK4SO/39fnz9Zckb1ClMQW9E0O7wx7YyI8meEwOrVDpYl/LzGaij4UhAPW3jFE1ZoxeTTk5GJ6litt6yXJFAWAlG1dy9kr8rbvNopSwHnmyap59Uk9/oZzskTbL69B4dmTSG4b0f+TupgHoFdmzjyzj/ZfKdYmV75IW/LGhRNE8v3t55bemFzEBaGsZLPP7+UYWKpsQxnEVi61MV1l511LXb+yAFejxxLATd2qiZrIgmUO4Ho27vjLD31zacDq2ojafNxr7Jj9NDwbuq9kx9BHLESwstwVW0VjhM6t06FfUFAIDCPefOCYTMX56aI57/FDu4TJ4RSFRvSYiWcBmYB1wNiOYIwBbJEKodYeTyyHuVMoKzt/cNQznEjd9Oqcq5OdNlpCYm0/st8Wk6YQ91e12M0aISi6ai1alProo7CsiptX8rfLxFdjuUUy/PIXpIV9Gzr3cJyLOwLQsSbye7dT/HZZ3D55YWlq9hrsbh6SUj84osufv9yAoHtFQugVLmLB1vowy1VLjKyJFA9CJS1vW9VTP9XR7Kmd2/WtquYaK/8oCjU6zc4fFR+4YWXeHLVEqzD+8Rqr4gdo7PjFf4FIXbg+nwfMG1awfje2ani8a/sbGELyiEQ+Es8ii9llgSqOwHPCvz16HszbOe0GJmSQRA4/MZUWzGMecCBwogUpSDEMNNI5sxRWS1WRcrAM8+I8XyxBGGOpCEJSALVksBsHGfHwRkT5NycsPD57UpOfPKO6lnBMUU9zaIVBGxA12dyzz0WwhZSPAdhB2ryZI/c3N8CMbh7L54fjpRdEoiagOsG8x44NHOiK1YOxXPwnBDf/eneoKoZLwPfFsWiOAUBljWKNWtOMXZs/HaKgQAMGhREUYRP6o+KAimvSwKSQLUgsAjUf20fOSjoWWJlZ3yG/S/80cvbtu6UawdHF0egeAUhTEdb1nDGjfPIyioun5p5T0yW33WXy86dR7AsYSFVBklAEqjmBLxQ8O7g3h3Hvnv6HjceV2oK3xQHZ4x3PdsS+xiK3fRbkoIQTeFDXPcZhgwJ8UUVLMOqysY4ZozHG2/YBINi/bSc2arKZyHLlgTKj8AJzwoMPPbBa/a+qWPianTk5MpP2DFqiNjvIIx2LioJaTQKQuTxexznZQYMCFWJP+iSpCjv++LLYcwYmDjRwXGEcqgiF3blLZjMTxKQBCIEvvIcZ+jBVya6+1/4Q75r4xqORiiHbQ9dF/Jc50Vc94loxI1WQXg4zn0EAm/Sv7/D3LnR5F094wif17ff7vLsszau+4sY2xRXPZnKWksCsUkgS/yPH5j+dGjX2DtdMXFbU8PxhW+y9ddXuq4VECu5fh2tnNEqCJGfeJsegW1PYuhQL/yGHaphQHfvhl69QsyalYvrCt+3NVgTRttEZDxJoEYTmCP+14/NfzWw+e7eIevg7holrBey2fe30ewYM0zYfpqA4wjf01EvSy2NghDgxHjdKDzvTiZODNCzZ4gdO2oG0Lfegk6dQqxatZ1QqDuwuGYIJqWQBCSBEgh87Dmh7rnffr1947DM0ImP3y4hevW4HdyzjU139rQPvfpcHni3Q9jCf6nmXEqrIArIvIzjdGHNms20a+cwdqzYWFdwr3qdhYK79lqHwYPh9OmXsKwuxa0Lrl7CydpKApJAlAQ2eLbV1ck99dL2kYPEWL0jHPxUx+AG8zjwjyfZMLSDk7d17WbPcYRF5xllkaWsCkKUtSncmVrWGMaOzaNdO5tXX6XabKoT/i5GjSKs4BYtEp9BfXGce4C8soCUaSQBSaDaE8jFDfcB/XK+XLhz/aAMZ9/UMYROFLsSNGaEFnMoR9+fyYbB7e0DLz6V59nWaM8Ov/CWeVfg+SgIAUbYapqI42Swe/dsbrvNJSPD5uWXIRijm1D27s1XDM2ahZgy5TiW9TiW1UEOKcVMO5cVkQSqmsDHnm118Gxr5KFXJ51Yd20zR4zj24f3VXW9Ci3ftQIczXqJ9QPb2LueuMO1Du153XMc4YP+OeC8JorPV0EUVHgPrnsrrpvBzp2vc9ddIVJTbR59FL4tchd3QdqKPwtTIfPnw/XXO1x4oceUKUcJBEZj2+nAlBhwxF7xDGQJkoAkUBoCwjvdZC9kp7vBvDGHXpt8bO21zbxtj/7cyV62AM+Nep63NGWWKm5g57dhvxJrB6Tau8b90rYO7Z6F57bGdYQLu3LRZkWZ+y5VRc+IvA3XFZV7nJycO5k69VdMntyUjAyLm24yueEGyMwULjvPSFJBP3NzYfFimDvX4513QmRn6xjGp7juNFxXbAuXlmorCL3MVhKoQQSE/4mJXsj+M/DznM8X/CZ7SVYvrU7dUL1+g/XkfoOUxK69Uf0JFS+y65K7bS0nFr/D8X+/YQW+22wqhrnbs61pgLCpdLi8K1HeCqKgfqKi47GsCcAlbN58IxMm/IKxY9NJTLTp3VulTx+Nzp2hY0do1KggXdnO4gth2zbCblJXroSPPrJYtUrHdcEwlmNZbwD/wrZr1hq2stGSqSQBSaD0BMQL5VwvZIul782cU9k3Hp3/yk1HsqZfoqiqV6tt11BSt35mQvsfhx0A+Zq2QlG10pdyRgr7yAHydmwgb/NqTq5a4pxatcR1Tp80FNO317OCwnPk255tfVmRBkQrSkEUiCmWVC0PH8HgSKAtJ0/2ZsGC3ixc2JdgMC0cMSnJpk0bj/R0naZNVdLSIDER/H5ISACfD06eBLHvIicn/7eYS9i/32HnTrHU1sC2VRTFwefbRCCwMOJV7TMsq3rMMBUQq8SzsFsuPIVUdCiYIRPnyrCVLsur6Cca9/mLF80/e7YlvipSPMfplbthRa+8besGeFYwA8/TFN1wfOktQ2bjFrqZ0lgz0tIRXuW02kmg6+HfwligG8hFzCEIHxWhY4exDu9xrUN7Q8HvtijO6Zzwv6di+g96IesjXFd4ilzqWcGCJl7hD6JqPCv9IFY9oBPQHsgA0vD7WwKNcN0kPM/Acfy4ro6m5aGqDqp6GkXJwXG+w7bFONueyLLUDZGzGDuUoWQCpVoPXXJ2MkYcELg54t42DkQts4hmpD8TfVo7oKmiG+loWjNcLwnPrY3nap7j1EJRQ4qmBVAUC1U9icdBzwqIFZUHI75nRJ+2Hjhe5trIhJKAJCAJSAKSgCQgCUgCkoAkIAlIApKAJCAJSAKSgCQgCUgCkoAkIAlIApKAJCAJSAKSQHUh8H9i07b0Qt6eRAAAAABJRU5ErkJgggA=" 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最简单的消息队列，生产者-消费者模式。一端产生消息，发送到队列，另一端消费者收取消息。

consume_simple.py

 #coding:UTF-8

 import pika

 import time

 # 建立实例

 connection = pika.BlockingConnection(pika.ConnectionParameters(

                'localhost'))

 # 声明管道

 channel = connection.channel()

 channel.queue_declare(queue='hello')

 def callback(ch, method, properties, body):  

     print "ch",ch

     print "method",method

     print "properties",properties

     print "body",body

     print(" [x] Received %r" % body)

27 # 消费消息

 channel.basic_consume(

     callback,  # 如果收到消息，就调用callback函数来处理消息

     queue='hello',  # 你要从那个队列里收消息

     )

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

 channel.start_consuming()  # 开始消费消息

productor_simple.py

 #coding:UTF-8

 import pika

 # 建立一个实例

 connection = pika.BlockingConnection(

     pika.ConnectionParameters('localhost')

     )

 # 声明一个管道，在管道里发消息

 channel = connection.channel()

 # 在管道里声明queue

 channel.queue_declare(queue='hello')

 channel.basic_publish(exchange='',

                       routing_key='hello',  # queue名字

                       body='Hello World!')  # 消息内容

 print(" [x] Sent 'Hello World!'")

 connection.close()  # 队列关闭

先运行消费者

在运行生产者

观察消费者获取的消息

RabbitMQ实现RPC

RPC的要求是等待获得返回值，而RabbitMQ常出现的场景是异步等待。这就要求RabbitMQ可以立即返回结果。使用了两种技术：

一、为调用指明id，要求id和结果一起返回，使用id来判断是哪一个函数的调用返回；

二、指明返回的队列名，返回结果时指明返回队列的名字确保会正确返回到调用者。

工作流程：

1. 客户端创建message时指定reply_to队列名、correlation_id标记调用者。
2. 通过队列，服务端收到消息。调用函数处理，然后返回。
3. 返回的队列是reply_to指定的队列，并携带correlation_id。
4. 返回消息到达客户端，客户端根据correlation_id判断是哪一个函数的调用返回。

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" alt="" />

#coding:UTF-8

import pika

import uuid

import time

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,  # 只要一收到消息就调用on_response

                                   no_ack=True,

                                   queue=self.callback_queue)  # 收这个queue的消息

    def on_response(self, ch, method, props, body):  # 必须四个参数

        # 如果收到的ID和本机生成的相同，则返回的结果就是我想要的指令返回的结果

        if self.corr_id == props.correlation_id:

            self.response = body

    def call(self, n):

        self.response = None  # 初始self.response为None

        self.corr_id = str(uuid.uuid4())  # 随机唯一字符串

        self.channel.basic_publish(

                exchange='',

                routing_key='rpc_queue',  # 发消息到rpc_queue

                properties=pika.BasicProperties(  # 消息持久化

                    reply_to = self.callback_queue,  # 让服务端命令结果返回到callback_queue

                    correlation_id = self.corr_id,  # 把随机uuid同时发给服务器

                ),

                body=str(n)

        )

        while self.response is None:  # 当没有数据，就一直循环

            # 启动后，on_response函数接到消息，self.response 值就不为空了

            self.connection.process_data_events()  # 非阻塞版的start_consuming()

            # print("no msg……")

            # time.sleep(0.5)

        # 收到消息就调用on_response

        return int(self.response)

if __name__ == '__main__':

    fibonacci_rpc = FibonacciRpcClient()

    print(" [x] Requesting fib(7)")

    response = fibonacci_rpc.call(7)

    print(" [.] Got %r" % response)

#coding:UTF-8

import pika

import time

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='',  # 把执行结果发回给客户端

            routing_key=props.reply_to,  # 客户端要求返回想用的queue

            # 返回客户端发过来的correction_id 为了让客户端验证消息一致性

            properties=pika.BasicProperties(correlation_id = props.correlation_id),

            body=str(response)

    )

    ch.basic_ack(delivery_tag = method.delivery_tag)  # 任务完成，告诉客户端

if __name__ == '__main__':

    connection = pika.BlockingConnection(pika.ConnectionParameters(

            host='localhost'))

    channel = connection.channel()

    channel.queue_declare(queue='rpc_queue')  # 声明一个rpc_queue ,

    channel.basic_qos(prefetch_count=1)

    # 在rpc_queue里收消息,收到消息就调用on_request

    channel.basic_consume(on_request, queue='rpc_queue')

    print(" [x] Awaiting RPC requests")

    channel.start_consuming()