Remote procedure call (RPC)

(using the amqp.node client)


This tutorial assumes RabbitMQ is installed and 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 second tutorial we learned how to use Work Queues to distribute time-consuming tasks among multiple workers.

But what if we need to run a function on a remote computer and wait for the result? Well, that's a different story. This pattern is commonly known as Remote Procedure Call or RPC.

In this tutorial we're going to use RabbitMQ to build an RPC system: a client and a scalable RPC server. As we don't have any time-consuming tasks that are worth distributing, we're going to create a dummy RPC service that returns Fibonacci numbers.

Callback queue

In general doing RPC over RabbitMQ is easy. A client sends a request message and a server replies with a response message. In order to receive a response we need to send a 'callback' queue address with the request. We can use the default queue. Let's try it:

ch.assertQueue('', {exclusive: true});

ch.sendToQueue('rpc_queue',new Buffer('10'), { replyTo: queue_name });

# ... then code to read a response message from the callback queue ...

Message properties

The AMQP protocol predefines a set of 14 properties that go with a message. Most of the properties are rarely used, with the exception of the following:

  • persistent: Marks a message as persistent (with a value of true) or transient (false). You may remember this property from the second tutorial.
  • content_type: Used to describe the mime-type of the encoding. For example for the often used JSON encoding it is a good practice to set this property to: application/json.
  • reply_to: Commonly used to name a callback queue.
  • correlation_id: Useful to correlate RPC responses with requests.

Correlation Id

In the method presented above we suggest creating a callback queue for every RPC request. That's pretty inefficient, but fortunately there is a better way - let's create a single callback queue per client.

That raises a new issue, having received a response in that queue it's not clear to which request the response belongs. That's when the correlation_id property is used. We're going to set it to a unique value for every request. Later, when we receive a message in the callback queue we'll look at this property, and based on that we'll be able to match a response with a request. If we see an unknown correlation_id value, we may safely discard the message - it doesn't belong to our requests.

You may ask, why should we ignore unknown messages in the callback queue, rather than failing with an error? It's due to a possibility of a race condition on the server side. Although unlikely, it is possible that the RPC server will die just after sending us the answer, but before sending an acknowledgment message for the request. If that happens, the restarted RPC server will process the request again. That's why on the client we must handle the duplicate responses gracefully, and the RPC should ideally be idempotent.


digraph { bgcolor=transparent; truecolor=true; rankdir=LR; node [style="filled"]; // subgraph cluster_C { label="Client"; color=transparent; C [label="C", fillcolor="#00ffff"]; }; subgraph cluster_XXXa { color=transparent; subgraph cluster_Note { color=transparent; N [label="Request\nreplyTo=amqp.gen-Xa2...\ncorrelationId=abc", fontsize=12, shape=note]; }; subgraph cluster_Reply { color=transparent; R [label="Reply\ncorrelationId=abc", fontsize=12, shape=note]; }; }; subgraph cluster_XXXb { color=transparent; subgraph cluster_RPC { label="rpc_queue"; color=transparent; RPC [label="{<s>||||<e>}", fillcolor="red", shape="record"]; }; subgraph cluster_REPLY { label="replyTo=amq.gen-Xa2..."; color=transparent; REPLY [label="{<s>||||<e>}", fillcolor="red", shape="record"]; }; }; subgraph cluster_W { label="Server"; color=transparent; W [label="S", fillcolor="#00ffff"]; }; // C -> N; N -> RPC:s; RPC:e -> W; W -> REPLY:e; REPLY:s -> R; R -> C; }

Our RPC will work like this:

Putting it all together

The Fibonacci function:

function fibonacci(n) {
  if (n == 0 || n == 1)
    return n;
    return fibonacci(n - 1) + fibonacci(n - 2);

We declare our fibonacci function. It assumes only valid positive integer input. (Don't expect this one to work for big numbers, and it's probably the slowest recursive implementation possible).

The code for our RPC server rpc_server.js looks like this:

#!/usr/bin/env node

var amqp = require('amqplib/callback_api');

amqp.connect('amqp://localhost', function(err, conn) {
  conn.createChannel(function(err, ch) {
    var q = 'rpc_queue';

    ch.assertQueue(q, {durable: false});
    console.log(' [x] Awaiting RPC requests');
    ch.consume(q, function reply(msg) {
      var n = parseInt(msg.content.toString());

      console.log(" [.] fib(%d)", n);

      var r = fibonacci(n);

        new Buffer(r.toString()),


function fibonacci(n) {
  if (n == 0 || n == 1)
    return n;
    return fibonacci(n - 1) + fibonacci(n - 2);

The server code is rather straightforward:

The code for our RPC client rpc_client.js:

#!/usr/bin/env node

var amqp = require('amqplib/callback_api');

var args = process.argv.slice(2);

if (args.length == 0) {
  console.log("Usage: rpc_client.js num");

amqp.connect('amqp://localhost', function(err, conn) {
  conn.createChannel(function(err, ch) {
    ch.assertQueue('', {exclusive: true}, function(err, q) {
      var corr = generateUuid();
      var num = parseInt(args[0]);

      console.log(' [x] Requesting fib(%d)', num);

      ch.consume(q.queue, function(msg) {
        if ( == corr) {
          console.log(' [.] Got %s', msg.content.toString());
          setTimeout(function() { conn.close(); process.exit(0) }, 500);
      }, {noAck: true});

      new Buffer(num.toString()),
      { correlationId: corr, replyTo: q.queue });

function generateUuid() {
  return Math.random().toString() +
         Math.random().toString() +

Now is a good time to take a look at our full example source code for rpc_client.js and rpc_server.js.

Our RPC service is now ready. We can start the server:

$ ./rpc_server.js
 [x] Awaiting RPC requests

To request a fibonacci number run the client:

$ ./rpc_client.js 30
 [x] Requesting fib(30)

The design presented here is not the only possible implementation of a RPC service, but it has some important advantages:

Our code is still pretty simplistic and doesn't try to solve more complex (but important) problems, like:

If you want to experiment, you may find the rabbitmq-management plugin useful for viewing the queues.