This tutorial assumes RabbitMQ is installed and running on localhost on the 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 or RabbitMQ community Slack.

Remote procedure call (RPC)

(using the amqp.node client)

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.

A note on RPC

Although RPC is a pretty common pattern in computing, it's often criticised. The problems arise when a programmer is not aware whether a function call is local or if it's a slow RPC. Confusions like that result in an unpredictable system and adds unnecessary complexity to debugging. Instead of simplifying software, misused RPC can result in unmaintainable spaghetti code.

Bearing that in mind, consider the following advice:

  • Make sure it's obvious which function call is local and which is remote.
  • Document your system. Make the dependencies between components clear.
  • Handle error cases. How should the client react when the RPC server is down for a long time?

When in doubt avoid RPC. If you can, you should use an asynchronous pipeline - instead of RPC-like blocking, results are asynchronously pushed to a next computation stage.

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 exchange. Let's try it:

channel.assertQueue('', {
  exclusive: true

channel.sendToQueue('rpc_queue', Buffer.from('10'), {
   replyTo: queue_name

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

Message properties

The AMQP 0-9-1 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:

  • When the Client starts up, it creates an anonymous exclusive callback queue.
  • For an RPC request, the Client sends a message with two properties: reply_to, which is set to the callback queue and correlation_id, which is set to a unique value for every request.
  • The request is sent to an rpc_queue queue.
  • The RPC worker (aka: server) is waiting for requests on that queue. When a request appears, it does the job and sends a message with the result back to the Client, using the queue from the reply_to field.
  • The client waits for data on the callback queue. When a message appears, it checks the correlation_id property. If it matches the value from the request it returns the response to the application.

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(error0, connection) {
  if (error0) {
    throw error0;
  connection.createChannel(function(error1, channel) {
    if (error1) {
      throw error1;
    var queue = 'rpc_queue';

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

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

      var r = fibonacci(n);

        Buffer.from(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:

  • As usual we start by establishing the connection, channel and declaring the queue.
  • We might want to run more than one server process. In order to spread the load equally over multiple servers we need to set the prefetch setting on channel.
  • We use Channel.consume to consume messages from the queue. Then we enter the callback function where we do the work and send the response back.

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(error0, connection) {
  if (error0) {
    throw error0;
  connection.createChannel(function(error1, channel) {
    if (error1) {
      throw error1;
    channel.assertQueue('', {
      exclusive: true
    }, function(error2, q) {
      if (error2) {
        throw error2;
      var correlationId = generateUuid();
      var num = parseInt(args[0]);

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

      channel.consume(q.queue, function(msg) {
        if ( == correlationId) {
          console.log(' [.] Got %s', msg.content.toString());
          setTimeout(function() {
          }, 500);
      }, {
        noAck: true

          correlationId: correlationId,
          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:

# => [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:

  • If the RPC server is too slow, you can scale up by just running another one. Try running a second rpc_server.js in a new console.
  • On the client side, the RPC requires sending and receiving only one message. As a result the RPC client needs only one network round trip for a single RPC request.

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

  • How should the client react if there are no servers running?
  • Should a client have some kind of timeout for the RPC?
  • If the server malfunctions and raises an exception, should it be forwarded to the client?
  • Protecting against invalid incoming messages (eg checking bounds, type) before processing.

If you want to experiment, you may find the management UI useful for viewing the queues.

Production [Non-]Suitability Disclaimer

Please keep in mind that this and other tutorials are, well, tutorials. They demonstrate one new concept at a time and may intentionally oversimplify some things and leave out others. For example topics such as connection management, error handling, connection recovery, concurrency and metric collection are largely omitted for the sake of brevity. Such simplified code should not be considered production ready.

Please take a look at the rest of the documentation before going live with your app. We particularly recommend the following guides: Publisher Confirms and Consumer Acknowledgements, Production Checklist and Monitoring.

Getting Help and Providing Feedback

If you have questions about the contents of this tutorial or any other topic related to RabbitMQ, don't hesitate to ask them on the RabbitMQ mailing list.

Help Us Improve the Docs <3

If you'd like to contribute an improvement to the site, its source is available on GitHub. Simply fork the repository and submit a pull request. Thank you!

1 "Hello World!"

The simplest thing that does something

2 Work queues

Distributing tasks among workers (the competing consumers pattern)

3 Publish/Subscribe

Sending messages to many consumers at once

4 Routing

Receiving messages selectively

5 Topics

Receiving messages based on a pattern (topics)


Request/reply pattern example

7 Publisher Confirms

Reliable publishing with publisher confirms