Tymon Tobolski

Event sourcing on Rails with RabbitMQ

This article was originally published at Monterail Blog

A year ago I wrote about one of our dashboard applications and how we solved performance issues using Faye delayed messaging (now available as gem!).

From the very beginning we started with Service Oriented Architecure, which turned out to be the best decision we ever made. Each application in the suite (there are 8 of them at the time of writing) is responsible for only one part of the business process. Then there is a dashboard that shows the user the most important information from all other apps.

Unfortunately, this is not everything. One of the most valuable features of this project is its ability to import/export data between applications. What’s more, it requires both write and read interfaces between applications – instead of star we have to deal with mesh.

The concurrent growth of features and users in the ecosystem has led to a number of performance issues. All the connections between applications were made using HTTP APIs in an “ask for X” manner and the suite started to become very sluggish.

We went through a thought process to redesign the architecture and reduce the dependencies, but since the core functionality of each application is separate and well-defined, there really wasn’t much that we could do to improve it. What’s more, we found that in the future it could be worthwhile to extract some functionality into new separate applications.

The obvious answer was this:

Let’s just toss in HTTP cache and it’ll be fine

It wasn’t.

The biggest problem we had with HTTP cache is max-age.

There are two basic use cases in the system: - User performs some action, goes to another application and wants to see updated data - User goes to her dashboard and wants to see the current status of data (which might not have changed for days)

It is impossible to find a perfect number of seconds for how long the resource should be cached. If the time duration is too low there will be too many HTTP requests even if the data hasn’t changed. If it is too high we will get loads of emails from users informing us that the system is not working since they will be presented with outdated information.

The whole point of caching is to improve performance, not spoil the experience.

So what can we do?

The answer is: Revert the architecture

Message passing to the rescue

Object-Oriented Programming principles encourage Tell, Don’t Ask pattern. If we take it to the higher, architectural level, the solution makes itself.

Instead of the consumer asking the producer for a resource each time, let’s make the producer tell the consumer about changes.

Enough storytelling; it’s time to get our hands dirty and replace old-school HTTP APIs with awesome RabbitMQ messaging.

What is RabbitMQ?

RabbitMQ is an Open Source message broker / queueing system written in Erlang implementing AMQP. Their tag line states:

Messaging that just works

and I’ve found this to really be true.

To get an overview of what can be achieved using RabbitMQ see example topologies diagrams.

We will be using Pub-Sub topology with multiple fanout exchanges and queues.

Setting up RabbitMQ

This part depends on your operating system. There are number of guides on the RabbitMQ website.

If you happen to use Mac OS X and have Homebrew installed all you need to do is run

brew install rabbitmq

and then start it with


After starting rabbitmq-server you can access the Admin UI at http://localhost:15672. The default user is guest with password guest. The Admin UI is extremely useful when working with and debugging RabbitMQ. We’ll explore some of this in the following sections.

Simple architecture: Dashboard for a blog

Let’s imagine two applications: a typical Blog application with posts and a Dashboard application that displays 5 recently created posts. Instead of building an HTTP API in Blog so that Dashboard could ask for recent posts we will make Blog tell about each new post.

Blog -> Dashboard

In the diagram above there is:

  • Blog - a typical rails app backed by SQL database
  • P - RabbitMQ Producer
  • X - RabbitMQ Exchange
  • Queue - RabbitMQ Queue
  • C - RabbitMQ Consumer
  • Dashboard - rails app backed by Redis

After a post is created in Blog application it will go to Producer which will then send a message to Exchange. Exchange will put the message into a Queue. Then the Consumer, connected to this Queue, will grab the message and update Dashboard’s Redis-based cache.

This may sound a little complicated, but thanks to great ruby libraries you will notice that there is not too much work to do.

Blog post Publisher

First we need to create a basic scaffold for posts

rails new blog
cd blog
rails generate scaffold post title:string body:text
rake db:migrate
rails server

The interface for managing posts should be now available at http://localhost:3000/posts

Now we need to create a RabbitMQ Producer. Let’s call it Publisher. We will be using bunny - “A dead easy to use RabbitMQ Ruby client”.

Let’s put that into Gemfile

# blog/Gemfile
gem "bunny"

and run bundle install.

Now it’s time to implement our Publisher.

# blog/app/services/publisher.rb
class Publisher
  # In order to publish message we need a exchange name.
  # Note that RabbitMQ does not care about the payload -
  # we will be using JSON-encoded strings
  def self.publish(exchange, message = {})
    # grab the fanout exchange
    x = channel.fanout("blog.#{exchange}")
    # and simply publish message

  def self.channel
    @channel ||= connection.create_channel

  # We are using default settings here
  # The `Bunny.new(...)` is a place to
  # put any specific RabbitMQ settings
  # like host or port
  def self.connection
    @connection ||= Bunny.new.tap do |c|

Now we need to call Publisher.publish every time a new Post is created:

# blog/app/controllers/posts_controller.rb
class PostsController < ApplicationController
  # ...

  def create
    @post = Post.new(post_params)

    if @post.save
      # Publish post data
      Publisher.publish("posts", @post.attributes)

      redirect_to @post, notice: 'Post was successfully created.'
      render :new

  # ...

That’s it!

Remember to restart rails server before continuing.

You can now create a new post, then go to RabbitMQ Admin UI, select “Exchanges”, then blog.posts and you should see something like this:

blog.posts exchange

When you scroll down the page you’ll notice that there is no binding for this exchange.

blog.posts exchange bindings

This basically means that messages sent to this exchange are not going anywhere.

Now it’s time to setup a queue between the exchange and Dashboard applications and the consumer that will update local cache.

Note on durability

RabbitMQ Pub/Sub tutorial uses on-demand random queues created when client connects to the server. This is good for some use cases, but not ours. In case the Dashboard applications goes down (for any reason) the temporary queue would be deleted and messages sent from Blog will never reach Dashboard. That’s why we need a static, durable queue that will hold messages in case Dashboard consumer disconnects and deliver every message after reconnecting.

Dashboard Consumer

If you are familiar with tools like Sidekiq or Resque this part will feel like home.

There is another great RabbitMQ ruby library made especially for processing messages that come from queues. It’s called sneakers and was created by @jondot. (You should definitely check out his blog!)

Let’s start with creating new rails app

rails new dashboard
cd dashboard

add some gems

# dashboard/Gemfile
gem 'redis-rails'
gem 'redis-namespace'
gem 'sneakers'

and run bundle install.

Both redis and sneakers require some setup

Redis setup

# dashboard/config/initializers/redis.rb
$redis = Redis::Namespace.new("dashboard:#{Rails.env}", redis: Redis.new)

Sneakers setup

# dashboard/Rakefile

# load sneakers tasks
require 'sneakers/tasks'

# dashboard/config/initializers/sneakers.rb
Sneakers.logger.level = Logger::INFO # the default DEBUG is too noisy

Recent Posts service

Since we are not using ActiveRecord, we need some place to put functionality related to recent posts. Let’s make a service called RecentPosts.

# app/services/recent_posts.rb
class RecentPosts
  KEY = "recent_posts" # redis key
  STORE_LIMIT = 5      # how many posts should be kept

  # Get list of recent posts from redis
  # Since redis stores data in binary text format
  # we need to parse each list item as JSON
  def self.list(limit = STORE_LIMIT)
    $redis.lrange(KEY, 0, limit-1).map do |raw_post|

  # Push new post to list and trim it's size
  # to limit required storage space
  # `raw_post` is already a JSON string
  # so there is no need to encode it as JSON
  def self.push(raw_post)
    $redis.lpush(KEY, raw_post)
    $redis.ltrim(KEY, 0, STORE_LIMIT-1)

Dashboard view

Dashboard application needs to have some view so we can see if it works correctly.

# dashboard/app/controllers/home_controller.rb
class HomeController < ApplicationController
  def index
    @posts = RecentPosts.list
# dashboard/app/views/home/index.html.erb
<h2>Recently updated posts</h2>


    <% @posts.each do |post| %>
        <td><%= post[:title] %></td>
    <% end %>
# dashboard/config/routes.rb
Rails.application.routes.draw do
  root to: "home#index"


Finally, the sneakers worker. You’ll probably notice that it looks very similar to sidekiq workers.

# dashboard/app/workers/posts_worker.rb
class PostsWorker
  include Sneakers::Worker
  # This worker will connect to "dashboard.posts" queue
  # env is set to nil since by default the actuall queue name would be
  # "dashboard.posts_development"
  from_queue "dashboard.posts", env: nil

  # work method receives message payload in raw format
  # in our case it is JSON encoded string
  # which we can pass to RecentPosts service without
  # changes
  def work(raw_post)
    ack! # we need to let queue know that message was received

That’s it, the Dashboard app is ready!

In order to start workers run:

WORKERS=PostsWorker rake sneakers:run

In RabbitMQ Admin UI you can see that the dashboard.posts queue was created - navigate to “Queues” -> dashboard.posts.

dashboard.posts queue

Now, if you create a new post in Blog app the message will go to blog.posts exchange but the dashboard.posts queue will still be empty. Why? We need to setup a binding between exchange and queue.

Putting it all together

We need to tell blog.posts exchange to send incoming messages to dashboard.posts queue. This could be done in RabbitMQ Admin UI, but it’s much better to have this binding as declarative configuration that can be automatically executed (e.g. during deploy).

We will use the same bunny library as we used for publishing messages in Blog application.

# config/Rakefile
namespace :rabbitmq do
  desc "Setup routing"
  task :setup do
    require "bunny"

    conn = Bunny.new

    ch = conn.create_channel

    # get or create exchange
    x = ch.fanout("blog.posts")

    # get or create queue (note the durable setting)
    queue = ch.queue("dashboard.posts", durable: true)

    # bind queue to exchange


Now all we need to do is run:

rake rabbitmq:setup

Let’s inspect blog.posts exchange bindings in RabbitMQ Admin UI.

blog.posts exchange bindings

Now every Post created will be published as a message that goes to blog.posts exchange, is routed to dashboard.posts queue, is taken by PostsWorker, and is put into redis by RecentPosts service.

Complex architectures

The previous example shows how you can connect two applications. In the real world, however, the connections are much more complicated.

Consider the following diagram

complex architecture

Here are some differences from the previous example:

  • Blog now publishes many messages to many exchanges
  • Dashboard consumes messages from many queues
  • Admin - another application that is both a producer and consumer
  • Exchange ↔ Queue binding is much more complicated - it can be either one to many (blog.posts) or many to one (*.page_views)
  • Admin is a consumer backed by SQL instead of redis

Let’s go through some of those points

Cache storage

Choosing the appropriate storage for our cache is a very broad topic. I’m going to stick with just a few examples that will give you an overview.

Recent posts

On our Dashboard, we are only interested in the last five posts. This is a perfect use case for redis list. We atomically push new items to the front of the list using LPUSH, we always fetch the first few items using LRANGE, and we can easily limit the storage using LTRIM. There is no need to do any sorting or filtering. Redis gives us everything that we need and nothing more.

Page views statistics

Another good use case for redis backend is gathering page views statistics. Using redis INCR, INCRBY or HINCR it is easy to atomically increment counters without having to worry about race conditions. You can see an example of this at PageViews service.

Structured cache with filtering and sorting

Of course redis in not a silver bullet. There might be a case when you need the ability to filter or sort cached records. In such a case, we can create a regular SQL database model and store required information in it. See an example of storing blog posts in Admin app using Blog::Post model and Blog::PostsWorker

Exchange ↔ Queue bindings

Complex architectures will require more complex bindings. Fortunately, this can be solved in the same way as the previous example.

All we need to do is modify the setup task:

# config/Rakefile
namespace :rabbitmq do
  desc "Setup routing"
  task :setup do
    require "bunny"

    conn = Bunny.new

    ch = conn.create_channel

    # connect one exchange to multiple queues
    x = ch.fanout("blog.posts")
    ch.queue("dashboard.posts", durable: true).bind("blog.posts")
    ch.queue("admin.posts", durable: true).bind("blog.posts")

    # connect mutliple exchanges to the same queue
    x = ch.fanout("admin.page_views")
    ch.queue("dashboard.page_views", durable: true).bind("admin.page_views")

    x = ch.fanout("blog.page_views")
    ch.queue("dashboard.page_views", durable: true).bind("blog.page_views")


Final notes

RabbitMQ has enormous potential for dealing with a complex Service-Oriented system. Using durable queues allows us to maintain data consistency even if one part of the system breaks. For Dashboard-like applications, users can still access cached data even if the underlying application goes down.

On-change message passing gives a significant performance boost when compared to continuous API calls. It is a win in terms of speed, durability and availability. Theoretically we give up some temporary consistency and real-time data updates. On most occasions, however, we’ve found that when a user moves between applications the cache is already updated and the recent data is already available.

A working example of complex architecture is available at our GitHub.

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