Full Stack Voting App

Overview
In this tutorial, we'll deploy a containerized full stack application to ReleaseHub. Our example voting application will allow users to vote for their favourite category, and then view the results of the votes. We'll use multiple stacks and frameworks for our app to illustrate the breadth and flexibility of deployments using ReleaseHub.
Our codebase will comprise of three services that will be containerized and managed by a docker-compose file. Additionally, we will use Postgres as a database and Redis as a message broker, to offload some of the computational load to the worker service. Our services will be:
Vote: A frontend and some server-side code that will push the vote made by a user to Redis. This will be built in Python, using the Flask framework.
Result: A frontend that uses a websocket API to poll data from its server-side implementation to provide real-time updates of votes. This will be a Node.js application that uses Express to serve an Angular frontend. The frontend will use Socket.IO to manage the websocket connection.
Worker: The background task processor that reads from Redis and creates entries in our Postgres database to represent the results of the votes. The worker will be implemented using Java.
Our completed voting application will look like this:
And the result application will look like this:
You can find the completed code for the project here.
Fork and clone the repository
Create a fork of the repository on your version control hosting provider (GitHub, GitLab, or Bitbucket). Ensure that the provider you fork the repository to is the provider you have integrated with ReleaseHub.
Once you have forked the repository, you can clone it to your development machine to get started.
You do not need to have the repository clone to deploy the application to ReleaseHub, but it is useful to be able to work through the code and understand the codebase.
Project structure
Vote application
Our vote application is a small Flask web app that accepts POST requests from the index.html file it bundles and serves statically via a GET request to /.
If you take a look at the vote/app.py file, you should see the code below:
@app.route("/", methods=['POST','GET'])
 def hello():
     voter_id = request.cookies.get('voter_id')
     if not voter_id:
         voter_id = hex(random.getrandbits(64))[2:-1]
​
     vote = None
​
     if request.method == 'POST':
         redis = get_redis()
         vote = request.form['vote']
         data = json.dumps({'voter_id': voter_id, 'vote': vote})
         redis.rpush('votes', data)
​
     resp = make_response(render_template(
         'index.html',
         option_a=option_a,
         option_b=option_b,
         hostname=hostname,
         vote=vote,
     ))
     resp.set_cookie('voter_id', voter_id)
     return resp
This code has a few responsibilities:
When an HTTP request is received, we assign a voter ID to the caller, if one is not already present as a cookie on the request.
If the HTTP request is a POST request, we connect to Redis, and push a JSON payload containing voter data onto a Redis queue called votes.
If the HTTP request is a GET request, we simply return the index.html template file, with a few parameters.
The most important parameters provided to our template are option_a and option_b.
option_a = os.getenv('OPTION_A', "Cats")
option_b = os.getenv('OPTION_B', "Dogs")
These are the categories that a user can vote for. If the environment variables for OPTION_A and OPTION_B aren’t set, the default options will be "Cats" and "Dogs".
Worker application
The worker application is purely a backend service, written in Java.
On startup, it establishes a connection to Redis and the PostgreSQL database.
...
class Worker {
  public static void main(String[] args) {
    try {
      Jedis redis = connectToRedis("redis");
      Connection dbConn = connectToDB("db");
...
		}
	}
}
As part of the connection to our database, the worker application also creates the necessary database tables.
...
PreparedStatement st = conn.prepareStatement(
        "CREATE TABLE IF NOT EXISTS votes (id VARCHAR(255) NOT NULL UNIQUE, vote VARCHAR(255) NOT NULL)");
      st.executeUpdate();
...
It then watches the Redis queue called votes for new items.
while (true) {
        String voteJSON = redis.blpop(0, "votes").get(1);
        JSONObject voteData = new JSONObject(voteJSON);
        String voterID = voteData.getString("voter_id");
        String vote = voteData.getString("vote");
​
        System.err.printf("Processing vote for '%s' by '%s'\n", vote, voterID);
        updateVote(dbConn, voterID, vote);
      }
When a new item is found, it calls a method called updateVote, which handles writing the result of a vote to the PostgreSQL database.
static void updateVote(Connection dbConn, String voterID, String vote) throws SQLException {
    PreparedStatement insert = dbConn.prepareStatement(
      "INSERT INTO votes (id, vote) VALUES (?, ?)");
    insert.setString(1, voterID);
    insert.setString(2, vote);
​
    try {
      insert.executeUpdate();
    } catch (SQLException e) {
      PreparedStatement update = dbConn.prepareStatement(
        "UPDATE votes SET vote = ? WHERE id = ?");
      update.setString(1, vote);
      update.setString(2, voterID);
      update.executeUpdate();
    }
  }
Result application
The result application, in a similar fashion to the vote application, serves an index.html file via its / route.
More interestingly, it exposes a websocket API using Socket.IO.
io.sockets.on('connection', function (socket) {
​
  socket.emit('message', { text : 'Welcome!' });
​
  socket.on('subscribe', function (data) {
    socket.join(data.channel);
  });
});
On startup, the result application establishes a connection to the PostgreSQL database.
async.retry(
  {times: 1000, interval: 1000},
  function(callback) {
    pool.connect(function(err, client, done) {
      if (err) {
        console.error("Waiting for db");
      }
      callback(err, client);
    });
  },
  function(err, client) {
    if (err) {
      return console.error("Giving up");
    }
    console.log("Connected to db");
    getVotes(client);
  }
);
Once a connection has been successfully established, it calls a getVotes() function using the database client.
This function reads the vote results from the database (which were written to it via the worker), and publishes them to a Socket.IO-managed channel called scores.
function getVotes(client) {
  client.query('SELECT vote, COUNT(id) AS count FROM votes GROUP BY vote', [], function(err, result) {
    if (err) {
      console.error("Error performing query: " + err);
    } else {
      var votes = collectVotesFromResult(result);
      io.sockets.emit("scores", JSON.stringify(votes));
    }
​
    setTimeout(function() {getVotes(client) }, 1000);
  });
}
Our client-side code (anchored at result/views/app.js) reads from the scores channel, and updates the result application’s frontend accordingly.
...
var updateScores = function(){
    socket.on('scores', function (json) {
       data = JSON.parse(json);
       var a = parseInt(data.a || 0);
       var b = parseInt(data.b || 0);
​
       var percentages = getPercentages(a, b);
​
       bg1.style.width = percentages.a + "%";
       bg2.style.width = percentages.b + "%";
​
       $scope.$apply(function () {
         $scope.aPercent = percentages.a;
         $scope.bPercent = percentages.b;
         $scope.total = a + b;
       });
    });
  };
...
Docker Compose
Each of the applications described above are containerized using a dockerfile in their respective directories. We can use docker-compose to coordinate and run our applications together, as well as run containerized versions of Redis and PostgreSQL.
Below is the complete docker-compose.yml file required to build and run our applications.
version: "3"
​
services:
  vote:
    build: ./vote
    command: 
    - python 
    - app.py
    ports:
      - "5001:80"
    depends_on:
      - "redis"
      - "db"
​
  result:
    build: ./result
    command: 
    - nodemon 
    - server.js
    ports:
      - "5002:80"
    depends_on:
      - "redis"
      - "db"
​
  worker:
    build:
      context: ./worker
    depends_on:
      - "redis"
      - "db"
​
  redis:
    image: redis:alpine
​
  db:
    image: postgres:9.4
    environment:
      POSTGRES_HOST_AUTH_METHOD: "trust"
Run the project locally
To run our project locally, ensure you have Docker installed, and run the following command in the root of the project:
docker-compose up 
The vote application will be accessible via port 5001 on localhost, and the Result application will be available via port 5002.
Deploy to ReleaseHub
Once we’ve created the applications and set up our docker-compose.yaml file, we’re ready to deploy our app to ReleaseHub.
Ensure you’ve forked the repository before we get started.
Create and configure a new application
On your ReleaseHub dashboard, click Create New App.
You will be taken to the Create Your Application screen. On this screen, refresh your repositories by clicking the refresh button, and then select the forked version of the example-voting-app repository from the dropdown list.
Using the docker-compose.yml file, ReleaseHub will automatically detect the services and their configuration.
Finally, give your application a short but appropriate name. This will be used in your hostnames for the environment created.
Once you’re satisfied with the configuration, click Generate App Template.
Next you can modify the Application Template that ReleaseHub has generated. The Application Template describes the way your application works. For the purposes of this tutorial, it isn’t necessary to edit it, so click Save & Continue.
Lastly, before building and deploying our application to an environment, we have the opportunity to add build arguments, set environment variables, or configure Just-In-Time File Mounts. We don’t need any of these for the moment, so we can hit Start Build & Deploy.
After kicking off a build a deployment, ReleaseHub will build the applications from the latest commit on the master branch.
ReleaseHub will also create an ephemeral environment for the built applications to be deployed into.
The initial build and deployment shouldn’t take more than a few minutes, as ReleaseHub will build and publish our Docker images for us.
Once our environment has been set up, we should see a toast notification indicating that the deployment was successful.
We can view the details about our environment by clicking through to the Environments tab.
The detailed environment view will give us in-depth details about the environment, and the instances deployed into it. Additionally, it will tell us the hostname URLs with which to access the services.
We can click on the hostname URL for the vote application to tinker with making votes. You can share this URL with other people to vote, too.
To view the results in real time, you can navigate to the hostname URL for the result application.
Changing environment variables
Our default environment variables for OPTION_A and OPTION_B were set to “Cats” and “Dogs”, but perhaps we’d like our users to choose between “Python” and “JavaScript”.
To modify this, we can navigate back to our Application Dashboard, and click on the Settings tab.
From here, we can modify the default environment variables for all environments. We will add two variables, for OPTION_A and OPTION_B respectively, and then click Save As New Version.
To apply this configuration to our environment, navigate back to the Environments tab, and click through to our Environment Details view. Click through to Settings via the left navigation bar, and select Apply, under the "Apply Latest Configuration" option.
This will apply the latest configuration changes to our live environment, and redeploy it.
Once our deployment is complete, we should be able to navigate back to the vote application and see our new voting categories in action!
Next steps
In this tutorial we’ve learned how to set up and deploy a non-trivial project with multiple services using ReleaseHub. We’ve also looked at how to configure databases using Docker on ReleaseHub. Additionally, we learned how to modify environment configurations, and redeploy afterward.
A good next step might be to create an environment specifically for a development branch of the project, so that you can iterate on your project without impacting a production deployment.

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Outline

Overview

Fork and clone the repository

Run the project locally

Deploy to ReleaseHub

Create and configure a new application

Changing environment variables

Next steps