Update of Python Runtime in Meilix

Meilix Generator is a webapp uses flask with Python, Werkzeug, and Jinja 2. It triggers Travis to release in an ISO. It is deployed on Heroku. An older python version caused the webapp to load very slowly and it also become unsupported so there was a need to update the python version. In this blog post we walk through the update of Python in the project.

We can specify an explicit version of Python to be used to run your application. For example, if you require Python 2, add the following to your Pipfile:

[requires]
Python_version = "2.7"

 

Then run $ pipnv lock to generate Pipfile.lock and push to Heroku.

Another way:

If we are using pip, we can supply a runtime.txt file.

$ cat runtime.txt
python-3.6.1

 

Building of the webapp (Example)

The webapp build in Heroku and provide us a log. The log presents the packages installed and its version. Log also shows if any newer version is present for the package.

While building webapp, we get this as a log:

-----> Python app detected
! The latest version of Python 3 is python-3.6.5 (you are using python-3.6.1, which is unsupported).
! We recommend upgrading by specifying the latest version (python-3.6.5).

 

This confirms that we need to update python version and so thus we edited the runtime.txt

Now building the same webapp, we get:

Python app detected
-----> Found python-3.6.1, removing
-----> Installing python-3.6.5
-----> Installing pip

 

It already using older python, so it need first to remove the older version and then it install the latest one.

The same implementation can be seen in the history.

Reference:

Flask – The Microframework

Heroku Python Runtime

Auto Deployment of Badgeyay Backend by Heroku Pipeline

Badgeyay project is now divided into two parts i.e front-end of Ember JS and back-end with REST-API programmed in Python. One of the challenging job is that, it should support the uncoupled architecture. Now, we have to integrate Heroku deployed API with Github which should auto deploy every Pull Request made to the Development Branch and help in easing the Pull Request review process.

In this blog, I’ll be discussing how I have configured Heroku Pipeline to auto deploy every Pull request made to the Development Branch and help in easing the Pull Request review process  in Badgeyay in my Pull Request.
First, Let’s understand Heroku Pipeline and its features. Then we will move onto configuring the Pipeline file to run auto deploy PR.. Let’s get started and understand it step by step.

What is Heroku Pipeline ?

A pipeline is a group of Heroku apps that share the same codebase. Each app in a pipeline represents one of the following steps in a continuous delivery workflow:

  • Review
  • Development
  • Staging
  • Production

A common Heroku continuous delivery workflow has the following steps:

  • A developer creates a pull request to make a change to the codebase.
  • Heroku automatically creates a review app for the pull request, allowing    developers to test the change.
  • When the change is ready, it’s merged into the codebase Default branch.
  • The Default branch is automatically deployed to staging for further testing.
  • When it’s ready, the staging app is promoted to production, where the change is available to end users of the app.

In badgeyay, I have used Review App and Development App steps for auto deployment of Pull Request.

Pre – requisites:

  • You should have admin rights of the Github Repository.
  • You should be the owner of the Heroku deployed app.
  • For creating a Review App , Below mentioned files are needed to be in the root of the project repository to trigger the Heroku Build.

1. App.json

{
    "name": "BadgeYay-API",
    "description": "A fully functional REST API for badges generator using flask",
    "repository": "https://github.com/fossasia/badgeyay/backend/",
    "keywords": [
        "badgeyay",
        "fossasia",
        "flask"
    ],
    "buildpacks": [
        {
            "url": "heroku/python"
        }
    ]
}
2. Procfile

web: gunicorn --pythonpath backend/app/ main:app

 

Now, I have fulfilled all the prerequisites needed for integrating Github repository to Heroku Deployed Badgeyay API. Let’s move to Heroku Dashboard of the Badgeyay API and implement auto deployment of every Pull Request.

Step 1 :

Open the heroku Deployed App on the dashboard. Yow will see following tabs in top of the dashboard.

Step 2 :

Click on Deploy and first create a new pipeline by giving a name to it and choose a stage for the pipeline.

Step 3 :

  • Choose a Deployment Method. For the badgeyay project, I have  integrated Github for auto deployment of PR.
  • Select the repository and connect with it.
  • You will receive a pop-up which will ensure that repository is connected to Heroku.

Step 4 :
Enable automatic deploys for the Github repository.

Step 5 :

Now after adding the pipeline, present app get nested under the pipeline. Click on the pipeline name on the top and now we have a pipeline dashboard like this :

Step 6:

Now for auto deployment of PR, enable Review Apps by filling the required information like this :

Step 7:

Verify by creating a test PR after following every above mentioned steps.

 

Now we are all done with setting up auto deployment of every pull request to badgeyay repository.

This is how I have configured Heroku Pipeline to auto deploy every Pull request made to the Development Branch and help in easing the Pull Request review process.

About Author :

I have been contributing in open source organization FOSSASIA, where I’m working on a project called BadgeYaY. It is a badge generator with a simple web UI to add data and generate printable badges in PDF.

Resources:

  • Heroku Pipelines Article – Link

Badgeyay: Integrating EmberJS Frontend with Flask Backend

Badgeyay is a simple badge generator with a simple web UI that generates a printable badge in PDFs. The project had gone through different cycles starting from a Flask server to a CLI application then a python library and now API Interface for generation of badges.

According to latest changes in the project structure, now the frontend and backend are independent components developed in Ember JS and Flask respectively. Now there is a need to connect the frontend to the backend, which means the user should see the response on the same page without refresh, if the badge generated successfully. AJAX would fit right into the spot. Asynchronous Javascript and XML also known as AJAX, will enable us to perform asynchronous operation on the page without refreshing the page.

We can make an API call to the Server running in backend or deployed on heroku, but the server is not suitable for doing CORS(Cross-Origin Resource Sharing), ability to share the resources on server with the client having different domain names, but as the server and the frontend are not hosted on the same host  so there is a need to enable the server to accept CORS request calls.

Now the challenges were:

  • Enabling Flask Server to accept CORS requests.
  • AJAX query for sending request to the Flask server.

Procedure

  1. Giving the form an id and creating an AJAX request to the Flask server (may be localhost or deployed on heroku).
<form id=”form1″ action=”” method=”post” enctype=”multipart/form-data” onsubmit=”return validate()”>

 

When the generate button is clicked, an AJAX request is made to the server to generate badges and at the same time prevent the page from refreshing. In the AJAX request we set the CORS header to allow the domain.

 

<script type=”text/javascript”>
$(document).ready(function () {
$(‘#form1’).submit(function (event) {
event.preventDefault();
$.ajaxSetup({
headers: {“Access-Control-Allow-Origin”: “*”}
});
$.ajax({
url: “http://badgeyay-api.herokuapp.com/api/v1.0/generate_badges”,
data: $(this).serialize(),
type: ‘POST’,
success: function (data) {…},
error: function (error) {…}
})
});
})
</script>

 

  1. Import the library and enable the API endpoint to accept CORS requests.
from flask_cors import CORS
cors = CORS(app, resources={r”/api/*”: {“origins”: “*”}})

 

  1. Add Logic for appending the download link by extracting the download link from the response and replacing the static text in the template with the download link, also changing the download variable to the filename, by stripping the base url from the download link.
if (data[“response”][0][“type”] === “success”) {
$(‘#success’).css(‘visibility’, ‘visible’);
let link = data[“response”][0][“download_link”];
link = link.replace(“backend/app/”, “http://badgeyay-api.herokuapp.com/”);
$(‘#badge-link’).attr(“href”, link);
link = link.replace(“static/badges/”, “”);
$(‘#badge-link’).attr(“download”, link);
}

 

  1. Output the success on the page.
<div id=”success” style=”visibility: hidden;”>
<div class=”flash-success”>Your badges have been created successfully.</div>
<div class=”text-center”>
<a id=”badge-link” href=”http://badgeyay-api.herokuapp.com/static/badges/{{msg}}-badges.pdf”
class=”btn btn-success”
download=”{{msg}}-badges.pdf”>Download as
PDF</a>
</div>
</div>

 

  1. Frontend and Backend now are connected to each other.The Server now accepts CORS requests and response is generated after the user requests from Frontend.

 

The Pull Request with the above changes is on this Link

Topics Involved

Working on this issue (Link)  involves following topics :

  • Enabling Flask Server for CORS
  • Request Headers
  • AJAX request for CORS.

References

Installing Susper Search Engine and Deploying it to Heroku

Susper is a decentralized Search Engine that uses the peer to peer system yacy and Apache Solr to crawl and index search results.

Search results are displayed using the Solr server which is embedded into YaCy. All search results must be provided by a YaCy search server which includes a Solr server with a specialized JSON result writer. When a search request is made in one of the search templates, a HTTP request is made to YaCy. The response is JSON because that can much better be parsed than XML in JavaScript.

In this blog, we will talk about how to install Susper search engine locally and deploying it to Heroku (A cloud application platform).

How to clone the repository

Sign up / Login to GitHub and head over to the Susper repository. Then follow these steps.

  1. Go ahead and fork the repository
https://github.com/fossasia/susper.com

2.   Get the clone of the forked version on your local machine using

git clone https://github.com/<username>/susper.com.git

3. Add upstream to synchronize repository using

git remote add upstream https://github.com/fossasia/susper.com.git

Getting Started

The Susper search application basically consists of the following :

  1. First, we will need to install angular-cli by using the following command:
npm install -g @angular/[email protected]

2. After installing angular-cli we need to install our required node modules, so we will do that by using the following command:

npm install

3. Deploy locally by running this

ng serve

Go to localhost:4200 where the application will be running locally.

How to Deploy Susper Search Engine to Heroku :

  1. We need to install Heroku on our machine. Type the following in your Linux terminal:
wget -O- https://toolbelt.heroku.com/install-ubuntu.sh | sh

This installs the Heroku Toolbelt on your machine to access Heroku from the command line.

  1. Create a Procfile inside root directory and write
web: ng serve
  1. Next, we need to login to our Heroku server (assuming that you have already created an account).

Type the following in the terminal:

heroku login

Enter your credentials and login.

  1. Once logged in we need to create a space on the Heroku server for our application. This is done with the following command
heroku create
  1. Add nodejs buildpack to the app
heroku buildpacks:add –index 1 heroku/nodejs
  1. Then we deploy the code to Heroku.
git push heroku master
git push heroku yourbranch:master # If you are in a different branch other than master

Resources

User Guide for the PSLab Remote-Access Framework

The remote-lab framework of the pocket science lab has been designed to enable user to access their devices remotely via the internet. The pslab-remote repository includes an API server built with Python-Flask and a webapp that uses EmberJS. This post is a guide for users who wish to test the framework. A series of blog posts have been previously written which have explored and elaborated various aspect of the remote-lab such as designing the API server, remote execution of function strings, automatic deployment on various domains etc. In this post, we shall explore how to execute function strings, execute example scripts, and write a script ourselves.

A live demo is hosted at pslab-remote.surge.sh . The API server is hosted at pslab-stage.herokuapp.com, and an API reference which is being developed can be accessed at pslab-stage.herokuapp.com/apidocs . A screencast of the remote lab is also available

Create an account

Signing up at this point is very straightforward, and does not include any third party verification tools since the framework is under active development, and cannot be claimed to be ready for release yet.

Click on the sign-up button, and provide a username, email, and password. The e-mail will be used as the login-id, and needs to be unique.

Login to the remote lab

Use the email-id used for signing up, enter the password, and the app will redirect you to your new home-page, where you will be greeted with a similar screen.

Your home-page

On the home-page, you will find that the first section includes a text box for entering a function string, and an execute button. Here, you can enter any valid PSLab function such as `get_resistance()` , and click on the execute button in order to run the function on the PSLab device connected to the API server, and view the results. A detailed blog post on this process can be found here.

Since this is a new account, no saved scripts are present in the Your Scripts section. We will come to that shortly, but for now, there are some pre-written example scripts that will let you test them as well as view their source code in order to copy into your own collection, and modify them.

Click on the play icon next to `multimeter.py` in order to run the script. The eye icon to the right of the row enables you to view the source code, but this can also be done while the app is running. The multimeter app looks something like this, and you can click on the various buttons to try them out.

You may also click on the Source Code tab in order to view the source

Create and execute a small python script

We can now try to create a simple script of our own. Click on the `New Python Script` button in the top-bar to navigate to a page that will allow you to create and save your own scripts. We shall write a small 3-line code to print some sinusoidal coordinates, save it, and test it. Copy the following code for a sine wave with 30 points, and publish your script.

import numpy as np
x=np.linspace(0,2*np.pi,30)
print (x, np.sin(x))

Create a button widget and associate a callback to the get_voltage function

A small degree of object oriented capabilities have also been added, and the pslab-remote allows you to create button widgets and associate their targets with other widgets and labels.
The multimeter demo script uses this feature, and a single line of code suffices to demonstrate this feature.

button('Voltage on CH1 >',"get_voltage('CH1')","display_number")

You can copy the above line into a new script in order to try it out.

Associate a button’s callback to the capture routines, and set the target as a plot

The callback target for a button can be set to point to a plot. This is useful if the callback involves arrays such as those returned by the capture routines.

Example code to show a sine wave in a plot, and make button which will replace it with captured data from the oscilloscope:

import numpy as np
x=np.linspace(0,2*np.pi,30)
plt = plot(x, np.sin(x))
button('capture 1',"capture1('CH1',100,10)","update-plot",target=plt)
Figure: Demo animation from the plot_test example. Capture1 is connected to the plot shown.
Resources

Creating an Elementary Oscilloscope in PSLab’s Remote Framework

The last couple of blog posts explained how we could put together the versatility of ember components, the visual appeal of jqplot, the flexibility of Python Flask, and the simplicity of Python itself in order to make simple scripts for PSLab that would could be run on a server by a remote client anywhere on the web. We have also seen how callbacks could be assigned to widgets created in these scripts in order to make object oriented applications. In this blog post, we shall see how to assign a capture method to a button, and update a plot with the received data. It will also demonstrate how to use ember-lodash to perform array manipulations.

Specifying the return data type in the callback success routine

For a more instructive write-up on assigning callbacks, please refer to these posts .

Whenever the callback assigned to a button is a function that returns an array of elements, and the target for the resultant data is a plot, the stacking order of the returned array must be specified in order to change its shape to suit the plotting library. The default return data from a capture routine (oscilloscope) is made up of separate arrays for X coordinate and Y coordinate values. Since JQplot requires [X,Y] pairs , we must specify a stacking order of ‘xy’ so that the application knows that it must convert them to pairs (using lodash/zip)  before passing the result to the plot widget. Similarly, different stacking orders for capture2, and capture4 must also be defined.

Creating an action that performs necessary array manipulations and plots the received data

It can be seen from the excerpt below, that if the onSuccess target for a callback is specified to be a plot in the actionDefinition object, then the stacking order is checked, and the returned data is modified accordingly

Relevant excerpt from controllers/user-home.js/runButtonAction

if (actionDefinition.success.type === 'update-plot') {
  if (actionDefinition.success.stacking === 'xy') {
    $.jqplot(actionDefinition.success.target, [zip(...resultValue)]).replot();
  } else if (actionDefinition.success.stacking === 'xyy') {
    $.jqplot(actionDefinition.success.target, [zip(...[resultValue[0], resultValue[1]]), zip(...[resultValue[0], resultValue[2]])]).replot();
  } else if (actionDefinition.success.stacking === 'xyyyy') {
    $.jqplot(actionDefinition.success.target, [zip(...[resultValue[0], resultValue[1]]), zip(...[resultValue[0], resultValue[2]]), zip(...[resultValue[0], resultValue[3]]), zip(...[resultValue[0], resultValue[4]])]).replot();
  } else {
    $.jqplot(actionDefinition.success.target, resultValue).replot();
  }
}

 

With the above framework in place, we can add a plot with the line plt = plot(x, np.sin(x)) , and associate a button with a capture routine that will update its contents with a single line of code: button(‘capture1’,”capture1(‘CH1’,100,10)”,”update-plot”,target=plt)

Final Result

The following script created on the pslab-remote platform makes three buttons and plots, and sets the buttons to invoke capture1, capture2, and capture4 respectively when clicked.

import numpy as np
x=np.linspace(0,2*np.pi,30)
plt = plot(x, np.sin(x))
button('capture 1',"capture1('CH1',100,10)","update-plot",target=plt)

plt2 = plot(x, np.sin(x))
button('capture 2',"capture2(50,10)","update-plot",target=plt2,stacking='xyy')

plt3 = plot(x, np.sin(x))
button('capture 4',"capture4(50,10)","update-plot",target=plt3,stacking='xyyyy')

 

 

 

 

 

 

 

 

 

 

 

 

Resources

 

Including a Graph Component in the Remote Access Framework for PSLab

The remote-lab software of the pocket science lab enables users to access their devices remotely via the Internet. It includes an API server designed with Python Flask, and a web-app designed with EmberJS that allows users to access the API and carry out various tasks such as writing and executing Python scripts. In a series of blog posts, various aspects of this framework such as  remote execution of function strings, automatic deployment on various domains, creating and submitting python scripts which will be run on the remote server etc have already been explored.  This blog post deals with the inclusion of a graph component in the webapp that will be invoked when the user utilises the `plot` command in their scripts.

The JQPLOT library is being used for this purpose, and has been found to be quite lightweight and has a vast set of example code .

Task list for enabling the plotting feature
  • Add a plot method to the codeEvaluator module in the API server and allow access to it by adding it to the evalGlobals dictionary
  • Create an EmberJS component for handling plots
    • Create a named div in the template
    • Invoke the Jqplot initializer from the JS file and pass necessary arguments and data to the jqplot instance
  • Add a conditional statement to include the jqplot component whenever a plot subsection is present in the JSON object returned by the API server after executing a script
Adding a plot method to the API server

Thus far, in addition to the functions supported by the sciencelab.py instance of PSLab, users had access to print, print_, and button functions. We shall now add a plot function.

def plot(self,x,y,**kwargs):
self.generatedApp.append({"type":"plot","name":kwargs.get('name','myPlot'),"data":[np.array([x,y]).T.tolist()]})

 

The X,Y datasets provided by the user are stacked in pairs because jqplot requires [x,y] pairs . not separate datasets.

We also need to add this to evalGlobals, so we shall modify the __init__ routine slightly:

self.evalGlobals['plot']=self.plot
Building an Ember component for handling plots

First, well need to install jqplot:   bower install –save jqplot

And this must be followed by including the following files using app.import statements in ember-cli-build.js

  • bower_components/jqplot/jquery.jqplot.min.js
  • bower_components/jqplot/plugins/jqplot.cursor.js
  • bower_components/jqplot/plugins/jqplot.highlighter.js
  • bower_components/jqplot/plugins/jqplot.pointLabels.js
  • bower_components/jqplot/jquery.jqplot.min.css

In addition to the jqplot js and css files, we have also included a couple of plugins we shall use later.

Now we need to set up a new component : ember g component jqplot-graph

Our component will accept an object as an input argument. This object will contain the various configuration options for the plot

Add the following line in templates/components/jqplot-graph.hbs:

style="solid gray 1px;" id="{{data.name}}">

The JS file for this template must invoke the jqplot function in order to insert a complete plot into the previously defined <div> after it has been created. Therefore, the initialization routine must override the didInsertElement routine of the component.

components/jqplot-graph.js

import Ember from 'ember';

export default Ember.Component.extend({
  didInsertElement () {
    Ember.$.jqplot(this.data.name,this.data.data,{
        title: this.title,

        axes: {
          xaxis: {
            tickInterval: 1,
            rendererOptions: {
            minorTicks: 4
            }
          },
        },
        highlighter: {
          show: true, 
          showLabel: true, 

          tooltipAxes: 'xy',
          sizeAdjust: 9.5 , tooltipLocation : 'ne'
        },				  
        legend: {
          show: true,
          location: 'e',
          rendererOptions: {
            numberColumns: 1,
          }
        },
        cursor:{ 
          show: true,
          zoom:true, 
          showTooltip:false
          } 

    });
  }
});

Our component is now ready to be used , and we must make the necessary changes to user-home.hbs in order to include the plot component if the output JSON of a script executed on the server contains it.

The following excerpt from the results modal shows how the plot component can be inserted

{{#each codeResults as |element|}}
	{{#if (eq element.type 'text')}}
		{{element.value}}<br>
	{{/if}}
	{{#if (eq element.type 'plot')}}
		{{jqplot-graph data=element}}
	{{/if}}
{{/each}}            

Most of the other components such as buttons and spans have been removed for clarity. Note that the element object is passed to the jqplot-graph component as an argument so that the component may configure itself accordingly.

In conclusion, the following screencast shows what we have created. A simple plot command creates a fancy plot in the output which includes data point highlighting, and can be easily configured to do a lot more. In the next blog post we shall explore how to use this plot to create a persistent application such as an oscilloscope.

Resources:

 

Working of One Click Deployment Buttons in loklak

Today’s topic is deployment. It’s called one-click deployment for a reason: Developers are lazy. It’s hard to do less than clicking on one button, so that’s our goal to make use of one click button in loklak.

For one click buttons we only need a central build server, which is our loklak_server. Everything written here was based on Apache ant, but later on ant build was deprecated and loklak server started to use gradle build. We wanted to make the process of provisioning and setting up a complete infrastructure of your own, from server to continuous integration tasks, as easy as possible. These button allows you to do all of that in one click.

How does it work?

You can see the one click buttons in the README page of loklak_server repository.

These repositories may include a different files like scalingo.json for scalingo, docker-compose.yml and docker-cloud.yml for docker cloud etc files at their root, allowing them to define a few things like a name, description, logo and build environment (Gradle build in the case of loklak server). Once you’ve clicked on any of the buttons, you will be redirected to respective apps and prompted with this information for you to review before confirming the fork.

This will effectively fork the repository in your account. Once the repo is ready, you can click on it. You will then be asked to “activate” or “deploy” your branch, allowing it to provision actual servers and run tasks. At the same time, you will be asked to review and potentially modify a few variables that were defined in the predefined files (for eg: app.json for heroku) of the apps. These are usually things like the Git URL of the repo for loklak, or some of the details related to the cloud provider you want to use (eg: Digital Ocean).

Once you confirmed this last step, your branch i.e., most probably master branch of loklak server repo is activated and the button will start provisioning and configuring your servers, along with the tasks which may allow you to build and deploy your app. In most of the cases, you can go to the tasks/setup section and run the build task that will fetch loklak server’s code, build it and deploy it on your server, all configurations included and will get a public IP.

What’s next

In loklak we are also introducing new one click “AZURE” button, then the users can also start deploying loklak in azure platform.

Resources

PSLab Remote Lab: Automatically deploying the EmberJS WebApp and Flask API Server to different domains

The remote-lab software of the pocket science lab enables users to access their devices remotely via the internet. Its design involves an API server designed with Python Flask, and a web-app designed with EmberJS that allows users to access the API and carry out various tasks such as writing and executing Python scripts. For testing purposes, the repository needed to be setup to deploy both the backend as well as the webapp automatically when a build passes, and this blog post deals with how this can be achieved.

Deploying the API server

The Heroku PaaS was chosen due to its ease of use with a wide range of server software, and support for postgresql databases. It can be configured to automatically deploy branches from github repositories, and conditions such as passing of a linked CI can also be included. The following screenshot shows the Heroku configuration page of an app called pslab-test1. Most of the configuration actions can be carried out offline via the Heroku-Cli

 

In the above page, the pslab-test1 has been set to deploy automatically from the master branch of github.com/jithinbp/pslab-remote . The wait for CI to pass before deploy has been disabled since a CI has not been setup on the repository.

Files required for Heroku to deploy automatically

Once the Heroku PaaS has copied the latest commit made to the linked repository, it searches the base directory for a configuration file called runtime.txt which contains details about the language of the app and the version of the compiler/interpretor to use, and a Procfile which contains the command to launch the app once it is ready. Since the PSLab’s API server is written in Python, we also have a requirements.txt which is a list of dependencies to be installed before launching the application.

Procfile

web: gunicorn app:app –log-file –

runtime.txt

python-3.6.1

requirements.txt

gunicorn==19.6.0
flask >= 0.10.1
psycopg2==2.6.2
flask-sqlalchemy
SQLAlchemy>=0.8.0
numpy>=1.13
flask-cors>=3.0.0

But wait, our app cannot run yet, because it requires a postgresql database, and we did not do anything to set up one. The following steps will set up a postgres database using the heroku-cli usable from your command prompt.

  • Point Heroku-cli to our app
    $ heroku git:remote -a pslab-test1
  • Create a postgres database under the hobby-dev plan available for free users.
    $ heroku addons:create heroku-postgresql:hobby-dev

    Creating heroku-postgresql:hobby-dev on ⬢ pslab-test1… free
    Database has been created and is available
    ! This database is empty. If upgrading, you can transfer
    ! data from another database with pg:copy
    Created postgresql-slippery-81404 as HEROKU_POSTGRESQL_CHARCOAL_URL
    Use heroku addons:docs heroku-postgresql to view documentation

  • The previous step created a database along with an environment variable HEROKU_POSTGRESQL_CHARCOAL_URL . As a shorthand, we can also refer to it simply as CHARCOAL .
  • In order to make it our primary database, it must be promoted

    $ heroku pg:promote HEROKU_POSTGRESQL_CHARCOAL_URL
    The database will now be available via the environment variable DATABASE_URL

  • Further documentation on creating and modifying postgres databases on Heroku can be found in the articles section .

At this point, if the app is in good shape, Heroku will automatically deploy its contents to pslab-test1.herokuapp.com. We can test it using a developer tool such as Postman, or make our own webapp to use it.

Deploying the EmberJS WebApp

Since we are using the free plan on Heroku which only allows one dyno, our EmberJS webapp which shares the repository cannot be deployed on the same heroku server. Therefore, we must look for other domains where the frontend can be deployed.

Surge.sh allows easy deployment of Ember apps, and we shall set up our CI’s configuration file .travis.yml to do this for us when a pull request is made, and the build passes

This excerpt from .travis.yml only shows parts relevant to deployment on Surge.sh

after_success:
– pushd frontend
– bash surge_deploy.sh
– popd

Once the build has passed, the after_success hook executes a script called surge_deploy.sh which is located in the directory of the webapp.

Contents of surge_deploy.sh

#!/usr/bin/env bash
if [ “$TRAVIS_PULL_REQUEST” == “false” ]; then
echo “Not a PR. Skipping surge deployment”
exit 0
fi

ember build –environment=’production’

export REPO_SLUG_ARRAY=(${TRAVIS_REPO_SLUG//\// })
export REPO_OWNER=${REPO_SLUG_ARRAY[0]}
export REPO_NAME=${REPO_SLUG_ARRAY[1]}

npm i -g surge

# Details of a dummy account. So can be added to vcs.
export SURGE_LOGIN=j********[email protected]
export SURGE_TOKEN=4********************************f
export DEPLOY_DOMAIN=https://${REPO_NAME}.surge.sh
surge –project ./dist –domain $DEPLOY_DOMAIN;

The variables SURGE_LOGIN and SURGE_TOKEN must be specified, otherwise Surge will open a login prompt, and since there is no way to feed details into a prompt in a Travis build, it will timeout and fail. The surge token can be obtained with a simple `surge login` followed by `surge token` on your system’s terminal.

Final Application

A user’s homepage on the webapp deployed at pslab-remote.surge.sh . The EmberJS app has been configured to send all AJAX requests to the API server located at pslab-remote.herokuapp.com .

Resources

One Click Deployment Button for loklak Using Heroku with Gradle Build

The one click deploy button makes it easy for the users of loklak to get their own cloud instance created and deployed in their heroku account and can be used according to their flexibility. Heroku uses an app.json manifest in the code repo to figure out what add-ons, config and other deployment steps are required to make the code run. This is used to configure and deploy the app.

Once you have provide the app name and then click on deploy button, Heroku will start deploying the loklak server to a new app on your account:

When setup is complete, you can open the deployed app in your browser or inspect it in Dashboard.

All these steps and requirements can now be encoded in an app.json file and placed in a repo alongside a button that kicks off the setup with a single click.

App.json is a manifest format for describing apps and specifying what their config requirements are. Heroku uses this file to figure out how code in a particular repo should be deployed on the platform. Here is the loklak’s app.json file which used gradle build pack:

{
	"name": "Loklak Server",
	"description": "Distributed Tweet Search Server",
	"logo": "https://raw.githubusercontent.com/loklak/loklak_server/master/html/images/loklak_anonymous.png",
	"website": "http://api.loklak.org",
	"repository": "https://github.com/loklak/loklak_server.git",
	"image": "loklak/loklak_server:latest-master",
	"env": {
		"BUILDPACK_URL": "https://github.com/heroku/heroku-buildpack-gradle.git"
	}
}

 

If you are interested you can try deploying the peer from here itself. Checkout how simple it can be to deploy.

Deploy button:

Deploy

Resources: