Creating a Notification in Open Event Android App

It is a good practice to show user a notification for alerts and have their attention for important events they want to remember. Open Event Android app shows notifications for the actions like bookmarks, upcoming events etc. In this blog we learn how to create similar kind of alert notification.


Displaying notification after bookmarking a track

NotificationCompat is available as part of the Android Support Library, so the first step is opening your project’s module-level build.gradle file and adding the support library to the dependencies section. First we initialize the notification manager with the context of application so a user can see notification irrespective of where it is in app.

NotificationManager mManager = (NotificationManager) this.getApplicationContext().getSystemService(NOTIFICATION_SERVICE);
int id = intent.getIntExtra(ConstantStrings.SESSION, 0);
String session_date;
Session session = realmRepo.getSessionSync(id);

We then get the info we want to display in the notification from the intent. While adding an action to your notification is optional, the reality is that the vast majority of applications add actions to their notifications. We define a notification action using a PendingIntent. In this instance, we update our basic notification with a PendingIntent.

Intent intent1 = new Intent(this.getApplicationContext(), SessionDetailActivity.class);
intent1.putExtra(ConstantStrings.SESSION, session.getTitle());
intent1.putExtra(ConstantStrings.ID, session.getId());
PendingIntent pendingNotificationIntent = PendingIntent.getActivity(this.getApplicationContext(), 0, intent1, PendingIntent.FLAG_UPDATE_CURRENT);
Bitmap largeIcon = BitmapFactory.decodeResource(getResources(), R.mipmap.ic_launcher);

We also test the condition for the OS version to display the marker image, see image 1 for reference. The minimum requirement for a notification are:

  • An icon: Create the image you want to use and then add it to you project’s ‘drawable’ folder. Here notification shows bookmark option
  • Title text. You can set a notification’s title either by referencing a string resource, or by adding the text to your notification directly.
  • Detail text. This is the most important part of your notification, so this text must include everything the user needs to understand exactly what they’re being notified about.
int smallIcon = R.drawable.ic_bookmark_white_24dp;
if (Build.VERSION.SDK_INT < Build.VERSION_CODES.LOLLIPOP) smallIcon = R.drawable.ic_noti_bookmark;

String session_timings = String.format("%s - %s",
       DateConverter.formatDateWithDefault(DateConverter.FORMAT_12H, session.getStartsAt()),
       DateConverter.formatDateWithDefault(DateConverter.FORMAT_12H, session.getEndsAt()));
session_date = DateConverter.formatDateWithDefault(DateConverter.FORMAT_DATE_COMPLETE, session.getStartsAt());

Finally we build notification using notification builder having various options to set text style, small icons, big icon etc., see the complete class here,

NotificationCompat.Builder mBuilder = new NotificationCompat.Builder(this)
       .setContentText(session_date + "\n" + session_timings)
       .setStyle(new NotificationCompat.BigTextStyle().bigText(session_date + "\n" + session_timings))



UI automated testing using Selenium in Badgeyay

With all the major functionalities packed into the badgeyay web application, it was time to add some automation testing to automate the review process in case of known errors and check if code contribution by contributors is not breaking anything. We decided to go with Selenium for our testing requirements.

What is Selenium?

Selenium is a portable software-testing framework for web applications. Selenium provides a playback (formerly also recording) tool for authoring tests without the need to learn a test scripting language. In other words, Selenium does browser automation:, Selenium tells a browser to click some element, populate and submit a form, navigate to a page and any other form of user interaction.

Selenium supports multiple languages including C#, Groovy, Java, Perl, PHP, Python, Ruby and Scala. Here, we are going to use Python (and specifically python 2.7).

First things first:
To install these package run this code on the CLI:

pip install selenium==2.40
pip install nose

Don’t forget to add them in the requirements.txt file

Web Browser:
We also need to have Firefox installed on your machine.

Writing the Test
An automated test automates what you’d do via manual testing – but it is done by the computer. This frees up time and allows you to do other things, as well as repeat your testing. The test code is going to run a series of instructions to interact with a web browser – mimicking how an actual end user would interact with an application. The script is going to navigate the browser, click a button, enter some text input, click a radio button, select a drop down, drag and drop, etc. In short, the code tests the functionality of the web application.

A test for the web page title:

import unittest
from selenium import webdriver

class SampleTest(unittest.TestCase):

    def setUpClass(cls):
        cls.driver = webdriver.Firefox()

    def test_title(self):
        self.assertEqual(self.driver.title, 'Badgeyay')

    def tearDownClass(cls):


Run the test using nose

Clicking the element
For our next test, we click the menu button, and check if the menu becomes visible.

elem = self.driver.find_element_by_css_selector(".custom-menu-content")


Uploading a CSV file:
For our next test, we upload a CSV file and see if a success message pops up.

def test_upload(self):
        Imagepath = os.path.abspath(os.path.join(os.getcwd(), 'badges/badge_1.png'))
        CSVpath = os.path.abspath(os.path.join(os.getcwd(), 'sample/vip.png.csv'))
        self.driver.find_element_by_css_selector("form .btn-primary").click()
        success = self.driver.find_element_by_css_selector(".flash-success")
        self.assertIn(u'Your badges has been successfully generated!', success.text)


The entire code can be found on:

We can also use the Phantom.js package along with Selenium for UI testing purposes without opening a web browser. We use this for badgeyay to run the tests for every commit in Travis CI which cannot open a program window.


Open Event Server: Creating/Rebuilding Elasticsearch Index From Existing Data In a PostgreSQL DB Using Python

The Elasticsearch instance in the current Open Event Server deployment is currently just used to store the events and search through it due to limited resources.

The project uses a PostgreSQL database, this blog will focus on setting up a job to create the events index if it does not exist. If the indices exists, the job will delete all the previous the data and rebuild the events index.

Although the project uses Flask framework, the job will be in pure python so that it can run in background properly while the application continues its work. Celery is used for queueing up the aforementioned jobs. For building the job the first step would be to connect to our database:

from config import Config
import psycopg2
conn = psycopg2.connect(Config.SQLALCHEMY_DATABASE_URI)
cur = conn.cursor()


The next step would be to fetch all the events from the database. We will only be indexing certain attributes of the event which will be useful in search. Rest of them are not stored in the index. The code given below will fetch us a collection of tuples containing the attributes mentioned in the code:

       "SELECT id, name, description, searchable_location_name, organizer_name, organizer_description FROM events WHERE state = 'published' and deleted_at is NULL ;")
   events = cur.fetchall()


We will be using the the bulk API, which is significantly fast as compared to adding an event one by one via the API. Elasticsearch-py, the official python client for elasticsearch provides the necessary functionality to work with the bulk API of elasticsearch. The helpers present in the client enable us to use generator expressions to insert the data via the bulk API. The generator expression for events will be as follows:

event_data = ({'_type': 'event',
                  '_index': 'events',
                  '_id': event_[0],
                  'name': event_[1],
                  'description': event_[2] or None,
                  'searchable_location_name': event_[3] or None,
                  'organizer_name': event_[4] or None,
                  'organizer_description': event_[5] or None}
                 for event_ in events)


We will now delete the events index if it exists. The the event index will be recreated. The generator expression obtained above will be passed to the bulk API helper and the event index will repopulated. The complete code for the function will now be as follows:


def cron_rebuild_events_elasticsearch():
   Re-inserts all eligible events into elasticsearch
   conn = psycopg2.connect(Config.SQLALCHEMY_DATABASE_URI)
   cur = conn.cursor()
       "SELECT id, name, description, searchable_location_name, organizer_name, organizer_description FROM events WHERE state = 'published' and deleted_at is NULL ;")
   events = cur.fetchall()
   event_data = ({'_type': 'event',
                  '_index': 'events',
                  '_id': event_[0],
                  'name': event_[1],
                  'description': event_[2] or None,
                  'searchable_location_name': event_[3] or None,
                  'organizer_name': event_[4] or None,
                  'organizer_description': event_[5] or None}
                 for event_ in events)
   abc = helpers.bulk(es_store, event_data)


Currently we run this job on each week and also on each new deployment. Rebuilding the index is very important as some records may not be indexed when the continuous sync is taking place.

To know more about it please visit

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