ob Movies

This is a short guide on setting up the system and environment dependencies required for ob’s Movies database.

Your Mission

Greetings Earthling! You have been selected to test your skills among the Team Awesome,
DevOps ?

This challenge contains a directory – ob-devopsday/obmovies – with a Python project and a mongoDB dataset dump
(available on the releases section of the repo). The package dependencies for Python are resolved using pip. The project
was built using Flask framework. The application has some errors/omissions that are purposefully made. Once you have
solved the connection issue run pytest to confirm


  1. Solve the following tickets/TODOs in db.py:
    1. Paging,
    2. Faceted Search and
    3. User Management.
  2. Containerize obmovies webapp and deploy into a Kubernetes solution/tool of your choice.
  3. Use helm chart or daemonset templates to deploy the webapp.
Brownie points:
  • Create a CICD pipeline to build the app and deploy to your cluster.
  • Codify the creation of your cluster.

Movies Application Structure

Everything you will implement is located in the obmovies/db.py file, which contains all database interfacing methods.
The API will make calls to db.py to interact with a MongoDB. You have the option of using a local mongo server or
sign up for a free tiered Mongo Atlas account.

The unit tests in tests will verify these database access methods directly, without going through the API. The UI will
run these methods in integration tests, and therefore requires the full application to be running.

The API layer is fully implemented, as is the UI. If you need to run on a port other than 5000, you can edit the
index.html file in the build directory to modify the value of window.host.

Please do not modify the API layer in any way, movies.py and user.py under the obmovies/api directory. Doing
so will most likely result in the frontend application failing to validate some of the labs.

Local Development Environment Configuration

MongoDB Atlas and Compass

Mongo Atlas offers a free tiered option that provides sample datasets. You will be using the mflix collection for
this challenge. Optional: If you are interested in running a local mongoDB use the mongod-sampledata.gz available on the
Releases tab in git

Sign up for a free-tiered Mongo Atlas account. Create a project and a cluster with a name of your choosing. During the
creation of the cluster select “Load Sample Dataset”. You will need to whitelist your IP address to gain access.


Use virtualenv, to define your Python 3 environment.
You are required to have a Python 3 installed in your workstation.

You can find the virtualenv installation procedure on the PyPA website.

Once you’ve installed Python 3 and virtualenv, you will have to setup a
virtualenv environment:

  • Navigate to the obmovies python directory: cd obmovies
  • Create the virtual environment for obmovies: virtualenv -p YOUR_LOCAL_PYTHON3_PATH obmovies_venv
  • Activate the virtual environment: source obmovies_venv/bin/activate
  • You can deactivate the virtual environment with the following command: deactivate

Documentation: virtualenv installation

Python Library Dependencies

Once the Python 3 environment is activated, we need to install our python dependencies. Dependencies are defined in
requirements.txt, and can be installed with the following command: pip install -r requirements.txt

Running the Application

In the obmovies directory there are two files, called dotini_unix and dotini_win.

Rename this file to .ini with the following command:

mv dotini_unix .ini  # on Unix
ren dotini_win .ini # on Windows

Once the file has been renamed, open it, and enter your Atlas or local mongo server connection string as directed in the
comment. This is the information the driver will use to connect!

You can confirm connection to the database by running the unit test: pytest -m connection

To start the obmovies application, run the following command: python run.py

And then point your browser to: http://localhost:5000/

Running the Unit Tests

To run the unit tests for this challenge, you will use pytest. The challenges solved contains a module of unit tests
that you can call individually with a command like the following: pytest -m LAB_UNIT_TEST_NAME

Each ticket will contain the command to run that ticket’s specific unit tests.

When you are finished

  • You can submit your solution to [email protected]
  • Update README.MD so we know how to run your solution and the Kubernetes cluster solution/tool we should use.
  • Leave the git history in tact and don’t fork this repo as other candidates could then start building on your solution ?

Simple steps tp run this app


  • Docker – v18.09.7
  • Mk v1.17.0 running k8s 1.18

Second step :

  • Insert correct values and setup dotini_unix and rename to .ini ( as security measure it wont get uploaded to the public repo and ignored )

Third step:

  • cd obmovies && docker-compose up
    #can exit using ^+C
  • kubectl create -f deployment.yaml


View Github