Bungalow Take Home Project for Backend Developer Role

About This Project

This is a Django based assignment. We have created a base project for you to work from. You are free to vary from our original base if you would like to. We provide it with the intention of providing a common base for all candidates to work from and to hopefully save you a bit of time.

If you need an introduction to Django, their docs are an excellent place to start: https://docs.djangoproject.com/en/3.2

We encourage you to use the Django Rest Framework for developing your API. This is a framework that we use extensively at Bungalow, and it provides some nice functionality out of the box. https://www.django-rest-framework.org/

What to Build

We would like you to build an API that can be used to query some information about houses. Sample data is provided in the sample-data folder. We have provided the stub for a Django command to import the data. Finish writing this code. You should use Django's ORM to model the data and store it in a local database. Then, utilize the Django Rest Framework to provide an API to query the models. A very basic API design here would simply return all of the data available. You can choose to improve and refine this very basic API design, and we encourage you to do so. This will give us an opportunity to see how you approach API design. If you are running out of time, you can outline how you would have done things differently given more time.

How Will This Be Evaluated

We will use this project as our basis for our evaluation of your coding skill level as it relates to our team. To do this, we will review your code with an eye for the following:

• Design Choices - choice of functionality, readability, maintainability, extendability, appropriate use of language/framework features
• Does it work as outlined
• Testing - have you considered how you'd test your code?
• Documentation - have you provided context around decisions and assumptions that you have made?
• Polish - have you produced something that would be ready to go into a production system? if not, have you clearly stated what would be needed to get from where it is to that level of polish?

Time Expectations

We know you are busy and likely have other commitments in your life, so we don't want to take too much of your time. We don't expect you to spend more than 2 hours working on this project. That being said, if you choose to put more or less time into it for whatever reason, that is your choice. Feel free to indicate in your notes below if you worked on this for a different amount of time and we will keep that in mind while evaluating the project. You can also provide us with additional context if you would like to. Additionally, we have left a spot below for you to note. If you have ideas for pieces that you would have done differently or additional things you would have implemented if you had more time, you can indicate those in your notes below as well, and we will use those as part of the evaluation. For example, if you would have tested more, you can describe the tests that you would have written, and just provide 1 or 2 actual implemented tests.

Public Forks

We encourage you to try this project without looking at the solutions others may have posted. This will give the most honest representation of your abilities and skills. However, we also recognize that day-to-day programming often involves looking at solutions others have provided and iterating on them. Being able to pick out the best parts and truly understand them well enough to make good choices about what to copy and what to pass on by is a skill in and of itself. As such, if you do end up referencing someone else's work and building upon it, we ask that you note that as a comment. Provide a link to the source so we can see the original work and any modifications that you chose to make.

Setup Instructions

1. Fork this repository and clone to your local environment. If you make your fork private, please give access to the bungalow-engineering user.
2. Install a version of Python 3 if you do not already have one. We recommend Python 3.8 or newer.
3. You can use the built-in virtual environment creation within Python to create a sandboxed set of package installs. If you already have a preferred method of virtualenv creation, feel free to proceed with your own method. python -m venv env
4. You will need to activate your virtual environment each time you want to work on your project. Run the activate script within the env/bin folder that was generated.
5. We have provided a requirements.txt file you can use to install the necessary packages. With your virtualenv activated run: pip install -r requirements.txt
6. To run the django server run python manage.py runserver
7. To run the data import command run python manage.py import_house_data
8. You are now setup and ready to start coding.

Your Notes

Time Spent

Approximately 2.5 hours were spent on the completion of this project. Time breakdown w.r.t the subtask:

Subtask	Restructuring repo and Dockerizing	Analysing data, creating Home model and implementing import_house_data.py	Adding Serizalizer and Views for Listing homes and performing CRUD operations on Home model	Adding Unittests	Documentation
Hours spent	0.5	0.75	0.5	0.25	0.5

Assumptions

• Since the task requirement and project was initially setup for read-only users, listing homes and retrieving a single home features work fine. Create, Update and Delete operations are added but doesn't work as it would require setting up authentication process.
• Zillow data's relation to each home was unclear (for example, is zillow id always unique for each house?), therefore, a single model was created.
• As the code could be made production ready, instead of using virtual environment, the repository is restructured and dockerized.
• PostGIS database is used to enable adding any spatial data about homes in future.

Next Steps

Features

• Setting authentication and authorization process to allow users to perform complete CRUD operations on home instances.
• Update model to include validations on some fields like year_built, tax_year, num_bathrooms and num_bedrooms.
• Update List API to enhance filtering options. For instance, users should be able to search according to range of prices, year_built as well by address.
• Autocomplete address feature can be added for searching by address
• Add pagination to List API endpoint to limit the number of results per page

Testing

• Add tests for all crud operations and cover more scenarios for each endpoint
• Integrate CircleCI for running tests

How to Use

Project Setup

1. Follow the instructions on https://docs.docker.com/install/ to install docker
2. Follow the instructions on https://docs.docker.com/compose/install/ to install docker compose
3. Create a .env file under listings directory and copy the contents from .env.sample in it. Contact the developer for credentials.
4. From the project root, cd listings and run the following command to start the application server: sudo docker-compose up --build
5. docker-compose will run entrypoint.sh that handles model migrations as well as running import_house_data script to populate Home table using sample-data/data.csv
6. The app will run locally at http://127.0.0.1:8001

Interacting with API endpoints

In your browser, try the following URLs:

List All Homes

GET http://127.0.0.1:8001/api/list-all-homes/

Filter Homes by home_type, state, zipcode, num_bathrooms, num_bedrooms

Listing all MultiFamily2To4 type homes

GET http://127.0.0.1:8001/api/list-all-homes/?home_type=MultiFamily2To4&state=&zipcode=&num_bathrooms=&num_bedrooms=

Retrieve a Home instance

From the list API results, take uuid of any home instance and replace in the following URL:

GET http://127.0.0.1:8001/api/home/<home_uuid>/

It should return the same home instance as available in the List API results

Running Unittests

While the docker container is running, in a new terminal try the following:

• docker exec -it listings-backend /bin/sh
• python manage.py test api.tests