FireMongo

This project is a REST API for storing and retrieving data documents. It allows users to create new data documents by sending a POST request to the API endpoint.

The API supports the creation of multiple data documents at once, each identified by a unique ID. The created documents are stored in a MongoDB database.

The API also supports retrieving data documents by their ID, using a GET request to the appropriate endpoint. The project uses Python and the FastAPI web framework, with asynchronous programming using the asyncio library. It also uses the PyMongo library for interfacing with MongoDB.

The API includes error handling and input validation to ensure data integrity and prevent unexpected errors.

Getting Started

Setup of development environment

1. Create a new Python environment and activate.

   Conda (from scratch)

   export PYTHON_VERSION=3.10.10
   conda create --name fastapi python=PYTHON_VERSION
   conda activate fastapi

   Conda environment.yml file

   conda env create -f conda-environment.yml

   Virtual environment

   python -m venv ENV
   source ENV/bin/activate

2. Install dependencies in your environments

   pip install -r requirements.txt
   pip install -r requirements-dev.txt

Setup Environment Variables for the App

Requirements

• You need have you own MongoDB Atlas Cluster
• Get the connection details including the URI, username and password

Creating a .env file

• Create a new environment file named .env
• Copy the environment variables from example.env file from the root directory and paste it into the .env file created above
• Add the MongoDB Atlas URI with username and password next to the MONGODB_URI environment variable
• To generate a the secret key, run the following command:

  openssl rand -hex 32

Run the APP

Locally

Run the command below in the terminal

1. Linux

   scripts/server.sh

2. Windows

   uvicorn app.main:app --reload

3. Run with Uvicorn multiple workers

   uvicorn app.main:app --host 0.0.0.0 --port 8000 --workers 4

4. Run with Gunicorn & Uvicorn

   gunicorn app.main:app --workers 4 --worker-class uvicorn.workers.UvicornWorker --bind 0.0.0.0:8000

Deploy

Deploy on Docker

• Build the docker image with the following tag

  docker build . -t {DOCKERHUB_USERNAME}/firebase-realtime-db-emulator:latest

• Create and run the container

  docker compose up

Deploy on Okteto

okteto login
okteto deploy --build

About

The theme of this semester’s project was emulation where the goal was to develop a prototype system that emulates the interface and working of a big data system.

Project Developed for DSCI 551: Foundations of Data Management | Spring 2023

Developed By Kayvan Shah | M.S. in Applied Data Science | University of Southern California

References

1. Firebase. (n.d.). Use the Firebase Realtime Database REST API

2. The MongoDB documentation

3. MongoDB Atlas. (2021). Cloud-hosted MongoDB

4. Okteto. (2021). Okteto Cloud Documentation. Okteto Cloud.

5. Sebastian Ramirez et al. FastAPI. 2020. [Online].

6. Deta. (n.d.). Deta Space Documentation

7. Docker. (2021). Docker Documentation