Python + MongoDB Assessment API

This project is a RESTful API for managing employee data, built with FastAPI and MongoDB. It provides a full suite of CRUD operations, advanced querying capabilities, and JWT-based authentication.

Features

• FastAPI: A modern, fast (high-performance) web framework for building APIs.
• MongoDB: A NoSQL database for storing employee documents.
• Pydantic V2: For robust data validation and settings management.
• JWT Authentication: Secure endpoints for creating, updating, and deleting data.
• Advanced Querying:
  • List and paginate employees by department.
  • Search for employees by their skills.
  • Aggregate data to find the average salary per department.
• Full Test Suite: Unit and integration tests written with pytest.
• Docker Support: Instructions for running a local MongoDB instance with Docker.

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Getting Started

Follow these instructions to get the project up and running on your local machine.

Prerequisites

• Python 3.10+
• Pip (Python package installer)
• Docker (for running MongoDB)

1. Clone the Repository

git clone <repository_url>
cd <repository_directory>

2. Set Up a Virtual Environment

It's recommended to use a virtual environment to manage project dependencies.

# Create a virtual environment
python -m venv venv

# Activate the virtual environment
# On Windows
venv\Scripts\activate
# On macOS/Linux
source venv/bin/activate

3. Install Dependencies

Install all the required packages from requirements.txt.

pip install -r requirements.txt

4. Set Up Environment Variables

The application uses a .env file to manage configuration. Create one by copying the example file.

cp .env.example .env

You can modify the .env file if needed, but the defaults are configured for a standard local setup. Remember to change SECRET_KEY for a production environment.

5. Run MongoDB with Docker

The easiest way to run a local MongoDB instance is with Docker.

docker run -d -p 27017:27017 --name assessment-mongo mongo

This command will download the official MongoDB image and start a container in the background.

6. Run the Application

Use uvicorn to run the FastAPI application.

uvicorn app.main:app --reload --port 8000

The API will now be available at http://127.0.0.1:8000. You can access the interactive Swagger documentation at http://127.0.0.1:8000/docs.

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Running the Tests

To run the test suite, ensure you have followed the setup steps and have the MongoDB container running.

python -m pytest

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API Endpoints

Here are some curl examples for interacting with the API.

Authentication

First, you need to get a JWT token to access the protected routes. The test user credentials are:

• Username: testuser
• Password: testpassword

Get a Token

curl -X POST "http://127.0.0.1:8000/auth/token" \
-H "Content-Type: application/x-www-form-urlencoded" \
-d "username=testuser&password=testpassword"

The response will contain your access_token.

{
  "access_token": "your_jwt_token_here",
  "token_type": "bearer"
}

Export the token to a variable for easier use in subsequent requests:

export TOKEN="your_jwt_token_here"

Core CRUD APIs

1. Create Employee (POST /employees) Protected Route

curl -X POST "http://127.0.0.1:8000/employees/" \
-H "Authorization: Bearer $TOKEN" \
-H "Content-Type: application/json" \
-d '{
  "employee_id": "E123",
  "name": "John Doe",
  "department": "Engineering",
  "salary": 75000,
  "joining_date": "2023-01-15",
  "skills": ["Python", "MongoDB", "APIs"]
}'

2. Get Employee by ID (GET /employees/{employee_id})

curl -X GET "http://127.0.0.1:8000/employees/E123"

3. Update Employee (PUT /employees/{employee_id}) Protected Route

curl -X PUT "http://127.0.0.1:8000/employees/E123" \
-H "Authorization: Bearer $TOKEN" \
-H "Content-Type: application/json" \
-d '{
  "salary": 80000,
  "skills": ["Python", "MongoDB", "FastAPI"]
}'

4. Delete Employee (DELETE /employees/{employee_id}) Protected Route

curl -X DELETE "http://127.0.0.1:8000/employees/E123" \
-H "Authorization: Bearer $TOKEN"

Querying & Aggregation

5. List Employees by Department (GET /employees) With pagination and filtering.

# Filter by department and get the first page
curl -X GET "http://127.0.0.1:8000/employees/?department=Engineering&page=1&page_size=5"

6. Average Salary by Department (GET /employees/avg-salary)

curl -X GET "http://127.0.0.1:8000/employees/avg-salary"

7. Search Employees by Skill (GET /employees/search)

curl -X GET "http://127.0.0.1:8000/employees/search?skill=Python"