Billogram-Tech-Challenge

This is the repo containing Nagasudeep's solution to the coding challenge put forth by billogram. The application comprises of 2 endpoints for generation of discount codes and also for fetching the discount code assigned to a particular brand.

1.User Guide and Setup

1. In case you havent already, Install Python.
2. You will need PostgreSQL for managing your data store create your schema here and below I will show some commands used for connecting and interacting with the table in python.
3. Install Docker to create an image for you
4. Clone this repo: $ git clone https://github.com/nagavem/Billogram-Tech-Challenge.git
5. Install the dependencies: $ pip install -r requirements.txt
6. You can use swaggerUI at (http://127.0.0.1:8000/docs) but i recommend using Postman for API testing.
7. Run the app server using the command : uvicorn application:app --reload
8. The service is now exposed at (http://127.0.0.1:8000/) with the endpoints discount_code for discount generation and show_discounts for fetching the discount for a specific brand.

Database

The database is hosted locally on postgreSQL and is a simple one comprising of the Brands table where the brand_name, brand id and the discount codes are stored.

The following code in apllication.py shows how database connection was established following which cursor was used in combination with python to write and execute queries in code.

while True:
    try :
        conn = psycopg2.connect(host = 'localhost',database = 'fastapi',user = 'postgres',password= 'Lumosity@24',cursor_factory=RealDictCursor)
        cursor = conn.cursor()
        print("Database connection succesfull")
        break

    except Exception as error:
        print("Connection failed")
        print("Error: ",error)
        time.sleep(2)

Query Examples

'update "Brands" SET discount_codes = %s where brand_name = %s'
'select * from "Brands" where brand_name = %s'

2.Tools/Frameworks and Reasoning

FastAPI:

FastAPI was chosen as the API framework due to its support of asynchronous code, ease of development which leads to great speed in implementation and makes it easy to test.

PostGreSQL:

Due to the structured nature of the data for the given context a relational database was the chosen approach. PostgreSQL is usually a popular choice especially for production environments and has been chosen for this project. Additionally it supports ACID transactions robustly and has a vast community making it easy to gain support in almost all areas.

Postman

Postman is the goto API testing tool regardless of the assignment domain or context!! With capabilities to run tests in different environments and extensive documentation making it easy to use I would recommend that this be the one stop tool when it comes to testing the endpoints.

3.Limitations

This section discusses the limitations brought about largely due to the time constraints.

1. To start on a meta note: The documentation and this readme itself for that matter has plenty of scope for improvement 😅 with several features not being discussed in depth.Future work could involve creation of a project wiki 💻 💻.

2. DockerFile has been included however despite building, the application was not implemented by hosting on a docker image. Containerization is an important next step.:whale: :whale:

3. Data validation has not been factored in at any point.

4. Security guidelines not in place for db and even the application endpoints. 👮‍♂️ 👮‍♀️

5. The discount code obtained in the second service does not get flushed and persists in the db after being retrieved. Functionality can be improved to take care of this.

6. Commits were not regular and detailed.