Monk Repo

This repo consists of tasks for Monk Commerce as mentioned in the doc here.

I mainly wanted to highlight that I am efficient and adaptable working with different tech stacks. That's the reason I have 2 implementations here in this repo. I have Java and Python implementations. Given the time I can do the same with Express too in either TS/JS. I have also worked with C/C++ for performance based tooling requirement and I am also learning Go recently.

I believe this gives a good idea of my adaptabilty as per the requirement.

---

Repo structure

Below is the list of major components in the repo.

1. java_implementation: Consists of Java implementation of APIs.
2. python_implementation: Consists of Python implemenation of APIs.
3. utils: Consists of code to generate dummy data for testing.
4. Coupons.postman_collection.json: Consists of Json data for postman requests.

---

Tech Stack being used

SQLite DB:

I wanted to keep the db simple to use and test. So I am using SQLite. I believe this fits well for the current use case and I can anytime upgrade to other DBs like Postgres, MySQL etc.

Java Spring Boot:

I am using Java 17 and Spring Boot for writng the APIs and using Hibernate to do the DB transactions with SQLite.

Python FastAPI:

I am using python 3.11.5 with FastAPI for writing the APIs and using SQLAlchemy to do the DB transactions with SQLite.

Postman Collection for Test:

You can setup the Postman collection using the Coupons.postman_collection.json file to test the API calls after running the HTTP servers.

---

Setting up the Projects

Setting up the Java Project

Assuming you have already cloned the repo.

You can just go to java_implementation directory in your terminal and run the following command.

./mvnw spring-boot:run

This should run the server on http://127.0.0.1:3000. This also creates the java_implementation/db/monk.db DB file if not already present.

You can change the settings in java_implementation/src/main/resources/application.properties file.

Setting up the Python Project

Assuming you have already cloned the repo.

You can just go to python_implementation directory in your terminal and install the dependencies.

You can create a virtual environment (recommended) or you can just run the following command.

pip install -r requirements.txt

You can move into python_implemenation/app and run the following command.

uvicorn main:app --port 3000

This should run the server on http://127.0.0.1:3000. This also creates the python_implementation/app/monk.db DB file if not already present.

Running Utils

Assuming you have cloned the repo and already installed all the dependencies for python.

You can move to the utils directory and run the following command.

python create_dummy_data.py

This will take the contents from utils/csv_dummy_data csv files and create a SQLite DB file utils/temp.db which you can place in respective DB paths in the projects as mentioned below.

• java project: java_implementation/db/monk.db
• python project: python_implementation/app/monk.db

---

DB Schema

Below is the list of tables created and used in the SQLite DB.

• coupons

  -- coupons definition
  
  CREATE TABLE coupons (
          coupon_id integer,
          coupon_code varchar(50) not null unique,
          coupon_name varchar(255),
          coupon_type varchar(50),
          discount_value float,
          end_date timestamp,
          start_date timestamp,
          status varchar(20) check (status in ('active','inactive','expired')),
          primary key (coupon_id)
  );

  Table example with dummy data:

  coupon_id	coupon_code	coupon_name	coupon_type	discount_value	end_date	start_date	status
  1	DISCOUNT10	10% Off	percentage	10.0	2024-12-31 23:59:59.000000	2024-01-01 00:00:00.000000	active
  2	BLACKFRIDAY	Black Friday Sale	percentage	20.0	2024-11-25 23:59:59.000000	2024-11-25 00:00:00.000000	active
  3	SPRINGSALE	Summer Spring Sale	fixed	15.0	2024-05-31 23:59:59.000000	2024-03-01 00:00:00.000000	active
  4	XMAS20	Christmas Special	percentage	20.0	2023-12-31 23:59:59.000000	2023-12-01 00:00:00.000000	expired
  5	FREESHIP	Free Shipping	fixed	5.0	2024-12-31 23:59:59.000000	2024-06-01 00:00:00.000000	active

• coupon_details

  -- coupon_details definition
  
  CREATE TABLE coupon_details (
      coupon_detail_id integer,
      detail_key varchar(100),
      detail_value varchar(255),
      coupon_id bigint not null,
      primary key (coupon_detail_id)
  );

  Table example with dummy data:

  coupon_detail_id	detail_key	detail_value	coupon_id
  1	min_purchase	50.00	1
  2	category	clothing	1
  3	min_purchase	100.00	2
  4	category	electronics	2
  5	min_purchase	75.00	3

• coupon_usage

  -- coupon_usage definition
  
  CREATE TABLE coupon_usage (
      usage_id integer,
      discount_applied float,
      used_at timestamp,
      coupon_id bigint not null,
      order_id bigint,
      user_id bigint,
      primary key (usage_id)
  );

  Table example with dummy data:

  usage_id	discount_applied	used_at	coupon_id	order_id	user_id
  1	10.0	2024-01-05 12:30:00.000000	1	101	1
  2	20.0	2024-11-25 08:45:00.000000	2	102	2
  3	15.0	2024-03-10 14:00:00.000000	3	103	3
  4	5.0	2024-06-02 11:00:00.000000	5	104	4
  5	25.0	2025-01-01 09:00:00.000000	6	105	5

• orders

  -- orders definition
  
  CREATE TABLE orders (
          order_id integer,
          coupon_ids varchar(255),
          order_time timestamp not null,
          product_ids varchar(255) not null,
          user_id bigint,
          primary key (order_id)
  );
  

  Table example with dummy data:

  order_id	coupon_ids	order_time	product_ids	user_id
  1	[3, 7]	2024-01-05 12:30:00.000000	[1, 3, 5]	1
  2	[8, 2, 6]	2024-11-25 08:45:00.000000	[2, 6, 8]	2
  3	[5, 10]	2024-03-10 14:00:00.000000	[3, 7]	3
  4	[1, 9]	2024-06-02 11:00:00.000000	[4, 9, 6]	4
  5	[9, 4]	2025-01-01 09:00:00.000000	[5, 10]	5

• products

  -- products definition
  
  CREATE TABLE products (
      product_id integer,
      created_at timestamp not null,
      description varchar(500),
      name varchar(255) not null,
      price float not null,
      stock_quantity integer not null,
      updated_at timestamp,
      primary key (product_id)
  );

  Table example with dummy data:

  product_id	created_at	description	name	price	stock_quantity	updated_at
  1	2024-01-01 00:00:00.000000	Latest model with advanced features	Apple iPhone 15	999.99	50	2024-01-01 00:00:00.000000
  2	2024-02-15 00:00:00.000000	Flagship Android smartphone	Samsung Galaxy S24	899.99	40	2024-02-15 00:00:00.000000
  3	2024-03-01 00:00:00.000000	High performance laptop	Dell XPS 13	1299.99	30	2024-03-01 00:00:00.000000
  4	2024-04-10 00:00:00.000000	Professional business laptop	Lenovo ThinkPad	1099.99	20	2024-04-10 00:00:00.000000
  5	2024-05-05 00:00:00.000000	Apple laptop with M2 chip	MacBook Pro	2399.99	15	2024-05-05 00:00:00.000000

• users

  -- users definition
  
  CREATE TABLE users (
      user_id integer,
      created_at timestamp not null,
      email varchar(255) not null unique,
      first_name varchar(100),
      last_name varchar(100),
      password_hash varchar(255) not null,
      username varchar(100) not null unique,
      primary key (user_id)
  );

  Table example with dummy data:

  user_id	created_at	email	first_name	last_name	password_hash	username
  1	2024-01-01 00:00:00.000000	john@example.com	John	Doe	$2b$12$uVuA4o03US7uF2WVpA2Xzmm9ZDhZl3a5oFzJkY5XxX93t9iL1.Gte	john_doe
  2	2024-02-15 00:00:00.000000	jane@example.com	Jane	Smith	$2b$12$EaP5nWzA4dL9WGvNUKHz9/q3zClXz1XmfQF9D2zB.6ugTn5t0FZna	jane_smith
  3	2024-03-01 00:00:00.000000	bob@example.com	Bob	Jones	$2b$12$1Xc/JQnpZB57/8Vm6efhFgTc3IhgaBYvsl8wK8pD5aZ1p2wD1htM6	bob_jones
  4	2024-04-10 00:00:00.000000	alice@example.com	Alice	Brown	$2b$12$Apv7dFQU04Q1YIT5AaljcMcTmHhv3gWztd9jDzxhOMXxWOC7zVE0x	alice_brown
  5	2024-05-05 00:00:00.000000	charlie@example.com	Charlie	Clark	$2b$12$Pi89Zmh6vwhhbZ5r9H8OBg0wozEv5YxQpKT1OHXJ1j9LfgPwwFjLa	charlie_clark

---

APIs

This mainly consists of the following APIs.

1. Index API:

   url: http://127.0.0.1:3000/

   Get request to the main page. Just gives you a hello message.

2. Get Coupons API:

   url: http://127.0.0.1:3000/coupons/

   Get request to get all the coupons. This gets data from coupons table.

3. Get Coupon By Id API:

   url: http://127.0.0.1:3000/coupons/{id}

   Get request to get coupon passing the id as a path variable. This gets data from coupons table based on coupon_id.

4. Save new Coupon API:

   url: http://127.0.0.1:3000/coupons/

   Post request to save the coupon in the DB by passing the JSON body along with the request.

   // Example JSON body
   // cart-wise
   {
       "coupon_code" : "cart10",
       "coupon_name" : "10% cart discount",
       "coupon_type" : "cart-wise",
       "discount_value" : 10,
       "status" : "active",
       "start_date" : "2024-01-01 00:00:00.000000",
       "end_date" : "2024-12-31 23:59:59.000000",
       "details" :{
           "threshold":100
       }
   }

   details can be any form of valid Json object. This saves data to coupons table and coupon_details table.

5. Update Coupon By Id API:

   url: http://127.0.0.1:3000/coupons/{id}

   Put request to update the Coupon data in the DB by passing the JSON body along with the request.

   // Example JSON body
   // cart-wise
   {
       "coupon_code" : "cart30",
       "coupon_name" : "30% cart discount",
       "coupon_type" : "cart-wise",
       "discount_value" : 30,
       "status" : "active",
       "start_date" : "2024-01-01 00:00:00.000000",
       "end_date" : "2024-12-31 23:59:59.000000",
       "details" :{
           "threshold":1500
       }
   }

   This updates data in coupons table and coupon_details table.

6. Delete Coupon By Id:

   url: http://127.0.0.1:3000/coupons/{id}

   Delete request to delete the Coupon data in DB based on the Coupon Id.

   This deletes data from coupons table and coupon_details table.

7. Apply Coupon By Id:

   url: http://127.0.0.1:3000/coupons/apply-coupon/{id}

   Post request to apply coupon usage based on coupon id and sends the required details by JSON body along with the requeset.

   // example JSON body
   {
       "user_id": 1,
       "cart": {
           "items": [
               {
                   "product_id": 1,
                   "quantity": 6,
                   "price": 50
               },
               {
                   "product_id": 2,
                   "quantity": 3,
                   "price": 30
               },
               {
                   "product_id": 3,
                   "quantity": 2,
                   "price": 25
               }
           ]
       }
   }

   This updates records in orders table and coupon_usage table.

---

Observations based on the current implementation

In this implementation for both Java and Python, I am assuming that user and product data will already be present. If required CRUD APIs can be addded too.

I believe with more time I could have added some more data validations and unit tests too. For production, I would have switched the DB to Postgres or some other DB based on requirement. I also would have written some test to stress test the DB calls and APIs before production roll outs. Along with this I would have also liked to create docker images for both individual projects for easy deployments.

I believe this is a good base for further enhnacements in terms of API additions or data model modifications for the given time.