Ozzey / Databases-Project

Databases-Project

This is the repository of MIPT2025 Databases project.

• The database is for Online Shopping Website automation system.
• It contains 19 different tables

Conceptual Model

Logical Model

Physical Model (Description)

Table 1: bill

Column Name	Description	Data Type
BillID	Unique identifier for each bill	int(11)
SalesID	Unique identifier for each sales transaction related to the bill	int(11)

Table 2: category

Column Name	Description	Data Type
CategoryID	Unique identifier for each category	int(20)
CategoryNameID	Unique identifier for each category name	int(11)
SizeID	Unique identifier for each size	int(11)
BrandID	Unique identifier for each brand	int(11)
PatternID	Unique identifier for each pattern	int(11)
ColorID	Unique identifier for each color	int(11)
TypeID	Unique identifier for each type	int(11)

Table 3: categoryname

Column Name	Description	Data Type
CategoryNameID	Unique identifier for each category name	int(11)
CategoryNames	The actual name of the category	varchar(20)

Table 4: color

Column Name	Description	Data Type
ColorID	Unique identifier for each color	int(11)
ColorCode	The code for the color	varchar(20)

Table 5: customer

Column Name	Description	Data Type
CustomerID	Unique identifier for each customer	int(11)
CustomerName	The first name of the customer	varchar(20)
CustomerSurname	The last name of the customer	varchar(20)
CustomerPhoneNum	The phone number of the customer	int(15)

Table 6: department

Column Name	Description	Data Type
DepartmentID	Unique identifier for each department	int(11)
DepartmentName	The name of the department	varchar(50)

Table 7: exchange

Column Name	Description	Data Type
ExchangeID	Unique identifier for each exchange transaction	int(11)
ExchangeNumber	Unique identifier for each exchange	int(20)
ExchangeDate	The date of the exchange	date
ExchangeExplanation	An explanation of the exchange	varchar(300)
ProductID	Unique identifier for each product related to the exchange	int(11)
CustomerID	Unique identifier for the customer related to the exchange	int(11)

Table 8: pattern

Column Name	Description	Data Type
PatternID	Unique identifier for each pattern	int(11)
PatternName	The name of the pattern	varchar(20)

Table 9: personnel

Column	Description	Data Type
PersonnelID	Unique identifier for each personnel	int
PersonnelName	Name of the personnel	varchar(20)
PersonnelSurname	Surname of the personnel	varchar(20)
PersonnelPhoneNum	Phone number of the personnel	int(15)
PersonnelAddress	Address of the personnel	varchar(200)
EntryDate	Date the personnel started working	date
Salary	Salary of the personnel	int(20)
DepartmentID	Identifier for the department the personnel belongs to	int(11)
ID	Identifier for the position the personnel holds	int(11)
StoreID	Identifier for the store the personnel works at	int(11)

Table 10: position

Column	Description	Data Type
PositionID	Unique identifier for each position	int
PositionName	Name of the position	varchar(20)

Table 11: product

Column	Description	Data Type
ProductID	Unique identifier for each product	int
ProductName	Name of the product	varchar(20)
ProductPrice	Price of the product	int(20)
CategoryID	Identifier for the category the product belongs to	int(11)
Barcode	Barcode number for the product	int(20)

Table 12: returnn

Column	Description	Data Type
ReturnID	Unique identifier for each return	int
ReturnNumber	Unique identifier for each return number	int(20)
ReturnDate	Date the return was made	date
ReturnExplanation	Explanation for the return	varchar(300)
ProductID	Identifier for the product being returned	int(11)
CustomerID	Identifier for the customer making the return

Table 13: sales

Column Name	Description	Data Type
SalesID	Unique ID for each sales transaction	int(11)
SalesNumber	Sales number for each transaction	int(20)
SalesDate	Date of the sales transaction	varchar(30)
ProductID	ID of the product sold	int(11)
StoreID	ID of the store where the product was sold	int(11)
PersonnelID	ID of the personnel who made the sale	int(11)

Table 14: size

Column Name	Description	Data Type
SizeID	Unique ID for each size	int(11)
SizeName	Name of the size	varchar(20)

Table 15: stock

Column Name	Description	Data Type
StockID	Unique ID for each stock item	int(11)
StockNumber	Stock number for each stock item	int(20)
ProductID	ID of the product in stock	int(11)
StoreID	ID of the store where the product is in stock	int(11)

Table 16: store

Column Name	Description	Data Type
StoreID	Unique ID for each store	int(11)
BranchName	Name of the store branch	varchar(30)
StorePhoneNumber	Phone number of the store	int(15)
StoreAddress	Address of the store	varchar(200)
City	City where the store is located	varchar(20)
DepartmentID	ID of the department to which the store belongs	int(11)

Table 17: type

Column Name	Description	Data Type
TypeID	Unique ID for each type	int(11)
TypeName	Name of the type	varchar(20)

Table 18: user

Column Name	Description	Data Type
UserID	Unique ID for each user	int(11)
UserName	Username of the user	varchar(20)
Password	Password of the user	varchar(20)
Authorization	Authorization of the user	varchar(20)
PersonnelID	ID of the personnel associated with the user	int(11)

Table 19: brand

Column Name	Description	Data Type
BrandID	Unique identifier for each brand	int(11)
BrandName	The name of the brand	varchar(20)

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Credentials:

• Database name: 'postgres'
• Username: 'postgres'
• Host: 'localhost'
• Password: 'admin'
• Schema: 'public'

Tasks

Task 3:

• Creating Database Schema
• Creating Tables
• Modifying the tables for FK and other purposes

Task 8: Creating indices for the tables

Task 4: Inserting data into tables

Task 5: CRUD Requests

Task 6: SELECT queries

Task 7: Tests for Select Queries

Task 9: Creating 6 views submissions

• Views Scripts
• Tests for Views Scripts

Task 10: Creating stored functions

• Function scripts
• Tests for the functions

Task 11: Triggers

• Trigger 1: Trigger to update a column based on changes to another column:
• Trigger 2: Trigger to enforce a business rule:

Task 12: Analysis

Analysis.py contains code for generating following graphs:

1. The top 10 most expensive products
2. Heatmap of sales by month and store location

Note: fill_tables.py contains code for filling table "product" with 1000 random values. Run this file before running the script for creating graph.
But run it after testing all the scripts or it can mess with hard-coded expected results for the tests

Graph: