Tools & Technologies Used

1. .Net 8
2. Blazor Server
3. EF Core 8
4. MSSQL Server
5. ASP.NET Identity Framework with Blazor Identity Framework UI
6. Blazor Fluent UI

Requirements and Features

Defining the requirements and features is a crucial first step in designing a system. Let's break this down for each of our primary users: Customers, Restaurants, and Delivery Personnel.

Customer Features:

• 1. Account Management: Customers should be able to create and manage their accounts, which includes setting their addresses and payment information.
• 2. Restaurant Browsing: Customers should be able to browse different restaurants, view their menus, and see reviews and ratings.
• 3. Order Placement: Customers should be able to place orders from restaurants, specifying the items they want and any customizations.
• 4. Order Tracking: After placing an order, customers should be able to track the status of their order in real-time.
• 5. Payment Processing: Customers should be able to pay for their orders securely through the system.
• 6. Order History and Reordering: Customers should be able to see their past orders and easily place the same order again.
• 7. Rating and Reviews: After the delivery, customers should be able to rate and review both the restaurant and the delivery personnel.
• 8. Promotions and Discounts: Customers should be able to see any current promotions or discounts, and these should be automatically applied to their orders if they qualify.
• 9. Loyalty Program: Customers should earn points for every order, and should be able to see their point balance and redeem points for discounts.
• 10. Customer Support: Customers should be able to access customer support via live chat, phone, or email.

Restaurant Features:

• 1. Account Management: Restaurants need to manage their accounts, which includes setting their location, business hours, and bank account information for payouts.
• 2. Menu Management: Restaurants need to be able to update their menus, including adding new items, removing or disabling unavailable items, and updating prices.
• 3. Order Management: Restaurants need to be able to receive new orders, update the status of orders (e.g., preparing, ready for pickup), and see past orders.
• 4. Payout Management: Restaurants need to be able to see their earnings, manage payouts, and see transaction histories.
• 5. Rating and Review Responses: Restaurants should have the ability to respond to customer reviews.
• 6. Promotion Management: Restaurants should be able to offer promotions on their items, subject to any rules or restrictions set by the platform.
• 7. Support: Restaurants should also have access to customer support, in case they encounter any issues with order management, payments, etc.

Delivery Personnel Features:

• 1. Account Management: Delivery personnel needs to manage their accounts, including setting their availability and managing their banking information for payouts.
• 2. Delivery Management: Delivery personnel needs to be able to receive new delivery assignments, navigate to the restaurant and then to the customer, and confirm when deliveries have been completed.
• 3. Earnings Management: Delivery personnel needs to be able to see their earnings, manage payouts, and see their transaction history.
• 4. Loyalty Program System: The backend services also need to support the loyalty program, including point accrual and redemption.
• 5. Customer Support System: There needs to be a system in place for managing customer support inquiries, which could include a ticketing system, chat system, etc.

Workflow

For Customers

1. Account Creation and Management:
   • Customers can create an account using their email or through social media integration.
   • Account management includes setting up and editing personal details, delivery addresses, and payment information.
2. Restaurant Browsing and Filtering:
   • Customers can browse different restaurants.
   • Advanced filtering options allow sorting by distance, cuisine type, ratings, or delivery time.
3. Order Placement:
   • Customers select items from the restaurant’s menu and add them to their cart.
   • At checkout, a detailed summary of the order is provided, including items, quantities, total price, and estimated delivery time.
4. Payment Processing:
   • Multiple payment options are available, such as credit/debit cards and mobile banking. Cash on delivery can also be chosen.
5. Basic Order Tracking:
   • After placing an order, customers can track its status (e.g., order placed, being prepared, out for delivery).
6. Ratings and Reviews:
   • Post-delivery, customers are encouraged to rate and review the restaurant and delivery service.

For Restaurants

1. Account Creation and Management:
   • Restaurants set up their profiles, including location, business hours, and bank account information for payouts.
2. Menu Management:
   • Restaurants can update their menus, adding new items, modifying existing ones, and managing availability.
3. Order Management:
   • Restaurants receive orders, update the status (preparing, ready for pickup), and view past orders.
4. Basic Analytics and Reporting (Future Feature):
   • Basic reporting features for tracking sales, popular items, and customer preferences, with more advanced analytics tools planned for future implementation.

For Delivery Personnel

1. Account Creation and Management:
   • Delivery personnel manage their profiles, including setting their availability and bank information for payouts.
2. Delivery Management:
   • They receive delivery assignments, navigate to the restaurant and customer’s location, and confirm delivery completion.

General System Features

• Security: Basic security measures are in place, ensuring data protection and transaction safety.
• Scalability: The system is designed to handle an increasing number of users, orders, and data volume.
• User Interface: The system features a user-friendly interface for easy navigation and operation across all user types.

Future Enhancements

• Implementation of a comprehensive customer support system.
• Real-time location tracking for orders.
• Advanced analytics tools for restaurants.
• Feedback mechanism for continuous system improvement.

Estimations

Estimations for Initial Launch

1. Customer Base

• Target: 1,000,000 customers.
• Consideration: Diverse geographical locations, varying peak usage times.

2. Restaurant and Delivery Personnel

• Assumption: A ratio can be established for restaurants and delivery personnel to customers. For instance, 1 restaurant per 50 customers and 1 delivery person per 100 customers.
• Estimation: Approximately 20,000 restaurants and 10,000 delivery personnel.

3. Order Volume

• Assumption: Average order frequency per customer (e.g., 4 orders per month).
• Estimation: Around 4,000,000 orders per month.

4. Data Volume

• Estimation: Large data sets encompassing user profiles, order histories, menu items, etc.

5. Traffic and Usage Patterns

• Peak Times: Estimate peak traffic times (e.g., weekends, evenings).
• Load Variation: Plan for normal load and peak load scenarios.

Growth Projections

Scaling to 100x

• Long-Term Target: 100,000,000 customers.
• Implications: Exponential increase in restaurants and delivery personnel, order volume, and data handling.

Calculations

1. Restaurants:
   • Assumption: 1 restaurant per 50 customers.
   • Calculation: 1,000,000 customers / 50 = 20,000 restaurants.
2. Delivery Personnel:
   • Assumption: 1 delivery person per 100 customers.
   • Calculation: 1,000,000 customers / 100 = 10,000 delivery personnel.
3. Order Volume:
   • Assumption: Average 4 orders per customer per month.
   • Calculation: 1,000,000 customers * 4 orders per month = 4,000,000 orders per month.

Memory & Quantity Calculation for each Table

Table Name	Row Count Calculation	Average Record Size Calculation	Memory Size Calculation	Database
Account Table	103M (100M + 2M + 1M)	4 (INT) + 780 (NVARCHAR) + 9 (DECIMAL) + 16 (DATETIME) = 809 bytes	103M × 809 bytes = 83.347 GB	Azure SQL Database
Customer Table	100M (customers)	8 (INT) + 200 (NVARCHAR) + 16 (DATETIME) = 224 bytes	100M × 224 bytes = 22.4 GB	Azure SQL Database
Restaurant Table	2M (restaurants)	8 (INT) + 200 (NVARCHAR) + 16 (DATETIME) = 224 bytes	2M × 224 bytes = 448 MB	Azure SQL Database
Rider Table	1M (riders)	8 (INT) + 200 (NVARCHAR) + 16 (DATETIME) = 224 bytes	1M × 224 bytes = 224 MB	Azure SQL Database
Order Table	400M (100M customers × 4)	12 (INT) + 18 (DECIMAL) + 300 (NVARCHAR) + 16 (DATETIME) = 346 bytes	400M × 346 bytes = 138.4 GB	Azure Cosmos DB
Delivery Order Table	400M (orders)	20 (INT) + 32 (DATETIME) + 200 (NVARCHAR) = 252 bytes	400M × 252 bytes = 100.8 GB	Azure Cosmos DB
Location Table	402M (400M orders + 2M restaurants)	DECIMAL: 18 bytes (2 fields), NVARCHAR: 710 bytes (355 chars) = 728 bytes	402M × 728 bytes ≈ 292.66 GB	Azure Cosmos DB
Item Order Table	1.2B (400M orders × 3 items/order)	16 (INT) + 9 (DECIMAL) = 25 bytes	1.2B × 25 bytes = 30 GB	Azure Cosmos DB
Item Table	40M (20 items/restaurant × 2M restaurants)	INT: 8 bytes (2 fields), DECIMAL: 18 bytes (2 fields), NVARCHAR: 510 bytes (255 chars) = 536 bytes	40M × 536 bytes ≈ 21.44 GB	Azure SQL Database
Ledger Table	1.6B (4 transactions/order × 400M orders)	INT: 12 bytes (3 fields), DECIMAL: 9 bytes, DATETIME: 8 bytes, NVARCHAR: 400 bytes (200 chars) = 429 bytes	1.6B × 429 bytes ≈ 686.4 GB	Azure Cosmos DB

Notes:

• “Row Count Calculation” is based on the final growth projections. For example, the Account Table’s row count is the sum of customers, restaurants, and riders.
• “Average Record Size Calculation” is the sum of bytes used by each type of field in a record. For example, in the Account Table, we calculate the bytes for each field type (INT, NVARCHAR, DECIMAL, DATETIME) and sum them up.
• “Memory Size Calculation” multiplies the total row count by the average record size to estimate the total memory required for each table.
• These estimates are for planning purposes and actual database sizes may vary based on real-world data distributions, database engine specifics, and other factors like compression and indexing.

Architectural Considerations

With these calculations in hand, we can now consider the architectural aspects:

1. Scalability: The system must be able to scale effectively to handle the large number of users, restaurants, delivery personnel, and the high order volume.
2. Elasticity: The architecture should dynamically adapt to varying loads, especially during peak times like weekends and evenings.
3. Reliability and Availability: The system should be designed for high reliability and availability, including redundant components and failover mechanisms to handle system failures without impacting user experience.
4. Performance: Optimize for fast response times and efficient processing to handle real-time operations like order tracking and payment processing.
5. Security and Data Management: Implement robust security measures for data protection, and design a system that can handle large data sets (user profiles, order histories, menu items, etc.) with durability and integrity.
6. User Interface: Ensure the system is user-friendly for all user types (customers, restaurants, delivery personnel).
7. Integration of Services: Seamlessly integrate various services like account management, payment processing, order tracking, customer support, etc.

Architectural Implications

With such significant growth projections, several key architectural considerations must be addressed:

1. Advanced Scalability: The system must be designed for extreme scalability, capable of handling 100 times the initial user and transaction volume.
2. Robust Infrastructure: The infrastructure must be robust enough to support the exponential increase in users, orders, and data, possibly requiring cloud solutions with global distribution and high resilience.
3. Enhanced Performance: Performance optimization becomes even more critical, with a focus on efficient processing and low latency to handle a substantially higher volume of real-time transactions.
4. Data Handling and Storage: Data management strategies need to be scalable and efficient, capable of handling exponentially larger datasets.
5. Advanced Analytics: Implementing advanced analytics tools will be essential for understanding customer behavior, optimizing restaurant performance, and streamlining delivery operations at this scale.
6. Automated Scaling and Load Balancing: Automated scaling solutions and sophisticated load balancing will be necessary to efficiently manage the variable and high-volume traffic.
7. Continuous System Improvement: A feedback mechanism should be in place for continuous system improvement, considering user feedback, system performance data, and evolving business needs.