Design Restaurant Management System | System Design

In the modern restaurant industry, delivering exceptional dining experiences requires more than just good cuisine. Restaurant Management Systems have emerged as the secret ingredient for efficient operations and customer satisfaction.

We are going to design a restaurant management system where users can effortlessly discover nearby restaurants. They can seamlessly get menus, explore ratings, and conveniently place orders online, with the added flexibility of choosing between dine-in or delivery to any location within the specified range.

Requirements

Functional requirements

• Allow customers to search restaurants, view menus, place orders.
• Restaurant staff can manage orders, update menus.
• Customers can view ratings of the restaurants.
• Customers can either dine in or ask for delivery.
• Kitchen staff can view and manage food orders.
• Delivery drivers can view assigned delivery orders.
• Customers can track their orders.
• Customers can choose payment methods.

Non functional requirements

• Scalability: It ensures the system remains responsive during peak demand, adapts to the addition of new features, and handles fluctuations in traffic.
• Reliability: Reliability is paramount for customer trust and business continuity. Users must have confidence in the system’s consistent and accurate order processing, and downtime should be minimized for restaurant owners and delivery agents relying on the platform.
• High Availability: A reliable and always-accessible system enhances customer trust, supports efficient restaurant operations, and ultimately contributes to the success of the entire restaurant management ecosystem.
• Strong consistency: Any changes in price, menu, location of user, payment details should reflect quickly.

Capacity Assumptions

To estimate the scale of the system and to get the idea about the storage requirements, we have to make some assumptions about the stored data and traffic.

Following are some assumptions for the given design.

• 10 million restaurants
• Daily active users = 100 million
• Active users per second = 1160 users/sec (approx.)
• Average Orders per minute = 250,000/min
• Average Orders per second = 4200 orders/second (approx.)
• Peak orders = 2 * Average orders per minute = 500,000 orders/minute

Storage Estimation

Let’s assume on average:

• Each customer row in DB is 1 KB (with address, profile info etc.)
• Each restaurant = 2 MB (details, menus, photos etc.)
• Each order = 1 KB.

Total storage needed:

100 million customers x 1KB per user = 100 GB
10 million restaurants x 2 MB per restaurant = 20 TB
500,000 orders per minute = 72 million orders per day (approx.)
72 million orders per day x 1 KB per order = 72 GB per day
Assuming we maintain orders for last 3 years:

72 GB per day x 365 days x 3 years = 77TB (approx.)

High Level Design

Within this framework, three fundamental services need management:

Client Services

Customers authenticate themselves to place orders at a restaurant. After order placement, they should have the capability to track their order status, with the flexibility to make payments through various methods.

Restaurant Admin Services

Authentication is the initial step for restaurant admins, who can then access all placed orders at their establishment. Admins can accept and process orders based on the user’s preference for dine-in or delivery. Additionally, they have the authority to modify restaurant-related details such as menus and prices.

Delivery Services

Delivery agents authenticate themselves and interact with Delivery Services, utilizing Map Services to track their location. The service notifies agents of nearby orders, which they can accept and subsequently deliver to the customer.

Necessary Components:

Client Interface

Customers engage with the system through mobile apps, web interfaces, or desktop applications.

Load Balancer

Evenly distributing incoming traffic across multiple servers, load balancers enhance high availability and optimize overall performance.

Authentication and Registration Services

This service handles user authentication and registration. All users, including customers, restaurant admins, and delivery agents, undergo authentication and registration processes.

Database

Storing user data, restaurant details, menus, orders, and metadata, the database serves as the central repository for the system.

Payment Services

Responsible for managing all payment-related data and services.

Order Placement Services

This service is tasked with placing orders at restaurants and managing all order-related processes.

Delivery Services

Focused on delivery-related operations, this service locates delivery agents and facilitates real-time package tracking using Map Services.

Restaurant Admin Services

Enabling restaurants to view and manage placed orders, this service also allows admins to modify menus, prices, and food images.

Map Services

Tracking user and delivery agent locations, Map Services collaborate with Delivery Services and customers for real-time location tracking.

Database Design

Customer

{

-customer_Id
-customer_name,
-address,
-phone_no,
-email

}

• Customer_Id (PK): Unique identifier for each customer.
• Customer_name: Stores the name of the customer.
• Address: Records the address of the customer.
• Phone_no: Stores the phone number of the customer.
• Email: Captures the email address of the customer.

Restaurant

{

-restaurant_id,
-restaurant_name,
-payment_details_id,
-location,
-menu_Id

}

• Restaurant_id (PK): Unique identifier for each restaurant.
• Restaurant_name: Stores the name of the restaurant.
• Payment_details_id (FK): Foreign key linked to the payment details table for crediting transactions.
• Location: Records the location of the restaurant.
• Menu_Id (FK): Foreign key pointing to the menu table for menu modifications by the restaurant admin.

Delivery agent

{

-agent_id,
-payment_details_id,
-phone_no,
-email,
-vechile_number,
-address

}

• Agent_id (PK): Unique identifier for each delivery agent.
• Payment_details_id (FK): Foreign key linked to the payment details table for agent’s account information.
• Phone_no: Stores the phone number of the delivery agent.
• Email: Captures the email address of the delivery agent.
• Vehicle_number: Records the vehicle number used by the agent for deliveries.
• Address: Stores the permanent address of the delivery agent.

Order

{

-order_id,
-customer_id,
-restaurant_id,
-agent_id,
-time_stamp,
-is_delivery,
-delivery_location,
-items_id,
-delivery_price,
-items_price,
-transcation_id:
-order_status

}

• Order_id (PK): Unique identifier for each order.
• Customer_id (FK): Foreign key linking to the customer table.
• Restaurant_id (FK): Foreign key linking to the restaurant table.
• Agent_id (FK): Foreign key linking to the delivery agent table (NULL for dine-in orders).
• Time_stamp: Records the timestamp when the order was placed.
• Is_delivery: Boolean value indicating whether the order is for delivery (true) or dine-in (false).
• Delivery_location: Stores the location for delivery orders (NULL for dine-in).
• Items_id (FK): Foreign key pointing to the table storing items and quantities.
• Delivery_price: Records the delivery charges calculated by the Delivery Services based on delivery proximity.
• Items_price: Stores the total sum of the price of ordered items.
• Order_status: Records the status of the order (e.g., delivered, cancelled).
• Transaction_id: Stores the transaction details for the specific order.

NoSQL data

Restaurant_rating

{

-restaurnat_id,
-rating

}

• Restaurant_id: Unique identifier for each restaurant.
• Rating: Stores the rating given to the restaurant.

Agent_rating

{

-agent_id,
-rating,

}

• Agent_id: Unique identifier for each delivery agent.
• Rating: Records the rating given to the delivery agent.

Which Database we will use ?

In selecting the appropriate database architecture, a relational database management system (RDBMS) such as SQL is deemed essential for tables housing critical information, like customer, restaurant, and agent details. This choice is made because of the necessity for high consistency, ensuring swift and accurate reflection of any alterations, such as changes in payment details or menu prices.

SQL’s adherence to ACID (Atomicity, Consistency, Isolation, Durability) properties makes it particularly well-suited for maintaining data integrity and ensuring reliability in such scenarios.

Conversely, for data that exhibits more relaxed consistency requirements, such as restaurant and agent ratings, a NoSQL database is a judicious choice.

NoSQL, characterized by BASE (Basically Available, Soft state, Eventually consistent) properties, aligns with the nature of these datasets. This allows for greater flexibility, accommodating the dynamic and non-critical nature of rating information, where immediate and stringent consistency is not a primary concern.

This hybrid approach harnesses the strengths of both SQL and NoSQL databases, tailoring the database architecture to the specific needs and characteristics of the data being managed.

Communicating with the server

Employing RESTful APIs for the Restaurant Management System proves advantageous due to their simplicity, uniform interface, and statelessness. REST’s straightforward design, using standard HTTP methods, facilitates ease of use and development, promoting interoperability across various platforms and programming languages.

The resource-centric approach aligns well with entities in the system, and the statelessness enables horizontal scalability, crucial for handling the anticipated high traffic in a restaurant management system. Additionally, RESTful APIs support flexibility in data formats, standardized error handling through HTTP status codes, and caching mechanisms, contributing to efficient communication, error identification, and improved performance.

1. User Registration

Endpoint:

POST /api/users/register

Request:

{

“username”: “salik”,
“password”: “secure_password”,
“email”: “”xyz@gmail.com”,
“address”: “123 Main St, City”,
“phone”: “+1234567890”

}

Response:

{

“message”: “User registered successfully”,
“userId”: “12345”,
“token”: “authentication_token”

}

2. User Authentication

Endpoint:

POST /api/users/login

Request:

{

“username”: “salik”,
“password”: “secure_password”

}

Response:

{

“message”: “Login successful”,
“userId”: “12345”,
“token”: “authentication_token”

}

3. Restaurant Search and Order Placement

1. Search Nearby Restaurants

Endpoint:

GET /api/restaurants/search?location=City&cuisine=Italian&rating=4

Response:

[

{

“restaurantId”: “abc123”,

“name”: “Italian Delight”,

“location”: “456 Main St, City”,

“cuisine”: “Italian”,

“rating”: 4.5

},

{

“restaurantId”: “def456”,

“name”: “Pizza Palace”,

“location”: “789 Main St, City”,

“cuisine”: “Italian”,

“rating”: 4.2

}

]

2. Place an Order

Endpoint:

POST /api/orders/place

Request:

{

“userId”: “12345”,

“restaurantId”: “abc123”,

“items”: [

{

“itemId”: “item1”,

“quantity”: 2

},

{

“itemId”: “item2”,

“quantity”: 1

}

],

“deliveryPreference”: “delivery”,

“paymentMethod”: “credit_card”

}

Response:

{

“orderId”: “order123”,
“message”: “Order placed successfully”,
“totalAmount”: 25.99

}

Restaurant Management:

View Orders for a Restaurant

Endpoint:

GET /api/restaurants/orders?restaurantId=abc123

Response:

[

{

“orderId”: “order123”,

“userId”: “user456”,

“items”: [

{

“itemId”: “item1”,

“quantity”: 2

},

{

“itemId”: “item2”,

“quantity”: 1

}

],

“totalAmount”: 25.99,

“status”: “pending”

},

// Additional orders…

]

Delivery and Real-time Tracking:

Track Delivery Order

Endpoint:

GET /api/delivery/track?orderId=order123

Response:

{

“orderId”: “order123”,

“status”: “in_transit”,

“location”: {

“latitude”: 37.7749,

“longitude”: -122.4194

}

}

Microservices Used

Let’s drill deeper into each microservices:

Low level design of restaurant management system

Client

Customers initiate requests to the load balancer for various services, such as user registration, restaurant searches, and order placement. Additionally, delivery agents and restaurant admins utilize the load balancer to send requests for registration or authentication.

Load Balancer

This vital component monitors server health, rerouting traffic in the event of failures to ensure minimal downtime. Not only does it enhance security by mitigating DDoS attacks, but it also plays a crucial role in scaling applications. Available in hardware, software, and cloud-based forms, load balancers act as traffic managers, directing the flow in modern IT infrastructures.

API Services

Serving two key functions, API services handle user authentication, ensuring only authorized users access the system. Once authenticated, these services act as intermediaries for communication with various microservices such as order placement, payment, and search. For example, implementing Kafka for message queuing ensures reliable communication between microservices.

Authentication Services

Offering robust user authentication and user registration functionalities, these services are pivotal for security and onboarding new users. The registration process assigns a unique user ID and securely stores essential user information, including names, addresses, and payment details, within the system’s database.

Payment Services

Offering a variety of payment options, Payment Services ensure flexibility and convenience for settling bills. They validate payments, confirming transaction accuracy and the availability of funds. This process prevents errors, fraud, or unsuccessful payments, ensuring a smooth and trustworthy payment experience. For instance, integrating with a payment gateway like Stripe or PayPal enhances transaction security.

CDN

Specializing in storing static assets like dish images and menus, CDNs reduce the load on the main server, enhancing content accessibility. Positioned strategically, CDNs ensure swift data retrieval, minimizing latency, and speeding up content delivery. For example, using Akamai or Amazon CloudFront for CDN services optimizes the loading time of menu pages, improving the overall user experience.

Restaurant Search Services

Enabling users to search for restaurants based on criteria like ratings and cuisines, these services leverage ElasticSearch for rapid data retrieval in a distributed database. For example, users can easily find top-rated Italian restaurants in their vicinity by utilizing ElasticSearch’s powerful search capabilities.

Order Placement Services

Managing the entire order life cycle, these services provide information to restaurants and update customers on order status. The Order Queue facilitates communication with Restaurant Services, while requests for delivery include interactions with Delivery Services. Implementing Apache Kafka as a message broker ensures efficient order processing and communication between services.

Delivery Services

Facilitating order pickup, these services establish seamless communication between agents and customers, enabling real-time order tracking. They are instrumental in ensuring successful deliveries and collecting customer feedback. For instance, Delivery Services efficiently coordinate the delivery of food from restaurants to customers using real-time tracking tools like Google Maps.

Map Services

Searching for nearby agents and tracking orders, Map Services provide real-time location updates to customers. Using technologies like Google Maps, it connects users to the service for order tracking through long polling, minimizing traffic while providing timely updates.

Notification Queue

Services wishing to send notifications to users push messages to the Notification Queue, with Notification Services consuming these requests, subsequently sending notifications to users. For example, Kafka can be employed to implement a robust and scalable notification queue.

Cache

Storing ongoing requests for quick retrieval, the cache provides speedy access to customers and other microservices. Redis cache, for example, efficiently stores and retrieves frequently accessed data, improving overall system performance.

Database

Storing user data, restaurant information, and menus, the database is shared and replicated across various servers. Horizontal sharding, exemplified by MySQL for high consistency in user and transaction data, is complemented by NoSQL for non-critical metadata such as restaurant ratings and delivery agent details. For instance, MongoDB can be used for its flexibility in handling unstructured data like ratings and agent information.

Workflow of the system

Below is the step by step process:

• The customer initiates the registration process through registration services, providing all necessary details and receiving a unique customer ID.
• Returning customers utilize authentication services for verification.
• Subsequently, customers explore nearby restaurants using search services, making selections from the CDN-stored restaurant details and menus.
• Upon making choices, Order Placement Services capture order specifics, encompassing items and quantities. Users then opt for food delivery or dine-in, with the system tracking order status.
• The Order Placement Services channel the order to the Order Queue, subsequently retrieved by Restaurant Placement Services for acceptance and processing by the restaurant admin.
• In dine-in scenarios, when the food is ready, Order Placement Services notify customers via the Notification Queue. Notification Services handle the request, sending notifications to the customer.

Below is the step by step process for delivery:

• Order Placement Services notify the Delivery Queue, prompting Delivery Services to locate nearby delivery agents.
• Once an agent accepts the order, they connect with the user through Map Services, allowing customers to track agent locations at regular intervals.
• Payment transactions are facilitated by Payment Services, redirecting to the respective bank’s payment portal or UPI services.
• Upon successful delivery, the order status is updated to “delivered,” with order details stored in the database.
• Customers can also provide restaurant ratings, saved in a NoSQL database.

Further optimizations for the system

To further enhance the efficiency of the Restaurant Management System, several optimizations can be considered:

• Caching strategies, such as implementing a Content Delivery Network (CDN) for static assets like images and menus, can significantly improve response times and reduce server load.
• Employing asynchronous processing for non-time-sensitive tasks, like order notifications, can enhance system responsiveness.
• Efficient database indexing, sharding, and replication strategies should be implemented to ensure fast data retrieval and high availability.
• Load balancing can be optimized dynamically to distribute traffic effectively, and the system should incorporate thorough security measures, including encryption for sensitive data and secure API endpoints.
• Regular performance monitoring and analysis will aid in identifying and addressing potential bottlenecks, ensuring the continued optimization of the Restaurant Management.