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Restaurant Management System — 75-Minute Interview Guide

Quick Start

5-Minute Overview

A restaurant management platform handling reservations, table management, menu ordering, kitchen coordination, bill calculation, and payment processing. Core flow: Reservation → Seating → Order → Kitchen → Billing → Payment.

Key Entities

Entity Purpose
Table Physical seating (status: FREE/RESERVED/OCCUPIED)
Reservation Customer hold on table
MenuItem Menu item with price & availability
Order Customer order lifecycle
KitchenTicket Preparation task tracking
Payment Bill processing

6 Design Patterns

  1. Singleton: Central RestaurantSystem coordinator
  2. Strategy: PricingStrategy (Regular, HappyHour, Member)
  3. Observer: Events (OrderPlaced, KitchenReady, BillPrepared)
  4. State: OrderStatus enum (RECEIVED→PREPARING→READY→SERVED→PAID)
  5. Factory: OrderFactory.create() for order creation
  6. Command: PlaceOrderCommand, PayBillCommand operations

Critical Points

✅ Prevent double-booking → Atomic table reservation + lock
✅ Kitchen coordination → KitchenTicket with status tracking
✅ Bill calculation → ItemPrice × Qty + Tax + Service - Discount
✅ Concurrent orders → Thread-safe Singleton
✅ Scaling → Microservices, Kafka, distributed locks

System Overview

Problem Statement

Restaurants manage multiple concurrent operations: reservations, table occupancy, food ordering, kitchen coordination, and payment. System must prevent table double-booking, ensure kitchen workflow, and provide accurate billing with flexible pricing.

Core Workflow

Reservation → Check-in → Order → Kitchen → Billing → Payment → Cleanup

Requirements & Scope

Functional Requirements

✅ Customer reservation with availability checking
✅ Table management (status tracking)
✅ Order management (add items, modify, cancel)
✅ Menu pricing with strategy-based discounts
✅ Kitchen ticket generation and tracking
✅ Bill calculation (tax, service charge, discounts)
✅ Payment processing
✅ Notifications (reservation, order status)

Non-Functional Requirements

✅ Support 100+ concurrent customers
✅ <500ms reservation lookup
✅ <200ms order placement
✅ <1s bill calculation
✅ 99.9% uptime

Out of Scope

❌ Payment gateway integration
❌ Multi-location synchronization
❌ Inventory management
❌ Delivery orders

Architecture & Design Patterns

1. Singleton Pattern

Problem: Race conditions on table assignment
Solution: Thread-safe single RestaurantSystem instance

class RestaurantSystem:
    _instance = None
    _lock = threading.Lock()

    def __new__(cls):
        if cls._instance is None:
            with cls._lock:
                if cls._instance is None:
                    cls._instance = super().__new__(cls)
        return cls._instance

2. Strategy Pattern (Pricing)

Problem: Multiple pricing models need to coexist
Solution: Pluggable strategies

class PricingStrategy(ABC):
    @abstractmethod
    def apply(self, subtotal: float) -> float:
        pass

class HappyHourPricing(PricingStrategy):
    def apply(self, subtotal: float) -> float:
        return subtotal * 0.8  # 20% off

class MemberPricing(PricingStrategy):
    def apply(self, subtotal: float) -> float:
        return subtotal * 0.85  # 15% off

3. Observer Pattern

Problem: Kitchen, billing, notifications need real-time updates
Solution: Abstract Observer interface

class RestaurantObserver(ABC):
    @abstractmethod
    def update(self, event: str, payload: Dict):
        pass

class KitchenObserver(RestaurantObserver):
    def update(self, event: str, payload: Dict):
        if event == "ORDER_PLACED":
            # Send to kitchen display
            pass

4. State Pattern

Problem: Orders have valid & invalid state transitions
Solution: Enum-based state management

class OrderStatus(Enum):
    RECEIVED = "received"
    PREPARING = "preparing"
    READY = "ready"
    SERVED = "served"
    PAID = "paid"
    CANCELLED = "cancelled"

5. Factory Pattern

Problem: Order creation scattered in code
Solution: Centralized factory

class OrderFactory:
    _counter = 0

    @staticmethod
    def create_order(table: Table, items: List[MenuItem]) -> Order:
        OrderFactory._counter += 1
        order_id = f"ORD{OrderFactory._counter}"
        return Order(order_id, table, items)

6. Command Pattern

Problem: Operations need logging and undo
Solution: Command objects

class PlaceOrderCommand:
    def __init__(self, system, table, items):
        self.system = system
        self.table = table
        self.items = items

    def execute(self):
        return self.system.place_order(self.table, self.items)

Core Entities & UML Diagram

┌──────────────────────────────────────────────┐
│     RestaurantSystem (Singleton)             │
├──────────────────────────────────────────────┤
│ - tables: Dict[str, Table]                   │
│ - reservations: Dict[str, Reservation]       │
│ - orders: Dict[str, Order]                   │
│ - pricing_strategy: PricingStrategy          │
├──────────────────────────────────────────────┤
│ + reserve_table(): Reservation               │
│ + check_in(): bool                           │
│ + place_order(): Order                       │
│ + process_payment(): Payment                 │
│ + calculate_bill(): Bill                     │
└──────────────────────────────────────────────┘
            │
    ┌───────┼───────┐
    ▼       ▼       ▼
  Table  Customer MenuItem
    │       │
    └─ Reservation
        │
    Order
        │
    KitchenTicket

Interview Q&A

Q1: How prevent double-booking?

A: Atomic reservation with thread locks. Only one replica can mark table RESERVED at a time.

Q2: Reservation vs Order difference?

A: Reservation is time-limited hold (30-60 min). Order is actual food ordered after check-in.

Q3: How coordinate kitchen?

A: KitchenTicket workflow (PENDING → PREPARING → READY → SERVED).

Q4: Calculate bills accurately?

A: subtotal → pricing_strategy.apply() → add tax + service → total.

Q5: Why Strategy for pricing?

A: Different rules (happy hour, member, group) swapped without modifying Order.

Q6: Scale to multiple restaurants?

A: Each restaurant gets own RestaurantSystem instance. Independent operation.

Q7: Handle no-show reservations?

A: Background job checks time. After 15 min: status=NO_SHOW, table=FREE.

Q8: Prevent order modification after kitchen receives?

A: Only modify in RECEIVED state. PREPARING status locks order.

Q9: Handle payment failures?

A: Retry 3x. On fail: Bill status=FAILED, customer notified.

Q10: What metrics to track?

A: Table utilization, avg order value, processing time, payment success rate, reservation conversion.

Scaling Q&A

Q1: Scale to 1000+ tables across 100 restaurants?

A: Each restaurant independent RestaurantSystem. Shared analytics DB aggregates.

Q2: Handle 1000 orders/hour peak?

A: Queue orders if latency > 500ms. Async kitchen tickets. Cache menu prices.

Q3: Prevent table overbooking across replicas?

A: Distributed lock (Redis) for atomic reservation globally.

Q4: Payment concurrency?

A: Optimistic locking with version numbers. Read → attempt update if version matches.

Q5: Scale kitchen operations?

A: Kafka topic with multiple partition workers. Parallel prep stations.

Q6: Ensure 99.9% uptime?

A: Multi-replica setup, health checks, RTO < 30s, RPO < 5min.

Q7: Test at scale?

A: Load test 1000 concurrent orders. Monitor latency p99, error rate, DB connections.

Demo Scenarios

Demo 1: Reservation & check-in
Demo 2: Order placement & kitchen
Demo 3: Bill calculation with pricing
Demo 4: Payment processing
Demo 5: Pricing strategy switch

Key Takeaways

Aspect Implementation
Table Management Singleton + locks
Pricing Strategy pattern
Kitchen Coordination Observer pattern
Order Flow State machine
Bill Accuracy Clear calculation
Scalability Multiple instances + events

Ready for your interview! 👨‍🍳