Problem Statement

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Food Delivery - Problem Statement

Reading Time: 20 minutes

Business Context

A food delivery platform connects three parties: customers ordering food, restaurants preparing meals, and delivery drivers transporting orders. Systems like DoorDash, Uber Eats, and Grubhub have transformed food delivery by providing real-time tracking, dynamic pricing, and efficient logistics.

Core Problem

Design a food delivery system that:

  • Connects customers, restaurants, and drivers
  • Provides real-time order tracking
  • Optimizes driver assignment and routing
  • Handles payments and settlements
  • Manages restaurant menus and inventory
  • Ensures food quality and delivery speed

Functional Requirements

1. Customer Features

  • Restaurant Discovery: Search, browse, filter by cuisine/rating/distance
  • Menu Browsing: View items, prices, descriptions, photos
  • Order Placement: Add to cart, customize items, apply promos
  • Payment: Multiple payment methods, split payments
  • Order Tracking: Real-time driver location, ETA updates
  • Ratings & Reviews: Rate restaurant and driver

2. Restaurant Features

  • Menu Management: Add/edit items, prices, availability
  • Order Management: Accept/reject orders, preparation time
  • Inventory Tracking: Mark items out of stock
  • Analytics Dashboard: Sales, popular items, ratings
  • Payout Management: View earnings, settlement schedule

3. Driver Features

  • Order Assignment: Accept/reject delivery requests
  • Navigation: Turn-by-turn directions to restaurant and customer
  • Status Updates: Picked up, on the way, delivered
  • Earnings Tracking: View earnings, tips, bonuses
  • Availability Management: Go online/offline

4. Platform Features

  • Dynamic Pricing: Surge pricing during peak hours
  • Driver Matching: Assign nearest available driver
  • Route Optimization: Minimize delivery time
  • Fraud Detection: Prevent fake orders, account abuse
  • Customer Support: Chat, phone, refunds

Non-Functional Requirements

1. Performance

  • Search Latency: < 500ms for restaurant search
  • Order Placement: < 2 seconds
  • Driver Assignment: < 30 seconds
  • Location Updates: Every 5-10 seconds
  • ETA Calculation: < 1 second

2. Availability

  • Uptime: 99.9% (8.7 hours downtime/year)
  • Peak Hours: Handle 3x normal traffic (lunch, dinner)
  • Graceful Degradation: Core features work during partial outages

3. Scalability

  • Users: 10 million customers
  • Restaurants: 100,000 restaurants
  • Drivers: 1 million drivers
  • Daily Orders: 5 million orders/day
  • Peak Orders: 2,000 orders/second

4. Consistency

  • Order State: Strong consistency (no duplicate orders)
  • Inventory: Eventual consistency acceptable
  • Location: Real-time updates (5-10 second lag acceptable)
  • Payments: Strong consistency (no double charges)

Key Challenges

1. Real-Time Location Tracking

Challenge: Track thousands of drivers in real-time with minimal latency

Considerations:

  • GPS accuracy and battery consumption
  • Network connectivity issues
  • Efficient storage and querying of location data
  • Privacy concerns

2. Driver Matching Algorithm

Challenge: Assign optimal driver to each order

Considerations:

  • Distance to restaurant
  • Driver availability and capacity
  • Estimated preparation time
  • Traffic conditions
  • Driver ratings and acceptance rate
  • Batch assignments for efficiency

3. ETA Calculation

Challenge: Accurate delivery time estimates

Considerations:

  • Restaurant preparation time (historical data)
  • Distance and traffic conditions
  • Driver speed and route
  • Weather conditions
  • Order complexity

4. Dynamic Pricing

Challenge: Balance supply and demand

Considerations:

  • Current demand (orders per area)
  • Driver availability
  • Time of day and day of week
  • Weather conditions
  • Special events
  • Customer price sensitivity

5. Three-Sided Marketplace

Challenge: Balance interests of customers, restaurants, and drivers

Considerations:

  • Fair commission rates
  • Driver earnings vs customer prices
  • Restaurant profitability
  • Platform sustainability
  • Quality vs speed tradeoffs

Success Metrics

Business Metrics

  • Order Volume: Daily orders, growth rate
  • GMV: Gross Merchandise Value
  • Take Rate: Platform commission percentage
  • Customer Retention: Monthly active users, churn rate
  • Restaurant Retention: Active restaurants, churn rate
  • Driver Retention: Active drivers, churn rate

Operational Metrics

  • Delivery Time: Average time from order to delivery
  • Order Accuracy: % of orders delivered correctly
  • Cancellation Rate: % of orders cancelled
  • Driver Utilization: % of time drivers are on delivery
  • Restaurant Response Time: Time to accept orders

Technical Metrics

  • Search Latency: P95 < 500ms
  • Order Placement Success: > 99%
  • Driver Assignment Time: P95 < 30 seconds
  • Location Update Frequency: Every 5-10 seconds
  • API Availability: 99.9% uptime

User Personas

1. Busy Professional

  • Needs: Quick ordering, reliable delivery, quality food
  • Pain Points: Long wait times, cold food, wrong orders
  • Usage: Lunch and dinner, 3-5 times/week

2. Family Household

  • Needs: Large orders, multiple cuisines, scheduled delivery
  • Pain Points: High delivery fees, limited options
  • Usage: Weekends, special occasions

3. Restaurant Owner

  • Needs: More customers, efficient operations, fair commissions
  • Pain Points: High platform fees, order management complexity
  • Usage: Daily operations, menu updates

4. Delivery Driver

  • Needs: Flexible hours, good earnings, efficient routing
  • Pain Points: Low pay, long wait times, unclear instructions
  • Usage: Part-time or full-time work

Scope Boundaries

In Scope

  • Restaurant discovery and search
  • Order placement and tracking
  • Driver assignment and routing
  • Real-time location updates
  • Payment processing
  • Ratings and reviews
  • Basic analytics

Out of Scope

  • Restaurant POS integration (separate system)
  • Grocery delivery (different logistics)
  • Alcohol delivery (regulatory complexity)
  • Drone/robot delivery (future feature)
  • Restaurant supply chain management
  • Driver background checks (third-party service)

Interview Discussion Points

Clarifying Questions to Ask

  1. Scale and Geography:

    • How many cities/regions?
    • Urban vs suburban vs rural?
    • International expansion plans?

  2. Order Types:

    • Delivery only or pickup too?
    • Scheduled orders or immediate only?
    • Group orders supported?

  3. Driver Model:

    • Independent contractors or employees?
    • Multi-order delivery (batching)?
    • Driver incentives and bonuses?

  4. Restaurant Integration:

    • Menu sync frequency?
    • Inventory management?
    • Order confirmation process?

  5. Payment Model:

    • Commission structure?
    • Driver payment frequency?
    • Refund and dispute handling?

Key Points to Emphasize

  1. Real-Time Systems: Location tracking, ETA updates
  2. Matching Algorithm: Efficient driver assignment
  3. Scalability: Handle peak hours (lunch, dinner)
  4. Reliability: Order state consistency
  5. User Experience: Fast search, accurate ETAs

Real-World Examples

DoorDash

  • Strengths: Wide restaurant selection, DashPass subscription
  • Architecture: Microservices, real-time tracking, ML for ETAs
  • Scale: 25M+ users, 450K+ restaurants

Uber Eats

  • Strengths: Uber driver network, global presence
  • Architecture: Shared infrastructure with Uber rides
  • Scale: 100M+ users, 800K+ restaurants

Grubhub

  • Strengths: Early market entry, restaurant partnerships
  • Architecture: Restaurant-focused features
  • Scale: 33M+ users, 300K+ restaurants

Next Steps

After understanding the problem:

  1. Define scale and constraints (02-scale-constraints.md)
  2. Design high-level architecture (03-architecture.md)
  3. Model database schema (04-database-design.md)
  4. Design APIs (05-api-design.md)

Estimated Reading Time: 20 minutes