Design a Restaurant Food Ordering Application

Problem Statement

Create a system architecture for a ride-sharing platform similar to Uber or Lyft that connects passengers with nearby drivers. The platform must support millions of concurrent users across multiple cities, handling real-time location tracking, dynamic pricing, and efficient driver-passenger matching. The system should minimize wait times for passengers while maximizing driver utilization.

Your design should handle peak traffic periods where ride requests can spike 10x normal volume (concerts, sporting events, rush hour). Consider that drivers and passengers are constantly moving, requiring continuous location updates and recalculation of ETAs. The system must remain highly available as downtime directly impacts revenue and user trust.

Key Requirements

Functional

• Real-time matching -- Connect passengers with nearby available drivers within seconds based on location, vehicle type, and estimated pickup time
• Live location tracking -- Track driver and passenger locations continuously, updating positions every 3-5 seconds during active trips
• Dynamic fare calculation -- Compute trip costs based on distance, time, traffic conditions, and surge pricing multipliers
• Trip management -- Handle complete trip lifecycle from request through pickup, journey, payment, and rating
• Driver availability -- Allow drivers to toggle online/offline status and accept/reject ride requests

Non-Functional

• Scalability -- Support 50 million daily active users with 5 million concurrent rides across 500+ cities
• Reliability -- Maintain 99.99% uptime with graceful degradation during partial failures
• Latency -- Match drivers to passengers within 2 seconds; deliver location updates with <500ms latency
• Consistency -- Ensure exactly-once ride assignment (no double-booking); eventual consistency acceptable for non-critical features

What Interviewers Focus On

Based on real interview experiences, these are the areas interviewers probe most deeply:

1. Geospatial Indexing and Location Tracking

The core challenge is efficiently finding nearby drivers when a passenger requests a ride. With millions of drivers constantly moving, naive approaches like scanning all drivers or simple lat/long range queries don't scale.

Hints to consider:

• Explore geohashing or quadtree structures to partition geographic space into manageable regions
• Discuss how to handle drivers moving between geo-boundaries without losing track
• Consider using in-memory data stores like Redis with geospatial indexes for sub-second queries
• Think about how granularity affects both query performance and accuracy

2. Real-Time Communication and WebSocket Management

Both drivers and passengers need instant updates about location changes, ride status, and ETA adjustments. Traditional request-response patterns introduce too much latency and overhead.

Hints to consider:

• Evaluate WebSocket connections versus long polling for different mobile network conditions
• Design a strategy for maintaining millions of concurrent persistent connections
• Discuss message broker architectures (Kafka, RabbitMQ) for decoupling location updates from business logic
• Plan for reconnection handling when mobile devices lose connectivity

3. Matching Algorithm and Assignment Strategy

Deciding which driver to assign involves multiple factors beyond just distance -- driver ratings, vehicle type, traffic patterns, and fair distribution of rides among drivers.

Hints to consider:

• Balance between optimal global assignment and fast local assignment with acceptable accuracy
• Discuss timeout strategies when drivers don't respond to requests
• Consider implementing a scoring system that weighs multiple factors (distance, rating, acceptance rate)
• Handle edge cases like multiple passengers requesting rides from the same location simultaneously

4. Surge Pricing and Dynamic Fare Calculation

During high-demand periods, prices must adjust dynamically to balance supply and demand while remaining transparent and fair to users.

Hints to consider:

• Design a system to detect demand spikes in real-time using location-based metrics
• Discuss how to compute surge multipliers without causing price volatility
• Consider caching strategies for base fare rates while allowing dynamic adjustments
• Think about regulatory compliance and price transparency requirements

5. Payment Processing and Financial Transactions

Handling payments involves third-party integrations, split payments between platform and drivers, refunds, and ensuring financial accuracy across millions of transactions daily.

Hints to consider:

• Use idempotency keys to prevent duplicate charges during network retries
• Implement eventual consistency with reconciliation jobs for financial records
• Design for PCI compliance when handling payment methods
• Consider pre-authorization holds versus final charges after trip completion

Suggested Approach

Step 1: Clarify Requirements

Confirm the system's scope and constraints with your interviewer. Ask about geographic coverage (single city vs global), expected request volume (thousands vs millions per second), and whether advanced features like ride pooling or scheduled rides are in scope. Clarify SLA requirements for matching latency and acceptable data staleness for driver locations. Determine if you need to design the payment processing system or can assume a third-party integration.

Step 2: High-Level Architecture

Sketch the core components: API Gateway for mobile clients, Location Service for tracking driver/passenger positions, Matching Service for pairing drivers with passengers, Trip Management Service for lifecycle handling, and Pricing Service for fare calculation. Include a real-time notification system using WebSockets or push notifications. Add data stores: relational database for trip history and user accounts, Redis for active driver locations and real-time state, and a time-series database for location history and analytics.

Step 3: Deep Dive on Geospatial Matching

Walk through the complete flow when a passenger requests a ride. Explain how you partition the map using geohashing (suggest h3 or S2 geometry) to create hierarchical grids. When a request arrives, query the passenger's geohash and neighboring cells to find active drivers. Retrieve driver locations from Redis sorted sets with geospatial indexes. Rank candidates by distance, estimated pickup time, and rating. Send ride requests to top 3-5 drivers with a 15-second timeout. If no acceptance, expand the search radius and retry. Discuss how to handle high-concurrency scenarios where multiple passengers compete for the same driver using distributed locks or compare-and-swap operations.

Step 4: Address Secondary Concerns

Discuss how WebSocket servers maintain persistent connections with clients, using a pub-sub system (Redis Pub/Sub or Kafka) to broadcast location updates to subscribers. Explain horizontal scaling of WebSocket servers using consistent hashing to route users to specific servers. Cover surge pricing calculations using real-time metrics from the matching service to detect demand-supply imbalances per geographic region. Address payment processing with idempotent APIs, asynchronous settlement, and reconciliation jobs. Mention observability through distributed tracing for debugging failed matches and monitoring latency metrics for matching and location updates.