Designing a Ride-Matching System for Carpooling (SDE1 Interview breakdown)
We will breakdown the system design problem asked in blablacar for SDE1
Welcome to ZenMode Newsletter, where we pick apart a simple-to-complex system design interview questions (L2- L7) and focus on the depth and nuance expected in top-tier interviews.
This question was asked by BlaBlacar for an SDE1 position.
Design a Ride-Matching System for Carpooling.
🔔Focus: At this level, interviewers expect you to identify core features (functional) and basic system qualities (non-functional) without diving too deep into complex architecture (e.g., microservices or sharding).
🏝️Approach: Start with functional requirements to show you understand the problem, then layer in non-functional requirements and constraints to demonstrate awareness of real-world challenges.
⚖️Trade-offs: Be ready to discuss trade-offs (e.g., faster matching vs. higher accuracy) if prompted.
If you'd like to try it first, do so before reading on
1. Requirements & Constraints
1.1 Functional Requirements
These define what the system must do to meet user needs and business goals.
User Registration and Profiles
Users (drivers and riders) can sign up and create profiles with basic details (e.g., name, contact info, preferences).
Drivers can specify vehicle details (e.g., car type, capacity).
Ride Posting
Drivers can post available rides with details like start location, destination, date/time, available seats, and optional preferences (e.g., no smoking, music choice).
Ride Search and Matching
Riders can search for rides based on start location, destination, and time.
The system matches riders with drivers based on route compatibility, timing, and preferences.
Provide a list of potential matches with details (e.g., driver name, ETA, cost).
Ride Booking
Riders can request to join a ride, and drivers can approve or reject requests.
Notify both parties (driver and rider) once a match is confirmed.
Route Optimization
Suggest optimal pickup/drop-off points or slight detours (if acceptable to the driver) to accommodate riders.
Payment Integration
Riders can pay drivers through the system (e.g., split fuel costs or fixed fee).
Support digital payment methods (e.g., credit card, wallet).
Ride Status Tracking
Both drivers and riders can track the ride status (e.g., "Scheduled," "In Progress," "Completed").
Provide real-time updates like ETA or delays.
Rating and Feedback
After the ride, both drivers and riders can rate each other and leave feedback.
1.2 Non-Functional Requirements
These define the qualities or constraints of the system, focusing on performance, scalability, and usability.
Scalability
The system should handle thousands of users and ride requests concurrently (e.g., 10,000 active users in a city).
Support growth to multiple cities or regions over time.
Performance
Ride matching should occur within 2-3 seconds of a search request.
Real-time updates (e.g., ETA) should refresh every 5-10 seconds.
Reliability
Ensure 99.9% uptime to avoid disruptions in ride scheduling or tracking.
Handle edge cases like network failures gracefully (e.g., retry mechanisms).
Availability
The system should be accessible 24/7, as carpooling can happen anytime.
Security
Protect user data (e.g., location, payment info) with encryption (e.g., HTTPS, data at rest).
Authentication users to prevent unauthorized access.
Usability
The interface (web/app) should be intuitive for non-technical users.
Supports mobile devices with a responsive design.
Latency
API response times should be under 200ms for critical operations (e.g., ride search, booking).
Cost Efficiency
Optimize server and database usage to minimize operational costs (e.g., efficient matching algorithms).
1.3 Constraints
These are limitations or boundaries within which the system must operate.
Geographical Scope
Initially limited to a single city or region, with potential expansion later.
Capacity Limits
Each vehicle has a fixed number of seats (e.g., 1-6), which the system must respect.
Time Sensitivity
Matches must account for real-time traffic and driver schedules, requiring integration with a mapping API (e.g., Google Maps).
Budget
Assume a lean infrastructure (e.g., cloud-based with minimal servers) suitable for a startup or small-scale deployment.
Legal/Regulatory
Comply with local transportation laws (e.g., no commercial rides unless licensed).
Ensure user privacy per regulations like GDPR or CCPA.
Technology Stack
To keep it simple for an SDE1-level design, use widely available tools (e.g., REST APIs, SQL/NoSQL databases).
In this post, we are tasked with designing a Ride-Matching System for Carpooling. This system aims to connect individuals driving their vehicles (Drivers) with others needing a ride along similar routes (Riders), facilitating shared commutes or trips to reduce costs, traffic congestion, and environmental impact.
While designing a robust, scalable, and trustworthy carpooling system involves significant complexity, especially concerning real-time location tracking, efficient matching algorithms, safety features, and payment processing. Figure 1 illustrates a conceptual mobile interface for such a system.
Carpooling platforms like Waze Carpool, BlaBlaCar (for longer distances), or Scoop operate on these principles. Let's explore some hypothetical, yet plausible, statistics to understand the potential scale and importance:
Potential Daily Active Users (DAU): Millions in large metropolitan areas.
Rides Facilitated Daily: Hundreds of thousands to millions globally.
Average Trip Distance: Varies significantly (commutes vs. long-distance). Assume 15 miles (24 km) for commutes.
Peak Hours: Concentrated during morning and evening commute times, posing significant load challenges.
Environmental Impact: Significant potential reduction in CO2 emissions and traffic volume if widely adopted.
Cost Savings: Riders save on fares compared to ride-hailing, Drivers offset fuel and vehicle costs.
These points highlight that a carpooling system needs to be highly reliable, efficient, scalable, and trustworthy to succeed.