Problem and Scope
Design a ride-sharing service like Uber that connects riders with nearby drivers and facilitates the entire ride experience from requesting to completion.
Start by confirming the core user journey, exclusions, success criteria, and the constraints that materially affect the architecture.
Requirements to Clarify
A strong answer should establish scope before choosing components.
Functional requirements:
- Users should be able to input a start location and a destination to get a fare estimate
- Users should be able to request a ride and be matched with a nearby available driver in real-time
- Drivers should be able to accept or decline a ride request and navigate to the rider's location and destination
- The system should track the real-time location of drivers to enable matching with riders
- The system should handle the entire ride lifecycle from request to completion
Non-functional requirements:
- Low latency for matching riders with drivers (< 1 minute to match or notify of no availability)
- High consistency for matching to ensure a ride is matched to only one driver and vice versa
- High availability for all services except the critical matching component
- Ability to handle high throughput during peak hours and special events (hundreds of thousands of requests in a region)
- Location data must be updated frequently to ensure accurate matching
Scale and Capacity
Use the workload to justify storage, partitioning, caching, and reliability decisions. Clarify or challenge these assumptions rather than treating them as unquestionable facts:
- Millions of active drivers worldwide
- Tens of millions of riders
- Location updates from drivers every few seconds
- Potential for extreme surge events (sports games, concerts, etc.)
- Global service across different cities, countries, and regions
Architecture Discussion
Walk through the important read and write paths, identify ownership boundaries, and explain how the design behaves when dependencies fail. Cover these areas explicitly:
- Location tracking and management system
- Ride matching service
- Fare estimation service
- Real-time driver-rider communication
- Navigation integration
- Payment processing
- Distributed storage for user profiles, rides, and location data
- Efficiently storing and querying real-time location data
- Ensuring consistency in the matching process
- Handling high volumes of location updates
- Managing surge pricing and demand
- Providing low-latency matching across global regions
Follow-up Questions
Expect the interviewer to test the consequences of your choices. Practise answering these questions with a concrete decision, its benefit, and its cost:
- How would your location service handle the high volume of driver location updates?
- Focus: Assess the candidate's answer to "How would your location service handle the high volume of driver location updates" for explicit assumptions, a workable mechanism, failure behavior, and consequential trade-offs.
- What approach would you take to ensure no driver is matched to multiple rides simultaneously?
- Focus: Assess the candidate's answer to "What approach would you take to ensure no driver is matched to multiple rides simultaneously" for explicit assumptions, a workable mechanism, failure behavior, and consequential trade-offs.
- How would your system handle a sudden surge in ride requests after a major event?
- Focus: Assess the candidate's answer to "How would your system handle a sudden surge in ride requests after a major event" for explicit assumptions, a workable mechanism, failure behavior, and consequential trade-offs.
- What trade-offs did you consider when designing your geospatial indexing strategy?
- Focus: Assess the candidate's answer to "What trade-offs did you consider when designing your geospatial indexing strategy" for explicit assumptions, a workable mechanism, failure behavior, and consequential trade-offs.
- How would you scale your matching service to handle global traffic?
- Focus: Assess the candidate's answer to "How would you scale your matching service to handle global traffic" for explicit assumptions, a workable mechanism, failure behavior, and consequential trade-offs.
Evaluation Rubric
MockMe evaluates the answer across the following dimensions. A complete answer should connect claims to requirements and explain consequential trade-offs.
- Requirements and scope (15%): Clarifies and prioritizes the required behavior for Users should be able to input a start location and a destination to get a fare estimate; Users should be able to request a ride and be matched with a nearby available driver in real-time; Drivers should be able to accept or decline a ride request and navigate to the rider's location and destination. Establishes the constraints that materially affect Design Uber, including Low latency for matching riders with drivers (< 1 minute to match or notify of no availability); High consistency for matching to ensure a ride is matched to only one driver and vice versa. Strong evidence includes Separates the critical path from secondary features and resolves ambiguous requirements before choosing components.
- Architecture and interfaces (20%): Presents coherent ownership boundaries and end-to-end flows covering Location tracking and management system; Ride matching service; Fare estimation service; Real-time driver-rider communication. Strong evidence includes Defines interfaces and traces important success, retry, and failure paths across the proposed components.
- Data and scaling (25%): Uses workload assumptions such as Millions of active drivers worldwide; Tens of millions of riders to justify capacity and partitioning decisions. Explains the data, state, or model strategy for Distributed storage for user profiles, rides, and location data; Efficiently storing and querying real-time location data. Strong evidence includes Quantifies a dominant workload, identifies the first bottleneck, and explains how the design evolves as that workload grows.
- Reliability, correctness, and safety (20%): Explains concrete failure behavior, recovery, and operational safeguards for Ensuring consistency in the matching process; Low latency for matching riders with drivers (< 1 minute to match or notify of no availability); High consistency for matching to ensure a ride is matched to only one driver and vice versa. Strong evidence includes States the required correctness or consistency boundary and covers retries, partial failure, observability, and safe degradation.
- Communication and trade-offs (20%): Drives a structured discussion and compares consequential alternatives for Handling high volumes of location updates; Managing surge pricing and demand; Providing low-latency matching across global regions. Strong evidence includes Makes assumptions explicit, answers the question asked, and explains both the benefit and cost of major decisions.