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LLD Case Study: Ride-Sharing Booking Engine
Object-oriented design of the Uber/Lyft booking and matching engine — entities, state machines, fare calculation, and concurrency. Focused on LLD class design, not distributed architecture.
LLD vs. HLD — What This Interview Is Actually Testing
The ride-sharing system appears in both LLD and HLD interviews, and the correct scope is completely different. HLD asks: how do you match 1M concurrent riders with 500K drivers across geographically sharded partitions? How does the matching service scale? What's the consistency model for driver location updates?
LLD asks: what are your classes? How do Trip, Driver, and MatchingEngine relate? How does a Trip transition from REQUESTED to COMPLETED without corrupting state? What pattern prevents a driver from being assigned two trips simultaneously?
If you start talking about Kafka, geohashing, or Redis in an LLD interview, you've signaled that you don't know the difference. The LLD interviewer wants to see clean OOP — strong entity modeling, correct use of composition vs. inheritance, State and Strategy patterns applied correctly, and awareness of the one real concurrency problem: preventing double-assignment of a driver.
What Interviewers Evaluate in This Problem
Three things separate strong candidates. First: modeling Trip as the central aggregate that owns both rider and driver references — not having Rider own a list of trips and Driver own a list of trips separately. Second: using the Strategy pattern for MatchingEngine so different matching algorithms (nearest-first, highest-rated-first, surge-pricing-aware) are swappable. Third: identifying the driver double-assignment problem and solving it with atomic state transition — not with a lock around the entire matching loop, which kills throughput.