Design a hybrid parking management and on-demand ride system (similar to an autonomous vehicle fleet operation). Key constraints and behaviors: (1) Customers summon vehicles at parking lot exits and wait for pickup. (2) Vehicles cycle through multiple spot types in the lot — e.g., they move from charging spots (replenishing power) to maintenance spots (inspection/repair) and finally to regular (non-charging) waiting spots before being dispatched to a customer. (3) The system must manage the full vehicle lifecycle across these spot categories and orchestrate efficient dispatch. As a follow-up, discuss which performance metrics you would design and monitor for such a system.
Naive approach with serious trade-off — being authored.
Solid baseline with reasonable trade-offs — being authored.
Production-grade approach with explicit trade-off rationale — being authored.
Naive approach with serious trade-off — being authored.
Solid baseline with reasonable trade-offs — being authored.
Production-grade approach with explicit trade-off rationale — being authored.
Cover happy path. Clarify scope. Identify the obvious bottleneck. Pick a reasonable storage and reasonable scaling approach.
All of the above plus: explicit failure handling, durability vs latency trade-offs, choose the right batching/caching strategy, articulate why.
All of the above plus: organizational concerns (rollout, migration, on-call), quantitative analysis, multi-region considerations, what could go wrong with the proposed solution at 10x scale.
Common mistakes: Treating the system as a pure ride-hailing design and ignoring the internal vehicle lifecycle / spot management component; Not modeling the vehicle state machine explicitly, leading to unclear transition logic; Forgetting concurrency issues when multiple dispatchers try to assign the same vehicle
What passers do: Explicitly modeling the vehicle state machine with legal transitions before jumping to services; Addressing spot-allocation consistency (locking mechanism) proactively; Proposing concrete, measurable performance metrics with justification
Why people fail: Focusing only on the customer-facing ride request flow without addressing internal fleet/spot management; Skipping data model and jumping directly to high-level boxes without detail; Unable to articulate relevant metrics beyond generic 'latency and throughput'
Edge cases probed: Vehicle runs out of charge while in a regular (non-charging) spot before being dispatched — needs re-routing to charging spot; All charging spots are occupied when a vehicle needs to charge; Customer cancels request after vehicle is already dispatched; Simultaneous ride requests when only one AVAILABLE vehicle exists (race condition in assignment)
Alternative approaches: Event-driven with Kafka for state transitions (Decouples services and enables replay/audit of vehicle state history, but introduces eventual consistency — a vehicle might appear AVAILABLE briefly while already being assigned in another partition.); Centralized monolithic coordinator (Simpler to reason about strong consistency for spot allocation and dispatch, but becomes a single point of failure and bottleneck at scale.); Geospatial dispatch (if multi-lot or large lot) (Use PostGIS or Redis GEO commands to find nearest available vehicle to a requested exit gate — more accurate but adds infrastructure complexity.)