I started with the Vehicle hierarchy and worked outward, which felt natural but I spent too long on the OOP side before getting to spot assignment.
Start by clarifying requirements and constraints (e.g., spot types, vehicle sizes, payment methods, concurrency). Then design the class hierarchy with clear responsibilities, focusing on extensibility and separation of concerns. Walk through the entry/exit flow, explaining spot assignment, ticket creation, and payment processing, while discussing trade-offs and potential bottlenecks.
Pro tip: Demonstrate awareness of real-world constraints like concurrent access and scalability by mentioning locking strategies or optimistic concurrency, and suggest using design patterns like Strategy for payment and Factory for spot assignment to keep the system flexible.
Ask questions to understand expected scale, spot types, vehicle types, payment methods, and any special features like reserved spots or EV charging. This ensures the design meets the actual needs.
Define core classes: ParkingLot (singleton), Level, ParkingSpot (with subclasses for Compact, Large, etc.), Vehicle (with subclasses Car, Truck, Motorcycle), and Ticket. Establish relationships and responsibilities.
Explain how to find an available spot based on vehicle size, possibly using a strategy pattern. Discuss data structures for efficient lookup (e.g., hashmap of spot types per level).
Walk through the steps: vehicle enters, ticket generated with timestamp and assigned spot; vehicle exits, payment calculated based on duration, ticket closed, spot freed. Include payment processing integration.
Address concurrency (locking), scalability (multiple levels, distributed), and potential extensions (reservations, dynamic pricing). Mention patterns used and why.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the scenario: are these gates physical turnstiles, API endpoints, or database connections? Then propose a layered concurrency strategy that combines optimistic locking, queuing, and idempotency to handle simultaneous entry and exit events without race conditions or deadlocks. Emphasize trade-offs between consistency, latency, and throughput, and how you would monitor and adjust the approach based on real-world load.
Pro tip: Mention that you would first try to avoid shared mutable state altogether by using event sourcing or a partitioned queue per gate, which often eliminates the need for complex locking. Also, highlight that you would measure contention points before optimizing, as premature concurrency control can hurt performance.
Ask about the nature of the gates (physical or logical), expected throughput, consistency requirements (e.g., can we allow temporary overcapacity?), and failure modes. This ensures your solution addresses the real problem.
Determine what state is shared across gates (e.g., current occupancy count, ticket validation, seat availability) and where race conditions could occur. Map out read/write patterns.
Choose appropriate techniques such as optimistic locking, pessimistic locking, atomic operations, or message queues. Explain how you would apply them to entry and exit flows, possibly using separate queues for each direction.
Ensure that duplicate events (e.g., a gate retrying a request) do not corrupt the count. Use idempotency keys, deduplication, or transactional outbox patterns.
Compare latency vs. consistency, scalability vs. complexity, and explain how you would monitor contention, adjust lock granularity, and handle failures gracefully.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pretty straightforward after the base design.
Start by clarifying the current design and assumptions, then propose extending it with a hierarchical model where each level is a sub-structure. Discuss data structures, algorithms for allocation and navigation, and trade-offs between simplicity and scalability.
Pro tip: Emphasize that multi-level parking introduces new constraints like inter-level navigation and capacity balancing; showing awareness of these non-obvious challenges demonstrates depth.
Ask about the current design, expected scale, and specific needs like vehicle types, entry/exit points, and real-time constraints. Confirm whether levels are independent or interconnected.
Propose a hierarchical data model: a ParkingLot composed of multiple Levels, each with spots. Consider using a tree or graph to represent connections between levels (e.g., ramps, elevators).
Adapt allocation and search algorithms to consider multiple levels. For allocation, use a strategy that balances occupancy across levels; for navigation, compute paths between levels using graph traversal.
Discuss trade-offs: centralized vs. distributed control per level, latency of cross-level operations, and consistency. Mention potential bottlenecks and how to mitigate them (e.g., caching, sharding by level).
Recap the key extensions and trade-offs, then ask if the interviewer wants to dive deeper into any area. This shows collaboration and ensures alignment.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I introduced a SpotType enum and subclassed Spot into ChargingSpot.
Start by clarifying the requirements and scope, such as whether this is for a marketplace, navigation app, or fleet management. Then outline a high-level design covering data modeling, APIs, and integration points, and discuss trade-offs like real-time availability vs. consistency and scalability.
Pro tip: Demonstrate awareness of real-world constraints like unreliable charging station data and the need for idempotent booking operations. Mention how you would handle edge cases such as concurrent reservations and payment failures.
Ask questions to understand the use case: Is this for finding chargers, booking them, or managing a fleet? What scale and latency requirements exist?
Sketch the main components: a database for charging spots, an API for CRUD operations, and integration with external data sources like Open Charge Map.
Define schemas for charging spots (location, connector type, power output, availability) and reservations (user, spot, time slot, status).
Design RESTful endpoints for searching, reserving, and updating spots. Include authentication, rate limiting, and error handling.
Discuss trade-offs: real-time availability vs. eventual consistency, SQL vs. NoSQL, and how to scale with caching and sharding.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Tacked a Reservation class onto the Ticket and talked about a time-windowed hold on a spot.
Start by clarifying the requirements and constraints of advance reservations, such as what resources are being reserved, the expected scale, and consistency needs. Then propose a data model and system architecture that handles concurrency, expiration, and notifications, while discussing trade-offs between different approaches.
Pro tip: Demonstrate awareness of real-world constraints like double-booking prevention and scalability by mentioning specific techniques (e.g., optimistic locking, distributed locks) and their trade-offs. Also, consider how reservations integrate with existing systems and how to handle failures gracefully.
Ask questions to understand what is being reserved (e.g., seats, inventory, time slots), the expected load, consistency requirements, and any business rules like cancellation policies.
Propose a schema for reservations, including fields like resource ID, user ID, start/end time, status, and timestamps. Consider using a separate table for reservations and indexes for efficient querying.
Discuss strategies to prevent double-booking, such as database transactions with isolation levels, optimistic locking with versioning, or distributed locks. Mention trade-offs between consistency and availability.
Explain how reservations are created, confirmed, expired, or cancelled. Include background jobs for expiration and notifications (e.g., email/SMS reminders).
Address scalability concerns (e.g., sharding, caching) and how the reservation system integrates with existing services (e.g., payment, inventory). Discuss monitoring and failure recovery.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.