I started with the class hierarchy and it went fine, Vehicle and Spot and Ticket are all pretty natural.
Start by clarifying requirements and constraints, then define core entities and their relationships using object-oriented principles. Design a clean API that encapsulates parking logic, and discuss scalability, concurrency, and extensibility. Walk through a concrete example to demonstrate correctness.
Pro tip: Emphasize separation of concerns: keep spot allocation, pricing, and persistence as independent services or modules. This shows you think beyond basic OOP and consider real-world maintainability and scalability.
Ask about expected scale, vehicle types, spot sizes, levels, pricing rules, and concurrency needs. Confirm functional and non-functional requirements.
Define classes like ParkingLot, Level, ParkingSpot, Vehicle, Ticket, and Payment. Establish relationships (e.g., ParkingLot has Levels, Level has Spots).
Specify methods for park(vehicle), leave(ticket), and query methods like getAvailableSpots(vehicleType). Ensure the API is intuitive and covers all use cases.
Describe strategies for finding suitable spots (e.g., first-fit, best-fit) and ensuring thread safety with locks or atomic operations.
Mention how to extend for new vehicle types or spot sizes, and how to scale horizontally (e.g., sharding by level or using a distributed lock).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about keeping a per-level queue of free spots indexed by type.
Start by clarifying the problem: define what 'next available spot' means (e.g., nearest in time or space) and the data model (levels, spot types, availability). Then propose an efficient data structure like a segment tree or priority queue per level and spot type, and discuss query and update complexities, scalability, and concurrency.
Pro tip: Mention that in a real system like Uber, you'd likely use a distributed cache (e.g., Redis) with sorted sets for fast lookups, and handle consistency with optimistic locking or versioning to avoid race conditions when multiple users search simultaneously.
Ask questions to understand the scale (number of levels, spot types, spots), definition of 'next available' (e.g., nearest, earliest), and read/write patterns. This ensures you design the right solution.
Propose efficient structures like a segment tree or Fenwick tree for range queries per level and spot type, or a priority queue (min-heap) for earliest availability. Consider a hash map for O(1) access to level-type combinations.
Outline how to query across levels and spot types: e.g., iterate over levels and types, query each structure for the next available spot, and compare results to find the global best. Discuss time complexity (e.g., O(log n) per query).
Explain how to scale horizontally (sharding by level or region) and handle concurrent updates (e.g., locking, optimistic concurrency, or atomic operations). Mention caching for hot data.
Compare your approach with alternatives (e.g., database indexes, in-memory grids) and justify choices based on latency, consistency, and cost. Mention monitoring and fallback strategies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements and constraints, such as whether the assignment must be strictly atomic and what consistency guarantees are needed. Then propose a solution using a database transaction with row-level locking or a distributed lock, and discuss trade-offs like latency, scalability, and failure handling. Finally, mention alternative approaches like optimistic concurrency control or a queue-based system.
Pro tip: Emphasize that true atomicity often requires a single source of truth, and discuss how you would handle edge cases like network partitions or lock timeouts to show depth.
Ask about the expected scale, latency requirements, and whether the system is distributed. Confirm that the goal is to prevent double-booking and ensure atomicity.
Suggest using a database transaction with SELECT FOR UPDATE or a distributed lock (e.g., Redis Redlock) to serialize access to the spot assignment.
Compare locking approaches: database locks are simpler but may not scale; distributed locks add complexity but handle scale. Mention optimistic concurrency as an alternative.
Explain how to handle lock timeouts, retries, and idempotency to avoid deadlocks or double assignments in failure scenarios.
Conclude with a recommended approach based on the clarified requirements, and note any monitoring or metrics needed to ensure correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the existing design and the new requirements (EV charging, reservations, license-plate entry/exit). Then propose extensions to the data model, services, and APIs, focusing on scalability, consistency, and real-time constraints. Discuss trade-offs and potential bottlenecks, and outline how you would validate the design.
Pro tip: Emphasize idempotency and exactly-once processing for license-plate events to avoid double-charging or incorrect spot allocation, and consider using a distributed lock or reservation system to handle concurrency.
Ask questions to understand the scale, existing architecture, and specific needs for EV charging (e.g., power levels, payment), reservations (e.g., advance booking, cancellation), and license-plate entry/exit (e.g., accuracy, latency).
Propose new entities: EVChargingSpot (with attributes like connector type, power), Reservation (with time slots, user, spot), and LicensePlateEvent (with plate number, timestamp, gate ID). Define relationships and indexes for efficient queries.
Outline new microservices or extensions: Reservation Service (handles booking, availability), Charging Service (manages charging sessions, billing), and Access Control Service (processes license plate events, validates entry/exit). Define REST/gRPC endpoints and event flows.
Discuss partitioning (e.g., by region or spot ID), caching for availability, and consistency models (e.g., strong consistency for reservations, eventual for analytics). Use queues for asynchronous processing of license plate events.
Cover scenarios like concurrent reservations, license plate misreads, charging session interruptions, and payment failures. Discuss trade-offs between latency and accuracy, and propose monitoring and fallback mechanisms.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Made this a separate PricingPolicy class that the Ticket references at checkout.
Start by clarifying the requirements: what are the different spot types, how are rates determined, and how is the minimum charge applied. Then propose a data model that separates pricing rules from spot instances, using a flexible structure like a pricing policy table with rate tiers and minimum charge fields. Finally, discuss how to enforce the minimum charge in the billing logic, considering factors like proration and time granularity.
Pro tip: Mention that the minimum charge should be applied per billing period (e.g., per hour or per day) and consider edge cases like partial hours or early termination. Also, highlight the importance of making the pricing policy versioned and auditable for financial compliance.
Ask questions to understand the spot types, how rates vary (e.g., by time of day, location, demand), and the exact semantics of the minimum charge (e.g., per hour, per day, per session).
Propose a schema with a pricing_policy table that includes fields like spot_type, hourly_rate, minimum_charge, and effective dates. Consider using a JSON column for flexible rate tiers or a separate rate_tiers table.
Explain how to compute the charge: calculate the duration, multiply by the hourly rate, then apply the minimum charge if the computed amount is lower. Discuss proration for partial hours and how to handle rounding.
Address scenarios like overlapping policies, policy changes mid-billing period, and spot types with no minimum charge. Mention the need for idempotent billing operations.
Discuss how to cache pricing policies for performance, version policies for audit trails, and log billing decisions for debugging and compliance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.