How to Design a Parking Lot System — System Design Interview Guide

Before diving into the architecture of a parking lot system, you need to clarify requirements with your interviewer. This step demonstrates that you think before you code and understand that real-world systems require explicit scope definition. Start by identifying the core functional requirements that define what the system must do, then establish non-functional requirements that constrain how the system operates. For a parking lot system, the primary functional requirements include the following capabilities that users and internal systems need.

Capacity estimation helps you make informed architectural decisions. For a parking lot system, consider the expected scale in terms of daily active users, requests per second, data storage growth, and bandwidth requirements. Start with reasonable assumptions: assume millions of daily active users for a production-grade system. Calculate read and write ratios since most systems are read-heavy with ratios around 10:1 or higher. Estimate storage needs by multiplying average object size by daily creation rate by retention period. For network bandwidth, multiply request rate by average response size. These calculations inform decisions about caching strategy, database selection, and whether to optimize for reads or writes. In your interview, round numbers aggressively and state your assumptions clearly — interviewers value the process of estimation more than precise numbers.

The high-level architecture for a parking lot system follows a distributed microservices pattern with clear separation of concerns. At the top level, clients connect through a load balancer to an API gateway that handles authentication, rate limiting, and request routing. Behind the gateway, individual services handle specific domain responsibilities. The key components that form the backbone of this system are listed below. Each component is designed to be independently deployable and scalable, communicating through well-defined interfaces using either synchronous REST/gRPC calls for request-response patterns or asynchronous message queues for event-driven workflows.

The database design for a parking lot system must balance normalization for data integrity with denormalization for query performance. ParkingLots (id, name, location, total_spaces, floors). Spaces (id, lot_id, floor, zone, type, status, sensor_id). Sessions (id, space_id, vehicle_plate, entry_time, exit_time, amount). Reservations (id, lot_id, user_id, start_time, end_time, space_type). Choose your database engine based on the access patterns: use PostgreSQL for transactional data requiring ACID guarantees, Redis for caching and real-time counters, Elasticsearch for full-text search, and Cassandra or DynamoDB for high-write-throughput time-series data. Each table should have appropriate indexes based on common query patterns — analyze your API endpoints to determine which columns need indexing. Consider partitioning strategies for tables that will grow beyond single-node capacity. Use composite keys where natural partitioning exists in the data model.

A well-designed API for a parking lot system follows RESTful conventions with clear resource naming, appropriate HTTP methods, and consistent response formats. GET /api/lots?location=&available=true; GET /api/lots/:id/availability; POST /api/sessions/entry {lot_id, plate}; POST /api/sessions/exit {session_id, payment_method}; POST /api/reservations {lot_id, start, end} All endpoints should support pagination using cursor-based pagination for real-time data or offset-based for static lists. Include rate limiting headers (X-RateLimit-Remaining, X-RateLimit-Reset) in responses. Use proper HTTP status codes: 200 for success, 201 for creation, 400 for client errors, 404 for not found, 429 for rate limiting, and 500 for server errors. Version your API from day one using URL versioning (v1/v2) to allow backward-compatible evolution. Authentication should use JWT tokens with short expiry and refresh token rotation.

IoT sensors for real-time space detection. Edge computing at each lot for gate control. Central system for reservations and analytics. MQTT for sensor data streaming. Local fallback for network outages. Batch sync space status every 30 seconds to central system. Additionally, implement horizontal scaling at every layer: stateless application servers behind load balancers, database read replicas for query distribution, and connection pooling to manage database connections efficiently. Use circuit breakers between services to prevent cascade failures. Implement health checks and graceful degradation so the system continues operating (possibly with reduced functionality) when individual components fail. Monitor key metrics including p50, p95, and p99 latency, error rates, throughput, and resource utilization. Set up alerting thresholds based on SLOs rather than arbitrary limits. Plan for capacity with at least 2x headroom above current peak traffic to handle organic growth and unexpected spikes.

Every architectural decision involves trade-offs, and demonstrating awareness of these trade-offs is what separates strong candidates from average ones in system design interviews. Sensor-based vs camera-based detection. Reserved vs first-come spaces. Flat vs dynamic pricing. Local vs cloud processing for gate decisions. When discussing trade-offs in your interview, structure your reasoning as: state the options, explain the pros and cons of each, justify your choice for the given requirements, and acknowledge when you would choose differently under different constraints. There is rarely a single correct answer — what matters is your ability to reason about the implications of each choice and pick the one that best serves the stated requirements and constraints.