Design and evolve a reservation system

Last updated: Mar 29, 2026

Quick Overview

Design and evolve a reservation system evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

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Design and evolve a reservation system

Nordstrom

Jul 17, 2025, 12:00 AM

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Design and evolve a reservation system

System Design: Online Reservation System

You are designing a backend platform to book time-based slots for services or events (e.g., appointments, classes, tables). Each resource offers time slots with a fixed capacity. The system must prevent double-booking under high concurrency and support lifecycle operations.

Assume a single region to start, with the need to scale reads (availability lookups) more than writes (bookings). Payments are out of scope; focus on reservation integrity.

Requirements

Core capabilities
- Create, modify, and cancel reservations.
- Prevent double-booking and overselling capacity.
APIs
- Specify external-facing endpoints and request/response behaviors.
- Define idempotency strategy for write APIs.
Data model
- Entities, relationships, and key indexes/constraints.
Consistency and concurrency
- Consistency guarantees for reads/writes.
- Concurrency control approach to avoid double-booking.
Capacity, waitlists, and expiration
- Capacity checks, holds, waitlist mechanics, and TTL/expiration handling.
Trade-offs
- Optimistic vs. pessimistic locking.
- Relational vs. NoSQL stores.
- Centralized vs. sharded inventory.
Scaling and reliability
- Partitioning, caching, queues, rate limiting, backpressure.
- Monitoring, alerting, and failure recovery patterns.
Security and privacy
- Key risks and mitigations.
Deployment
- Choose between blue/green, canary, and rolling; justify your choice.
Product engineering principles

What principles and risks do you prioritize when designing features/services?

Constraints & Assumptions

Preserve the scope, facts, inputs, and requested outputs from the prompt above.
If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.
Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.

Clarifying Questions to Ask Guidance

Clarify users, core use cases, read/write patterns, scale, latency, availability, and data retention.
State explicit assumptions before making sizing or architecture decisions.
Prioritize the functional path first, then address reliability, security, observability, and rollout.

What a Strong Answer Covers Guidance

A scoped requirements summary with concrete non-goals and success metrics.
API, data model, architecture, consistency, capacity, and operations.
Reasoned trade-offs among simple and scalable designs, including bottlenecks and failure modes.
A validation, monitoring, migration, and launch plan appropriate for the risk level.

Follow-up Questions Guidance

What breaks first at 10x traffic or data volume?
How would you degrade gracefully during dependency failures?
What metrics and alerts would prove the design is healthy after launch?

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