I started with the data model, which felt natural.
Start by clarifying requirements and constraints, then design an object-oriented model with clear entities and relationships, followed by a high-level system architecture that separates concerns and ensures scalability and security. Emphasize authorization rules and data consistency, and discuss trade-offs between different design choices.
Pro tip: Demonstrate maturity by explicitly addressing authorization at the data access layer and considering audit trails for compliance, which is critical in travel and expense systems like Navan.
Ask questions to understand the scope: expected volume, read/write patterns, latency requirements, and any compliance needs. Confirm that comments are auxiliary and not core to the booking process.
Define entities like User, Booking, Comment, and Admin, with relationships and methods for adding/editing comments. Include authorization logic in the model or service layer.
Propose a layered architecture: API gateway, authentication/authorization service, comment service, and data storage. Consider using a relational database for strong consistency and a cache for performance.
Detail how regular users can only modify their own comments, while admins can modify any. Discuss role-based access control (RBAC) and enforcement at the API and data layers.
Compare SQL vs NoSQL, synchronous vs asynchronous writes, and caching strategies. Mention how the design can scale with increasing bookings and comments.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements: scale (thousands of reservations), consistency needs, and whether the update is synchronous or asynchronous. Then propose an asynchronous batch processing architecture with a job queue, chunked processing, and idempotency, while discussing trade-offs like eventual consistency and failure recovery.
Pro tip: Emphasize idempotency and observability: design each batch job to be safely retryable and provide admins with progress tracking and audit logs. This shows you understand production concerns beyond just the happy path.
Ask about the expected scale, latency tolerance, consistency requirements, and whether the update must be atomic. Confirm if admins need immediate feedback or can wait for asynchronous completion.
Propose an endpoint that accepts a filter or list of reservation IDs and the new comment, validates the request, and enqueues a batch job. Return a job ID for status tracking.
Use a message queue and worker pool to process reservations in chunks (e.g., 100-1000 per batch). Ensure idempotency by tracking processed IDs and using database transactions per chunk.
Implement retries with exponential backoff, dead-letter queues for persistent failures, and compensating actions if needed. Consider eventual consistency and how to handle partial failures.
Expose job status, progress, and error details via API. Allow admins to cancel or retry failed batches. Log all changes for auditing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
They explicitly asked me to state my assumptions before designing.
Start by explicitly stating your assumptions about comment history, auditability, and scale, then explain how each assumption shapes your design decisions. Tie these assumptions to Navan's business context (travel and expense management) to show you understand the domain. Invite the interviewer to correct or refine your assumptions to demonstrate collaboration.
Pro tip: Frame assumptions as testable hypotheses and mention how you'd validate them with stakeholders or data, showing you balance speed with rigor. Also, note that auditability often requires immutable logs and retention policies, which can impact storage costs and query performance.
State assumptions about what comment history includes (e.g., edits, deletions, threading) and how it's accessed (e.g., chronological, searchable). Explain how this affects data modeling and storage.
Assume audit logs must be immutable, tamper-proof, and retained for a certain period (e.g., 7 years for compliance). Discuss how this influences database choices (e.g., append-only stores) and access controls.
Assume scale in terms of users, comments per day, read/write ratio, and growth rate. Explain how these numbers drive decisions on sharding, caching, and indexing.
For each assumption, briefly state the architectural consequence (e.g., 'If audit logs are immutable, we'll use a write-once store like Amazon QLDB'). This shows you can translate assumptions into design.
Acknowledge that assumptions may be wrong and propose how you'd validate them (e.g., with product managers, by analyzing existing data). This demonstrates adaptability and collaboration.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.