Meta·Software Engineer·Onsite - System Design / Architecture·Senior

Senior

Jul 2026

Summary

Meta system design round for a software engineer role. The whole session was built around one meaty question about building a robust API endpoint, and it went deeper than I expected across a lot of dimensions.

Questions Asked (1)

Design the full validation and error-handling strategy for an API endpoint that computes the top-K most frequent values from a user-submitted array. Cover input validation, auth, rate limiting, idempotency, error codes, partial failures, timeouts, retries, observability, and graceful degradation for bad or oversized payloads. Include example API contracts and error response shapes.

System DesignAPI & IntegrationsTechnical Trade-offs

Author's notes

This question sprawled in every direction and I kept second-guessing whether to go broad or deep first.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints (e.g., expected array size, K range, latency SLA, auth model). Then structure your answer around the request lifecycle: input validation, auth, rate limiting, idempotency, processing with timeouts and partial failure handling, and finally observability and graceful degradation. Provide concrete API contracts and error response shapes to illustrate your design decisions.

Pro tip: Emphasize idempotency and partial failures: use idempotency keys for safe retries and design the API to return partial results with warnings when some elements are invalid, rather than failing the entire request. This shows you prioritize user experience and system resilience.

1. Clarify Requirements and Constraints

Ask about expected payload size, K limits, latency SLA, auth mechanisms, and whether the operation is synchronous or asynchronous. This ensures your design meets actual needs.

2. Define API Contract and Validation Rules

Specify the endpoint (e.g., POST /top-k), request/response schemas, and validation rules for array size, element types, and K. Include error codes for invalid inputs.

3. Design Security and Traffic Management

Cover authentication (e.g., API keys, OAuth), authorization, rate limiting (per user/IP), and idempotency using idempotency keys to prevent duplicate processing.

4. Handle Processing, Timeouts, and Partial Failures

Describe how to process large arrays with timeouts, retries with backoff, and partial failure handling (e.g., skip invalid elements and return warnings). Include graceful degradation for oversized payloads.

5. Implement Observability and Error Responses

Define structured error response shapes with codes, messages, and details. Add logging, metrics, and tracing for monitoring. Explain how to degrade gracefully (e.g., return 413 for oversized payloads).

Key Points to Mention

Input validation: enforce max array size, element type, K bounds; return 400 with detailed errors.
Authentication and rate limiting: use API keys/OAuth, apply per-user rate limits, return 429 with Retry-After.
Idempotency: support Idempotency-Key header to ensure safe retries and prevent duplicate processing.
Timeouts and retries: set server-side timeouts, use exponential backoff for retries, and return 504 on timeout.
Partial failures: process valid elements, return partial results with warnings for invalid ones, and use 207 Multi-Status if needed.
Observability: log requests with correlation IDs, emit metrics (latency, error rates), and trace processing steps.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.