Design a Real-Time Suggestions Service 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.
Design a system that provides real-time typeahead/autocomplete suggestions as users type. Cover API design and request/response schemas, data modeling and indexing (e.g., prefix trees or inverted indexes), ranking signals and personalization, latency targets (e.g., <100 ms P
95), caching layers, scalability, freshness and backfill pipelines, A/B experimentation hooks, and failure handling.
Quick Answer: Design a Real-Time Suggestions Service 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.
Design a real-time typeahead/autocomplete service for a consumer-facing web and mobile application. Users see suggestion updates on each keystroke. Assume global traffic, multiple locales, and both anonymous and signed-in users.
Requirements
Design the system and cover:
API design and request/response schemas
Data modeling and indexing (e.g., prefix trees, FST, inverted indexes)
Ranking signals and personalization
Latency targets and budgets (e.g., P95 < 100 ms)
Caching layers (edge, in-memory, distributed)
Scalability and capacity planning
Freshness and backfill pipelines (batch + streaming)
A/B experimentation hooks and telemetry
Failure handling and graceful degradation
State assumptions where needed and justify trade-offs.
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?