Design a scalable like/unlike system (in context of Roblox Marketplace items, games, or generic social media items). Core functional requirements: (1) A user can like or unlike any item (idempotent); (2) Any user can view the total like count for any item; (3) Any user can check whether they have liked a specific item; (4) A user can retrieve the full list of items they have previously liked. Some variants also include: dislike functionality with state transitions (like→dislike, dislike→like), a leaderboard of most-liked items, and geographic popular items. Scale: ~1M QPS reads and ~100K–1M QPS writes.
Naive approach with serious trade-off — being authored.
Solid baseline with reasonable trade-offs — being authored.
Production-grade approach with explicit trade-off rationale — being authored.
Naive approach with serious trade-off — being authored.
Solid baseline with reasonable trade-offs — being authored.
Production-grade approach with explicit trade-off rationale — being authored.
Naive approach with serious trade-off — being authored.
Solid baseline with reasonable trade-offs — being authored.
Production-grade approach with explicit trade-off rationale — being authored.
Naive approach with serious trade-off — being authored.
Solid baseline with reasonable trade-offs — being authored.
Production-grade approach with explicit trade-off rationale — being authored.
Naive approach with serious trade-off — being authored.
Solid baseline with reasonable trade-offs — being authored.
Production-grade approach with explicit trade-off rationale — being authored.
Naive approach with serious trade-off — being authored.
Solid baseline with reasonable trade-offs — being authored.
Production-grade approach with explicit trade-off rationale — being authored.
Naive approach with serious trade-off — being authored.
Solid baseline with reasonable trade-offs — being authored.
Production-grade approach with explicit trade-off rationale — being authored.
Cover happy path. Clarify scope. Identify the obvious bottleneck. Pick a reasonable storage and reasonable scaling approach.
All of the above plus: explicit failure handling, durability vs latency trade-offs, choose the right batching/caching strategy, articulate why.
All of the above plus: organizational concerns (rollout, migration, on-call), quantitative analysis, multi-region considerations, what could go wrong with the proposed solution at 10x scale.
Common mistakes: Staying at high-level architecture without specifying Redis key names, data types, or DB query patterns; Not proactively driving the interview — waiting for interviewer prompts instead of leading the deep dive; Not addressing the hot/popular item problem (hotkey) unprompted; Proposing synchronous DB writes without caching or queuing, which cannot handle 1M QPS; Forgetting idempotency: not handling double-like or concurrent like/unlike race conditions; Not defining state transitions for like→unlike or like→dislike clearly; Answering only the basic 2 FRs and not covering user's liked-items list or leaderboard; Not discussing sharding strategy or DB choice justification
Interviewer hints: Interviewer explicitly asked: 'What does the Redis key look like?' when candidate stayed at high level; Interviewer asked: 'What does the DB query look like when you write or read a like?' to push for schema detail; Interviewer guided toward hotkey handling when candidate didn't mention it; Interviewer probed: 'How do you make the like count update visible to users quickly?'; Interviewer continuously guided the candidate throughout when stuck
What passers do: Proactively clarified all 3–4 functional requirements before designing; Explicitly stated Redis key schemas and DB table schemas with column names; Addressed the hotkey/hot-item problem without being prompted; Combined Kafka (write decoupling) + Redis (fast read/write) + DB (persistence) cleanly; Drove the interview rather than waiting for prompts; Discussed idempotency and concurrent write handling; Covered both write path and read path explicitly
Why people fail: High-level answer that never got to specific implementation details (keys, queries, schemas); Did not handle hot item / burst write scenario; Could not articulate Redis key design or DB query when probed; Passive in the interview — answered questions but did not proactively identify and address design challenges; Missed idempotency and state transition correctness entirely; Good clarification but poor technical depth in the design itself
Edge cases probed: Concurrent like/unlike from the same user (race condition → duplicate likes); Hot/popular item with millions of simultaneous likes (hotkey problem); Exact vs. approximate like count (tradeoff at high scale); User liking their own item (business rule clarification); Like count going negative on unlike if counter not guarded; State transition correctness: like → unlike → like idempotency; Like → dislike transition and dual counter update atomicity; Stale cache read after unlike (cache invalidation timing)
Alternative approaches: CDC (Change Data Capture) pattern (Cleaner separation of concerns — primary DB captures writes, CDC stream feeds downstream counter service and cache. Adds operational complexity (need Debezium or similar); eventual consistency for counts.); Synchronous DB counter with optimistic locking (Simpler architecture; accurate counts. Does not scale to 1M WPS — DB becomes bottleneck; hotkey contention on popular items.); Kafka + Flink stream processing (Handles very high throughput with real-time aggregation via Flink windowing. Higher infrastructure cost; more complex failure recovery; introduces latency vs. direct Redis increment.); Kafka + batch consumer (no Flink) (Simpler than Flink; batch writes to DB and Redis reduce write amplification. Slight lag before counts are updated; less granular stream processing.); Client-side optimistic update + SSE reconciliation (Immediately reflects user's own like action in UI without waiting for backend confirmation. Counts may be approximate/stale until SSE pushes server truth; complex client-side rollback on failure.)
What you just read — canonical solution, follow-up arc, what passing candidates actually did — exists for all 34 Roblox questions, refreshed monthly from new candidate reports.