I went straight to the Redis sorted-set for the leaderboard and that was the right call.
Start by clarifying requirements and scale, then design a high-level architecture that separates daily puzzle serving, answer submission, and leaderboard computation. Focus on data modeling for efficient scoring and leaderboard queries across locale, day, and all-time dimensions, and discuss trade-offs between real-time and batch processing.
Pro tip: Emphasize idempotency and anti-cheat measures for daily submissions, and propose a tiered storage strategy (e.g., Redis for real-time leaderboards, Cassandra for historical data) to balance performance and cost.
Ask about user volume, daily active users, locale granularity, leaderboard update frequency, and consistency requirements. Establish assumptions for scale (e.g., millions of DAU) and latency targets.
Outline core components: API gateway, puzzle service, submission service, scoring service, leaderboard service, and data stores. Describe the flow from puzzle retrieval to answer submission and leaderboard updates.
Design schemas for puzzles, submissions, scores, and leaderboards. Consider keys for locale, date, and user to support efficient queries. Choose databases (e.g., Redis for sorted sets, Cassandra for time-series) and explain partitioning and indexing strategies.
Detail server-side scoring logic (e.g., correctness, time, streaks). Explain how to compute and update leaderboards: real-time via Redis sorted sets, and batch for all-time using MapReduce or stream processing. Discuss consistency and eventual vs strong consistency.
Discuss trade-offs: real-time vs batch, cost vs latency, consistency vs availability. Address scaling: sharding by locale/date, caching, read replicas, and handling peak loads. Mention anti-cheat and idempotency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the scenario: a submission burst occurs when many users in different time zones hit midnight simultaneously, causing a spike in write traffic. Then propose a multi-layered solution that combines client-side jitter, server-side rate limiting, and asynchronous processing to smooth the load and ensure reliability.
Pro tip: Emphasize that the goal is not to eliminate the burst but to make it graceful—prioritize user experience by ensuring submissions are accepted quickly and processed reliably, even if slightly delayed.
Ask about the expected scale (e.g., number of locales, users per locale), latency requirements, and whether submissions must be processed in real-time or can be queued.
Explain that the burst is due to synchronized user behavior across time zones, and discuss potential impacts like overloaded databases, increased latency, or dropped requests.
Suggest adding random jitter to client submission times or spreading submissions over a window (e.g., midnight ± 5 minutes) to reduce peak load.
Implement rate limiting, request queuing, and asynchronous processing (e.g., message queues) to absorb the burst and process submissions at a controlled rate.
Use auto-scaling for backend services, monitor queue depths and error rates, and have fallback mechanisms (e.g., retries, dead-letter queues) to handle failures.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Client-side scoring is an obvious no for anything competitive, so I dismissed it fast.
Start by clarifying the scoring use case and requirements (e.g., real-time vs. batch, latency, consistency, security). Then compare client-side, server-side, and serverless options across dimensions like latency, scalability, cost, and maintainability. Finally, recommend a hybrid or context-specific solution, justifying trade-offs and mentioning Uber's scale and existing infrastructure.
Pro tip: Emphasize that scoring logic often needs to be consistent and secure, so client-side scoring is risky unless it's purely for UI feedback. At Uber's scale, a serverless approach can handle spiky traffic, but consider cold starts and cost; sometimes a dedicated service is better.
Ask about the scoring context: is it for driver/rider ratings, fraud detection, or promotions? Determine latency needs, data sensitivity, and expected traffic patterns.
Discuss pros: instant feedback, reduced server load. Cons: security risks (tampering), inconsistency across devices, and limited compute for complex models.
Highlight benefits: centralized logic, security, consistency, and access to full data. Drawbacks: potential latency, scaling challenges, and cost of maintaining servers.
Explain serverless (e.g., AWS Lambda) advantages: auto-scaling, pay-per-use, reduced ops. Challenges: cold starts, execution time limits, and potential vendor lock-in.
Propose a solution based on requirements, e.g., hybrid: client for simple UI scoring, serverless for complex or bursty scoring, server for consistent low-latency needs. Justify with trade-offs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.