Start by clarifying requirements and scale (e.g., read/write ratio, consistency needs) to frame the design. Then walk through the high-level components: API design, data schema, counter architecture, ranking algorithm, abuse prevention, and reliability mechanisms. Emphasize trade-offs and justify choices based on scalability and business needs.
Pro tip: Highlight the importance of idempotency and eventual consistency in counter updates, and propose a hybrid approach (e.g., combining real-time counters with periodic batch reconciliation) to balance accuracy and performance.
Ask questions to understand expected traffic (e.g., reviews per second, upvotes per review), read/write patterns, consistency requirements, and reward calculation rules. This informs architectural decisions.
Define RESTful or GraphQL endpoints for posting reviews, voting, and fetching reviews with sorting. Design a schema that separates review content, votes, and counters, using appropriate data stores (e.g., SQL for reviews, NoSQL for votes).
Propose a counter service using sharded counters or a distributed cache (e.g., Redis) with write-behind persistence. For ranking, discuss algorithms like Wilson score or time-decayed popularity, and how to compute them efficiently.
Outline measures like rate limiting, vote verification (e.g., one vote per user per review), anomaly detection, and CAPTCHA. For reliability, discuss idempotent vote processing, retries, dead-letter queues, and periodic reconciliation of counters.
Summarize key trade-offs (e.g., consistency vs. latency, accuracy vs. cost) and suggest extensions like real-time analytics, A/B testing of ranking, or integration with reward systems.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.