This was the main question and it took the full session.
Start by clarifying functional and non-functional requirements, then design the data model and APIs for ratings, reviews, and voting. Discuss trade-offs in storage, consistency, and scalability, and detail the reward mechanism with idempotency and fraud prevention.
Pro tip: Emphasize idempotency and exactly-once processing for rewards to avoid duplicate payouts, and consider using a ledger system for financial transactions. Also, discuss how to handle vote manipulation and ensure fairness in helpfulness sorting.
Ask about expected traffic, read/write ratios, consistency needs, and reward criteria. Define functional requirements: users can rate items (1-5 stars), write reviews, view average ratings, sort by recency/helpfulness, and upvote/downvote reviews.
Choose a database (e.g., SQL for transactions, NoSQL for scale) and design schemas for users, items, reviews, votes, and rewards. Consider denormalization for average ratings and helpfulness scores to optimize reads.
Design RESTful or GraphQL endpoints for submitting reviews, voting, and fetching sorted reviews. Explain how to compute helpfulness (e.g., Wilson score) and handle pagination for large datasets.
Detail how to trigger rewards when a review reaches a threshold of net upvotes. Use a message queue and idempotent consumer to process reward events, and integrate with a payment system via a ledger.
Discuss caching strategies (e.g., Redis for average ratings), sharding, and consistency models. Trade-offs: strong vs. eventual consistency for votes, real-time vs. batch reward processing, and fraud detection.
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My first instinct was sharding the counter across multiple rows and summing them, which is fine, but I didn't immediately connect that the uniqueness constraint still lives on the canonical votes table separate from the counter.
Start by clarifying the requirements and scale, then propose a sharded counter design that distributes writes across multiple nodes while enforcing one vote per user via a separate unique constraint. Discuss trade-offs between consistency, latency, and complexity, and how to handle hot shards and eventual consistency.
Pro tip: Mention that you can use a combination of sharding and asynchronous aggregation to handle spikes, but ensure idempotency and deduplication to enforce one vote per user. Also, consider using a write-optimized store like Cassandra with a unique key on (user_id, review_id) to prevent duplicate votes.
Ask about the expected scale, read/write patterns, latency requirements, and consistency needs. Confirm that one vote per user is a hard constraint and that the vote count can be eventually consistent.
Propose splitting the vote counter into N shards (e.g., by review_id + shard_id) to distribute writes. Each vote increments a random shard, and reads sum all shards. This reduces contention on a single key.
Use a separate store (e.g., a database with a unique constraint on (user_id, review_id)) to record votes. This can be done asynchronously or transactionally, but must be idempotent to handle retries.
If a review goes viral, shards may still become hot. Consider dynamic sharding, write buffering (e.g., in-memory aggregation with periodic flush), or using a distributed queue to smooth spikes.
Talk about consistency vs. latency (e.g., eventual consistency for counts), read amplification (summing shards), and potential use of caching or materialized views for reads. Mention monitoring and auto-scaling.
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Start by explaining how you would detect the drift through monitoring and reconciliation, then describe a safe, incremental fix that avoids downtime. Emphasize idempotent corrections, validation, and communication with stakeholders.
Pro tip: Mention using a shadow table or dual-write with reconciliation to validate fixes before applying them to production, and always have a rollback plan.
Use monitoring alerts, anomaly detection, or reconciliation jobs to identify discrepancies between source events and aggregates. Check logs for the bug's time window.
Quantify the scope: which aggregates are affected, how many records, and the magnitude of overcounting. Determine if the bug is still active.
Plan an idempotent correction: recompute aggregates from raw events or apply compensating adjustments. Use a shadow table or batch process to avoid locking.
Deploy the fix in small batches, monitor for errors, and validate each batch before proceeding. Use feature flags or canary releases if possible.
After correction, run reconciliation checks to ensure aggregates match expected values. Set up alerts for future drifts and document the incident.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Two separate problems and I conflated them at first.
Start by acknowledging the distributed transaction problem and the need for exactly-once semantics. Propose using idempotency keys and a state machine to track payment status, with reconciliation to handle unknown outcomes. Emphasize that true exactly-once requires idempotent operations and a reliable way to resolve in-flight transactions.
Pro tip: Mention that exactly-once is achieved through at-least-once delivery plus idempotent processing, and that the payment provider's API often supports idempotency keys to deduplicate requests.
Recognize that the timeout creates an unknown state: the payment may or may not have been processed. This is a classic distributed transaction problem requiring coordination between the database and external payment service.
Use a unique idempotency key for each payment attempt, generated before the first call. Ensure the payment provider supports idempotency so retries with the same key don't double-charge.
Track payment status in your database (e.g., PENDING, COMPLETED, FAILED). On timeout, mark as PENDING and trigger a reconciliation process to query the payment provider for the final status.
Periodically reconcile pending payments by querying the provider using the idempotency key. If the payment succeeded, update the record; if not, safely retry or fail.
Use database transactions to update the reward record and payment status together. Consider outbox pattern or event sourcing to reliably publish events and avoid inconsistencies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Honestly the question I was least prepared for.
Start by clarifying the requirements: how the helpfulness score is computed, how often it updates, and the read patterns (e.g., sorting by score, filtering by product). Then propose a denormalized read model that stores the precomputed score and supports efficient sorting, and discuss indexing strategies to handle the access patterns. Finally, address trade-offs like staleness, write amplification, and scalability.
Pro tip: Mention that you'd precompute the helpfulness score asynchronously and store it in a dedicated read store (like a search index or a materialized view) to avoid expensive on-the-fly calculations, and use a composite index that includes the score and other common filters to optimize query performance.
Ask about how the helpfulness score is defined, how frequently it changes, and the expected query patterns (e.g., sort by score descending, pagination, filtering by product or category).
Propose a denormalized read model that stores the precomputed helpfulness score along with review data, optimized for reads. Consider using a search index (e.g., Elasticsearch) or a materialized view in a database.
Suggest indexes that support the main query patterns: a composite index on (product_id, helpfulness_score DESC) for sorting within a product, and possibly additional indexes for other filters. Discuss covering indexes to avoid lookups.
Explain how the score is updated (e.g., via a batch job or stream processing) and how to propagate changes to the read model. Discuss trade-offs between consistency and latency, and how to handle stale scores.
Talk about partitioning/sharding strategies, caching, and the impact on write throughput. Mention alternatives like computing score on the fly for small datasets vs. precomputing for large scale.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.