Start by clarifying requirements and scale, then design the data model and API. Focus on how to efficiently support per-user lookups, per-user history, and per-item counts, discussing trade-offs between consistency, latency, and storage.
Pro tip: Emphasize idempotency and atomicity for reaction changes, and consider using a write-through cache or change data capture to keep counts and history in sync without overloading the primary database.
Ask about expected QPS, number of users, items, and reaction types. Clarify consistency needs (e.g., eventual vs strong) and whether history needs to be paginated or time-ordered.
Propose a schema: a reactions table (user_id, item_id, reaction_type, timestamp) with a unique constraint on (user_id, item_id). Consider separate stores for per-user history and per-item counts, or derive them from the reactions table with appropriate indexes.
Specify endpoints: POST /reactions to add/switch/remove (idempotent), GET /users/{id}/reactions/{item_id} for per-user lookup, GET /users/{id}/history for liked items, GET /items/{id}/reactions for counts. Ensure operations are atomic and handle concurrent updates.
Discuss sharding by user_id or item_id, caching hot counts, using a message queue for asynchronous count updates, and read replicas for history queries. Consider trade-offs between consistency and latency.
Cover idempotency, race conditions, and failure recovery. Discuss whether to use a single datastore (e.g., SQL with indexes) vs specialized stores (e.g., Redis for counts, Cassandra for history) and justify choices.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying that idempotency and exactly-once semantics are related but distinct: idempotency ensures repeated requests have the same effect, while exactly-once delivery is impossible in distributed systems, so we aim for effectively-once processing. Then describe a concrete design using idempotency keys, deduplication stores, and transactional guarantees, and discuss trade-offs like latency, storage cost, and failure modes.
Pro tip: Mention that exactly-once is a myth in distributed systems—focus on at-least-once delivery with idempotent processing to achieve effectively-once semantics. This shows you understand the theoretical limits and practical realities.
Define what 'react()' does and what 'exactly-once' means in this context. Distinguish between idempotency (same result on retry) and exactly-once delivery (impossible without coordination).
Use a unique idempotency key per request, typically generated by the client. Store the key and the result of processing in a durable, transactional store (e.g., database with unique constraint).
Use atomic operations (e.g., INSERT ... ON CONFLICT DO NOTHING) or distributed locks to ensure only one request with a given key is processed. If a duplicate arrives while the first is in-flight, either wait or return a conflict.
Make the processing and the idempotency record update atomic (e.g., in a single transaction). If side effects are external (e.g., sending email), use outbox pattern or two-phase commit to avoid partial failures.
Acknowledge costs: storage for keys, latency from coordination, and complexity. Explain how to handle key expiration, retries, and what happens if the idempotency store fails.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with a relational DB for the user-item reaction table since the access patterns are key-value-ish but you also need the userId scan for listLikedItems.
Start by clarifying requirements: scale (millions of reactions per second), read/write patterns, consistency needs, and query patterns. Then propose a database (e.g., Cassandra for high write throughput, or a combination of Redis for counters and a relational DB for metadata) and design a schema that supports efficient writes and reads. Justify your choices with trade-offs and mention how you'd handle hot items and aggregation.
Pro tip: Show awareness of Roblox's massive scale by discussing sharding and eventual consistency, and mention that you'd use a write-optimized store for reactions and a separate system for counts to avoid hotspots.
Ask about scale (e.g., reactions per second, total items), read/write ratio, latency requirements, and consistency needs (e.g., is eventual consistency acceptable for counts?).
Select a database based on requirements: e.g., Cassandra for high write throughput and scalability, Redis for fast counters, or a combination. Explain why it fits.
Propose a table schema for storing individual reactions, with partition key (e.g., item_id) and clustering key (e.g., user_id, reaction_type, timestamp) to allow efficient queries and writes.
Design a separate table or use a counter system (e.g., Redis or Cassandra counters) to maintain aggregated counts per item and reaction type, ensuring fast reads.
Discuss how to handle hot partitions (e.g., sharding by item_id with a salt), eventual consistency, and trade-offs between storage cost and read performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where I backed myself into a corner.
Start by clarifying the system requirements and data access patterns, then propose a sharding strategy based on the dominant query pattern. Compare sharding by userId and itemId in terms of query efficiency, data distribution, and scalability, and discuss how to handle cross-shard queries.
Pro tip: Mention that the choice depends on the read/write ratio and whether the system is more user-centric or item-centric; for Roblox, user-centric operations like loading a player's inventory are frequent, so sharding by userId often makes sense, but you can also consider a hybrid approach or secondary indexes.
Ask about the scale (number of users, items), read/write patterns, and latency requirements to understand the system's needs.
Discuss how sharding by userId groups all data for a user together, making user-centric queries efficient and ensuring even distribution if user activity is uniform.
Discuss how sharding by itemId groups all data for an item together, benefiting item-centric queries like fetching all owners of an item, but potentially causing hotspots for popular items.
Analyze trade-offs: query performance for common operations, data distribution and hotspots, scalability, and complexity of cross-shard queries.
Recommend a sharding key based on the dominant access pattern, and suggest mitigations like caching, secondary indexes, or a hybrid approach for cross-shard queries.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by framing the core tension: strong consistency for like counts requires synchronous writes and reads, which hurts latency and scalability, while eventual consistency trades accuracy for performance. Then discuss specific techniques like write-behind caching, read replicas, and idempotent counters, and finally explain when stale counts are acceptable based on user experience and business impact.
Pro tip: Mention that like counts are often non-critical and can be eventually consistent, but you must handle edge cases like a user liking and immediately refreshing—use optimistic UI updates or per-user consistency to avoid confusion.
Ask about expected read/write ratio, latency SLAs, and whether counts must be exact or approximate. This shows you tailor solutions to context.
Explain strong vs. eventual consistency: strong ensures accuracy but adds latency and reduces availability; eventual improves performance but may show stale data.
Describe patterns like write-through/write-behind caching, read replicas, sharded counters, and asynchronous aggregation to balance trade-offs.
Discuss when stale counts are okay: non-critical social proof, high-traffic pages, or when eventual consistency is imperceptible to users.
Cover scenarios like a user's own like reflecting immediately (read-your-writes) and handling count drift with reconciliation jobs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.