← Confluent Interview Insights
I spent too long on the ingestion pipeline and didn't get to the mobile-specific API stuff until they nudged me.
Start by clarifying requirements and scale, then design the ingestion pipeline with deduplication and ranking, followed by the API layer. Compare REST and RPC tradeoffs for different client needs, and detail pagination, caching, and client-specific adaptations.
Pro tip: Emphasize how Confluent's event streaming platform (Kafka) can be leveraged for scalable ingestion and real-time processing, showing alignment with the company's core technology.
Ask about expected QPS, number of publishers, article volume, latency requirements, and client types. This ensures the design meets actual needs and shows you think before coding.
Outline a pipeline using a message queue (e.g., Kafka) to ingest articles, then process for deduplication (e.g., SimHash) and ranking (e.g., ML models). Mention scalability and fault tolerance.
Decide on REST for public, cacheable endpoints and gRPC for internal, high-performance communication. Discuss tradeoffs: REST simplicity vs RPC efficiency, and how to adapt for mobile (e.g., GraphQL or field selection).
Use cursor-based pagination for stable, efficient paging. Implement caching at CDN, API gateway, and service levels with appropriate TTLs and invalidation strategies.
Provide tailored responses for mobile (reduced payload, compression) vs web (richer data). Consider API versioning, content negotiation, or separate endpoints.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements (scale, latency, cost) and then propose a two-part solution: a human-like username generator using curated word lists and patterns, and a uniqueness enforcement mechanism combining a Bloom filter for fast probabilistic checks with a persistent store for exact verification. Discuss tradeoffs of Bloom filters (false positives, memory vs. accuracy) and how to handle them, and mention operational aspects like monitoring and scaling.
Pro tip: Emphasize that false positives in Bloom filters are acceptable if you have a fallback to exact storage, but false negatives are not—so design the system to never reuse a username even if it means occasionally rejecting a valid one. Also, consider using a deterministic generator with a counter to guarantee uniqueness without collisions.
Ask about expected daily volume, latency requirements, storage budget, and whether usernames need to be memorable or just human-like. Confirm that 'never reused' means globally unique forever.
Propose using a combination of common first names, last names, and optional numbers or separators, sourced from curated lists. Use randomness with patterns (e.g., first.last, firstlast123) to mimic human choices, and avoid sequential or predictable patterns.
Use a Bloom filter for fast probabilistic membership checks to avoid hitting the database for most collisions. On a positive (possibly false positive), verify against a persistent store (e.g., a database or distributed KV store) that holds all issued usernames. If truly unique, issue and add to both structures.
Discuss false positive rate, memory usage, and how to tune parameters (size, hash functions) based on expected volume. Mention that false positives cause extra database lookups but are safe; false negatives are impossible if implemented correctly. Consider alternatives like cuckoo filters or deterministic generation.
Propose sharding the Bloom filter and storage by username prefix or hash to scale horizontally. Discuss persistence, replication, and how to handle failures (e.g., if the Bloom filter is lost, rebuild from the store).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.