← Confluent Interview Insights
This was basically eight questions stitched into one.
Start by clarifying requirements (read-heavy ratio, consistency needs, scale) and then walk through the design layer by layer: OS-level optimizations, storage engine, partitioning, replication, consistency, failure handling, hotspot mitigation, and capacity planning. Emphasize trade-offs and justify choices based on read-heavy workload characteristics.
Pro tip: Confluent values Kafka-like scalability and durability; mention how you'd leverage log-structured storage and consider integrating with Kafka for change data capture or event sourcing. Also, explicitly discuss how you'd measure and monitor performance metrics to validate design decisions.
Ask about read/write ratio, latency SLAs, consistency requirements, data size, and geographic distribution. State assumptions to scope the design.
Choose a storage engine (e.g., LSM-tree for write-heavy, B-tree for read-heavy) and discuss OS-level tuning like page cache, direct I/O, and file system choices to optimize read performance.
Explain how to partition data (e.g., consistent hashing) for even distribution and low-latency reads, and design replication (e.g., leader-follower, quorum) for fault tolerance and read scalability.
Select a consistency model (e.g., eventual consistency for read-heavy) and describe failure detection, recovery, and data repair mechanisms (e.g., anti-entropy, hinted handoff).
Address hotspots via techniques like key salting, caching, and load balancing. Discuss capacity planning: estimating storage, throughput, and scaling out horizontally.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.