← Confluent Interview Insights
Start by clarifying that Kafka is a distributed commit log, not a traditional database, and frame your design around append-only logs, partitioning, and replication. Walk through the core components—producers, brokers, consumers, and metadata management—while highlighting trade-offs between consistency, availability, and latency. Emphasize how your design decisions align with Confluent's business goals and customer needs.
Pro tip: Focus on the product implications of technical trade-offs—e.g., how partition count affects ordering guarantees and operational complexity—to show you think like a PM, not just an engineer.
Ask clarifying questions about scale, latency, durability, and use cases to define the problem scope. State assumptions about throughput, data retention, and consistency requirements.
Describe the fundamental components: partitioned append-only logs, brokers for storage and serving, producers for writes, consumers for reads, and a metadata layer (e.g., KRaft or ZooKeeper). Explain how data is distributed and replicated.
Discuss choices like replication factor, acks, partitioning strategy, and retention policies, and their impact on durability, availability, and performance. Highlight trade-offs between consistency and latency.
Explain how the system scales horizontally, handles broker failures, and ensures data durability. Mention techniques like leader election, ISR, and rack awareness.
Tie technical decisions to customer value, operational costs, and competitive differentiation. Discuss how features like exactly-once semantics or tiered storage enable new use cases.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.