Start by clarifying requirements: write throughput, query patterns (e.g., full-text search, filters, aggregations), retention, and consistency needs. Then propose a scalable architecture using a distributed log store (e.g., Kafka) for ingestion and a search-optimized store (e.g., Elasticsearch) for queries, discussing trade-offs and optimizations.
Pro tip: Emphasize the separation of write and read paths to handle high ingestion rates and complex queries independently, and mention how you would handle schema evolution and time-based partitioning for efficient retention.
Ask about expected write volume, query types (search, filters, aggregations), latency requirements, retention policies, and consistency needs to scope the design.
Propose a pipeline: ingestion via a distributed message queue (e.g., Kafka) for durability and buffering, then indexing into a search-optimized store (e.g., Elasticsearch) for queries.
Design the log schema (timestamp, service, level, message, metadata) and indexing strategy (e.g., inverted index for text, time-based indices for efficient range queries and retention).
Discuss partitioning (e.g., by time or service), replication for fault tolerance, and backpressure handling to ensure high availability and durability.
Explain how to optimize queries (caching, pre-aggregation, columnar storage) and trade-offs between consistency, latency, and cost (e.g., eventual consistency vs. strong consistency).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.