Start by clarifying requirements: log volume, schema, query types, latency and retention needs. Then propose a scalable architecture with a durable ingestion pipeline (e.g., Kafka) feeding a storage layer optimized for time-series logs (e.g., Elasticsearch or ClickHouse), and discuss trade-offs between indexing, storage cost, and query performance. Finally, address operational concerns like backpressure, fault tolerance, and scaling.
Pro tip: Emphasize the importance of a schema-on-write approach with structured logs to enable efficient indexing and querying, and mention how you would handle schema evolution without downtime.
Ask about log volume (events/sec), data size, query patterns (search, aggregation, real-time dashboards), latency requirements (near real-time), retention period, and consistency needs.
Propose a pipeline: producers -> ingestion service (e.g., Kafka) -> processing (parsing, enrichment) -> storage (e.g., Elasticsearch, ClickHouse) -> query service. Ensure components are decoupled and scalable.
Design a structured log schema with common fields (timestamp, service, level, message, metadata). Discuss indexing strategies (e.g., inverted index for text, columnar for analytics) and partitioning by time for efficient queries.
Explain how to scale ingestion (partitioned topics, consumer groups), storage (sharding, replication), and query (load balancing, caching). Address backpressure, data durability, and exactly-once semantics.
Discuss trade-offs: indexing vs. storage cost, latency vs. throughput, consistency vs. availability. Mention optimizations like compression, tiered storage, and query caching.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.