This thing had so many layers I didn't know where to start.
Start by clarifying requirements and scale (e.g., logs per second, retention, query latency). Then design a pipeline: ingestion via a distributed message queue, storage in a time-partitioned store with appropriate indexing, and query/aggregation layers that use pre-aggregation and caching for performance. Discuss trade-offs and estimate complexity for key operations.
Pro tip: Emphasize partitioning and pre-aggregation to handle scale: partition by time and log source, and maintain pre-aggregated counters/histograms to avoid expensive real-time scans. This shows you understand how to balance cost, latency, and accuracy in a high-volume system.
Ask about expected log volume (e.g., millions per second), retention period, query patterns (filtering, counting, histograms), latency SLAs, and consistency needs. This shapes the entire design.
Propose a scalable ingestion layer: agents collect logs, push to a distributed queue (e.g., Kafka) for buffering and decoupling. Discuss partitioning by log source or time for parallelism and ordering.
Select a storage system optimized for time-series or log data (e.g., Elasticsearch, ClickHouse, or custom time-partitioned store). Define schema with indexed attributes (timestamp, level, service, message) and consider columnar storage for efficient filtering and aggregation.
For filtering, use inverted indexes or columnar scans. For counting errors and histograms, pre-aggregate in stream processing (e.g., Flink) or maintain materialized views. Discuss deduplication via unique log IDs or idempotent writes.
Explain horizontal scaling via sharding, replication for availability, and caching for hot queries. Estimate time/space complexity: e.g., filtering O(n) but with indexes O(log n), aggregation O(1) with pre-aggregation, storage O(n) with compression.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.