Start by clarifying requirements and scale (10M sensors, 10s granularity, ~1M writes/sec, retention years). Then propose a scalable ingestion pipeline (e.g., Kafka + stream processing) and a storage layer optimized for time-series (e.g., time-partitioned columnar store or TSDB) with pre-aggregation for heat maps. Finally, discuss trade-offs around consistency, cost, and query performance, and how to ensure long-term stability.
Pro tip: Emphasize the need for downsampling and tiered storage to handle years of data cost-effectively, and mention that heat maps can be served from pre-aggregated tiles rather than raw data.
Ask questions to understand data volume, query patterns, latency requirements, and retention. Calculate write throughput (10M sensors / 10s = 1M writes/sec) and storage needs.
Propose a scalable, fault-tolerant ingestion layer using a distributed message queue (e.g., Kafka) and stream processing (e.g., Flink) to validate, transform, and route data.
Select a time-series database or a columnar store with time-based partitioning. Model data with sensor ID, timestamp, and value, and consider pre-aggregation for common queries.
Design query APIs for current, min, max over arbitrary time ranges. For heat maps, pre-compute spatial aggregations (e.g., grid tiles) and serve via a low-latency cache or CDN.
Discuss data retention policies, downsampling, tiered storage, and monitoring. Highlight trade-offs between consistency, latency, cost, and complexity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.