Start by clarifying requirements (read/write patterns, latency, consistency) and estimating scale (10M sensors × 0.1 writes/sec = 1M writes/sec, 864M readings/day). Then propose a high-level architecture: ingestion via a scalable message queue (e.g., Kinesis), stream processing for real-time aggregation, time-series storage for historical data, and a serving layer for heat map and queries. Finally, dive into trade-offs and optimizations for each component.
Pro tip: Emphasize partitioning and pre-aggregation: shard by sensor ID or geohash to distribute load, and pre-compute aggregates (e.g., per-minute min/max) to reduce query latency and storage costs. Also, discuss how to handle late-arriving data and ensure idempotency.
Ask about data retention, query patterns (real-time vs historical), latency SLAs, and consistency needs. Estimate write throughput (1M/sec) and storage (e.g., 864M readings/day × 10 bytes = ~8.6 GB/day).
Use a distributed message queue (e.g., Amazon Kinesis) to handle high write throughput. Partition by sensor ID or geohash for scalability. Consider batching and compression to reduce overhead.
For real-time heat map, use stream processing (e.g., Kinesis Analytics, Flink) to aggregate data into time windows and update a cache (e.g., Redis) or serving DB. For historical queries, store raw data in a time-series database (e.g., Amazon Timestream) or a columnar store (e.g., Parquet on S3) with pre-aggregated tables for min/max/current.
For heat map, serve aggregated data from a low-latency store (e.g., Redis) via an API. For historical queries, query pre-aggregated tables or use a query engine (e.g., Athena) over S3. Ensure APIs are scalable and cacheable.
Discuss trade-offs: consistency vs availability, cost vs latency, and complexity. Plan for fault tolerance, data durability, and handling late/out-of-order data (e.g., watermarks).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.