Start by clarifying requirements (event volume, latency, accuracy, tile format) and then propose a scalable architecture that separates ingestion, storage, and query serving. Use a spatial index and pre-aggregation to efficiently compute heatmap tiles for arbitrary bounding boxes, zoom levels, and time ranges.
Pro tip: Mention that heatmap tiles are typically served as vector tiles (e.g., Mapbox Vector Tiles) and that you can pre-aggregate at multiple zoom levels to balance query latency and storage cost. Also, discuss how to handle time-range queries by partitioning data by time (e.g., hourly/daily) and using a rollup strategy.
Ask about event volume (e.g., millions per minute), query latency SLA (e.g., <100ms), accuracy vs. performance trade-offs, and tile format (raster vs. vector). Also clarify time range granularity and retention.
Propose a scalable ingestion layer (e.g., Kafka) that buffers events and writes to a storage system. Consider partitioning by time and geo-hash to enable efficient writes and later queries.
Select a storage solution that supports spatial indexing (e.g., geohash, S2, or Quadtree) and time-series partitioning. Consider a columnar store (e.g., Parquet on S3) for cost-effective long-term storage and a fast lookup store (e.g., Redis, Cassandra) for recent data.
Pre-compute heatmap tiles at multiple zoom levels and time buckets (e.g., hourly, daily) to serve queries quickly. Use a tile server that can fetch and merge pre-aggregated tiles for the requested bounding box, zoom, and time range.
Discuss trade-offs between pre-aggregation and on-the-fly computation, storage cost vs. query latency, and accuracy vs. performance. Mention techniques like dynamic aggregation, caching, and approximate algorithms (e.g., HyperLogLog) if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.