Start by clarifying requirements and scale, then design a high-level architecture that separates ingestion, aggregation, and serving. Walk through each component in detail, emphasizing trade-offs and scalability. Conclude with how updates are pushed to drivers and how data retention is handled.
Pro tip: Emphasize the use of approximate geospatial indexing (like geohashes or S2 cells) to balance accuracy and performance, and discuss how you'd handle hot spots and load skew.
Ask questions to understand expected QPS, latency requirements, geographic granularity, and update frequency. Establish that the system must handle millions of drivers and riders globally.
Propose a scalable ingestion layer (e.g., Kafka) to collect rider demand events and driver location updates. Discuss partitioning by geographic region for parallelism.
Explain how to aggregate demand in real-time using a stream processing framework (e.g., Flink, Spark Streaming). Use geospatial indexing (geohash, S2) to bucket events into cells and compute counts per cell over sliding windows.
Store aggregated heatmap data in a low-latency store (e.g., Redis, Cassandra) and push updates to driver apps via WebSockets or long polling. Discuss how to handle frequent updates and minimize bandwidth.
Outline retention policies: keep recent data for real-time heatmap, and optionally store historical data for analytics. Discuss scaling strategies: sharding by region, auto-scaling, and handling hot spots.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.