I jumped straight into the data ingestion layer and kind of forgot to nail down what kind of heatmap we were even talking about.
Start by clarifying the scope: what kind of heatmap (e.g., geospatial demand heatmap for Uber) and its key requirements (real-time vs batch, granularity, scale). Then design a pipeline: data ingestion, aggregation, storage, and serving, discussing trade-offs at each stage. Finally, address scalability, latency, and cost, and how to handle updates and queries efficiently.
Pro tip: Emphasize that heatmaps are often pre-aggregated for performance, and discuss how to balance freshness with cost by using a lambda or kappa architecture. Also, mention the importance of choosing the right spatial indexing (e.g., geohash, S2) for efficient querying.
Ask questions to understand the use case: Is it real-time or historical? What is the geographic granularity? What is the expected query pattern (e.g., by region, time range)? What are the scale and latency requirements?
Outline the main components: data sources (e.g., driver/rider events), ingestion (e.g., Kafka), processing (e.g., Spark/Flink), storage (e.g., time-series DB, geospatial index), and serving layer (e.g., API, caching).
Decide on the data model: how to represent spatial cells (e.g., geohash, S2, H3) and time buckets. Discuss pre-aggregation strategies to reduce query latency and storage costs.
Discuss trade-offs: real-time vs batch processing, storage vs compute, accuracy vs performance. Mention techniques like downsampling, caching, and using approximate algorithms (e.g., HyperLogLog) if needed.
Explain how the system scales horizontally, handles failures, and ensures data consistency. Mention partitioning, replication, and monitoring.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.