I started with the ingestion side because that felt most concrete to me.
Start by clarifying requirements and scale, then design a pipeline that ingests events into a stream processor for real-time aggregation and a batch store for historical data. Propose a tile-based serving layer that precomputes heatmap tiles at multiple zoom levels using a quadtree or geohash-based spatial index, and discuss trade-offs between latency, cost, and accuracy.
Pro tip: Emphasize how you would handle the 'hot' real-time path versus the 'cold' historical path separately, and mention using a time-series database or columnar store for efficient range queries. Also, proactively discuss how to handle late-arriving data and ensure idempotency in the aggregation pipeline.
Ask about expected event volume, geographic distribution, zoom levels, latency requirements for real-time vs. historical, and accuracy needs. This shapes the entire architecture.
Propose a scalable ingestion layer (e.g., Kafka) to buffer events, then split into real-time stream processing (e.g., Flink) for immediate aggregation and batch processing (e.g., Spark) for historical data. Store aggregated data in a time-series or columnar database optimized for spatial and temporal queries.
Explain how to precompute heatmap tiles at multiple zoom levels using a spatial index (e.g., quadtree, geohash) and store them in a tile server or CDN. For real-time, generate tiles on-the-fly from recent aggregates; for historical, serve precomputed tiles.
Discuss trade-offs: precomputation vs. on-the-fly, accuracy vs. performance, cost of storing all zoom levels. Mention techniques like dynamic tile generation, caching, and approximate algorithms (e.g., sampling) for high zoom levels.
Cover late data, idempotency, backfill, and how to scale with increasing load. Mention monitoring, alerting, and potential bottlenecks (e.g., hot partitions).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This came as a follow-up and I fumbled it a bit.
Start by defining the problem context: heatmap tiles are visualizations of aggregated spatial data, often used for demand/supply patterns. Then compare precomputing (offline batch generation) versus on-demand (real-time computation) across dimensions like latency, cost, freshness, and scalability. Conclude with a recommendation based on use case, such as using precomputed tiles for historical analysis and on-demand for real-time dashboards.
Pro tip: Mention that a hybrid approach—precomputing frequently accessed tiles and computing others on demand—often balances cost and freshness, and discuss how caching and incremental updates can optimize both.
Ask about data volume, update frequency, latency requirements, and query patterns to understand the trade-off space.
Briefly explain what each approach entails: precomputing generates tiles ahead of time and stores them; on-demand computes tiles when requested.
Analyze trade-offs in terms of latency, cost (compute/storage), data freshness, scalability, and complexity.
Discuss how caching, incremental updates, and tiered storage can mitigate downsides of each approach.
Suggest which approach fits scenarios like real-time monitoring vs. historical analysis, and justify with trade-offs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through time-series storage and rolling aggregations.
Start by clarifying the system's scale, data sources, and query patterns to tailor the pipeline design. Then describe a multi-stage aggregation pipeline (e.g., using Kafka, Flink, and a time-series database) that balances latency, throughput, and cost. Finally, justify a tiered retention policy based on data value, compliance, and storage costs.
Pro tip: Tie your retention policy to Uber's specific use cases (e.g., real-time pricing vs. historical analytics) and mention how you'd automate data lifecycle management with tools like Apache Airflow or cloud-native solutions.
Ask about data volume, velocity, variety, and query latency requirements to understand the system's constraints. Identify key stakeholders and their needs (e.g., real-time dashboards vs. batch reports).
Propose a pipeline architecture: ingestion (Kafka), stream processing (Flink/Spark Streaming), storage (time-series DB like Cassandra or Druid), and serving layer. Explain how you'd handle windowing, joins, and exactly-once semantics.
Propose a tiered retention strategy: hot data (last 7 days) in fast storage, warm data (last 90 days) in cheaper storage, and cold data (older) in archival storage like S3 Glacier. Justify based on access patterns and compliance.
Discuss trade-offs between latency, cost, and complexity. Explain how the pipeline scales horizontally and how retention policies can be adjusted dynamically based on business needs.
Summarize your approach and invite feedback. Validate assumptions with the interviewer and adjust based on their input.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.